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基于递归变分贝叶斯推理的具有时变定位不确定性的多车辆协同目标跟踪

Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference.

作者信息

Chen Xiaobo, Wang Yanjun, Chen Ling, Ji Jianyu

机构信息

Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China.

School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.

出版信息

Sensors (Basel). 2020 Nov 13;20(22):6487. doi: 10.3390/s20226487.

DOI:10.3390/s20226487
PMID:33202934
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7697773/
Abstract

Cooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. However, the localization signal usually provided by the satellite-based navigation system is rather susceptible to dynamic driving environment, thus influencing the effectiveness of cooperative tracking. In order to implement reliable cooperative tracking, especially when the statistical characteristic of the relative localization noise is time-varying and uncertain, this paper presents a recursive Bayesian framework which jointly estimates the state of the target and the cooperative vehicle as well as the localization noise parameter. An online variational Bayesian inference algorithm is further developed to achieve efficient recursive estimate. The simulation results verify that our proposed algorithm can effectively boost the accuracy of target tracking when the localization noise dynamically changes over time.

摘要

通过车际通信连接的多车辆协同目标跟踪是提高目标状态估计的一种很有前景的方法。协同跟踪的有效性很大程度上取决于主车辆与协同车辆之间相对定位的准确性。然而，基于卫星的导航系统通常提供的定位信号很容易受到动态驾驶环境的影响，从而影响协同跟踪的有效性。为了实现可靠的协同跟踪，特别是当相对定位噪声的统计特性随时间变化且不确定时，本文提出了一种递归贝叶斯框架，该框架联合估计目标和协同车辆的状态以及定位噪声参数。进一步开发了一种在线变分贝叶斯推理算法以实现高效的递归估计。仿真结果验证了我们提出的算法在定位噪声随时间动态变化时能够有效地提高目标跟踪的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/685370e06833/sensors-20-06487-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/56419ef78a46/sensors-20-06487-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/4841855b0da2/sensors-20-06487-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/c820f6c22d3a/sensors-20-06487-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/2c98da4c4d1e/sensors-20-06487-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/e831d0c6f2ad/sensors-20-06487-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/c85b02de494e/sensors-20-06487-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/df8d5704ed6f/sensors-20-06487-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/ce9a1f7dd1cf/sensors-20-06487-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/df9b27b42737/sensors-20-06487-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/685370e06833/sensors-20-06487-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/56419ef78a46/sensors-20-06487-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/4841855b0da2/sensors-20-06487-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/c820f6c22d3a/sensors-20-06487-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/2c98da4c4d1e/sensors-20-06487-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/e831d0c6f2ad/sensors-20-06487-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/c85b02de494e/sensors-20-06487-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/df8d5704ed6f/sensors-20-06487-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/ce9a1f7dd1cf/sensors-20-06487-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/df9b27b42737/sensors-20-06487-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1873/7697773/685370e06833/sensors-20-06487-g010.jpg

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本文引用的文献

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Robust Cooperative Multi-Vehicle Tracking with Inaccurate Self-Localization Based on On-Board Sensors and Inter-Vehicle Communication.基于车载传感器和车际通信的具有不准确自身定位的稳健协作多车辆跟踪
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2
Probabilistic Linear Discriminant Analysis Based on L-Norm and Its Bayesian Variational Inference.基于 L-范数的概率线性判别分析及其贝叶斯变分推断。
IEEE Trans Cybern. 2022 Mar;52(3):1616-1627. doi: 10.1109/TCYB.2020.2985997. Epub 2022 Mar 11.
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L1 -norm low-rank matrix factorization by variational Bayesian method.
基于变分贝叶斯方法的 L1-范数低秩矩阵分解。
IEEE Trans Neural Netw Learn Syst. 2015 Apr;26(4):825-39. doi: 10.1109/TNNLS.2014.2387376. Epub 2015 Jan 15.