Sensor Control Using Cauchy-Schwarz Divergence via Gaussian Mixture Multi-Bernoulli Filter

doi:10.3969/j.issn.0372-2112.2020.04.012

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Sensor Control Using Cauchy-Schwarz Divergence via Gaussian Mixture Multi-Bernoulli Filter

更新时间：2025-07-08

- Sensor Control Using Cauchy-Schwarz Divergence via Gaussian Mixture Multi-Bernoulli Filter
- Acta Electronica Sinica Vol. 48, Issue 4, Pages: 706-716(2020)
- 作者机构：
  
  兰州理工大学电气工程与信息工程学院,甘肃,兰州,730050
- 作者简介：
- 基金信息：
  
  National Defense Basic Scientific Research Program (No.JCKY2018427C002);National Natural Science Foundation of China (No.61873116, No.51668039, No.61763029);Science and Technology Project of Gansu Province (No.18JR3RA137)
- DOI：10.3969/j.issn.0372-2112.2020.04.012
  CLC： TP274
- Published Online：25 April 2020，
  
  Published：2020
- 稿件说明：
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Sensor Control Using Cauchy-Schwarz Divergence via Gaussian Mixture Multi-Bernoulli Filter[J]. Acta Electronica Sinica, 2020, 48(4): 706-716.
DOI：

Sensor Control Using Cauchy-Schwarz Divergence via Gaussian Mixture Multi-Bernoulli Filter[J]. Acta Electronica Sinica, 2020, 48(4): 706-716. DOI： 10.3969/j.issn.0372-2112.2020.04.012.

摘要

针对多目标跟踪中的传感器控制问题，本文基于有限集统计（FISST）理论，利用高斯混合多伯努利滤波器研究并提出相应的传感器控制策略.首先，文中给出容积卡尔曼高斯混合势均衡多目标多伯努利滤波器（CK-GMCBMeMBerF）的实现形式，并提取高斯混合分量近似多伯努利密度.然后，研究两个高斯混合之间的柯西施瓦兹（Cauchy-Schwarz）散度的求取，推导多目标概率密度变化所对应的信息增益，并以此为基础提出相应的传感器控制策略.此外，结合CK-GMCBMeMBer，详细推导了目标势的后验期望（PENT）准则的高斯混合（GM）实现过程，以GM-PENT作为评价准则进行相应的传感器控制方法的研究.最后，仿真实验验证了所提算法的有效性.

Abstract

In consideration of the sensor control for multi-target tracking

this paper proposes the corresponding sensor control strategy using Gaussian mixture multi-Bernoulli filter based on the FInite Set Statistics (FISST) theory. First

this paper gives the implementation of the Cubature Kalman Gaussian Mixture Cardinality Balanced Multi-target Multi-Bernoulli Filter (CK-GMCBMeMBerF)

and extracts the Gaussian mixture component to approximate multi-Bernoulli density. In addition

we study the solution of the Cauchy-Schwarz divergence between the two Gaussian mixture distributions

and derive the information gain corresponding to the change of multi-target probability density. Then

the corresponding sensor control strategy is proposed. Moreover

a detailed Gaussian Mixture (GM) implementation of the posterior expected number of targets (PENT) criteria is given based on CK-GMCBMeMBerF

and the corresponding sensor control strategy is studied with GM-PENT as the evaluation criteria. Finally

simulation results verify the effectiveness of these proposed algorithms.

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