Joint tracking and classification of extended targets with complex shapes

Liping WANG; Ronghui ZHAN; Yuan HUANG; Jun ZHANG; Zhaowen ZHUANG

doi:10.1631/FITEE.2000061

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Joint tracking and classification of extended targets with complex shapes

Regular Papers | Updated：2022-06-06

- Joint tracking and classification of extended targets with complex shapes
  Enhanced Publication
- 复杂形状的扩展目标联合跟踪与分类
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 6, Pages: 839-861(2021)
- Affiliations：
  
  National Key Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  Ronghui ZHAN, E-mail: zhanrh@nudt.edu.cn
- Funds：
- DOI：10.1631/FITEE.2000061
  CLC： TN953
- Published：2021-06，
  
  Received：06 February 2020，
  
  Revised：01 June 2021，
- Accepted：
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LIPING WANG, RONGHUI ZHAN, YUAN HUANG, et al. Joint tracking and classification of extended targets with complex shapes. [J]. Frontiers of information technology & electronic engineering, 2021, 22(6): 839-861.
DOI：

LIPING WANG, RONGHUI ZHAN, YUAN HUANG, et al. Joint tracking and classification of extended targets with complex shapes. [J]. Frontiers of information technology & electronic engineering, 2021, 22(6): 839-861. DOI： 10.1631/FITEE.2000061.

摘要

本文解决具有复杂形状的单扩展目标联合跟踪与分类（joint tracking and classification

JTC）问题。为描述复杂形状，首先利用随机超曲面模型（random hypersurface model

RHM）将空间扩展状态建模为星凸形状，并将其作为目标分类的特征信息。利用两个向量对目标状态建模，以减轻高维状态空间和严重非线性观测模型对目标状态估计的影响，并利用归一化傅立叶描述子的欧氏距离度量获得类别概率更新的解析解。因此，该方法被称为"JTC-RHM方法"。此外，为解决检测不确定和杂波情况下的单扩展目标JTC问题，将所提JTC-RHM方法整合到Bernoulli滤波框架中，提出JTC-RHM-Ber滤波算法。特别地，推导了该滤波算法的递推表达式。仿真结果表明：（1）与基于随机矩阵模型的JTC算法相比，所提JTC-RHM方法能更准确地对不同形状、相似大小的目标进行分类；（2）与基于星凸RHM的扩展目标跟踪算法相比，所提算法对目标状态性能估计更优；（3）所提JTC-RHM-Ber滤波算法在状态检测和估计方面具有良好性能，能够正确地实现目标分类。

Abstract

This paper addresses the problem of joint tracking and classification (JTC) of a single extended target with a complex shape. To describe this complex shape

the spatial extent state is first modeled by star-convex shape via a random hypersurface model (RHM)

and then used as feature information for target classification. The target state is modeled by two vectors to alleviate the influence of the high-dimensional state space and the severely nonlinear observation model on target state estimation

while the Euclidean distance metric of the normalized Fourier descriptors is applied to obtain the analytical solution of the updated class probability. Consequently

the resulting method is called the "JTC-RHM method." Besides

the proposed JTC-RHM is integrated into a Bernoulli filter framework to solve the JTC of a single extended target in the presence of detection uncertainty and clutter

resulting in a JTC-RHM-Ber filter. Specifically

the recursive expressions of this filter are derived. Simulations indicate that: (1) the proposed JTC-RHM method can classify the targets with complex shapes and similar sizes more correctly

compared with the JTC method based on the random matrix model; (2) the proposed method performs better in target state estimation than the star-convex RHM based extended target tracking method; (3) the proposed JTC-RHM-Ber filter has a promising performance in state detection and estimation

and can achieve target classification correctly.

关键词

扩展目标傅里叶描述子联合跟踪与分类随机超曲面模型伯努利滤波器

Keywords

Extended targetFourier descriptorsJoint tracking and classificationRandom hypersurface modelBernoulli filter

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