针对目标跟踪的两种基于最大期望算法测量值偏差估计过程的性能分析

陆志华; 朱梦尧; 叶庆卫; 周宇

doi:10.1631/FITEE.1800214

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针对目标跟踪的两种基于最大期望算法测量值偏差估计过程的性能分析

常规文章 | Updated：2022-05-19

- 针对目标跟踪的两种基于最大期望算法测量值偏差估计过程的性能分析
- Performance analysis of two EM-based measurement bias estimation processes for tracking systems
- 信息与电子工程前沿（英文） 2018年19卷第9期页码：1151-1165
- Affiliations：
  
  College of Information Science and Engineering, Ningbo University, Ningbo 315211, China
  School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
- Author bio：
  
  Zhi-hua LU, E-mail: luzhihua@nbu.edu.cn
  [ "Meng-yao ZHU, E-mail: zhumengyao@shu.edu.cn" ]
  [ "Qing-wei YE, E-mail: yeqingwei@nbu.edu.cn" ]
  [ "Yu ZHOU, E-mail: zhouyu@nbu.edu.cn" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61601254) and the KC Wong Magna Fund of Ningbo University, China
- DOI：10.1631/FITEE.1800214
  中图分类号： TP73
- 收稿：2018-04-07，
  
  修回：2018-;9-25，
  
  纸质出版：2018-09
- Accepted：
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陆志华, 朱梦尧, 叶庆卫, 等. 针对目标跟踪的两种基于最大期望算法测量值偏差估计过程的性能分析[J]. 信息与电子工程前沿（英文）, 2018,19(9):1151-1165.

Zhi-hua LU, Meng-yao ZHU, Qing-wei YE, et al. Performance analysis of two EM-based measurement bias estimation processes for tracking systems[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1151-1165.
陆志华, 朱梦尧, 叶庆卫, 等. 针对目标跟踪的两种基于最大期望算法测量值偏差估计过程的性能分析[J]. 信息与电子工程前沿（英文）, 2018,19(9):1151-1165. DOI： 10.1631/FITEE.1800214.

Zhi-hua LU, Meng-yao ZHU, Qing-wei YE, et al. Performance analysis of two EM-based measurement bias estimation processes for tracking systems[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(9): 1151-1165. DOI： 10.1631/FITEE.1800214.

摘要

在现实目标跟踪问题中，传感器采集的测量值，比如系统缺陷，可能存在一定程度偏差。为正确估计目标的运动轨迹，测量值偏差的识别与估计将成为首要任务。首先，提出两种状态空间模型，分别把测量值偏差视为随机变量和常数。其次，假设测量值样本数足够大，基于最大期望算法，利用扩展卡尔曼滤波和平滑，针对两种模型提出不同的循环迭代偏差估计过程。最后，分析这两种估计过程的全局收敛结果，揭示其内在联系。结果表明，第二种估计过程比第一种估计过程更简单准确，并且收敛速度更快。批量处理测量值样本后，可同时获得目标运动轨迹和测量值偏差的估计结果。

Abstract

In target tracking

the measurements collected by sensors can be biased in some real scenarios

e.g.

due to systematic error. To accurately estimate the target trajectory

it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization (EM) algorithm

for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing

we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly

we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore

the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct

the target trajectory can be simultaneously estimated with the measurement bias

after processing a batch of measurements. These results are confirmed by our simulations.

关键词

Keywords

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