Open Access
 Issue JNWPU Volume 38, Number 2, April 2020 359 - 365 https://doi.org/10.1051/jnwpu/20203820359 17 July 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## 1 多目标BOT系统模型

t时刻第m个目标第s个观测站的状态方程和量测方程分别为

## 2 多观测站多目标PMHT算法

### 2.1 多传感器PMHT算法

PMHT算法的优点是在避免求解关联变量K的情况下, 求解目标状态X的后验概率分布, 即最大化p(X|Z)

### 2.2 基于UKF的多目标纯方位PMHT算法

Q(X(n+1); X(n))与下式具有相同的导数, 其中

## 3 仿真分析

### 3.1 静止多观测站仿真实验

2种方法在δr2=0.1°时的位置均方根误差均小于δr2=0.5°时, 说明相同量测噪声情况下, 基于UKF平滑算法的纯方位PMHT算法的跟踪性能优于基于EKF平滑算法的纯方位PMHT算法。量测噪声越小, 2种方法的位置均方根误差越小, 跟踪性能越优, 实际目标跟踪过程中应尽可能减小环境噪声, 以提高多目标跟踪性能。

 图1静止多观测站纯方位多目标跟踪PMHT量测
 图2位置均方根误差, 量测噪声δr2=0.5°
 图3位置均方根误差, 量测噪声δr2=0.1°

### 3.2 机动单观测站仿真实验

 图4机动单观测站纯方位多目标跟踪PMHT量测
 图5基于UKF的纯方位PMHT算法位置均方根误差

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## All Figures

 图1静止多观测站纯方位多目标跟踪PMHT量测 In the text
 图2位置均方根误差, 量测噪声δr2=0.5° In the text
 图3位置均方根误差, 量测噪声δr2=0.1° In the text
 图4机动单观测站纯方位多目标跟踪PMHT量测 In the text
 图5基于UKF的纯方位PMHT算法位置均方根误差 In the text

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