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摘要:
针对现有机动目标跟踪算法在广义未知扰动下跟踪峰值误差过大的问题，提出一种多模型最小上限滤波(MMUBF)算法。在多模型框架下利用最小上限滤波对不同模式下状态进行递推估计，根据滤波结果及模型后验概率估计加权在线辨识扰动分量，将其引入各模式下似然概率计算以减弱其对模型概率更新的影响。同时，为进一步提升模式匹配精度，利用修正因子自适应调整马尔可夫转移概率矩阵。此外，通过计算每个步骤的浮点运算数量，分析所提算法的计算复杂度。具有时变未知扰动的机动目标跟踪仿真结果表明：相比于现有交互式多模型滤波、自适应交互式多模型滤波、灰狼优化算法改进后的自适应交互式多模型滤波和基于单模型的最小上限滤波算法，所提算法在不同量测噪声、过程噪声、调整系数及概率修正阈值水平下皆具有更小峰值误差和更高估计精度。
Abstract:This paper presents the multiple model upper bound filter (MMUBF) for maneuvering target tracking, since the tracking error is too big in existing algorithms when encountering with generalized unknown disturbances. In the multi-model framework, the minimum upper bound filter is implemented as the corresponding sub-filter in every mode to realize state update recursively. Then, the unknown disturbance is identified online according to the filtered result and the posterior mode probability, and the resultant estimate of disturbance is adopted to re-calculate the likelihood in each mode to eliminate the effect of the existence of unknown disturbance on the update of the posterior mode probability. Meanwhile, in order to further improve the model matching accuracy, the Markov transition probability matrix is adaptively adjusted using correction factors. In addition, the computational complexity of the algorithm is analyzed by calculating the number of floating-point operations at each step. In the meantime, the Markov transition probability matrix is adaptively modified using correction factors to further increase the model matching accuracy. Additionally, the number of floating-point operations at each step is calculated in order to examine the algorithm's computational complexity. Regarding various levels of measurement noises, process noises, adjustment coefficients, and probability correction threshold, the simulation results of maneuvering target tracking with time-varying unknown disturbances demonstrate that the suggested algorithm effectively suppresses the tracking error and has higher estimation accuracy than the existing interacting multiple model filter, adaptive interacting multiple model filter, improved adaptive interacting multiple model filter by gray wolf optimization algorithm, and single model-based minimum upper bound filter.
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图 6 不同量测噪声水平下各算法均方根误差均值对比
Figure 6. Comparison of ARMSE of each algorithms with different levels of measurement noises
图 7 不同过程噪声水平下各算法均方根误差均值对比
Figure 7. Comparison of ARMSEs of each algorithms with different levels of process noises
图 8 不同模式转移概率修正阈值下各算法均方根误差均值对比
Figure 8. Comparison of ARMSE of each algorithms with different mode transition probability amending thresholds
图 9 不同调整系数下各算法均方根误差均值对比
Figure 9. Comparison of ARMSEs of each algorithms with different adjustment coefficients
图 10 不同$ \alpha $下MMUBF算法均方根误差均值
Figure 10. ARMSEs of MMUBF algorithm with different values of $ \alpha $
表 1 每个时刻MMUBF算法各步骤的计算复杂度
Table 1. Computational complexity of each step of MMUBF algorithm at each moment
步骤 浮点运算数量 1 $ 5{M^2} + 3{M^2}{n^2} + 3{M^2}n - 3M{n^2} - 3Mn - 3M $ 2 $ M(8{n^3} + 8m{n^2} + 4{m^2}n + 10{m^3} + 5{m^2} - 2{n^2} - n - 3mn) $ 3 $ 2Mmn + M{m^3} + 2M{m^2} + 5Mm + {M^2} - M - m $ 4 $ 2{M^2} + 2M $ 5 $ 2M{n^2} + 4Mn - 2{n^2} - 4n $ 
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[1] 王昱淇, 卢宙, 蔡云泽. 基于一致性的分布式变结构多模型方法[J]. 自动化学报, 2021, 47(7): 1548-1557.WANG Y Q, LU Z, CAI Y Z. Consensus-based distributed variable structure multiple model[J]. Acta Automatica Sinica, 2021, 47(7): 1548-1557(in Chinese). [2] SU J, LI Y, ALI W. Underwater passive manoeuvring target tracking with isogradient sound speed profile[J]. IET Radar, Sonar & Navigation, 2022, 16(9): 1415-1433. [3] ZHENG Z, CAI S C. A collaborative target tracking algorithm for multiple UAVs with inferior tracking capabilities[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(10): 1334-1350. [4] LI B, YANG Z P, CHEN D Q, et al. Maneuvering target tracking of UAV based on MN-DDPG and transfer learning[J]. Defence Technology, 2021, 17(2): 457-466. [5] MA J, GUO X T. Combination of IMM algorithm and ASTRWCKF for maneuvering target tracking[J]. IEEE Access, 2020, 8: 143095-143103. [6] 何友, 修建娟, 刘瑜, 等. 雷达数据处理及应用[M]. 4版. 北京: 电子工业出版社, 2022.HE Y, XIU J J, LIU Y, et al. Radar data processing with applications[M]. 4th ed. Beijing: Publishing House of Electronics Industry, 2022(in Chinese). [7] MA Y J, ZHAO S Y, HUANG B. Multiple-model state estimation based on variational Bayesian inference[J]. IEEE Transactions on Automatic Control, 2019, 64(4): 1679-1685. [8] SHEN C, XU D J, HUANG W, et al. An interacting multiple model approach for state estimation with non-Gaussian noise using a variational Bayesian method[J]. Asian Journal of Control, 2015, 17(4): 1424-1434. [9] GOFF G M, BLACK J T, BECK J A. Orbit estimation of a continuously thrusting spacecraft using variable dimension filters[J]. Journal of Guidance, Control, and Dynamics, 2015, 38(12): 2407-2420. [10] LU P. Comparative study of state and unknown input estimation for continuous-discrete stochastic systems[J]. Franklin Open, 2022, 1: 1-8. [11] ANGELI A, DESMET W, NAETS F. Deep learning of multibody minimal coordinates for state and input estimation with Kalman filtering[J]. Multibody System Dynamics, 2021, 53(2): 205-223. [12] 肖楚晗, 李炯, 雷虎民, 等. 基于AVSIMM算法的高超声速再入滑翔目标跟踪[J]. 北京航空航天大学学报, 2019, 45(2): 413-421.XIAO C H, LI J, LEI H M, et al. Hypersonic reentry gliding target tracking based on AVSIMM algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 413-421(in Chinese). [13] HUANG Y H, WU Y, YAO L X, et al. A class of distributed variable structure multiple model algorithm based on posterior information of information matrix[J]. Journal of Shanghai Jiaotong University (Science), 2022, 27(5): 671-679. [14] QIN Y M, LIANG Y, YANG Y B, et al. Adaptive upper-bound linear mean square error filter of Markovian jump linear systems with generalized unknown disturbances[C]//Proceedings of the 18th International Conference on Information Fusion. Piscataway: IEEE Press, 2015: 1833-1839. [15] QIN Y M, LIANG Y, YANG Y B, et al. Minimum upper-bound filter of Markovian jump linear systems with generalized unknown disturbances[J]. Automatica, 2016, 73: 56-63. [16] YANG Y B, QIN Y M, LIANG Y, et al. Adaptive filter for linear systems with generalized unknown disturbance in measurements [C]//Proceedings of the 16th International Conference on Information Fusion. Piscataway: IEEE Press, 2013: 1336-1341. [17] QIN Y M, LIANG Y, YANG Y B, et al. Adaptive filter of non-linear systems with generalised unknown disturbances[J]. IET Radar, Sonar & Navigation, 2014, 8(4): 307-317. [18] 叶瑾, 许枫, 杨娟, 等. 一种改进的时变转移概率AIMM跟踪算法[J]. 应用声学, 2020, 39(2): 246-252.YE J, XU F, YANG J, et al. An improved AIMM tracking algorithm based on adaptive transition probability[J]. Journal of Applied Acoustics, 2020, 39(2): 246-252(in Chinese). [19] 游航航, 韩其松, 余敏建, 等. 基于AIGWO-IMMUKF的目标跟踪算法[J]. 北京航空航天大学学报, 2020, 46(10): 1826-1833.YOU H H, HAN Q S, YU M J, et al. Target tracking algorithm based on AIGWO-IMMUKF[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1826-1833(in Chinese). [20] YANG Y B, LIU Z G, QIN Y M, et al. Iteratively distributed instrumental variable-based pseudo-linear information filter for angle-only tracking[J]. ISA Transactions, 2023, 138: 359-372. -
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