Loose and Tight GNSS/INS Integrations: Comparison of Performance Assessed in Real Urban Scenarios

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1 sensors Article Loose and Tight GNSS/INS Integrations: Comparison Performance Assessed Real Urban Scenarios Gianluca Falco *, Marco Pi and Gianluca Marucco Istituto Superiore Mario Boella, Toro 10138, Italy; (M.P.); (G.M.) * Correspondence: falco@ismb.it; Tel.: Academic Editor: Jörg F. Wagner Received: 30 November 2016; Accepted: 19 January 2017; Published: 29 January 2017 Abstract: Global Navigation Satellite Systems (GNSSs) rema prcipal mean positiong many applications and systems, but several types environment, performance standalone receivers is degraded. Although many works show benefits tegration between GNSS and Inertial Navigation Systems (INSs), tightly-coupled architectures are maly implemented pressional devices and are based on high-grade Inertial Measurement Units (IMUs). This paper vestigates performance improvements enabled by tight tegration, usg low-cost sensors and a mass-market GNSS receiver. Performance is assessed through a series tests carried out real urban scenarios and is compared agast commercial modules, operatg standalone mode or featurg loosely-coupled tegrations. The paper describes developed tight-tegration algorithms with a terse mamatical model and assesses ir efficacy from a practical perspective. Keywords: GNSS/INS tegration; horizontal positiong errors; urban navigation 1. Introduction Durg last years, re has been an creasg demand for accurate estimate users position many systems and applications, such as drivg assistance systems and autonomous vehicles. In addition to enhanced performance different types operational environments, developers seek novative strategies for reliable systems at affordable costs [1 3]. Unfortunately, urban environment poses some most severe challenges to Global Navigation Satellite Systems (GNSSs) that rema prcipal mean positiong for outdoor navigation. Indeed, presence buildgs and trees duce signal reflections and attenuations that turn provoke measurements affected by errors. In severe cases, number visible satellites is not sufficient and receivers are unable to provide Position Velocity and Time (PVT) data. Although multiple constellations improve satellites visibility [4], performance standalone receivers urban settgs can be enhanced followg two ma strategies. First, receivers can be augmented with sensors, such as wheel odometers [5], Inertial Navigation Systems (INSs) [6 8], Light Detection and Rangg (LIDAR) [9], or can be combed with or terrestrial systems, such as Wi-Fi networks. Second, position accuracy standalone receivers can be improved with novative signal processg, such as high sensitivity trackg loops [10], Cooperative Positiong [11], or 3-Dimensional (3-D) buildg models to predict satellite visibility, as proposed [12,13]. In this paper, we assess performance two well-known algorithms employed to tegrate GNSS receivers and INSs. They represent two most common sensors that are typically used a wide range applications. Small, robust and low-cost ertial sensors (e.g., Micro Electrical Mechanical Sensors (MEMS) [7]) have been available on market for several years and are combed with Global Positiong System (GPS) receivers especially for land vehicle navigation. Durg last decade, different approaches for GPS/INS tegration have been adopted [14] and many m have been vestigated for different grades Inertial Measurement Units (IMUs). The three most Sensors 2017, 17, 255; doi: /s

2 Sensors 2017, 17, common tegration strategies are: loosely-coupled [15], tightly-coupled [16] and ultra-tight tegration [17,18]. Sce ultra-tight tegration volves baseband signal processg GNSS receivers (i.e., digital trackg loops), that is typically not accessible usg commercial products, this paper compares loosely and tightly-coupled techniques employg Commercial Off The Shelf (COTS) modules. Briefly, basic difference between m is type data shared by GPS receiver and INS sensors. In loosely-coupled technique, positions and velocities estimated by GPS receiver are blended with INS navigation solution, while case tightly-coupled method, GPS raw measurements (i.e., pseudorange and Doppler observables) are processed through a unique Kalman filter with measurements comg from ertial sensors to estimate PVT. The ma advantage tight tegration [16] is possibility to update hybrid navigation solution also scenarios with poor signal quality or limited coverage, thanks to prediction pseudoranges and Doppler trends. Many papers highlight benefits tightly-coupled architectures urban environments. For example, [3] authors developed a tight method with additional constrats on velocity and height to mata INS errors bounded case GPS outage. In [19], an algorithm able to reduce effect multipath and monitor quality pseudoranges has been implemented with a tight architecture. Similarly, [10], a correction is applied to received GPS signals to smooth errors affectg pseudoranges. In [20], authors vestigated combed use tightly-coupled tegration and an f-le Kalman filter to provide a high accuracy position and attitude solution for urban environment. Eventually, reference [21] analyzes advantages a tightly-coupled algorithm as a position, velocity, and attitude estimator for autonomous navigation. A statistical sensitivity analysis was performed by considerg effects map aidg, differential corrections and carrier phases. Even if tightly-coupled method has shown remarkable advantages harsh environments, such architecture is still a research topic [16 22] or is maly implemented only for pressional applications and based on high-grade IMUs. In fact, most MEMS IMUs [23,24] contue to be tegrated with GNSS receivers, through loosely-coupled schemes [23]. However, years ahead, an improvement performance MEMS IMUs that will enable low-cost positiong systems based on tightly-coupled architecture is expected. In this paper, we vestigate performance improvements enabled by tight tegration, usg low-cost sensors. Performance is assessed a real urban context and is compared agast those obtaed by a commercial MEMS IMU, loosely tegrated with a mass-market GPS receiver. Although many papers describe loosely-coupled tegrations for automotive applications [20,25,26], limited work [27] has been done to assess efficacy tight navigation solutions, based on low-cost devices, from a practical perspective. Most papers compare loosely-coupled system performance only with respect to a tight solution obtaed through a highly precise GPS Real Time Kematic (RTK) with an accurate tactical grade IMU [19]. Or papers estimate positional solution accuracy obtaed through a loosely-coupled and tightly-coupled architecture exclusively via simulation by reducg f-le number satellites visibility [15,25]. Therefore, many examples available literature, re is no fair comparison figures merit. The paper provides a detailed description ma features tightly-coupled algorithm that has been designed on top a mass-market, sgle-frequency GPS receiver and a low-cost INS. Then, paper describes results some tests performed three urban scenarios: first open sky conditions second drivg through narrow streets downtown Tur, third along a straight avenue trees. More specifically, paper is organized as follows: after this troduction, first section briefly describes architecture a loosely-coupled GPS/INS system and ma features designed tightly-coupled algorithm. The followg section presents scenarios where we run test campaign and metrics used to assess performance algorithms under vestigation. In last section, results are shown and thoroughly discussed.

3 Sensors 2017, 17, Sensors 2017, 17, Navigation Algorithms Based on Global Navigation Satellite System (GNSS)/Inertial Navigation Systems (INS) Integrations 2. Navigation Algorithms Based on Global Navigation Satellite System (GNSS)/Inertial InNavigation this section, Systems we briefly (INS) Integrations recall architecture a loose tegration that is embedded with many commercial devices, cludg commercial MEMS IMU used for comparison test In this section, we briefly recall architecture a loose tegration that is embedded with campaign. many commercial The sectiondevices, also provides cludg mamatical commercial details MEMS about IMU used design for comparison tightly-coupled test algorithm campaign. as well The assection some sights also provides on itsmamatical real-time implementation details about ondesign an embedded tightly-coupled system. algorithm as well as some sights on its real-time implementation on an embedded system Common Loosely-Coupled Architecture 2.1. Common Loosely-Coupled Architecture The most common GNSS/INS tegration scheme is so-called loosely-coupled where positions and The velocities most common derived GNSS/INS by GNSS tegration signal processg scheme is are merged so-called asloosely-coupled updates where INS estimates positional positions formation, and velocities through derived a navigation by GNSS Kalman signal processg filter [7]. To are furr merged improve as updates accuracy INS navigation estimates solution, positional error formation, states are through fed back a navigation to INS Kalman mechanization filter [7]. equation To furr [6] improve to mitigate accuracy navigation solution, error states are fed back to INS mechanization equation [6] errors that affect IMU [3]. A simple block diagram loosely-coupled architecture is reported to mitigate errors that affect IMU [3]. A simple block diagram loosely-coupled architecture Figure 1. is reported Figure 1. Figure Figure 1. Block 1. Block diagram diagram aa common loosely-coupled architecture. architecture. The INS The mechanization INS mechanization equations equations are are used used to to convert ertial ertial measures measures (accelerations (accelerations and and angular rates along three orthogonal directions) from body frame (b-fame) to navigation angular rates along three orthogonal directions) from body frame (b-fame) to navigation frame (e.g., local l-frame or ECEF-frame) and y are omitted followg paper. frame (e.g., local l-frame or ECEF-frame) and y are omitted followg paper. A detailed description such equations can be found [7] for l-frame and [16] for A detailed ECEF-frame description transformations, such equations respectively. can Considerg be found an [7] ECEF-frame, for l-frame typical and error [16] states for ECEF-frame estimated transformations, by GPS/INS tegration respectively. Kalman Considerg filter are: an ECEF-frame, typical error states estimated by GPS/INS tegration Kalman filter are: x x y z Vx Vy Vz Ax Ay Az x y z fx f y f z (1) r v A f δx = δx δy δz δv x δv y δv z δa x δa y δa z δω x δω y δω z δ f x δ f y δ f z where r, v, A are }{{ } errors }{{ related }} to position, {{} velocity }{{ and } attitude }{{ while } (1) and f δr δv δa δω δ f are errors associated with IMU gyroscopes and accelerometers, respectively. where δr, δv, A generic δa aremeasurement errors related model to position, a discrete velocity time Kalman andfilter attitude can be while written δω as and stated δ f are (2): errors associated with IMU gyroscopes andz accelerometers, k Hk xk respectively. k (2) A generic measurement model a discrete time Kalman filter can be written as stated (2): where H is matrix that shows relationship between error states and measurements k z at k -th time stant, whereas k zis white noise associated with measurements with k k = H k x k + ν k (2) a covariance matrix equal to R k. In case loosely-coupled expressed ECEF coordates, z k where H k is matrix that shows relationship between error states and measurements z k at can be written as: k-th time stant, whereas ν k is white noise associated with measurements with a covariance matrix equal to R k. In case loosely-coupled expressed ECEF coordates, z k can be written as: z k = [X INS X GPS, Y INS Y GPS, Z INS Z GPS ] T (3)

4 k T INS GPS, INS GPS, INS GPS z X X Y Y Z Z (3) A dynamic model an error state discrete Kalman filter is: Sensors 2017, 17, x x (4) A dynamic model an error kstate discrete k 1 kkalman 1 kfilter 1 is: where is transition matrix that relates two consecutive error states vectors and δx k = Φ k 1 δx k 1 + µ k 1 (4) dicates noise propagation discrete time system. Details about design matrix where Φ is transition matrix that relates two consecutive error states vectors and µ dicates can be found [7]. noise propagation discrete time system. Details about design matrix Φ can be found [7] Common Tightly-Coupled Architecture 2.2. Common Tightly-Coupled Architecture A tightly-coupled algorithm uses a centralized Kalman filter that tegrates estimated A tightly-coupled algorithm uses a centralized Kalman filter that tegrates estimated pseudoranges pseudoranges ( (ρ GPS ) ) and and Doppler Doppler shift shift ( f req ( Doppler,GPS freq GPS Doppler,GPS ) from ) GNSS from receiver and GNSS formation receiver and formation position, position, velocity and velocity attitude and comg attitude from comg mechanization from equations mechanization ertial equations sensors. A simple block diagram a tightly-coupled architecture is reported Figure 2. ertial sensors. A simple block diagram a tightly-coupled architecture is reported Figure 2. Figure Figure 2. Block 2. Block diagram a common tightly-coupled architecture. Considerg Considerg an an ECEF-frame navigation, error states estimated by by GNSS/INS GNSS/INS tegration tegration Kalman Kalman filter can filter be can written be written as: as: x δx = x yδx δy z δz VδV x x VδV y y VδV z z δa A x δa x A y δa A z δω z x δω x y δω y z δ f x z δf f y δ x f z δt y δt f z t t (5) }{{}}{{}}{{}}{{}}{{}}{{} (5) r v A f b δr δv δa δω δ f δb where where b cludes δb cludes clock clock bias bias and and drift errors that affect GNSS GNSS receiver. receiver. Also Also case case a tightly-coupled architecture, centralized Kalman filter is formed by a dynamic error states a tightly-coupled architecture, centralized Kalman filter is formed by a dynamic error states model and a measurements model. Gog to detail, discrete transition matrix expressed ECEF-frame, can be written accordg to [16] as: k

5 Sensors 2017, 17, model and a measurements model. Gog to detail, discrete transition matrix Φ k expressed ECEF-frame, can be written accordg to [16] as: where: Φ k = I 3 3 T k I N e I 3 3 2T k Ωie e T k F k T k Cb,k3 3 e I 3 3 T k Ωie e T k Cb,k e I 3x3 + T k D g I T k D a N e represents tensor gravity gradients; Ωie e is Earth rotation rate; F k is skew-symmetric matrix accelerometers measured at time-k; Cb,k e is Direct Cose Matrix (DCM)computed from body to earth frame; D a and D g are time-constant diagonal matrices that defe a first-state Gauss-Markov model for accelerometers and gyroscopes, respectively; T k is time samplg terval between two consecutive executions dynamic model Kalman filter. In order to take to account how noise affectg INS sensors is distributed among state vectors parameter, (4) can be expanded as: δx k = Φ k 1 δx k 1 + G k 1 ω k 1 (7) where G k 1 is noise distribution matrix. A mamatical expression can be found [7]. The defition model noise vector µ k is reported (8): µ k = [ ( ) T, ( ) ] µ t,k, (µ a,k ) T, µ g,k µt,k, (µ aa,k ) T T T, µ gg,k µ k R 14,1 (8) (6) where: µ t,k and µ t,k are clock error and clock drift error noises at discrete time k, respectively; µ a,k and µ g,k are additive white noise components on three accelerometers gyros outputs; µ aa,k and µ gg,k are bias stabilities IMU accelerometers and gyros, respectively. Thus, covariance matrix Q noise components, as stated (8), can be computed as a diagonal matrix as reported [16]. A measure noise standard deviation related to receiver clock bias can be found [28], while stochastic noises that generate stability INS sensors (i.e., µ aa,k and µ gg,k ) are typically modelled as a 1st order Gauss-Markov process. The discrete form covariance matrix can be obtaed accordg to followg formula [3]: Q k 1 2 [ ] Φ k G k Q(t k )Gk T + G kq(t k )Gk T ΦT k t (9)

6 IMUs based on gyros and accelerometers not compensated temperature degrade accuracy INS navigation solution. An example a non-temperature compensated gyro is visible Figure 4a, where drift angular rate, accordg to variation Sensors 2017, 17, As far as measurements model tightly-coupled Kalman filter is concerned, accordg to (2), observation vector z k is defed as: where: z k = ζ sat,k ˆζ k (10) ζ sat,k = [ ρ k, ρ T k] R 2Nsat,1 is vector corrected pseudoranges ρ k and pseudorange rates ρ k at time stant k; ˆζ k = [ ˆρ k ˆρ T k] R 2Nsat,1 represents predicted pseudorange and pseudorange rate vector, computed from current estimate target trajectory. The observation matrix, dicated with H k, is time varyg case a tightly-coupled architecture and it depends on number satellites visibility. Such a matrix can be written as: H k = [ H ρ,k 0 Nsat 3 0 Nsat 3 0 Nsat 8 0 Nsat 3 H ρ,k 0 Nsat 3 0 Nsat 8 ] R 2Nsat,17 (11) where H ρ,k is Jacobian matrix non-lear relationship between user s position and clock and N sat pseudoranges ρ 1,..., ρ Nsat, respectively. It is possible to write H ρ,k as: H ρ,k = [ ] h[n] x (p) x= x = x x1 d 1 x x2 d 2. y y1 d 1 y y2 d 2. z z1 d 1 z z2 where d j is norm vector [x x j, y y j, z z j ] and [ x, y, z ] are estimated user s position coordates, whereas x 1 N, y 1 N, z 1 N represent N satellites positions ECEF-frame Designed Tightly-Coupled Architecture With respect to architecture described above, our design we cluded additional constrats and features order to improve performance tightly-coupled algorithm, specifically for landsensors applications. 2017, 17, 255 The block scheme new architecture is shown Figure 3, where additional 7 27 functions are marked green and detailed below. d (12) Figure Figure 3. Block 3. Block diagram diagram a designed a designed tightly-coupled tightly-coupled architecture. architecture Temperature Compensation

7 Sensors 2017, 17, Figure 3. Block diagram a designed tightly-coupled architecture Temperature Compensation Temperature Compensation IMUs based on gyros and accelerometers not compensated temperature degrade accuracy IMUs INS navigation based on solution. gyros and An accelerometers example a non-temperature not compensated compensated temperature gyro degrade is visible Figure accuracy 4a, where INS drift navigation angular solution. rate, An accordg example to a non-temperature variation temperature compensated over gyro time is visible a static condition, Figure 4a, is evident. where drift angular rate, accordg to variation temperature over time a static condition, is evident. (a) (b) Figure Figure Temperature Temperature variation variation over over time time (a). (a). Example Example compensation compensation for for Gyro-X Gyro-X compared compared with with same same measurements measurements without without compensation compensation (b). (b). Most low-cost MEMS IMUs available on market (e.g., [29]) are not temperature compensated. In our design, we estimated INS sensors biases f-le, stallg IMU to a temperature-controlled chamber and rotatg IMU different positions accordg to characterization tests proposed [30]. We repeated same procedure for different temperatures range 20 to 60 C. As an example, Figure 5 reports results related to gyros biases MEMS IMU InvenSense MPU-9150 [29]. Ideally, such a calibration should be performed on each MEMS IMU, as y have ir own characteristic curve. However, accordg to our experience and considerg objective this work, calibration performed on a sgle MEMS IMU is also representative or samples same type and manufacturer.

8 characterization tests proposed [30]. We repeated same procedure for different temperatures range 20 to 60 C. As an example, Figure 5 reports results related to gyros biases MEMS IMU InvenSense MPU-9150 [29]. Ideally, such a calibration should be performed on each MEMS IMU, as y have ir own characteristic curve. However, accordg to our experience and Sensors considerg 2017, 17, 255 objective this work, calibration performed on a sgle MEMS IMU is 8 also 25 representative or samples same type and manufacturer. Figure 5. InvenSense MPU Gyros Bias vs. Temperature. Figure 5. InvenSense MPU Gyros Bias vs. Temperature. A similar procedure was followed for accelerometers. The data collected durg characterization test were garedto toa alook-up table table that, that, turn, turn, was was used used to correct to correct IMU IMU measurements. measurements Gyros Gyros Recalibration Recalibration The The gyros gyros bias bias can can be be estimated estimated leavg leavg IMU IMU a static static condition condition for for a short short period period time. time. In In our our design, design, we we dedicated dedicated less less than than 1 s to to gyro gyro calibration. calibration. Assumg Assumg tightly-coupled tightly-coupled algorithm algorithm is is implemented implemented on on board board a system a system used for used road for navigation, road navigation, algorithm algorithm was designed was to designed run a new to run gyros a new calibration gyros calibration any time any vehicle time stops. vehicle stops Static Condition Detection Static Condition Detection In order to detect when vehicle stops, we implemented a simple strategy that checks In order to detect when vehicle stops, we implemented simple strategy that checks estimated velocity. When absolute value velocities along three axes is lower than a estimated velocity. When absolute value velocities along three axes is lower than certa threshold (e.g., 0.3 m/s) we decide for hyposis that vehicle is a static condition. certa threshold (e.g., 0.3 m/s) we decide for hyposis that vehicle is static condition. As long as this condition is verified, gyro recalibration is performed. Although false detections As long as this condition is verified, gyro recalibration is performed. Although false detections static condition have been observed rarely, y can potentially occur case limited satellites static condition have been observed rarely, y can potentially occur case limited visibility, as difficult urban environments. Therefore, proposed method could be improved with satellites visibility, as difficult urban environments. Therefore, proposed method could be clusion an additional sensor, such as an odometer. improved with clusion an additional sensor, such as an odometer Nonholonomic Constrats Nonholonomic Constrats In case a vehicle movg an urban scenario, presence tunnels or underpasses is frequent. In case In such a vehicle conditions, movg satellite an urban visibility scenario, is limited presence and tightly-coupled tunnels or underpasses algorithm can is only frequent. rely In on such IMU conditions, sensors to provide satellite visibility a navigation is limited solution. and It is tightly-coupled well known that algorithm MEMS IMU can positiong only rely on accuracy IMU tends sensors to diverge to provide after a a navigation few seconds. solution. In order It is to well improve known that performance MEMS IMU positiong algorithm accuracy case tends GNSS to diverge signals after outages, a few NonHolonomic seconds. In order Constrats to improve (NHC) performance were used [3]. We reasonably assume that vertical and lateral velocities, referred to as body frame (i.e., Vy b, Vz b ), are negligible and close to zero. However, it is important to highlight that such constrats can be considered a valid technique to reduce INS drift only case short outage, while case long outages (e.g., long tunnels or door parkg) additional sensors are needed (e.g., odometer).

9 Sensors 2017, 17, Vertical Velocity Constrat Sce vehicle moves on ground, vertical velocity can be assumed negligible. Therefore, we have modified measurement model Kalman filter as stated (10) by addg formation related to vertical velocity that can be bounded to zero. The new z k vector is written as: z k = [ζ sat,k ˆζ k, 0 V n d ] (13) where Vd n is vertical velocity calculated with respect to local l-frame. The observation matrix can be expressed as (14): H k = H ρ,k 0 Nsat 3 0 Nsat 3 0 Nsat 8 0 Nsat 3 H ρ,k 0 Nsat 3 0 Nsat 8 0, 0, 0 Ce,k n (3, 1), Cn e,k (3, 2), Cn e,k (3, 3) where Ce,k n is DCM matrix with respect to ECEF-frame to l-frame Raw Measurement Selection and Weights R 2Nsat+1,17 (14) In an urban environment, user has ten a limited visibility sky and it is necessary that few satellites view are weighted carefully PVT computation. A set parameters are considered to assess quality received signals and, turn, GNSS measurements. The signal quality relates to satellite elevation, presence multipath and or impairments, and is generally measured with Carrier to Noise density ration (C/N 0 ). Sce presence multipath is more likely to affect satellites with low elevations, our tightly-coupled algorithm adds two masks to exclude satellites with elevations lower than 10 and a satellite showg C/N 0 lower than 30 db-hz. Therefore, algorithm is based on a model for covariance matrix code-based measured pseudoranges, as proposed [1] and [31] specifically for harsh environments: σρ 2 N C 0 = a + b (15) where σ 2 ρ is variance on pseudorange estimates, a and b are empirical parameters, that are set equal to a = 1 and b = for semi-urban/urban scenarios accordg to [1]. Our model cludes also satellite elevation, thus formula (15) is simply rewritten as: 2.4. Insights on Practical Implementations σ 2 ρ = ( a + b 10 N C 0 10 s(elev) The described algorithm was implemented on an embedded system. Sce objective this paper is on comparison algorithms performances, we keep description stware implementation terse. Even if equations behd tightly-coupled scheme do not pose severe complexity constrats, some aspects need to be carefully considered practical implementations. The first issue volves synchronization between measurements. As well explaed [32], prciple low-cost GNSS receivers, MEMS IMU and or sensors generate asynchronous data. We developed a synchronization module that is used to tag measurements from GNSS receiver and from IMU with respect to same time scale. Such a module cludes a time counter that counts number periods processg unit ternal clock between two consecutive 1PPS signals. When a new measurement arrives, it is tagged to value counter. To avoid drifts, counter is reset at arrival a new 1 PPS pulse [33]. ) (16)

10 Sensors 2017, 17, The second important aspect we consider our implementation refers to INS mechanization equations for real-time applications. Even if most microcontrollers have remarkably enhanced ir computational capability, it is still important to reduce number operations volved execution tightly-coupled algorithm. Accordg to [34,35], an efficient algorithm for a strapdown ertial navigation system is based on splittg computg processes to low and high-speed segments. The first part is designed to take to account low frequency, large amplitude, body motions arisg from vehicle maneuvers, whereas second volves a relatively simple algorithm that is designed to keep track high frequency, low amplitude, motions vehicle. We followed this approach by choosg a computation rate for high-speed part equal to 100 Hz, and 10 Hz for low-speed one. The third aspect to consider design real-time system is management GNSS data latency. Every time we use a GNSS receiver, we get observables delayed with respect to 1 PPS signal. This is due to latency data output terface, to type (and quality) micro-processor and its clock speed. Eventually, additional delay refers to type measurements to be processed (e.g., differential code/carrier-phase based observables, multi-antenna GNSS receiver for precise attitude estimation, etc.). In order to manage GNSS data latency, INS measurements are buffered and processed only at proper time stants. For example, if a set new GNSS measurements is expected, because PPS signal has been received, but not yet data message, important variables such as estimated position, velocity, attitude, transition and covariance matrices Kalman filter as well as INS data are saved memory, while navigation solution is provided through IMU measurements only. Only when new data message is available, status Kalman filter is updated and buffers emptied. 3. Performance Assessment Real Urban Scenarios This section describes urban scenarios selected for experimental tests and presents metrics used to assess algorithms performances. The designed tightly-coupled architecture was implemented on an embedded system that was stalled on a vehicle and used for several data Sensors 2017, 17, collections along trajectory shown Figure 6 downtown Tur. Figure 6. Trajectory followed durg experimental tests Tur. Three Three portions portions were were selected, selected, as considered as considered more more relevant relevant for forobjective objective tests: tests: Zone 1, that is a car parkg area, characterized by good visibility sky; Zone 2, that is characterized by narrow streets and densely packed buildgs, limitg number satellites view. Moreover, that part trajectory, vehicle is expected to experience frequent stops and sharp turns; Zone 3, that is a straight avenue trees, surrounded by buildgs that likely generate multipath degradg received GNSS signals.

11 Sensors 2017, 17, Zone 2, that is characterized by narrow streets and densely packed buildgs, limitg number satellites view. Moreover, that part trajectory, vehicle is expected to experience frequent stops and sharp turns; Zone 3, that is a straight avenue trees, surrounded by buildgs that likely generate multipath degradg received GNSS signals. In addition to embedded system runng tightly-coupled algorithm, experimental Sensors setup 2017, cluded 17, 255 or COTS GNSS receivers and a commercial MEMS IMU, loosely-coupled with a mass-market GPS receiver. As reference, we used a tactical-grade IMU that is tightly-coupled with signal a survey-grade, conditions, assessed dual frequency agast GNSS a reliable receiver reference. (i.e., Novatel Neverless, SPAN-CPT recognizg [36]) able toimportance provide Real Timeantenna Kematic (RTK) practical positiong. operations, When we such also acarried receiverout is set additional to work tests RTK usg mode, low-cost its accuracy, patch antennas, terms but RMSE, is results aboutare 2 cmnot along reported horizontal here, because planey and 3do cmnot along provide vertical additional axis, respectively. sights on Thecomparison block diagram between and loose picture and tight tegrations. experimental setup is reported Figure 7. (a) (b) Figure Figure Setup Setup (a) (a) and and setup setup mounted mounted on on board board mounted mounted on on board board a car car (b). (b). The positiong performance was assessed through specific metrics. For all three zones, we To be more specific, prcipal components mounted on embedded system runng analysed number satellites view and Horizontal Positiong Accuracy (HPA). tightly-coupled algorithm were: Followg test procedures defed ETSI standard [38], metric used to characterize -HPA Awas low-cost horizontal 9-axis MEMS position IMU, error manufactured over a specified by InvenSense time terval, [29]; terms its mean, standard deviation and 95th percentile. Furrmore, we compared yaw angle, Along-Track (AT) and - A commercial GNSS module [37]; Cross-Track (CT) errors estimated by embedded system and same parameters provided by - A 720 MHz micro-controller Cortex-A8, runng QNX as operatg system. commercial Microstra MEMS IMU [23] list. For As mentioned, sake we clarity, compared Table our 1 summarizes solution agast hardware two commercial components standalone depicted receivers Figure (see 7a and blackir andnavigation green blocks techniques. FigureEach 7a and configuration Table 1 for has details) been associated same with type a aslabel that that usedis n used embedded system. followg The sections first was to comment configured to use results. only GPS satellites, second to use both GPS and GLONASS. Last, commercial MEMS IMU (red block Figure 7a) was Microstra 3DM-Gx3-45 [23] Table that 1. provided Hardware acomponents loosely-coupled and navigation GPS/INStechnique tegration used bydurg usg tests. embedded U-blox GPS receiver. Label More details on cost and performance Hardware all IMUs used Navigation setuptechnique are reported GPS+INS tightly-coupled Tight Embedded System algorithm Commercial MEMS IMU (i.e., GPS+INS loosely-coupled Microstra Microstra 3DM-Gx3-45) algorithm Standalone GPS receiver (i.e., NVS GPS Rx Standalone GPS PVT solution

12 Sensors 2017, 17, Appendix A. Note that all devices beg tested, cludg reference system, received GNSS signals from same antenna that was survey-grade AeroAntenna AT antenna. Indeed, quality antenna fluences positiong performance, but ma objective this work was comparison loose and tight tegrations under same signal conditions, assessed agast a reliable reference. Neverless, recognizg importance antenna practical operations, we also carried out additional tests usg low-cost patch antennas, but results are not reported here, because y do not provide additional sights on comparison between loose and tight tegrations. Table 1. Hardware components and navigation technique used durg tests. Label Hardware Navigation Technique Tight Embedded System GPS+INS tightly-coupled algorithm Microstra Commercial MEMS IMU (i.e., Microstra 3DM-Gx3-45) GPS+INS loosely-coupled algorithm GPS Rx Standalone GPS receiver (i.e., NVS NV08C-CSM) Standalone GPS PVT solution GPS+GLONASS Rx Standalone GPS/GLONASS receiver (i.e., NVS NV08C-CSM) Standalone GPS+GLONASS PVT solution Ref NOVATEL REFERENCE SYSTEM Dual Frequency GPS, RTK+INS (i.e., Novatel SPAN-CPT) tightly-coupled algorithm The positiong performance was assessed through specific metrics. For all three zones, we analysed number satellites view and Horizontal Positiong Accuracy (HPA). Followg test procedures defed ETSI standard [38], metric used to characterize HPA was horizontal position error over a specified time terval, terms its mean, standard deviation and 95th percentile. Furrmore, we compared yaw angle, Along-Track (AT) and Cross-Track (CT) errors estimated by embedded system and same parameters provided by commercial Microstra MEMS IMU [23] list. For sake clarity, Table 1 summarizes hardware components depicted Figure 7a and ir navigation techniques. Each configuration has been associated with a label that is n used followg sections to comment on results. 4. Results Field Tests This section shows results test campaign, dividg analysis among three zones terest. The performance devices beg tested is compared terms metrics mentioned above Zone 1: Car Parkg Area A zoomed view this area is reported Figure 8, where trajectories recorded by devices are reported on map with different colors. The vehicle was driven car park, formg several figure eights. It is worth notg that vehicle remaed a static condition for approximately 2 m before movg. On average, number satellites used by multi-constellation standalone receiver (black curve) was 16, while all or devices, configured to process only GPS signals, received 10 satellites. The advantage havg more satellites view is more evident or zones, where number satellites is sometimes not sufficient to provide positions, if receiver relies only on GPS. The horizontal positiong errors are plotted Figure 9 on North and East coordates. Note that such errors are computed by subtractg coordates estimated by devices under vestigation and those provided by reference receiver, at same time stants.

13 This section shows results test campaign, dividg analysis among three zones terest. The performance devices beg tested is compared terms metrics mentioned above Zone 1: Car Parkg Area Sensors 2017, 17, 255 A zoomed view this area is reported Figure 8, where trajectories recorded by devices are reported on map with different colors. Sensors 2017, 17, from 5 to 5 m along North coordate and from 3 to 4 along East coordate, respectively. The benefits multi-constellation cannot be noted this scenario due to high number GPS satellites view. Indeed, as evident from same level standard deviations and 95th Figure 8. Trajectories collected by devices beg tested Zone 1. Figure 8. Trajectories collected by devices beg tested Zone 1. percentiles, Figure performance GPS/GLONASS receiver (black columns 9b) is comparable with that obtaed by GPS receiver (green columns Figure 9b). The vehicle was driven car park, formg several figure eights. It is worth notg that HPE vehicle remaed a static condition for approximately 2 m before movg. On average, number satellites used by multi-constellation standalone receiver (black curve) was 16, while all or devices, configured to process only GPS signals, received 10 satellites. The advantage havg more satellites view is more evident or zones, where number satellites is sometimes not sufficient to provide positions, if receiver relies only on GPS. The horizontal positiong errors are plotted Figure 9 on North and East coordates. Note that such errors are computed by subtractg coordates estimated by devices under vestigation and those provided by reference receiver, at same time stants. From Figure 9, it is possible to observe how tightly-coupled algorithm (blue les) provides best performance terms precise estimates vehicle position. The tight tegration shows lowest standard deviations and 95th percentiles error on both coordates. Although a small bias approximately equal to 1 m affects position accuracy tight solution, we observe effect static condition constrats first part data collection, which matas estimated position constant while vehicle is actually still. Different reasons could have generated presence bias positional solution obtaed through designed tightly-coupled technique. Most likely, reference system used a higher number satellites to compute PVT with respect to our solution and had a lower dilution precision. In addition, reference system was set to work RTK mode, exploitg differential carrier-phase measurements, that allows for achievg an accuracy 2 cm. On contrary, our solution was based only on (a) code-based pseudoranges. The commercial MEMS IMU (red les) has an error that varies from 2 Metrics to 2 m both North and East axes, while standalone GPS receiver shows errors rangg (b) Figure 9. Horizontal positiong errors Zone 1. In (a) measurements over time: error North Figure 9. Horizontal positiong errors Zone 1. In (a) measurements over time: error North direction (above) and error East direction (below). In (b) metrics associated to error direction North (above) and error to In (b) metrics associated to error direction (left) and East errordirection East (below). direction (right). North direction (left) and to error East direction (right). Figure 10 shows yaw angles estimated by embedded system runng tightly-coupled algorithm (blue le) and those obtaed by commercial MEMS IMU (red le). The figure also plots cyan yaw angles estimated by reference receiver.

14 Sensors 2017, 17, From Figure 9, it is possible to observe how tightly-coupled algorithm (blue les) provides best performance terms precise estimates vehicle position. The tight tegration shows lowest standard deviations and 95th percentiles error on both coordates. Although a small bias approximately equal to 1 m affects position accuracy tight solution, we observe effect static condition constrats first part data collection, which matas estimated position constant while vehicle is actually still. Different reasons could have generated presence bias positional solution obtaed through designed tightly-coupled technique. Most likely, reference system used a higher number satellites to compute PVT with respect to our solution and had a lower dilution precision. In addition, reference system was set to work RTK mode, exploitg differential carrier-phase measurements, that allows for achievg an accuracy 2 cm. On contrary, our solution was based only on code-based pseudoranges. The commercial MEMS IMU (red les) has an error that varies from 2 to 2 m both North and East axes, while standalone GPS receiver shows errors rangg from 5 to 5 m along North coordate and from 3 to 4 along East coordate, respectively. The benefits multi-constellation cannot be noted this scenario due to high number GPS satellites view. Indeed, as evident from same level standard deviations and 95th percentiles, performance GPS/GLONASS receiver (black columns Figure 9b) is comparable with that obtaed by GPS receiver (green columns Figure 9b). Figure 10 shows yaw angles estimated by embedded system runng tightly-coupled algorithm Sensors 2017, (blue 17, 255 le) and those obtaed by commercial MEMS IMU (red le). The figure15 also 27 plots cyan yaw angles estimated by reference receiver. Figure 10. Yaw angles estimated by different MEMS IMU sensors under vestigation Zone 1. Figure 10. Yaw angles estimated by different MEMS IMU sensors under vestigation Zone 1. In In Figure Figure itial itial part part static static has has been been omitted omitted sce sce reference reference receiver receiver provided provided a valida attitude valid attitude value only value dynamic. only dynamic. The tightly-coupled The tightly-coupled algorithm needs algorithm tens needs seconds tens to converge seconds to to converge right solution: to right such behavior solution: is such duebehavior to low-cost is due IMU to used low-cost embedded IMU used system that embedded is not able system to provide that is anot valid able itial to provide headga angle valid due itial to headg high level angle due noise to affectg high itslevel gyroscopes. noise However, affectg after its gyroscopes. this transient, However, yawafter angle this estimated transient, by our yaw system angle isestimated comparable by to our that system is reference comparable receiver, to that as correspondg reference receiver, curvesas Figure correspondg 10 are almost curves superimposed. Figure 10 Conversely, are almost superimposed. quality Conversely, measurements provided quality by commercial measurements MEMS provided IMU seems by poor, commercial as difference MEMS with IMU respect seems topoor, reference as difference is on order with respect tens to degrees, reference even is open on skyorder conditions. tens Eventually, degrees, also even AT open andsky CTconditions. errors haveeventually, been calculated also andat canand be seen CT errors Figure have 11. been calculated and can be seen Figure 11.

15 comparable to that reference receiver, as correspondg curves Figure 10 are almost superimposed. Conversely, quality measurements provided by commercial MEMS IMU seems poor, as difference with respect to reference is on order tens degrees, even open sky conditions. Eventually, also AT and CT errors have been calculated and can be seen Sensors 2017, Figure 17, Figure 11. Along-Track (AT) and Cross-Track (CT) errors Zone 1. Figure 11. Along-Track (AT) and Cross-Track (CT) errors Zone 1. Considerg AT error, open sky conditions, tightly-coupled algorithm is able to bound it with 1 m, while commercial MEMS IMU shows an error rangg from 2 to 2 m. On or hand, Figure 11 confirms what was commented on above, that 1 m bias affects estimated positions also AT-CT frame by usg tightly-coupled algorithm. The commercial MEMS IMU shows a much smaller bias, although position estimates are less precise. The details loosely-coupled algorithm implemented commercial MEMS IMU are not known. Similar to fset experienced by our tightly-coupled algorithm, such a small bias is likely due to use code-based pseudorange measurements and to a lower number satellites used PVT. Moreover, bias experienced with Microstra is different from bias obtaed with tightly-coupled algorithm, because different strategies selection and weight satellite measurements cluded PVT computation Zone 2: Urban Canyon The left part Figure 12 shows trajectories recorded by devices beg tested Zone 2 that is characterized by narrow streets, tightly packed buildgs and reduced sky visibility. A snapshot path is visible on right side Figure 12 with a zoomed view, taken from Google Earth. In such a challengg scenario, number satellites view plays a fundamental role obtag accurate navigation performance. The number GPS satellites is remarkably reduced with respect to Zone 1. Although multi-constellation standalone receiver (black curve) still guarantees at least 12 satellites view most time, tightly-coupled algorithm worked, on average, with 5 6 satellites. The reference system tracked also satellites with degraded C/N 0 and had a higher number GPS satellites trackg with respect to mass market GPS receiver used by embedded system. Similar to Zone 1, horizontal positiong errors are plotted Figure 13 on North and East coordates, for all devices under vestigation.

16 obtaed with tightly-coupled algorithm, because different strategies selection and weight satellite measurements cluded PVT computation Zone 2: Urban Canyon The left part Figure 12 shows trajectories recorded by devices beg tested Zone 2 Sensors 2017, that 17, is 255characterized by narrow streets, tightly packed buildgs and reduced sky visibility. A snapshot path is visible on right side Figure 12 with a zoomed view, taken from Google Earth. (a) (b) Figure 12. Trajectories collected by devices beg tested Zone (a) and picture a narrow Figure 12. street Trajectories passed through collected durg by test devices (b). beg tested Zone 2 (a) and picture a narrow street passed through durg test (b). Sensors 2017, 17, In such a challengg scenario, number satellites view plays a fundamental role obtag accurate navigation performance. The HPE number GPS satellites is remarkably reduced with respect to Zone 1. Although multi-constellation standalone receiver (black curve) still guarantees at least 12 satellites view most time, tightly-coupled algorithm worked, on average, with 5 6 satellites. The reference system tracked also satellites with degraded C/N0 and had a higher number GPS satellites trackg with respect to mass market GPS receiver used by embedded system. Similar to Zone 1, horizontal positiong errors are plotted Figure 13 on North and East coordates, for all devices under vestigation. (a) Metrics (b) Figure 13. Horizontal positiong errors Zone 2. In (a) measurements over time: error Figure 13. Horizontal positiong errors Zone 2. In (a) measurements over time: error North North direction (above) and error East direction (below). In (b) metrics associated to error direction (above) North and direction error(left) and East to direction error (below). East direction In (b) (right). metrics associated to error North direction (left) and to error East direction (right). From Figure 13 we appreciate benefits tightly-coupled algorithm (blue les) with respect to loosely-coupled one (red les). The tight strategy provides accurate position estimates, with lowest standard deviations (i.e., 1.69 m on North and 2.10 m on East) and 95th percentiles (i.e., 2.13 m on North and 5.60 m on East) errors. In this test case, embedded system runng tightly-coupled algorithm outperformed commercial MEMS IMU. The horizontal position error was always lower than 5 m along North coordate, and lower than 8 m along East coordate. As expected, re is a general degradation

17 Sensors 2017, 17, From Figure 13 we appreciate benefits tightly-coupled algorithm (blue les) with respect to loosely-coupled one (red les). The tight strategy provides accurate position estimates, with lowest standard deviations (i.e., 1.69 m on North and 2.10 m on East) and 95th percentiles (i.e., 2.13 m on North and 5.60 m on East) errors. In this test case, embedded system runng tightly-coupled algorithm outperformed commercial MEMS IMU. The horizontal position error was always lower than 5 m along North coordate, and lower than 8 m along East coordate. As expected, re is a general degradation positiong performance all devices, passg from Zone 1 to Zone 2. Clearly, standalone GPS receiver (green les) does not fer similar performance and showed errors up to 30 m, with a standard deviation error on order ten meters. Furrmore, considerg measurements from two un-coupled GNSS receivers, we notice benefits brought by multiple constellations that results improved positiong performance when visibility satellites belongg to one constellation Sensors 2017, 17, is reduced. The multi-constellation receiver (black les) has significantly better performance with respect significantly to sgle better constellation performance receiver, with respect even comparable to sgle with constellation ones obtaed receiver, by even commercial comparable MEMS with IMU. ones obtaed by commercial MEMS IMU. Figure Figure shows shows yaw yaw angle angle estimated estimated by by embedded embedded system system runng runng tightly-coupled tightly-coupled algorithm algorithm and and commercial commercial MEMS MEMS IMU. IMU. Figure Figure Yaw Yaw angles angles estimated estimated by by different different MEMS MEMS IMU IMU sensors sensors under under vestigation vestigation Zone Zone In In Zone 2, 2, yaw angle estimated by by embedded system (blue le) followed trend yaw angle estimated by by reference (cyan le). In In certa time stants, it it is is possible to to note aa difference that that reaches values up up to 20 to 20.. However, accuracy yaw yaw angle, angle, as calculated as calculated by by tightly-coupled strategy, strategy, is much is much better better with respect with respect to thatto provided that provided by by commercial commercial MEMS MEMS IMU. InIMU. fact, In fact, standard standard deviation deviation error on error estimated on yaw estimated angle is yaw 5.28angle usg is 5.28 embedded usg system, embedded agast system, 44.3 agast measured 44.3 onmeasured angleson estimated angles byestimated commercial by MEMS commercial IMU. The MEMS AT IMU. and CT The errors AT and overct time errors are shown over time Figure are shown 15. Figure 15. The AT and AC errors are lower with tightly-coupled algorithm (blue le) with respect to loosely-coupled approach used by commercial MEMS IMU (red le). In this test case, standard deviation AT and CT errors are approximately 1.8 m and 2.06 m, agast 4.5 m and 4.6 m experienced with commercial MEMS IMU. The reason is twold: first, tight tegration allows for more degrees freedom design tegration Kalman filter; second, better accuracy estimated yaw angles is certaly an advantage to obta small AT and CT errors.

18

19 Sensors 2017, 17, Compared to Zone 2, this scenario appears less critical. Indeed, GNSS receiver used by embedded system tracks on average eight satellites. Obviously, multi-constellation receiver is able to track highest number satellites: this case, it is 15 most time. Only a few stants does number satellites drop to 14. Sensors The 2017, horizontal 17, 255 positiong error over time is plotted Figure 17, for all devices beg 20 tested. 27 HPE (a) Metrics (b) Figure 17. Horizontal positiong errors Zone Zone 3. In 3. (a) In measurements (a) over over time: time: error error North North direction direction (above) (above) and error and error East direction East direction (below). (below). In (b) In metrics (b) metrics associated associated to to error error North North direction direction (left) and (left) toand to error error East direction East direction (right). (right). Also Also this this scenario, scenario, tightly-coupled tightly-coupled algorithm algorithm provided provided lowest lowest error error with with respect respect to all to all or or sensors sensors used used test. test. We We notice notice that that standard standard deviation deviation horizontal horizontal errors errors is is lower lower than than 1 1 m m on on both both North North and and East East coordates, coordates, whereas whereas 95th 95th percentiles percentiles do do not not reach reach 2 m. m. The The results results this this test test case case are are similar similar to to those those obtaed obtaed open open sky sky conditions. conditions. This This proves proves robustness robustness tight tegration tight tegration that is that able is to provide able to provide reliable position reliable estimates position estimates also conditions also conditions low satellite visibility. Also this zone, embedded system outperformed commercial MEMS IMU (red les). However, from this test, it is possible to observe advantages brought by GNSS/INS tegration, eir followg a loosely-coupled (red les) or a tightly-coupled (blue les) architecture. Both showed superior performance with respect to standalone receivers, both sgle (green les) and multi-constellation (black les). In particular,

20 Sensors 2017, 17, low satellite visibility. Also this zone, embedded system outperformed commercial MEMS IMU (red les). However, from this test, it is possible to observe advantages brought by GNSS/INS tegration, eir followg a loosely-coupled (red les) or a tightly-coupled (blue les) architecture. Both showed superior performance with respect to standalone receivers, both sgle (green les) and multi-constellation (black les). In particular, designed tightly-coupled strategy matas a small standard deviation on estimated positions and, thanks to velocity Sensors 2017, 17, constrats, it matas solution constant whole time car is static. In this test case, two standalone 2, where sometimes receivers showed number similar performance. GPS satellites Contrary was not sufficient to Zone 2, to where compute sometimes user s number position, GPS this satellites scenario was advantage not sufficient to compute multi-constellation user s position, cannot be appreciated. this scenario Indeed, advantage durg test, multi-constellation number tracked cannot GPS be appreciated. satellites never Indeed, decreased durg below test, six and number dilution tracked precision GPS satellites remaed never below decreased 1.2. below six and dilution precision remaed below 1.2. Figure Figure shows shows yaw yaw angle angle estimated estimated by by embedded embedded system system runng runng tightly-coupled tightly-coupled algorithm algorithm and and commercial commercial MEMS MEMS IMU. IMU. Figure Figure Yaw Yaw angles angles estimated estimated by by different different MEMS MEMS IMU IMU sensors sensors under under vestigation vestigation Zone Zone The error estimated estimated yaw yaw angle angle is quite is large quite for large case for case loosely-coupled loosely-coupled algorithm implemented algorithm implemented commercial commercial MEMS IMU. MEMS Such aimu. poor Such quality a poor quality yaw estimate yaw is estimate le withis le performance with performance experienced experienced or zones. or zones. On contrary, designed tightly-coupled algorithm provided aa yaw angle similar to to that estimated by by reference, even even if if some some time time tervals tervals (e.g., (e.g., GPS GPS time time from from 301, ,290 to 301,315 to 301,315 and from and 301,600 from 301,600 to 301,635) to 301,635) difference difference between between m was m up to was 15 up. Conversely, to 15. Conversely, we observe we how observe yaw how angle yaw is mataed angle is mataed constant durg constant long durg static conditions long static experienced conditions byexperienced vehicle. by vehicle. Eventually, Figure 19 reports AT and CT errors computed Zone 3. The Eventually, loosely-coupled Figure 19 strategy reports provides AT and positions CT errors computed AT and CT Zone framework, 3. with errors to order few meters. The standard deviation AT and CT error is higher if compared to one experienced by designed tightly-coupled algorithm. Similar to Zone 2, poor estimate yaw angles affects positiong performance AT-CT frame. In Appendix B we have summarized results obtaed each scenario, for all receivers beg tested.

21 On contrary, designed tightly-coupled algorithm provided a yaw angle similar to that estimated by reference, even if some time tervals (e.g., GPS time from 301,290 to 301,315 and from 301,600 to 301,635) difference between m was up to 15. Conversely, we observe how yaw angle is mataed constant durg long static conditions experienced by Sensors vehicle. 2017, 17, Eventually, Figure 19 reports AT and CT errors computed Zone 3. Figure 19. AT and CT errors Zone Conclusions This paper presents assessment positiong performance a Global Navigation Satellite Systems (GNSS)/Inertial Navigation Systems (INS) tightly-coupled algorithm, measured real urban scenarios. The algorithm was designed to fuse measurements from a low-cost INS and a mass-market Global Positiong System (GPS) receiver. Results show a significant decrement positiong errors, if compared to those obtaed with or commercial devices. In particular, tightly-coupled algorithm provides better estimates vehicle position and attitude, with respect to a commercial GPS module, loosely tegrated with an ertial sensor. The improvement was measured followg a standardized testg method, considerg horizontal position error and yaw angle, as ma performance metrics. The experimental results reported this paper demonstrate possibility to employ tightly-coupled architectures also mass-market devices, ten employed applications where users move urban spaces. Examples clude pay-as-you-drive surances, trackg fleet for wter road matenance, systems for advanced driver assistance and autonomous vehicles. In years ahead, improvement Micro Electrical Mechanical Sensors (MEMS) technology and evolution GNSS, with enhanced signal formats, different frequency bands and more satellites view, are expected to furr crease positiong performance mass-market devices, enablg a variety new services for road users. Acknowledgments: The authors would like to thank anonymous reviewers and editors for ir help to improve paper. Author Contributions: G.M. and M.P. conceived and designed experiments; G.M. and G.F. performed experiments; G.F. analyzed data. Conflicts Interest: The authors declare no conflict terest. Appendix A In Table A1 we have also summarized ma features and price IMU sensors that have been used durg test urban area. Only gyros characteristics have been reported because y represent most important error sources case MEMS IMU. In fact, gyro error has a direct fluence on attitude accuracy sce GNSS Kalman filter updates can only

22 Sensors 2017, 17, directly compensate for position and velocity error while attitude error relies mostly on IMU calibration and proper gyro bias correction. Table A1. A selection sensor error distributions derived from ir datasheets, units given. The InvenSense is a consumer-grade MEMS. The Microstra is a factory-calibrated MEMS IMU and Novatel is a tactical-grade IMU. Sensor Manufacturer InvenSense Microstra Novatel Model MPU DM-Gx3-45 SPAN-CPT Type Sgle-chip IMU Factory calibrated IMU Factory calibrated IMU Price <100$ 3000$ 25000$ Gyroscope Errors Bias Offset ±20 dps * ±0.25 dps ± dps In-Run Stability Not Specified 18 /h ± 1 /h Noise dps/ Hz 0.03 dps/ Hz dps/ Hz Scale Factor Errors ±3% ±0.05% ±0.0015% Non-Lenearity ±0.2% FS ±0.03% Not Specified Cross-Axis Sensitivity ±2% FS Aligned to ±0.05 Not Specified Appendix B * dps = degree per second, FS = Full Scale. Table A2. Summary performance receivers beg tested Zone 1 (Open Sky). Sensor Manufacturer Custom-Board Microstra NVS NVS Model MPU DM-Gx3-45 NV08C-CSM NV08C-CSM Nav Algorithm Tightly-coupled Loosely-coupled GPS/GLONASS GPS Mean, St. Dev, 95th PCTL Position North [m] Position East [m] N.A. * N.A. Headg [deg] N.A. N.A N.A. N.A Along Track [m] N.A. N.A N.A. N.A N.A. N.A. Cross Track [m] N.A. N.A N.A. N.A. N.A.* = Not Available.

23 Sensors 2017, 17, Table A3. Summary performance receivers beg tested Zone 2 (Urban Canyon). Sensor Manufacturer Custom-Board Microstra NVS NVS Model MPU DM-Gx3-45 NV08C-CSM NV08C-CSM Nav Algorithm Tightly-coupled Loosely-coupled GPS/GLONASS GPS Mean, St. Dev, 95th PTCL Position North [m] Position East [m] N.A.* N.A. Headg [deg] N.A. N.A N.A. N.A N.A. N.A. Along Track [m] N.A. N.A N.A. N.A N.A. N.A. Cross Track [m] N.A. N.A N.A. N.A. N.A.* = Not Available. Table A4. Summary performance receivers beg tested Zone 3 (Avenue trees). Sensor Manufacturer Custom-Board Microstra NVS NVS Model MPU DM-Gx3-45 NV08C-CSM NV08C-CSM Nav Algorithm Tightly-coupled Loosely-coupled GPS/GLONASS GPS Mean, St. Dev, 95th PTCL Position North [m] Position East [m] N.A. * N.A. Headg [deg] N.A. N.A N.A. N.A N.A. N.A. Along Track [m] N.A. N.A N.A. N.A N.A. N.A. Cross Track [m] N.A. N.A N.A. N.A. N.A.* = Not Available. References 1. Carcanague, S.; Julien, O.; Vigneau, W.; Macabiau, C.; He, G. Fdg right algorithm: Low-cost, sgle-frequency GPS/GLONASS RTK for road users. Inside GNSS 2013, 8, Defa, A.; Favenza, A.; Falco, G.; Orgiazzi, D.; Pi, M. ASSIST: An Advanced Snow Plough and Salt Spreader Based on Innovative Space Based Technologies. In Proceedgs 28th International Technical Meetg Satellite Division Institute Navigation (ION GNSS+ 2015), Tampa Convention Center, Tampa, FL, USA, September 2015; pp

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