Multisensor CPHD Filter with Gaussian Mixture Implementation Based on Divide-Conquer and Greedy Algorithm

QU Chang-wen; FENG Qi; LI Ting-jun

doi:10.3969/j.issn.0372-2112.2018.10.022

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Multisensor CPHD Filter with Gaussian Mixture Implementation Based on Divide-Conquer and Greedy Algorithm

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- Multisensor CPHD Filter with Gaussian Mixture Implementation Based on Divide-Conquer and Greedy Algorithm
- Acta Electronica Sinica Vol. 46, Issue 10, Pages: 2472-2479(2018)
- 作者机构：
  
  1. 海军航空工程学院电子信息工程系,山东,烟台,264001
  2. 武警警官学院分队指挥系,四川,成都,610200
  3. 海军航空工程学院电子信息工程系,山东,烟台,264001
  4. 武警警官学院分队指挥系,四川,成都,610200
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.91538201)
- DOI：10.3969/j.issn.0372-2112.2018.10.022
  CLC： TN958.97
- Published Online：25 October 2018，
  
  Published：2018
- 稿件说明：
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QU Chang-wen, FENG Qi, LI Ting-jun. Multisensor CPHD Filter with Gaussian Mixture Implementation Based on Divide-Conquer and Greedy Algorithm[J]. Acta Electronica Sinica, 2018, 46(10): 2472-2479.
DOI：

QU Chang-wen, FENG Qi, LI Ting-jun. Multisensor CPHD Filter with Gaussian Mixture Implementation Based on Divide-Conquer and Greedy Algorithm[J]. Acta Electronica Sinica, 2018, 46(10): 2472-2479. DOI： 10.3969/j.issn.0372-2112.2018.10.022.

摘要

针对现有的多观测站概率假设密度滤波器实现中存在依赖观测站处理顺序、计算复杂度高等问题，文中提出一种基于分治-贪心算法的高斯混合多观测站势概率假设密度滤波器.假设观测站个数为

，每个观测站的量测个数为

，相对于暴力分析法，分治算法使得子集选取问题的计算复杂度从

（

）降到了

（

）.此外，在线性高斯模型假设条件下，给出多观测站势概率假设密度滤波实现的具体步骤.仿真结果证明，本文实现方法不受观测站处理顺序的影响，分治-贪心近似实现方法与暴力分析法的跟踪性能相当，但是运算耗时大大降低，提高了算法实现及应用的可行性.

Abstract

For the problem that the existing multisensor Probability Hypothesis Density (PHD) filters have limitations such as sensor order dependence or high computational requirements

a multisensor cardinalized probability hypothesis density (CPHD) filter with Gaussian mixture implementation based on Divide-conquer and Greedy algorithm is proposed in this paper. Especially the divide-conquer algorithm make the computation complex fall to

(

) from

(

)

compared with the brute analysis algorithm

where

is the number of sensors and

is the number of observations of each

sensor. In addition

the details of the implement of the multi-sensor CPHD filter are expressed in this paper. Finally

simulation results show that the algorithm proposed is not affected by the processing order

the proposed divide-conquer and greedy two-stage algorithm is equivalent to the tracking performance of the brute analysis

but the operation time greatly reduced

all of this improve the feasibility of implementation and application of multi-sensor CPHD filter.

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