Adaptive Non-Linear Joint Probabilistic Data Association for Vehicle Target Tracking

This paper investigated the problem of multi-target tracking (MTT) over a vehicle sensor network. A novel adaptive square root cubature joint probabilistic data association (ASRCJPDA) was proposed. Motivated by enhancing the stability of joint probabilistic data association (JPDA) in practical application, the proposed methodology implemented a numerically stabled cubature Kalman filter for JPDA state estimate process. It improved numerical stability and acquired more accurate estimated results. Additionally, enlightened by enhancing the real time efficiency of JPDA, an adaptive tracking gate designed for the JPDA measurement associate process was proposed. It combined with the kinematics of vehicle to reduce the computational complexity of data association, which improved the robustness of MTT in complex scenarios. The virtual vehicle target tracking scenarios were built in PreScan software in order to better simulate the real traffic condition. Simulations of target tracking examples are presented to show great effectiveness and superiority of the proposed method.

[1]  Marcus Baum,et al.  Network Flow Labeling for Extended Target Tracking PHD Filters , 2019, IEEE Transactions on Industrial Informatics.

[2]  Xuelong Li,et al.  Multi-Sensor Centralized Fusion without Measurement Noise Covariance by Variational Bayesian Approximation , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Thushara D. Abhayapala,et al.  A Gaussian-Sum Based Cubature Kalman Filter for Bearings-Only Tracking , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[4]  Tao Sun,et al.  Iterative Diffusion-Based Distributed Cubature Gaussian Mixture Filter for Multisensor Estimation , 2016, Sensors.

[5]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[6]  Quan Pan,et al.  Hybrid Classification System for Uncertain Data , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[7]  You He,et al.  Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association , 2017, Sensors.

[8]  Peng Wang,et al.  Multi-Target Tracking by Associating and Fusing the Multi-Bernoulli Parameter Sets , 2020, IEEE Access.

[9]  Qiang Fu,et al.  Generalised distance partitioning for multiple-detection tracking filter based on random finite set , 2017 .

[10]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[11]  Jing Wang,et al.  Adaptive tracking gate design for maneuvering target in clutter environment , 2017, 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC).

[12]  Florentin Wörgötter,et al.  Square-Root Sigma-Point Information Filtering , 2012, IEEE Transactions on Automatic Control.

[13]  Zhiyu Qu,et al.  A measurement-driven adaptive probability hypothesis density filter for multitarget tracking , 2015 .

[14]  Yaakov Bar-Shalom,et al.  Sonar tracking of multiple targets using joint probabilistic data association , 1983 .

[15]  Simon Haykin,et al.  Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations , 2010, IEEE Transactions on Signal Processing.

[16]  Simon Haykin,et al.  Cubature Kalman smoothers , 2011, Autom..

[17]  Xiangfeng Wang,et al.  Flexible Fusion Structure-Based Performance Optimization Learning for Multisensor Target Tracking , 2017, Sensors.

[18]  Gao Ang.  Overview of deterministic sampling filtering algorithms for nonlinear system , 2012 .

[19]  Claudio Fantacci,et al.  The Marginalized $\delta$-GLMB Filter , 2015, 1501.00926.

[20]  Xiong Wei.  Centralized Interacted Multiple Model Multisensor Fuzzy Joint Probabilistic Data Association Algorithm , 2008 .

[21]  Thia Kirubarajan,et al.  A practical bias estimation algorithm for multisensor-multitarget tracking , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[22]  M. Omair Ahmad,et al.  An Adaptive Turn Rate Estimation for Tracking a Maneuvering Target , 2020, IEEE Access.

[23]  Jun Liu,et al.  Adaptive Gaussian sum squared-root cubature Kalman filter with split-merge scheme for state estimation , 2014 .

[24]  Zishu He,et al.  Comparison of gating techniques for maneuvering target tracking in clutter , 2014, 17th International Conference on Information Fusion (FUSION).

[25]  Giuseppe Thadeu Freitas de Abreu,et al.  Adaptive Gating for Multitarget Tracking With Gaussian Mixture Filters , 2012, IEEE Transactions on Signal Processing.

[26]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

[27]  Liu Yu,et al.  Squared-root cubature information consensus filter for non-linear decentralised state estimation in sensor networks , 2014 .