Sensor Control Strategy Based on Gaussian Mixture Multi-target Filter

doi:10.3969/j.issn.0372-2112.2019.03.002

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Sensor Control Strategy Based on Gaussian Mixture Multi-target Filter

更新时间：2025-07-02

- Sensor Control Strategy Based on Gaussian Mixture Multi-target Filter
- Acta Electronica Sinica Vol. 47, Issue 3, Pages: 521-530(2019)
- 作者机构：
  
  1. 兰州理工大学电气工程与信息工程学院,甘肃,兰州,730050
  2. 西安交通大学电子与信息工程学院综合自动化研究所,陕西,西安,710049
  3. 兰州理工大学电气工程与信息工程学院,甘肃,兰州,730050
  4. 西安交通大学电子与信息工程学院综合自动化研究所,陕西,西安,710049
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61370037, No.61873116);Science and Technology Project of Gansu Province (No.18YF1GA065, No.18JR3RA137)
- DOI：10.3969/j.issn.0372-2112.2019.03.002
  CLC： TP274
- Published Online：25 March 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
Sensor Control Strategy Based on Gaussian Mixture Multi-target Filter[J]. Acta Electronica Sinica, 2019, 47(3): 521-530.
DOI：

Sensor Control Strategy Based on Gaussian Mixture Multi-target Filter[J]. Acta Electronica Sinica, 2019, 47(3): 521-530. DOI： 10.3969/j.issn.0372-2112.2019.03.002.

摘要

本文基于随机有限集的高斯混合多目标滤波器（Gaussian Mixture Multi-Target Filter，GM-MTF）提出几种传感器控制策略.首先，基于容积卡尔曼高斯混合多目标非线性滤波器，借助两个高斯分布之间的巴氏距离，推导GM-MTF的整体信息增益，并以此为基础提出相应的传感器控制策略.另外，设计高斯粒子的联合采样方法对多目标滤波器的预测高斯分量进行采样，用一组带权值的粒子去近似多目标统计特性，利用理想量测集对粒子的权值进行更新，继而研究利用Rnyi散度作为评价函数，提出一种适应性更好的传感器控制策略.最后，给出基于目标势的后验期望（Posterior Expected Number of Targets，PENT）评价的高斯混合实现过程.仿真实验验证了提出算法的有效性.

Abstract

This paper proposes several sensor control strategies via Gaussian mixture multi-target filter (GM-MTF) with random finite set.First

on the basis of the cubature Kalman Gaussian mixture multi-target nonlinear filter

the global information gain of the GM-MTF is deduced through the Bhattacharyya distance between the two Gaussian distributions.Then

taking advantage of this information distance

this paper proposes a corresponding sensor control strategy.Furthermore

a joint sampling method of Gaussian particle is designed to sample the predicted Gaussian component of multi-target filter.Subsequently

a set of weighted particles are used to approximate the multi-target statistical characteristic

and their weights are updated with the ideal measurement set.Next

a Rnyi divergence based sensor control strategy which has better adaptability is proposed.Finally

a detailed Gaussian mixture implementation of the posterior expected number of targets (PENT) is given.Simulation results verify the effectiveness of these algorithms.

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