Solution to the Anti-aircraft Fire Control Problem on a Naval Platform Using the Direct Geometric Model

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2 Gil, Vivas Introduction The classic problem of anti-aircraft fire control consists in the correct prediction of the future position of a target over the time in which it is intercepted by a missile (Berg, 1983). For naval antiaircraft fire, the weapon is on a platform that moves within six degrees of freedom, which requires maintaining the reference in orientation and displacement with respect to the world (Weiss, 1979) through an inertial navigator (I.N.S.) (Woodman, 27). Additionally, the cannon, the detection system, the inertial navigator, and the platform s center of gravity are in different places; thus, requiring further corrections. A solution consists in employing numerical methods to correct the cannon s orientation with respect to the line of sight (Elnashar, 213), using the relative velocity between the target and the platform to predict the future position. This generates error when the platform is mobile, given that the relative velocity to the platform and the target s absolute velocity are different. This error is greater inasmuch as the velocity of the platform is comparable to that of the target. In addition, the derived equations result complex and with many elements. Wang (212) employs the free-body diagram and coordinate conversion through Euler angles (Slabaugh, 1999) to find the kinematic model of a system with four stabilized cameras in an air balloon. This method develops conversions of coordinates successively, which also turns out complex due to the large amount of elements to treat. Conversion via Euler angles is used in multiple applications, including control of guided missiles (Ollerenshaw, 25) and unguided missiles (Hahn, 29); stabilization of cameras on vehicles (Zayed, 27), (Kumar, 28); dynamic models of underwater vehicles (Wadoo, 211); and models of inertial platforms (Dongsheng, 211). A way to simplify the movement equations is to use the matrix representation of vectors. This reduces the number of coefficients necessary for control, making it easier to construct models with multiple inputs and multiple outputs (Fossen, 211). Fossen (1991) uses an inverse dynamic model to model a naval artifact with six degrees of freedom. This implementation conceives the artifact as a rigid body and it is used to design controllers for governance and stabilization systems. Cabecinhas (212) uses a similar strategy for the dynamic model of a four-rotor helicopter. Kim (28) constructs a virtual model of a stabilization platform that works as a parallel robot capable of moving in six degrees of freedom. Through the inverse geometric model the length of six prismatic articulations is determined. On this virtual model, a drift (ronza) elevation system is tested for a machine gun and its respective sensors. This permits saving resources upon developing part of the field tests on the virtual model. Other tools may be employed to construct virtual models of weapons systems, like in Bo (211), where Virtool and High-Level Architecture (HLA) are used. Bearing in mind that the naval fire control problem can contemplate up to 14 degrees of freedom, it turns out complex to find its geometric model by independently performing coordinate transformations, with it being best to take advantage of the methodical manner of performing successive transformations as proposed by Khalil-Kleinfinger (1986). A variation is, then, proposed of this approach to construct a geometric model of the fire control problem, including platform movements and extending to integrate the movements of the target, the cannon located on the platform, and the missile in flight. This model will serve as a framework to design, test, and integrate controllers for the cannon or the direction of fire control. Bao-quan et al. (21) designed a controller for a cannon s servos on a virtual model. Upon developing a virtual model, huge costs are saved because this allows early detection of possible design problems and tests are reduced by using real surface units, real aircraft targets, and real weapons (Kim, 28). Solution to the fire control problem The following algorithm was designed to solve the fire control problem. 44

3 Solution to the Anti-aircraft Fire Control Problem on a Naval Platform Using the Direct Geometric Model Fig. 1. Algorithm proposed for fire control. TRACING SUBSYSTEM Course Roll Pitch Vertical Mov. Current Target Position FIRE CONTROL PREDICTOR Future Target Position Future Target Position Error Missile Position in Tv IS THE DIFFERENCE BETWEEN TARGET FUTURE POSITION AND MISSILE POSITION SMALL ENOUGH? Yes/No Flight time of Missile FIRE BOARD WHILE Absolut Elevation and distance to future target FUTURE TARGET SEEN FROM CANNON Cannon absolute coordinates M.G.D UNTIL CANNON POSITION MISSILE PATH - Advance distance - Descent degree - Descent distance - Right drift - Absolute elevation tube barrel - Right drift Absolut dial to future target M.G.D UNTIL MISSILE POSITION Course VERTICAL Roll REFERENCE Pitch SUBSYSTEM Vertical Mov. INERCIAL Course NAVIGATOR Roll Pitch Course Vertical Mov. Roll Pitch Vertical Mov. STABILIZATION Desired Elevation Desired Elevation YES NO CANNON Desired Elevation CONTROLLER Control Signal CANNON SUBSYSTEM Monitoring Subsystem This determines the current target position in rectangular coordinates with respect to the platform position over the surface. This position is defined by the point projected by the platform s center of gravity over the surface plane. The target s position is measured through a fire control director that indicates compass bearing, elevation, and distance; thereafter, the coordinates are translated to the point of reference. The direct geometric model of T 1D is found and the target s coordinates are obtained, thus: (1) (2) (3) Corrections to maintain the point of reference as fixed point Unlike the robots normally modeled through the Khalil-Kleinfinger (1986) technique, in this case the point of reference moves on a plane. Due to this, it is presumed that for each discrete time the pointof-reference position is fixed and the prior target positions are corrected according to the platform s displacement on the plane. It is assumed that the 45

4 Gil, Vivas Fig. 2. Direct geometric model monitoring system and parameters. FIRE DIRECTOR: measures the target s position in spherical coordenates Elevation Fire Director θ9d Z8D X9D Z9D X1D CURRENT TARGET Target POSITION R1D X8D Z1 X6D Z6D CG-DT in X R7D X7D CG-DT in Y R6D Z7D Z Z1 Z2 Course θ2 Vertical Movement R1 X3 Z3 REFERENCE POINT Pitch θ3 X2 X1 X CG-DT in Z R5D Roll θ4 Z5D Gravity Center Z4 X5D X4 SHIP MOVEMENT BY SEA ACTION: by roll sensor, pitch, course and vertical acceleration. (Inertial Navegator) Articulation σ α d Ɵ R 1 1 Height 2 Course 3 -π/2 Pitch-π/2 4 -π/2 Roll-π/2 5D 1 -π/2 -π/2 6D 1 -π/2 -π/2 7D 1 -π/2 -π/2 Center of Gravity- Z director Center of Gravity- Y director Center of Gravity- X director 8D -π/2 Ronza-π/2 9D -π/2 Elevation-π/2 1D 1 -π/2 Director- Target inertial navigator is 1% precise; however, in reality a deviation will exist. A P matrix is defined, which keeps the target s previous positions with respect to the point of reference that is always [x=, y=]. (4) P t is calculated in the following manner: (5) Where P is the platform s displacement in the sampling time, t. To find P, measure the instantaneous acceleration delivered by the inertial navigator, a i, and calculate such through Newton s laws of motion. (6) 46

5 Solution to the Anti-aircraft Fire Control Problem on a Naval Platform Using the Direct Geometric Model Where Vi t-1 is the platform s velocity in the prior time, which is calculated by integrating a i (Woodman, 27). Pt and the target s current position [x t, y t ] are the inputs to the predictor to calculate the target s future position. Predictor of the target s future position By using any prediction algorithm, like the Kalman filter (Berg, 1982), from the known trajectory, the target s position is sought in a future time equal to the missile s flight time, t v. The problem is that the target s future position is a function of the missile s flight time, which in turn is a function of the target s future position. To solve this, we first assume that the target s future position is equal to the current position, hence, the missile s flight time, t v, is calculated. This flight time serves to calculate a new future position of the target. When repeating this process several times, the difference between the target s future position and the missile s position in t v is reduced until it gets close to zero (Vila, 29). The missile s flight time is obtained from shooting charts. Shooting charts contain the ballistic parameters of a given type of ammunition. These charts are elaborated by manufacturers and are determined experimentally. From the elevation angle and the distance to target, we find the cannon s elevation angle, missile s angle of fall in its terminal part, and the missile s lateral deviation caused by the rotation on its own axis (Vila, 29). Another way is to mathematically model the missile s trajectory in the atmosphere based on parameters like the ballistic coefficient, initial velocity, and atmospheric pressure (Carlucci, 27). Direct geometric model from the point of reference to the end of the missile s trajectory The geometric model from the point of reference to the missile s position in t v permits comparing this last position with the target s future position at the same time, so that we can find a drift position and cannon elevation that reduces the distance between these two points to zero. The direct geometric model of T 14C is found and the missile s coordinates at moment t v are obtained, thus equations 7, 8 and 9 (see page 49). Calculation of drift and cannon elevation To complete all the parameters of the direct geometric model, determine where the cannon should be aimed with respect to the platform. Fig. 3. Geometric representation of the parameters provided by the shooting chart. Target s interception point Angle of descent Descent distance Side drift Angle of projection Advanced distance Elevation Angle Slope distance Missile path Height Range Angle Angle of target position Horizontal distance 47

6 Gil, Vivas Fig. 4. Direct geometric model from the point of reference to the end of the missile s trajectory and parameters. MISSILE POSITION IN Tv X14C Decoupling missile in flight θ1c X12C X13C Z14C X9C X1C X11C Descent R13C Drift R14C Cannon Elevation θ9c Z9C Advance R11C Descent Angle θ12c Z13C CANNON Z8C Z1C Z11C Z Z1 Z2 X3 Z3 Roll θ4 Z5C CG-CN in Z R5C X6C CG-CN in Y R6C Z6C X5C X4 CG-CN in X R7C X8C Cannon s Turning Gear θ8c Z7C X7C Picth θ3 Course θ2 X2 X1 Vertical Movement R1 X REFERENCE POINT Z4 GRAVITY CENTER SHIP MOVEMENT BY SEA ACTION: by roll sensor, pitch, course and vertical acceleration. (Inertial Navegator) Articulation σ α d Ɵ R 1 1 Height 2 Course 3 -π/2 Pitch-π/2 4 -π/2 Roll-π/2 5C 1 -π/2 -π/2 Center of Gravity- Z cannon 6C 1 -π/2 -π/2 Center of Gravity- Y cannon 7C 1 -π/2 -π/2 Center of Gravity- X cannon 8C -π/2 Drift-π/2 9C -π/2 Elevation-π/2 1C -π/2 Missile Decoupling 11C 1 Rise distance 12C π/2 Angle of descent 13C 1 -π/2 Descent 14C 1 π/2 Deviation 48

7 Solution to the Anti-aircraft Fire Control Problem on a Naval Platform Using the Direct Geometric Model (7) (8) (9) For this, the platform aspect is characterized with respect to the world through three unit vectors: the vertical that describes the platform plane, the bow that describes the direction, and the starboard that is perpendicular to the previous two. These are found through the following direct geometric models, from data on course, roll, and pitch. Fig. 5. Geometric bow and vertical models and parameters. X4 Z4 X2 Z2 X3 X2 VERTICAL=1 BOW=1 Z2 ROLL X3 Z Z1 COURSE/ YAW X1 PITCH Z3 Z Z1 COURSE/ YAW PITCH X1 Z3 X X 49

8 Gil, Vivas BOW Articulation σ α d Ɵ R 1 1 Course 2 -π/2 Pitch-π/2 3 -π/2 1 VERTICAL 1 Course 2 -π/2 Pitch-π/2 3 -π/2 Roll-π/ π/2 1 The components of this last vector are found in the directions of the vertical, bow, and starboard vectors performing the corresponding point products. (2) (21) (22) Finally, the coordinates of the bow, starboard, and vertical vectors of the platform are obtained, thus: (1) (11) (12) Fig. 6a. Cannon direction vector (M) with respect to the point of reference. Mv Vertical Zenit Me M (13) (14) East Bow (15) Starboard Mp North (16) Fig. 6b. Cannon direction vector (M) with respect to the platform. Upon defining the reference coordinates system, the necessary drift and cannon elevation are determined. For such, we determined the rectangular coordinates of the directions of compass bearing and elevation obtained through the shooting chart by constructing a geometric model equal to that described in Fig. 5 for the bow, except that the articulations are drift, elevation, and cannon tube, obtaining the coordinates of the directions of compass bearing and elevation obtained through the shooting chart, thus: Vertical Starboard Mv Me Mp M Elevation Turning Gear Bow (17) (18) (19) The component of the compass bearing-elevation vector in direction of the vertical vector permits finding the elevation, while the components in bow and starboard direction permit finding the compass bearing. 5

9 Solution to the Anti-aircraft Fire Control Problem on a Naval Platform Using the Direct Geometric Model (23) (24) If vector M e then the drift is toward the right (+), on the contrary it is to the left (-). Missile decoupling When the missile abandons the cannon tube to start its flight, it stops depending on the platform s orientation; hence, it is necessary to maintain the plane formed by the rise distance and the descent distance when turning over the angle of descent, perpendicular to the surface plane. For this, the decoupling articulation must be turned. To find this turn angle, the following geometrical model is constructed. The rise distance-descent distance plane is perpendicular to the surface when the left unit vector (z7) is perpendicular to the zenith. This occurs when: (25) (26) (27) Fig. 7. Geometric model and parameters of the left unit vector for the missile decoupling in flight. X7 Z7 X5 DECOUPLING MISSILE IN FLIGHT θ6 Z5 X6 LEFT UNIT CANNON ELEVATION θ5 Z4 FLIGHT θ6 X4 Z6 X2 CANNON S TURNING GEAR θ4 X3 Z2 ROLL θ3 Z Z1 PITCH θ2 X1 GRAVITY CENTER Z3 Articulation σ α d Ɵ R 1 Course Height 2 -π/2 Pitch-π/2 3 -π/2 Roll-π/2 COURSE θ1 4 -π/2 Drift-π/2 5 -π/2 Elevation-π/2 X 6 -π/2 Missile Decoupling 7 1 π/2 1 51

10 Gil, Vivas From the direct geometric model we obtain: (28) (29) (3) (31) (32) (33) Results To assess the model s performance, a virtual model is constructed of a surface platform with a shooting director and a cannon, according to the geometric models described previously, using VRML (Cellary, 212). The algorithm described in Fig. 1 is tested by using Matlab Simulink. The target s trajectory is generated through a free-ware flight simulator, YSflight, (Yamakawa, 29). To simplify the predictor it is presumed perfect when taking the target s future position in t v of the same trajectory recorded from the simulator. Likewise, the cannon s plant model or its controller is not considered. Fig. 8. Height, course, pitch, and roll signals of the platform movement. Angle (degrees) Height Course Angle (degrees) Pitch Angle (degrees)

11 Solution to the Anti-aircraft Fire Control Problem on a Naval Platform Using the Direct Geometric Model Roll Angle (degrees) Time (s) Fig. 9a. Perspective of the virtual model from the east (x=1.5, y=3.7, z=17.9). Fig. 9b. Perspective of the virtual model from the top (x=2.1, y=.6, z=16.5). In the previous images, the cannon is toward the bow and is represented as a sphere with a thin cylinder, the shooting director is over the superstructure and is represented as a cylinder that is the base, and a cone whose flat face represents the sensor that monitors the target. Note that the position of the cannon and shooting director is common and is taken as generic, but in practice it may be any over the platform, being this one of the advantages of this model because it permits modeling the position of the cannon or director in any part of the platform. From a trajectory of a target and simulated platform movements the algorithm is tested, obtaining the results represented in Figs. 9 to 11. Fig. 11a shows an error of 8.58x1-8 MRS in the missile s position with respect to the target s future position. This error is because the shooting chart uses an approximation in the missile s deviation. In Fig. 11b the deviation is equal to zero to observe the error attributable to the geometric model, with this error being 2.45x1-27 MRS. Conclusions This article has presented a new application of the Khalil-Kleinfinger method (1986) to geometrically model robots to solve the control problem of a naval anti-aircraft cannon shot, suggesting the applicability of this method to diverse problems. 53

12 Gil, Vivas This method facilitates the construction of the model, given that it simplifies the equations permitting easier design of controllers for the system. The model developed serves as generic platform to develop a fire control system by integrating all the system s components from the mean of detection to the missile in flight, being easily adaptable to multiple platforms including air and land. Future work will include the development of the controller for a cannon and the prediction algorithm of the target s future position. Fig. 1. Target s future position against missile s position at the end of its flight. Platform Target Future Position 1 Missile position in Tv 8 Zenit East - West South - North Fig. 11a. Error of the target s future position against the missile s position at the end of its flight, with (a); keep in mind the deviation. 1.8 x (m) Time (s) 54

13 Solution to the Anti-aircraft Fire Control Problem on a Naval Platform Using the Direct Geometric Model Fig. 11b. Error of the target s future position against the missile s position at the end of its flight, without (b); keep in mind the deviation. 1.5 x1-14 (m) Time (s) References BAO-QUAN, M. et al. Research on Dynamic Simulation of Remotely-Operated Weapon Stations Servo Control System. 21 IEEE International Conference on Intelligent Computation Technology and Automation. China, Changsha, 21 BERG, R.F. Estimation and prediction for manoeuvring target trajectories, IEEE Transactions of Automatic Control, Vol. 28, No. 3, March 1983, pp BO, B., et al. Research of Anti-aircraft Gun Weapon System Simulation Platform Based HLA and Virtools. 211 International Conference on Electronics, Communications and Control. China, Ningbo, pp , September, 211. CABECHINHAS, D. Integrated Solution to Quadrotor Stabilization and Attitude Estimation Using a Pan and Tilt Camera. 51st IEEE Conference on Decision and Control. USA, Maui, Hawaii, December, 212. CARTLUCCI, D. and JACOBSON, S. Ballistics Theory and Design of Guns and Ammunition. CRC Press, Taylor & Francis Group. Boca Raton, Florida, USA, 27. ISBN: CELLARY W. Interactive 3D Multimedia Content, Models for Creation, Management, Search and Presentation. Department of Information Technology Poznan University of Economics. Springer. Poland ISBN DONGSHENG L. et al. Research on Modelling and Simulation for Pitch/Roll Two-Axis Strap-down Stabilization Platform. The Tenth International Conference on Electronic Measurement & Instruments. The 54th Research Institute of CETC, Shijiazhuang, School of Instrumentation Science and Optoelectronic Engineering Beijing University of Aeronautics and Astronautics, China ELNASHAR, G. A mathematical model derivation of general fire control problem and solution scenario, School of Engineering-Egyptian 55

14 Gil, Vivas Armed Forces. Int. Journal of Modelling, Identification and Control, Vol. 2, No. 3, Cairo Egypt, 213. FOSSEN, T. Nonlinear Modelling and Control of Underwater Vehicles. Doctoral Thesis (PhD). Department of Engineering Cybernetics, Norwegian University of Science and Technology. Trondheim, Norway FOSSEN T, Handbook of Marine Craft Hydrodynamics and Motion Control, First Edition. Norwegian University of Science and Technology Trondheim, Norway. John Wiley & Sons Ltd. Trondheim, Norway ISBN: HAHN R. et al. Predictive Guidance of a Missile for Hit-to-Kill Interception. IEEE Transactions on Control Systems Technology, Vol. 17, No. 4, July KHALIL, W. Y KLEINFINGER, J.F. A new geometric notation for open and closed-loop robots IEEE International Conference on Robotics and Automation, San Francisco, USA, pp KIM D. et al. Stabilization Control for the Mobile Surveillance Robot using Motion Simulator. Chungnam National University, Dodaam Systems Co. International Conference on Control, Automation and Systems. Seoul, Korea. 28. KUMAR J. Stabilization System for Camera Control on an Unmanned Surface Vehicle. Thesis. Naval Postgraduate School. Monterey, California, USA. 28. NSN OLLERENSHAW D. and COSTELLO M. Model of Predictive Control of a Direct-Fire Missile Equipped With Canards. Oregon State University. Corvallis, Oregon, USA. 25. WADOO S. and KACHROO P. Autonomous Underwater Vehicles, Modelling, Control Design and Simulation. CRC Press, Taylir & Francis Group ISBN-13: SLABAUGH G. Downloaded from: soi.city.ac.uk/~sbbh653/publications/euler.pdf VIVAS O. Diseño y Control de Robots Industriales Teoría y Práctica. Elaleph, Buenos Aires, Argentina. 21. ISBN WOODMAN O. An introduction to inertial Navigation, Technical report Number 696, University of Cambridge, Computer Laboratory. Cambridge UK p. ISSN WANG, Y., et al. Strap-down stabilization for multi-load optoelectronic imaging platform. Beihang University, Chinese Academy of Sciences. Aircraft Engineering and Aerospace Technology: An International Journal. Beijing, China WEISS I. Y CROSS R. Ship Motion Effects on Gun Fire Control System Design. Naval Engineers Journal, P YAMAKAWA S. ysflight. software flight simulator. Available on line in: Version ZAYED M. and BOONAERT J. An Inertial Sensor for Mechanical Compensation of the Vehicle Vertical Movement Impact on In-Vehicle Embedded Camera Orientation. Proceedings of the 27 IEEE Intelligent Transportation Systems Conference. Seattle, Washington, USA

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