Electronics and Electrical Engineering and Control

Time-varying smooth variable structure filter based on interactive multi-model

  • Jian WANG ,
  • Lihui ZHOU ,
  • Jiafu CHEN ,
  • Xinqi LI ,
  • Linyang GUO ,
  • Zihao HE ,
  • Hao ZHOU
Expand
  • 1.School of Electronic and Information,Northwestern Polytechnical University,Xi’an 710129,China
    2.No. 365 Institute,Northwestern Polytechnical University,Xi’an 710065,China

Received date: 2024-01-17

  Revised date: 2024-02-27

  Accepted date: 2024-04-16

  Online published: 2024-04-25

Supported by

National Natural Science Foundation of China(62271409);Shaanxi Key Industry Innovation Chain Project(2018ZDCXL-G-12-2);Fundamental Research Funds for the Central Universities;National Polytechnical Laboratory for Integrated Aero-space Ground Ocean Gig Data Application Technology

Abstract

To overcome the problems of jitter and ineffective estimation of the unmeasured target state in the smooth variable structure filter, a time-varying smooth variable structure filter based on interactive multi-model is proposed. Firstly, the target state is estimated through the smooth variable structure filter. Subsequently, the jitter problem is solved by computing the time-varying smooth boundary layer and employing the tanh function instead of the saturation function to calculate the initial state gain. Then, Bayesian formulas are applied to recompute the covariance matrix and state gain to update the target state, effectively addressing the challenge of inefficient estimation of unmeasured states in the smooth variable structure filter. Finally, the algorithm is integrated with the interactive multi-model approach to achieve effective tracking of maneuvering targets. Simulation results demonstrate that the proposed algorithm maintains its effectiveness in tracking maneuvering targets under the conditions of model mismatch and changes in measurement noise or non-Gaussian measurement noise. The simulation results indicate that the method proposed has a significant improvement in tracking accuracy and robustness over typical target tracking methods.

Cite this article

Jian WANG , Lihui ZHOU , Jiafu CHEN , Xinqi LI , Linyang GUO , Zihao HE , Hao ZHOU . Time-varying smooth variable structure filter based on interactive multi-model[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(21) : 330167 -330167 . DOI: 10.7527/S1000-6893.2024.30167

References

1 KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering196082(1): 35-45.
2 GAO R, TRONARP F, S?RKK? S. Iterated extended Kalman smoother-based variable splitting for L1-regularized state estimation[J]. IEEE Transactions on Signal Processing201967(19): 5078-5092.
3 熊凯, 魏春岭, 李连升, 等. 基于扩维QLEKF的脉冲星/星间定向组合导航[J]. 航空学报202344(3): 526232.
  XIONG K, WEI C L, LI L S, et al. Pulsar/inter-satellite LOS integrated navigation based on augmented QLEKF[J]. Acta Aeronautica et Astronautica Sinica202344(3): 526232 (in Chinese).
4 JULIER S J, UHLMANN J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE200492(3): 401-422.
5 黄景帅, 李永远, 汤国建, 等. 高超声速滑翔目标自适应跟踪方法[J]. 航空学报202041(9): 323786.
  HUANG J S, LI Y Y, TANG G J, et al. Adaptive tracking method for hypersonic glide target[J]. Acta Aeronautica et Astronautica Sinica202041(9): 323786 (in Chinese).
6 ARASARATNAM I, HAYKIN S. Cubature Kalman filters[J]. IEEE Transactions on Automatic Control200954(6): 1254-1269.
7 陈林秀, 杨翔宇, 张航, 等.基于主动雷达/红外信息融合的复合制导方法[J].航空学报202243(S1): 727058.
  CHEN L X, YANG X Y, ZHANG H, et al. Composite guidance technology based on active radar/infrared information fusion[J]. Acta Aeronautica et Astronautica Sinica202243(S1): 727058 (in Chinese).
8 MOGHADDASI S S, FARAJI N. A hybrid algorithm based on particle filter and genetic algorithm for target tracking[J]. Expert Systems with Applications2020147: 113-188.
9 HABIBI S. The smooth variable structure filter[J]. Proceedings of the IEEE200795(5): 1026-1059.
10 GADSDEN S A, HABIBI S R. A new form of the smooth variable structure filter with a covariance derivation[C]∥ 49th IEEE Conference on Decision and Control (CDC). Piscataway: IEEE Press, 2010: 7389-7394.
11 AL-SHABI M, HABIBI S. New novel time-varying and robust smoothing boundary layer width for the smooth variable structure filter[C]∥ 2013 9th International Symposium on Mechatronics and its Applications (ISMA). Piscataway: IEEE Press, 2013: 1-6.
12 LI W J, GU H, SU W M. Partitioned time-varying smooth variable structure filter for airport target tracking[C]∥ 2016 CIE International Conference on Radar (RADAR). Piscataway: IEEE Press, 2016: 1-5.
13 ATTARI M, LUO Z Z, HABIBI S. An SVSF-based generalized robust strategy for target tracking in clutter[J]. IEEE Transactions on Intelligent Transportation Systems201617(5): 1381-1392.
14 CHEN Y, XU L P, WANG G M, et al. An improved smooth variable structure filter for robust target tracking[J]. Remote Sensing202113(22): 4612-4639.
15 AVZAYESH M, ABDEL-HAFEZ M, ALSHABI M, et al. The smooth variable structure filter: a comprehensive review[J]. Digital Signal Processing2021110: 102912.
16 AL-SHABI M, GADSDEN S A, HABIBI S R. Kalman filtering strategies utilizing the chattering effects of the smooth variable structure filter[J]. Signal Processing201393(2): 420-431.
17 GOODMAN J, HILAL W, GADSDEN S A, et al. Adaptive SVSF-KF estimation strategies based on the normalized innovation square metric and IMM strategy[J]. Results in Engineering202216: 100785.
18 LI Y W, LI G, LIU Y, et al. A novel smooth variable structure filter for target tracking under model uncertainty[J]. IEEE Transactions on Intelligent Transportation Systems202223(6): 5823-5839.
19 JIA S Y, ZHANG Y, WANG G H. Highly maneuvering target tracking using multi-parameter fusion singer model[J]. Journal of Systems Engineering and Electronics201728(5): 841-850.
20 鲁其兴, 汤新民, 周杨. 基于双变量自适应 “当前” 统计模型的场面4D轨迹跟踪预测[J]. 系统工程与电子技术202446(2): 675-683.
  LU Q X, TANG X M, ZHOU Y. Airport surface 4D trajectory tracking prediction based on bivariate adaptive “current” statistical model[J]. Systems Engineering and Electronics202446(2): 675-683 (in Chinese).
21 HUAI L L, LI B, YUN P, et al. Weighted maximum correntropy criterion-based interacting multiple-model filter for maneuvering target tracking[J]. Remote Sensing202315(18): 4513.
22 GADSDEN S A, HABIBI S R, KIRUBARAJAN T. A novel interacting multiple model method for nonlinear target tracking[C]∥ 2010 13th International Conference on Information Fusion. Piscataway: IEEE Press, 2010: 1-8.
23 GADSDEN S A, SONG Y, HABIBI S R. Novel model-based estimators for the purposes of fault detection and diagnosis[J]. IEEE/ASME Transactions on Mechatronics201318(4): 1237-1249.
24 JOHNSTON L A, KRISHNAMURTHY V. An improvement to the interacting multiple model (IMM) algorithm[J]. IEEE Transactions on Signal Processing200149(12): 2909-2923.
25 彭冬亮, 郭云飞, 薛安克. 三维高速机动目标跟踪交互式多模型算法[J]. 控制理论与应用200825(5): 831-836.
  PENG D L, GUO Y F, XUE A K. An interacting multiple model algorithm for a 3D high maneuvering target tracking[J]. Control Theory & Applications200825(5): 831-836 (in Chinese).
Outlines

/