Decentralized Sensor Selection Based on Multi-Target MS-OSPA Lower Bound

LIAN Feng; ZHANG Xiu-li; WEI Bo; HOU Li-ming; HAN Chong-zhao; WANG Wei

doi:10.3969/j.issn.0372-2112.2019.10.018

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Decentralized Sensor Selection Based on Multi-Target MS-OSPA Lower Bound

更新时间：2025-07-16

- Decentralized Sensor Selection Based on Multi-Target MS-OSPA Lower Bound
- Acta Electronica Sinica Vol. 47, Issue 10, Pages: 2158-2165(2019)
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,陕西,西安,710049
  2. 陕西省组合与智能导航重点实验室,陕西,西安,710068
  3. 中国电子科技集团公司第二十研究所,陕西,西安,710068
  4. 西安交通大学电子与信息工程学院,陕西,西安,710049
  5. 陕西省组合与智能导航重点实验室,陕西,西安,710068
  6. 中国电子科技集团公司第二十研究所,陕西,西安,710068
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.10.018
  CLC： TP391
- Published Online：25 October 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
LIAN Feng, ZHANG Xiu-li, WEI Bo, et al. Decentralized Sensor Selection Based on Multi-Target MS-OSPA Lower Bound[J]. Acta Electronica Sinica, 2019, 47(10): 2158-2165.
DOI：

LIAN Feng, ZHANG Xiu-li, WEI Bo, et al. Decentralized Sensor Selection Based on Multi-Target MS-OSPA Lower Bound[J]. Acta Electronica Sinica, 2019, 47(10): 2158-2165. DOI： 10.3969/j.issn.0372-2112.2019.10.018.

摘要

本文提出了一种分散式大规模多目标跟踪网络的传感器选择优化算法.该方法以多目标状态集和估计集间的均方最优子模式分配误差下界作为优化目标函数，根据加权Kullback-Leibler平均（Kullback-Leibler average，KLA）准则对局部多目标密度进行融合，最终采用坐标下降法来折中计算代价和跟踪精度.仿真实验在不同信噪比场景下验证了本方法的有效性.

Abstract

A sensor selection optimization algorithm is proposed for decentralized large-scale multi-target tracking network. In this method

the lower bound of mean square optimal sub-pattern assignment error between multi-target state set and its estimation is taken as optimized objective function while the rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. The coordinate descent method is proposed to compromise the computation cost and tracking accuracy. Simulations verify the effectiveness of our method under different signal-to-noise ratio scenarios.

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