1 问题模型
2 相关性分析
3 基于编队目标密度特性的航迹跟踪
4 仿真实验与结果分析
4.1 仿真环境
表1 三个编队的参数设置Tab.1 Parameter setting for three formations |
编队 | 目标 | 初始位置/ m | 速度/ (m/s) | 运动时间/ s |
---|---|---|---|---|
1 | (400,-500) | |||
1 | 2 | (450,-500) | (-12,6) | 1-50 |
3 | (350,-500) | |||
4 | (-700,-200) | |||
2 | 5 | (-730,-230) | (26,6) | 10-60 |
6 | (-670,-230) | |||
3 | 7 | (-200,800) | (5,-25) | 20-80 |
8 | (-230,830) | |||
9 | (-260,860) | |||
10 | (-290,890) |
4.2 仿真结果与分析
图3 λ=10时ET-GM-PHD算法单次仿真状态估计Fig.3 Single simulation state estimation using ET-GM-PHD algorithm when λ=10 |
图4 λ=10时本文算法单次仿真状态估计Fig.4 The single simulation state estimation algorithm in this paper when λ=10 |
图5 λ=10时ET-GM-PHD算法的目标数量估计Fig.5 Target number estimation of ET-GM-PHD algorithm when λ=10 |
图7 λ=1时ET-GM-PHD算法单次仿真状态估计Fig.7 Single simulation state estimation using ET-GM-PHD algorithm when λ=1 |
图8 λ=1时本文算法单次仿真状态估计Fig.8 The single simulation state estimation algorithm in this paper when λ=1 |
图9 λ=1时ET-GM-PHD算法的目标数量估计Fig.9 Target number estimation of ET-GM-PHD algorithm when λ=1 |
图10 λ=1时本文算法的目标数量估计Fig.10 The target number estimation of the algorithm in this paper when λ=1 |
图11 λ=50时ET-GM-PHD算法单次仿真状态估计Fig.11 Single simulation state estimation using ET-GM-PHD algorithm when λ=50 |
图12 λ=50时本文算法单次仿真状态估计Fig.12 The single simulation state estimation algorithm in this paper when λ=50 |
图13 λ=50时ET-GM-PHD算法的目标数量估计Fig.13 Target number estimation of ET-GM-PHD algorithm when λ=50 |