A Single Measurement PHD Filter with Unknown Target Birth Intensity

XU Cong-an; XIONG Wei; LIU Yu; HE You

doi:10.3969/j.issn.0372-2112.2016.10.003

您当前的位置：

首页 >

文章列表页 >

A Single Measurement PHD Filter with Unknown Target Birth Intensity

更新时间：2025-07-16

- A Single Measurement PHD Filter with Unknown Target Birth Intensity
- Acta Electronica Sinica Vol. 44, Issue 10, Pages: 2300-2307(2016)
- 作者机构：
  
  海军航空工程学院信息融合研究所,山东,烟台,264001
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.10.003
  CLC： TN957TP391
- Published：2016
- 稿件说明：
移动端阅览
XU Cong-an, XIONG Wei, LIU Yu, et al. A Single Measurement PHD Filter with Unknown Target Birth Intensity[J]. Acta Electronica Sinica, 2016, 44(10): 2300-2307.
DOI：

XU Cong-an, XIONG Wei, LIU Yu, et al. A Single Measurement PHD Filter with Unknown Target Birth Intensity[J]. Acta Electronica Sinica, 2016, 44(10): 2300-2307. DOI： 10.3969/j.issn.0372-2112.2016.10.003.

摘要

自适应新生目标强度PHD滤波器（PHD-M）在目标漏检时易发生错估或漏估，从而导致滤波器估计性能下降.为解决这一问题，提出了一种新生目标强度未知的单量测（single measurement）PHD滤波器（PHD-SM）并给出了其粒子实现方式.该文首先通过构建一步虚拟量测对漏检目标进行补偿，然后基于单量测PHD分解技术推导了PHD预测和更新公式，最后设计了一种无须聚类操作的多目标状态估计方法.仿真实验表明，在当检测概率

较小时，PHD-SM滤波器估计性能优于PHD-M滤波器，且检测概率越小，性能优势越明显.

Abstract

In situations where the targets cannot be detected in the surveillance region

the estimated performance of the adaptive target birth intensity probability hypothesis density (PHD) filter will get worse because of false or low estimate.To overcome this problem

with unknown target birth intensity

a single measurement PHD (PHD-SM) filter and its sequential Monte Carlo (SMC) method are proposed.First

the undetected targets are compensated through developing the one step virtual measurement set.Afterward

according to the single measurement decomposition technique of PHD

the predication and update equations are derived.Finally

a novel multi-target state estimation method is presented.The simulation results show that

when the detecti

on probability P

is small

PHD-SM filter has higher estimation performance.Moreover

the smaller the detection probability

the more significant advantage of estimation performance for PHD-SM filter.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Bearings-Only Multi-targets Tracking PHD in the Three Dimensional Space

Dense Multi-Target Adaptive Video Tracking Based on Signature

Rao-Blackwellized Particle Filter Based on Random Finite Sets Theory for Multi-Target Association and Tracking

Parallel Joint Probability Track Initiation Logic

Tracking Technique for Radar Network in the Presence of Multi-Range-False-Target Deception Jamming

Related Author

ZHAO Wei

YUAN Zhi-wei

HUANG Shu-cai

XIONG Zhi-gang

REN Xiao-yi

JIANG Sheng

WU Jian-kang

Related Institution

Air and Missile Defense College, Air Force Engineering University

School of Information Science and Engineering,Graduate University of Chinese Academy of Sciences.

CASC AcakemyXi’anShaanxi 710071China

Xidian UniversityXi’anShaanxi 710071China

CASC 401 Acakemy

⁰