Doppler and bearing tracking using fuzzy adaptive unscented Kalman filter

Document Type : Research Paper


Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Iran


The topic of Doppler and Bearing Tracking (DBT) problem is to achieve a target trajectory using the Doppler and Bearing measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. Several techniques were studied to deal with this topic, such as the unscented Kalman filter. Nevertheless, the performance of the filter depends directly on the prior knowledge, involving the accurate model, sufficient information of the noise distribution and the suitable initialization. To address these problems, in this paper, a new adaptive factor together with a fuzzy logic system is proposed for online adjusting the process and the measurement noise covariance matrices simultaneously. In the core of the proposed algorithm, the fault detection procedure is also adopted to reduce the computational time. The theoretical developments are investigated by simulations, which indicate the effectiveness of the proposed filter in DBT problem.


[1] A. Alkaya, Unscented Kalman filter performance for closed-loop nonlinear state estimation: a simulation case study, Electrical
Engineering, 96(4) (2014), 299-308.
[2] E. Cao, K. Jiang, Adaptive unscented Kalman fi lter for input estimations in Diesel-engine selective catalytic reduction systems,
Neurocomputing, 205 (2016), 329-335.
[3] Y. Chan, S. Rudnicki, Bearings-only and Doppler-bearing tracking using instrumental variables, IEEE Transactions on
Aerospace and Electronic Systems, 28(4) (1992), 1076-1083.
[4] G. Dikic, Z. Djurovic, Unbiased estimation of atmosphere attenuation coefficient, Electrical Engineering, 89(4) (2007), 343-
[5] P. Escamilla-Ambrosio, N. Mort, Adaptive Kalman fi ltering through fuzzy logic, Proceedings of the 7th UK Workshop On
Fuzzy Systems, Recent Advances and Practical Applications of Fuzzy, Neuro-Fuzzy, and Genetic Algorithm-Based Fuzzy
Systems, (2000), 26-27.
[6] C. Hajiyev, H. E. Soken, Robust adaptive unscented Kalman fi lter for attitude estimation of pico satellites, International
Journal of Adaptive Control and Signal Processing, 28(2) (2014), 107-120.
[7] K. Ho, Y. Chan, An asymptotically unbiased estimator for bearings-only and Doppler-bearing target motion analysis, IEEE
Transactions on Signal Processing, 54(3) (2006), 809-822.
[8] K. Jiang, E. Cao, L. Wei, NOx sensor ammonia cross-sensitivity estimation with adaptive unscented Kalman fi lter for Diesel-
engine selective catalytic reduction systems, Fuel, 165 (2016), 185-192.
[9] S. J. Julier, J. K. Uhlmann, New extension of the Kalman filter to nonlinear systems, Signal processing, sensor fusion, and
target recognition VI. International Society for Optics and Photonics, 3068 (1997), 182-194.
[10] D. Jwo, S. Lai, Navigation integration using the fuzzy strong tracking unscented Kalman fi lter, The Journal of Navigation,
62(2) (2009), 303-322.
[11] D. J. Jwo, C. H. Tseng, Fuzzy adaptive interacting multiple model unscented Kalman filter for integrated navigation, Control
Applications,(CCA) and Intelligent Control,(ISIC), (2009), 1684-1689.
[12] D. J. Jwo, C. F. Yang, C. H. Chuang, T. Y. Lee, Performance enhancement for ultra-tight GPS/INS integration using a
fuzzy adaptive strong tracking unscented Kalman fi lter, Nonlinear Dynamics, 73(1-2) (2013), 377-395.
[13] S. Koteswara Rao, Doppler-bearing passive target tracking using a parameterized unscented kalman fi lter, IETE Journal of
Research, 56(1) (2010), 69-75.
[14] D. Lee, G. Vukovich, R. Lee, Robust adaptive unscented Kalman fi lter for spacecraft attitude estimation using quaternion
measurements, Journal of Aerospace Engineering, 30(4) (2017), 9-17.
[15] K. Z. Liu, J. Li, W. Guo, P. Q. Zhu, X. H. Wang, Navigation system of a class of underwater vehicle based on adaptive
unscented Kalman fi ter algorithm, Journal of Central South University, 22(2) (2014), 550-557.
[16] X. Liu, H. J. Liu, Y. G. Tang, Q. Gao, Z. M. Chen, Fuzzy adaptive unscented Kalman filter control of epileptiform spikes in
a class of neural mass models, Nonlinear Dynamics, 76(2) (2014), 1291-1299.
[17] N. Nguyen, K. Dogancay, Improved pseudolinear Kalman fi lter algorithms for bearings-only target tracking, IEEE Transac-
tions on Signal Processing, 65(23) (2017), 6119-6134.
[18] J. Passerieux, D. Pillon, P. Blanc-Benon, C. Jauffret, Target motion analysis with bearings and frequencies measurements
via instrumental variable estimator (passive sonar), Acoustics, Speech, and Signal Processing, International Conference on.
IEEE, (1989), 2645-2648.
[19] H. W. Quan, Target tracking using extended Kalman filter with bearing and Doppler measurements, Applied Mechanics and Materials, 529 (2014), 379-382.
[20] B. Raeisy, A. A. Safavi, A. R. Khayatian, Optimized fuzzy control design of an autonomous underwater vehicle, Iranian
Journal of Fuzzy Systems, 9(2) (2012), 25-41.
[21] A. Rahimi, K. D. Kumar, H. Alighanbari, Enhanced adaptive unscented Kalman filter for reaction wheels, IEEE Transactions on Aerospace and Electronic Systems, 51(2) (2015), 1568{1575.
[22] U. Rajyalakshmi, R. P. Mallikarjuna, D. Lingaraju, Doppler-Bearing passive target tracking system for underwater target
detection using modi ed gain EKF, International Journal on Intelligent Electronic Systems, 5(2)(2011), 12-17.
[23] A. Rosenqvist, Asymptotic theory for a two-step pseudo-linear Doppler-bearing tracker, Computational statistics and data
analysis, 21(6) (1996), 647-660.
[24] M. N. Santhosh, S. K. Rao, R. P. Das, K. L. Raju, Underwater target tracking using unscented Kalman Filter, Indian Journal
of Science and Technology, 8(31) (2015), 1-5.
[25] H. E. Soken, C. Hajiyev, Adaptive unscented Kalman fi lter with multiple fading factors for pico satellite attitude estimation,
Recent Advances in Space Technologies, (2009), 541-546.
[26] H. E. Soken, C. Hajiyev, Adaptive fading UKF with Q-adaptation: application to picosatellite attitude estimation, Journal
of Aerospace Engineering, 26(3) (2011), 628-636.
[27] X. J. Tao, C. R. Zou, Z. Y. He, Passive target tracking using maximum likelihood estimation, IEEE Transactions on Aerospace
and Electronic Systems, 32(4) (1996), 1348-1354.
[28] V. Teuliere, O. Brun, Parallelisation of the particle fi ltering technique and application to doppler-bearing tracking of maneu- vering sources, Parallel Computing, 29(8) (2003), 1069-1090.
[29] R. Turner, C. E. Rasmussen, Model based learning of sigma points in unscented Kalman fi ltering, Neurocomputing, 80
(2012), 47-53.
[30] D. Wang, H. Lv, J. Wu, In- flight initial alignment for small UAV MEMS-based navigation via adaptive unscented Kalman
filtering approach, Aerospace Science and Technology, 61 (2017), 73-84.
[31] C. Yang, J. Collins, Improvement of honey bee tracking on 2D video with hough transform and Kalman fi lter, Journal of
Signal Processing Systems, 90(12) (2018), 1639-1650.
[32] H. Yang, W. Li, C. m. Luo, Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated
method, Journal of Central South University, 22(4) (2015), 1324-1333.
[33] L. Yang, M. Sun, K. Ho, Doppler-bearing tracking in the presence of observer location error, IEEE Transactions on Signal Processing, 56(8) (2008), 4082-4087.
[34] R. Zhan, J.Wan, Iterated unscented Kalman fi lter for passive target tracking, IEEE Transactions on Aerospace and Electronic Systems, 43(3) (2007), 1155-1163.
[35] Q. Zhang, T. L. Song, Improved bearings-only target tracking with iterated Gaussian mixture measurements, IET Radar, Sonar and Navigation, 11(2) (2016), 294-303.
[36] H. Zhou, H. Huang, H. Zhao, X. Zhao, X. Yin, Adaptive unscented Kalman fi lter for target tracking in the presence of
nonlinear systems involving model mismatches, Remote Sensing, 9(7) (2017), 657.
[37] H. Zhu, H. Hu, W. Gui, Adaptive unscented kalman fi lter for deep-sea tracked vehicle localization, Information and Automa-tion, (2009), 1056-1061.