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access icon free Gaussian sum pseudolinear Kalman filter for bearings-only tracking

© The Institution of Engineering and Technology
Received 24/05/2019, Accepted 04/11/2019, Revised 07/10/2019, Published 06/11/2019

The efficacy of a bearings-only tracking algorithm, to a great extent, depends on the target-sensor geometry and motion. Although the pseudolinear Kalman filter and its variants have demonstrated comparable performance with the elite non-linear filters, they still suffer from bias problems and the tracking performance is inevitably affected by the relative geometry and motion relationships. In this study, an observability metric based on classical control theory is first presented to characterise the relative relationships between the target and the sensor. Then an efficient bearings-only tracking algorithm called Gaussian sum pseudolinear Kalman filter is developed. It is based on the bias-compensated pseudolinear Kalman filter and is built within a Gaussian sum framework. In the novel algorithm, a splitting and merging procedure will be triggered when a low degree of observability is detected. Simulation results show the significant performance improvement of the proposed algorithm.

Inspec keywords: Kalman filters; state estimation; Gaussian processes

Other keywords: elite nonlinear filters; efficient bearings; tracking performance; bias-compensated pseudolinear Kalman filter; Gaussian sum framework; Gaussian sum pseudolinear Kalman filter; target-sensor geometry; motion relationships; relative geometry; bearings-only tracking algorithm

Subjects: Signal processing theory; Filtering methods in signal processing; Optical, image and video signal processing; Other topics in statistics; Optimisation techniques; Other topics in statistics

References

    1. 1)
      • 1. Ristic, B., Arulampalam, S., Gordon, N.: ‘Beyond the Kalman filter: particle filters for tracking applications’ (Artech House, Norwood, MA, USA, 2004).
    2. 2)
      • 17. Nguyen, N.H., Doğançay, K.: ‘Instrumental variable based Kalman filter algorithm for three-dimensional AOA target tracking’, IEEE Signal Process. Lett., 2018, 25, (10), pp. 1605–1609.
    3. 3)
      • 20. Kronhamn, T.R.: ‘Bearings-only target motion analysis based on a multihypothesis Kalman filter and adaptive ownship motion control’, IEE Proc. Radar Sonar Navig., 1998, 145, (4), pp. 247–252.
    4. 4)
      • 14. Doğançay, K.: ‘Bias compensation for the bearings-only pseudolinear target track estimator’, IEEE Trans. Signal Process., 2006, 54, (1), pp. 59–68.
    5. 5)
      • 24. Hermann, R., Krener, A.J.: ‘Nonlinear controllability and observability’, IEEE Trans. Autom. Control, 1977, 22, (5), pp. 728–740.
    6. 6)
      • 23. Gilitschenski, I., Kurz, G., Hanebeck, U.D.: ‘Bearings-only sensor scheduling using circular statistics’. Proc. 16th Int. Conf. on Information Fusion (FUSION), Istanbul, Turkey, July 2013, pp. 515–521.
    7. 7)
      • 10. Merlinge, N., Dahia, K., Piet-Lahanier, H., et al: ‘A box regularized particle filter for state estimation with severely ambiguous and non-linear measurements’, Automatica, 2019, 104, pp. 102–110.
    8. 8)
      • 19. Passerieux, J.M., Van Cappel, D.: ‘Optimal observer maneuver for bearings-only tracking’, IEEE Trans. Aerosp. Electron. Syst., 1998, 34, (3), pp. 777–788.
    9. 9)
      • 18. Le Cadre, J.P., Jauffret, C.: ‘Discrete-time observability and estimability analysis for bearings-only target motion analysis’, IEEE Trans. Aerosp. Electron. Syst., 1997, 33, (1), pp. 178–201.
    10. 10)
      • 27. Jiang, H., Cai, Y.: ‘Adaptive fifth-degree cubature information filter for multi-sensor bearings-only tracking’, Sensors, 2018, 18, (10), p. 3241.
    11. 11)
      • 6. Chen, X., Sun, R., Wang, F., et al: ‘Two-stage unscented Kalman filter algorithm for fault estimation in spacecraft attitude control system’, IET Control Theory Appl., 2018, 12, (13), pp. 1781–1791.
    12. 12)
      • 22. Zhang, H., Dufour, F., Anselmi, J., et al: ‘Piecewise optimal trajectories of observer for bearings-only tracking by quantization’. Proc. 20th Int. Conf. on Information Fusion (FUSION), Xi'an, China, July 2017, pp. 1458–1464.
    13. 13)
      • 3. Inoue, R.S., Terra, M.H., Cerri, J.P.: ‘Extended robust Kalman filter for attitude estimation’, IET Control Theory Appl., 2016, 10, (2), pp. 162–172.
    14. 14)
      • 16. Nguyen, N.H., Doğançay, K.: ‘Improved pseudolinear Kalman filter algorithms for bearings-only target tracking’, IEEE Trans. Signal Process., 2017, 65, (23), pp. 6119–6134.
    15. 15)
      • 7. Zhang, Y., Huang, Y., Li, N., et al: ‘Interpolatory cubature Kalman filters’, IET Control Theory Appl., 2015, 9, (11), pp. 1731–1739.
    16. 16)
      • 25. Leong, P.H., Arulampalam, S., Lamahewa, T.A., et al: ‘Gaussian-sum cubature Kalman filter with improved robustness for bearings-only tracking’, IEEE Signal Process. Lett., 2014, 21, (5), pp. 513–517.
    17. 17)
      • 13. Lindgren, A.G., Gong, K.F.: ‘Position and velocity estimation via bearing observations’, IEEE Trans. Aerosp. Electron. Syst., 1978, AES-14, (4), pp. 564–577.
    18. 18)
      • 12. Evensen, G.: ‘The ensemble Kalman filter: theoretical formulation and practical implementation’, Ocean Dyn., 2003, 53, (4), pp. 343–367.
    19. 19)
      • 8. Qin, W., Wang, X., Cui, N.: ‘Maximum correntropy sparse Gauss–Hermite quadrature filter and its application in tracking ballistic missile’, IET Radar Sonar Navig., 2017, 11, (9), pp. 1388–1396.
    20. 20)
      • 2. Xu, S., Doğançay, K., Hmam, H.: ‘Distributed pseudolinear estimation and UAV path optimization for 3D AOA target tracking’, Signal Process., 2017, 133, pp. 64–78.
    21. 21)
      • 15. Doğançay, K.: ‘3D pseudolinear target motion analysis from angle measurements’, IEEE Trans. Signal Process., 2015, 63, (6), pp. 1570–1580.
    22. 22)
      • 5. Peach, N.: ‘Bearings-only tracking using a set of range-parameterised extended Kalman filters’, IEE Proc. Control Theory Appl., 1995, 142, (1), pp. 73–80.
    23. 23)
      • 9. Arulampalam, M.S., Maskell, S., Gordon, N., et al: ‘A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking’, IEEE Trans. Signal Process., 2002, 50, (2), pp. 174–188.
    24. 24)
      • 4. Aidala, V.J., Hammel, S.E.: ‘Utilization of modified polar coordinates for bearings-only tracking’, IEEE Trans. Autom. Control, 1983, 28, (3), pp. 283–294.
    25. 25)
      • 28. Williams, R.K., Sukhatme, G.S.: ‘Observability in topology-constrained multi-robot target tracking’. Proc. 2015 IEEE Int. Conf. on Robotics and Automation (ICRA), Seattle, USA, May 2015, pp. 1795–1801.
    26. 26)
      • 21. Trémois, O., Le Cadre, J.P.: ‘Optimal observer trajectory in bearings-only tracking for manoeuvring sources’, IEE Proc. Radar Sonar Navig., 1999, 146, (1), pp. 31–39.
    27. 27)
      • 29. Tichavský, P., Muravchik, C.H., Nehorai, A.: ‘Posterior Cramér–Rao bounds for discrete-time nonlinear filtering’, IEEE Trans. Signal Process., 1998, 46, (5), pp. 1386–1396.
    28. 28)
      • 26. Leutnant, V., Krueger, A., Haeb-Umbach, R.: ‘A versatile Gaussian splitting approach to non-linear state estimation and its application to noise-robust ASR’. Proc. 12th Annual Conf. of the Int. Speech Communication Association (INTERSPEECH), Florence, Italy, August 2011, pp. 1641–1644.
    29. 29)
      • 11. Bréhard, T., Le Cadre, J.P.: ‘Hierarchical particle filter for bearings-only tracking’, IEEE Trans. Aerosp. Electron. Syst., 2007, 43, (4), pp. 1567–1585.
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