Maneuvering Target Tracking Algorithm Based on the Adaptive Augmented State Interracting Multiple Model
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摘要: 现有的增广状态-交互式多模型算法存在着依赖于量测噪声协方差矩阵这一先验信息的问题。当先验信息未知或不准确时，算法的跟踪性能将会下降。针对上述问题，该文提出一种自适应的变分贝叶斯增广状态-交互式多模型算法VB-AS-IMM。首先，针对增广状态的跳变马尔科夫系统，该文给出了联合估计增广状态和量测噪声协方差矩阵的变分贝叶斯推断概率模型。其次，通过理论推导证明了该概率模型是非共轭的。最后，通过引入一种“信息反馈+后处理”方案，提出联合后验密度的次优求解方法。所提算法能够在线估计未知的量测噪声协方差矩阵，具有更强的鲁棒性和适应性。仿真结果验证了算法的有效性。Abstract: The existing Augmented State-Interracting Multiple Model (AS-IMM) algorithm suffers from the problem that it relies on the prior information of the covariance matrix of the measurement noise. When the prior information is unavailable or inaccurate, the tracking performance of AS-IMM will be degraded. In order to overcome this problem, a novel adaptive Bayesian Variational Augmented State-Interracting Multiple Model (VB-AS-IMM) algorithm is proposed. Firstly, the variational Bayesian inference probabilistic model of the augmented state and the covariance matrix of the measurement noise for the jump Markovarian system is presented. Secondly, the probabilistic model is proven to be non-conjugated. Finally, by introducing a novel post processing method, the suboptimal solution to calculate the joint posterior distribution is proposed. The proposed algorithm can estimate the unknown covariance matrix of the measurement noise online, thus it is more robust and has higher adaptability. Simulation result verifies good performance of the proposed algorithm.
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表 1 算法的平均RMSE
算法 位置RMSE(m) 速度RMSE(m/s) MIMM 131.69 2.37 IMM 191.91 3.37 MAS-IMM 78.90 0.95 AS-IMM 90.94 1.47 VB-AS-IMM 79.74 0.96 
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BLOM H A P and BAR-SHALOM Y. The interacting multiple model algorithm for systems with Markovian switching coefficients[J]. IEEE Transactions on Automatic Control, 1988, 33(8): 780–783. doi: 10.1109/9.1299 CHANG C B and ATHANS M. State estimation for discrete systems with switching parameters[J]. IEEE Transactions on Aerospace and Electronic Systems, 1978, AES–14(3): 418–425. doi: 10.1109/TAES.1978.308603 ANDERSON B D O and MOORE J B. Optimal Filtering[M]. Englewood Cliffs: Prentice-Hall, 1979: 165–190. RAUCH H. Solutions to the linear smoothing problem[J]. IEEE Transactions on Automatic Control, 1963, 8(4): 371–372. doi: 10.1109/TAC.1963.1105600 KELLY C N and ANDERSON B D O. On the stability of fixed-lag smoothing algorithms[J]. Journal of the Franklin Institute, 1971, 291(4): 271–281. doi: 10.1016/0016-0032(71)90183-9 MOORE J B. Discrete-time fixed-lag smoothing algorithms[J]. Automatica, 1973, 9(2): 163–174. doi: 10.1016/0005-1098(73)90071-X MATHEWS V J and TUGNAIT J K. Detection and estimation with fixed lag for abruptly changing systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 1983, AES–19(5): 730–739. doi: 10.1109/TAES.1983.309374 CHEN Bing and TUGNAIT J K. Interacting multiple model fixed-lag smoothing algorithm for Markovian switching systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(1): 243–250. doi: 10.1109/7.826326 MORELANDE M R and RISTIC B. Smoothed state estimation for nonlinear Markovian switching systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(4): 1309–1325. doi: 10.1109/TAES.2008.4667711 LOPEZ R and DANÈS P. Low-complexity IMM smoothing for jump Markov nonlinear systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3): 1261–1272. doi: 10.1109/TAES.2017.2669698 BLEI D M, KUCUKELBIR A, and MCAULIFFE J D. Variational inference: A review for statisticians[J]. Journal of The American Statistical Association, 2017, 112(518): 859–877. doi: 10.1080/01621459.2017.1285773 TZIKAS D G, LIKAS A C, and GALATSANOS N P. The variational approximation for bayesian inference[J]. IEEE Signal Processing Magazine, 2008, 25(6): 131–146. doi: 10.1109/MSP.2008.929620 SARKKA S and NUMMENMAA A. Recursive noise adaptive kalman filtering by variational bayesian approximations[J]. IEEE Transactions on Automatic Control, 2009, 54(3): 596–600. doi: 10.1109/TAC.2008.2008348 AGAMENNONI G, NIETO J I, and NEBOT E M. Approximate inference in state-space models with heavy-tailed noise[J]. IEEE Transactions on Signal Processing, 2012, 60(10): 5024–5036. doi: 10.1109/TSP.2012.2208106 MA Yanjun, ZHAO Shunyi, and HUANG Biao. Multiple-model state estimation based on variational Bayesian inference[J]. IEEE Transactions on Automatic Control, 2019, 64(4): 1679–1685. doi: 10.1109/TAC.2018.2854897 DONG Peng, JING Zhongliang, and LEUNG H. Variational bayesian adaptive Cubature information filter based on Wishart distribution[J]. IEEE Transactions on Automatic Control, 2017, 62(11): 6051–6057. doi: 10.1109/TAC.2017.2704442 XU Hong, XIE Wenchong, YUAN Huadong, et al. Fixed-point iteration Gaussian sum filtering estimator with unknown time-varying non-Gaussian measurement noise[J]. Signal Processing, 2018, 153: 132–142. doi: 10.1016/j.sigpro.2018.07.017 LI Xiaorong and JILKOV V P. Survey of maneuvering target tracking. Part I. Dynamic models[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1333–1364. doi: 10.1109/TAES.2003.1261132 ZHOU Gongjian, GUO Zhengkun, CHEN Xi, et al. Statically fused converted measurement Kalman filters for phased-array radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(2): 554–568. doi: 10.1109/TAES.2017.2760798 -
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