FUSION Multitarget-Multisensor Bias Estimation. T. Kirubarajan July Estimation, Tracking and Fusion Laboratory (ETFLab)

Transcription

1 Multitarget-Multisensor Bias Estimation Estimation, Tracking and Fusion Laboratory (ETFLab) E. Taghavi, R. Tharmarasa, T. Kirubarajan T. Kirubarajan July 2017

2 Motivation (1) Fusion of different sensors is at the core of new distributed systems Sensors not only carry residual error, but also systematic errors If not compensated, fusion will not give a better state estimation Ultrasonic Infrared Digital Maps Distributed Tracking Systems Camera Radar/LIDAR Inertial Motion 2

3 Motivation (2) Practical systems are vulnerable to systematic errors The cost of calibration prevents the users to do the maintenance regularly There is a lack in mathematical modeling for different types of systematic biases Many of the proposed algorithms designed to work with two sensors only New measurement data like video measurements are now part of the systems 3

4 Bias estimation algorithm for multisensor multitarget tracking Offset and Scaling Biases Loss of Communication and Tracklets Decoupled Models based on Tracks Large Network of Sensors 4

5 Bias estimation algorithm for multisensor multitarget tracking General radar model Previously proposed algorithms use either the measurements or local tracks, associated covariance matrices and Kalman gains Here, only local tracks and associated covariance matrices, and reconstruct Kalman gains are used The update rate and multiple sensor constraints are relaxed 5

6 Bias estimation algorithm for multisensor multitarget tracking Proposed a technique to reconstruct the Kalman gain Fused Bias Estimation Algorithm (FBEA) handles multisensor multitarget bias estimation CRLB for multisensor multitarget based on the extension of two sensor case 6

7 Bias estimation algorithm for multisensor multitarget tracking Comparison between the two sensor case, with and without access to ground truth of Kalman gain Proposed Kalman gain reconstruction is effective 7

8 Bias estimation algorithm for multisensor multitarget tracking Comparison between the square root of diagonal elements of CRLB, square root of diagonal elements of the covariance matrix of bias estimation algorithm and RMSE of the bias estimation for the case of 5 sensors with NCV-NCV IMM estimator as local trackers (only the results for the first sensor are shown) 8

9 Bias estimation algorithm for multisensor multitarget tracking NEES for FBEA and three different local tracker estimators (Kalman filter, NCV-NCV IMM and NCA-NCV IMM) for sensor 1 (top) to sensor 5 (bottom) compared to the upper-bound of 95% probability interval 9

10 Bias estimation algorithm for multisensor multitarget tracking RMSE of local track (sensor 1) and the output of the fusion algorithm including offset biases for all sensors in logarithmic scale 10

11 Multisensor multitarget bearing only sensor registration Offset Biases Bias Modeling for a Sensor Network Lower Bound Calculation 11

12 Multisensor multitarget bearing only sensor registration Bearing-only bias model: Modeling the biases based on the noisy or estimated bearing only data Handling large biases in all the sensors involved in the modeling Modeling the biases in Cartesian coordinates and how they effect the measurements Decoupling the biases from the position of the targets in Cartesian coordinates Simulation results with realistic parameter setting Real time implementation through windowing The option to parallelize the computations 12

13 Multisensor multitarget bearing only sensor registration Bias models after decoupling: 13

14 Multisensor multitarget bearing only sensor registration θ 1 (k) θ 2 (k) θ 3 (k) θ 4 (k) b 1 b 2 b 3 b 4 Triangulation and Covariance Matrix Calculation for best pairs Sensors Position Bias Estimation (Genetic Algorithm) x 1 (k) y 1 (k) x 2 (k) y 2 (k) Batch Likelihood Function p Z b Pseudo Measurement Z b k = f(b 1, b 2, b 3, b 4 ) Z b 1: K = f 1:K (b 1, b 2, b 3, b 4 ) 14

15 Multisensor multitarget bearing only sensor registration Position RMSE with distributed tracking for corrected and original tracks of target 3 15

16 Multisensor multitarget bearing only sensor registration Position RMSE for corrected and original tracks for the four{sensor distributed tracking case and window size of 10 (Target 2) 16

17 Geo-registration and geo-location Definition of Registration Error Video measurement to geo-- registration Simulations with Realistic Error Parameters 17

18 Geo-registration and geo-location How a video image frame is created? X I (North) d I v Gimbal X v CM X b α az Z c, X g Y c, Y g Y v Y b Y I (East) 18

19 Geo-registration and geo-location How a video image frame is created? d I v CM Z v Z g Z I Z b X c X b α el X g X I, Y I Z c Plane X v 19

20 Geo-registration and geo-location How a video image frame is mapped to its Geo location? Y c O X c Y im X im f λ X ip q Y ip c p obj Z c 20

21 Geo-registration and geo-location The ability to implement the proposed method on the same or different platforms Targeting the two most effective biases, i.e., gimbal azimuth and elevation Based on common targets observed by both sensors X I (North) Target Target Mirror Target Mirror Y I (East) 21

22 Geo-registration and geo-location Precise values for bias, presumingly large Estimating the biases up to 5 with accuracy of less than 1 A decoupled bias model from geo-location of the targets An efficiently implementable geo-registration algorithm 22

23 Geo-registration and geo-location RMSE of geo-location estimates of common targets (C i : Camera i) The debiased geo{locations match the ideal bias-free geo-locations and do not have the large ripples in RMSE that are observed when biased geolocations are used 23

24 Geo-registration and geo-location Average RMSE of biased, debiased and bias-free geo-location estimates for Camera 1 For all common targets on the image plane, the results of the debiased geo-location estimates are similar to the ideal bias-free ones in terms of RMSE 24

25 References E. Taghavi, T. Tharmarasa, T. Kirubarajan, and Y. Bar-Shalom. Bias Estimation for Practical Distributed Multiradar Multitarget Tracking Systems. 16th International Conference on Information Fusion (FUSION), pp , Istanbul, Turkey, July E. Taghavi, T. Tharmarasa, T. Kirubarajan, Y. Bar-Shalom and M. McDonald. A Practical Bias Estimation Algorithm for Multisensor Multitarget Tracking. IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 4, October E. Taghavi, T. Tharmarasa, T. Kirubarajan, and M. McDonald. Multisensor Multitarget Bearing Only Sensor Registration. IEEE Transaction on Aerospace and Electronic Systems, To be appear in vol. 52, no. 4, August E. Taghavi, T. Tharmarasa, T. Kirubarajan, and M. McDonald. Geo registration: A Two Video Sensor Approach. Submitted to IEEE Transactions on Aerospace and Electronic Systems, April

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