ZHANG Hongwei, XIE Weixin. Constrained Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking[J]. Chinese Journal of Electronics, 2020, 29(3): 501-507. doi: 10.1049/cje.2020.02.006
Citation: ZHANG Hongwei, XIE Weixin. Constrained Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking[J]. Chinese Journal of Electronics, 2020, 29(3): 501-507. doi: 10.1049/cje.2020.02.006

Constrained Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking

doi: 10.1049/cje.2020.02.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61773267) and the Science and Technology Program of Shenzhen (No.20170302145519524, No.20170818102503604).
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  • Corresponding author: XIE Weixin (corresponding author) is a professor and Ph.D. supervisor of Shenzhen University. He principally engaged in the research intelligent information processing and pattern recognition.(Email:wxxie@szu.edu.cn).
  • Received Date: 2019-01-22
  • Rev Recd Date: 2019-02-16
  • Publish Date: 2020-05-10
  • To track the bearings-only maneuvering target tracking accurately online, the soft measurement constraints are implemented into the Unscented Kalman filtering (UKF). To deal with the soft measurement constraints, the Lasso regularization is added as the obstacle function. In doing this, the sampled sigma points can be restricted into the feasible region. To enhance the sampling efficiency, the global optimal solution is acquired by a heuristic optimizer. To smooth the outliers, the posterior distribution is approximated by a Gaussian mixture consists of the original and the modified priors with the fuzzy weighted factor. Simulated results indicate the accuracy and the computational efficiency of the proposed method.
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  • Nardone S. C., Lindgren A. G., Gong K. F., “Fundamental properties and performance of conventional bearing only target motion analysis”, IEEE Transactions on Automatic Control, Vol.29, No.9, pp.775-787, 1984.
    Y. Bar-Shalom, T. Kirubarajan, and X. R. Li, “Estimation with applications to tracking and navigation”, Hoboken, NJ: Wiley, 2001
    Yu Z., Xiaoyan W. U., Huang S., et al., “Optimality analysis of sensor-target geometries for bearing-only passive localization in three dimensional space”, Chinese Journal of Electronics, Vol.25, No.2, pp.391-396, 2016
    Li X. R., Jilkov V. P., “Survey of maneuvering target tracking—Part I. Dynamic models”, IEEE Transactions on Aerospace & Electronic Systems, Vol.39, No.4, pp.1333-1364, 2004.
    Henk A. P., Bar-Shalom, Yaakov Blom., “The interacting multiple model algorithm for systems with Markovian switching coefficients”, IEEE Transactions on Automatic Control, Vol.33, No.8, pp.780-783, 1988.
    Zhu H., Guo K., Chen S., “Fusion of Gaussian mixture models for maneuvering target tracking in the presence of unknown cross-correlation”, Chinese Journal of Electronics, Vol.25, No.2, pp.270-276, 2016.
    Li X. R., Jilkov V P., “A survey of maneuvering target tracking, part VI: approximate nonlinear density filtering in discrete time Proceedings of SPIE”, The International Society for Optical Engineering, Vol.7698, No.1, pp.1-12, 2010.
    Merwe R. V. D., Doucet A., Freitas N. D., et al., “The unscented particle filter”, International Conference on Neural Information Processing Systems. MITPress, pp.584-590, 2000.
    Julier S. J., Uhlmann J. K., “Unscented filtering and nonlinear estimation”, Proceedings of the IEEE, Vol.92, No.3, pp.401-422, 2004.
    A. Doucet, A. M. Johansen, “A tutorial on particle filtering and smoothing: Fifteen years later”, Handbook of Nonlinear Filtering, Vol.12, pp.656-704, 2009.
    Tronarp F., Garcia-Fernandez A. F., Sarkka S., “Iterative filtering and smoothing in non-lnear and non-Gaussian systems using conditional moments”, IEEE Signal Processing Letters, Vol.99. pp.408-412, 2018.
    Garcia-Fernandez, á. F, Morelande, R. M., and Grajal, “Truncated Unscented Kalman filtering,” IEEE Transactions on Signal Processing, Vol.60, No.7, pp.3372-3386, 2012.
    Li L Q, Wang X L, Liu Z X, et al., “Auxiliary truncated unscented Kalman filtering for bearings-only maneuvering target tracking”, Sensors, Vol.17, No.5. pp.972, 2017.
    H.Zhang, L. Li and W. Xie, “Constrained multiple model particle filtering for bearings-only maneuvering target tracking”, IEEE Access, Vol.6, pp.51721-51734, 2018.
    D.Simon, “Kalman filtering with state constraints: A survey of linear and nonlinear algorithms”, IET Control Theory and Applications, Vol.4, No.8, pp.1303-1318, 2010.
    Kim, SeungJean, Koh, K, Lustig, M., “An interior-point method for large-scale l1-regularized least squares”, IEEE Journal of Selected Topics in Signal Processing, Vol.1, No.4, pp.606-617, 2008.
    W. Qian, Optimal Estimation of Dynamic Systems, Second Edition, Chapman & Hall/crc Boca Raton Fl, 2012
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