Modified Two-Filter Smoothing Method for Complex Nonlinear Target Tracking

To improve target state estimate accuracy, two-filter smoothing method is generally utilized. With the method, it needs to acquire the inverse function of target dynamic motion. However, it is difficult to obtain the inverse function when the target motion is complex nonlinear. Though the inverse function can be obtained in some cases, it may leads to incorrect backward transition of target state. In this paper, a new state space model is presented for the backward smoothing by introducing static equation and pseudo-measurement. With the proposed model, the inverse function is avoided to be calculated. Meanwhile, under the new model, all filtering algorithms based on state space model can be utilized for backward smoothing. Taking EKF as an example, the EKF implementation algorithm for two-filter smoothing is derived with the proposed model. The effectiveness and superiority of the proposed technique are validated by the results of the corresponding numerical simulations.

[1]  Chunxia Li,et al.  Modified two-filter smoother based on static equation , 2016, 2016 CIE International Conference on Radar (RADAR).

[2]  Jianjun Ge,et al.  Information Theory for Future Detection System Construction , 2019, 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP).

[3]  Tao Zeng,et al.  Tracking with nonlinear measurement model by coordinate rotation transformation , 2014 .

[4]  Chunxia Li,et al.  Parallel Simulation Calculation and Visualization Technology of Network Radar System Actual Detection Power in Urban Building Environment , 2019, 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP).

[5]  Simon J. Godsill,et al.  Monte Carlo smoothing with application to audio signal enhancement , 2002, IEEE Trans. Signal Process..

[6]  Wujun Wang,et al.  Target tracking with a dynamic and adaptive selection of radars based on entropy , 2019 .

[7]  Markus Hürzeler,et al.  Monte Carlo Approximations for General State-Space Models , 1998 .

[8]  A. Doucet,et al.  Smoothing algorithms for state–space models , 2010 .

[9]  G. Peters,et al.  Monte Carlo Approximations for General State-Space Models , 1998 .

[10]  Tao Zeng,et al.  Improved weak space object tracking assisted by strong target , 2015 .

[11]  Nando de Freitas,et al.  Fast particle smoothing: if I had a million particles , 2006, ICML.

[12]  Yaakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Applications and Advances , 1992 .

[13]  J. Meditch.  A survey of data smoothing for linear and nonlinear dynamic systems , 1973 .

[14]  P. Fearnhead,et al.  A sequential smoothing algorithm with linear computational cost. , 2010 .

[15]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[16]  Multi-radar hybrid detection algorithm based on information entropy , 2016, 2016 CIE International Conference on Radar (RADAR).

[17]  A. Doucet,et al.  Monte Carlo Smoothing for Nonlinear Time Series , 2004, Journal of the American Statistical Association.

[18]  Li Chunxia,et al.  A Dynamic and Adaptive Selection Radar Tracking Method Based on Information Entropy , 2017 .

[19]  A Novel Method to Analyze Accuracy of Target Measurement Based on Entropy , 2017, 2017 4th International Conference on Information Science and Control Engineering (ICISCE).