基于机动频率自适应的增强层级融合算法

doi:10.12305/j.issn.1001-506X.2025.10.04

Abstract

Abstract:

To address the issues of filter divergence and track distortion in long-term multi-source track fusion tracking caused by reliance on empirical parameters, an enhanced hierarchical fusion algorithm based on maneuver frequency adaptive parameters is proposed. This algorithm improves the dependence of traditional fusion methods on empirical parameters and ideal reception conditions, enabling stable target tracking and generation of integrated track in complex scenarios. Experimental results show that the enhanced hierarchical fusion model incorporating maneuver frequency adaptation achieves an average optimization improvement of 36.16%. This algorithm significantly enhances the robustness and timeliness of the system in high dynamic environments by introducing an adaptive mechanism driven by maneuvering frequency and a hierarchical feedback structure, providing an effective solution for high-precision and stable fusion of multi-source track in complex scenarios.

Key words: adaptive maneuvering frequency, multi sensor cooperation, heterogeneous information source fusion, enhanced hierarchical fusion model

CLC Number:

Gaozhi ZHONG, Hongyi XU, Changbo HOU, Pengqi ZHAO, Haonan GUO. Enhanced hierarchical fusion algorithm based on adaptive maneuvering frequency[J]. Systems Engineering and Electronics, 2025, 47(10): 3155-3167.

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变量	符号	变量描述	注释
输入变量	$ \left[ {L,B,H} \right] $	经度、纬度、高度列表	基于WGS-84坐标系
输出变量	$ \left[ {{X_c},{Y_c},{Z_c}} \right] $	X 轴、Y 轴、Z 轴坐标列表	基于CGCS2000 地心坐标系

变量	符号	变量描述	注释
输入变量	$ \left[ {R,\theta ,\varepsilon } \right] $	原点距离、方位角、俯仰角列表	基于极坐标系
输出变量	$ \left[ {L,B,H} \right] $	经度、纬度、高度列表	基于WGS-84坐标系

变量	符号	变量描述	注释
输入变量	${{\boldsymbol{\hat x}}^{{i}}}$	传感器$i$的状态估计	基于CGCS2000 地心坐标系
	${{\boldsymbol{P}}^{{i}}}$	传感器$i$的误差协方差	−
	${{\boldsymbol{\hat x}}^{{j}}}$	传感器$j$的状态估计	基于CGCS2000 地心坐标系
	${{\boldsymbol{P}}^{{j}}}$	传感器$j$的误差协方差	−
输出变量	${\boldsymbol{\hat x}}$	融合后的状态估计	基于CGCS2000 地心坐标系
输出变量	${\boldsymbol{P}}$	融合后的误差协方差	−

变量	符号	变量描述	注释
输入变量	${{\boldsymbol{P}}_{{{ij}}}}$	传感器$i$和传感器$j$的互协方差	${{\boldsymbol{P}}_{{{ij}}}} ={\boldsymbol{ }}{{\boldsymbol{P}}_{{{ji}}}}$
输出变量	$ {{\boldsymbol{D}}_{{{ij}}}} $	传感器$i$和传感器$j$的估计误差	${{\boldsymbol{D}}_{{{ij}}}} ={{\boldsymbol{D}}_{{{ji}}}}$

变量	符号	变量描述	注释
输入变量	$\hat {\boldsymbol{x}}_{k/k}^i$	传感器$i$在k时刻的状态估计	−
	$\hat {\boldsymbol{x}}_{k/k-1}^i$	传感器i在k时刻的状态预测	基于上一时刻
	${\boldsymbol{P}}_{k/k-1}^i$	传感器$i$在k时刻的估计协方差	−
	$\hat {\boldsymbol{x}}_{k/k-1}$	系统航迹在k时刻的状态预测	基于上一时刻
	$ {\boldsymbol{P}}_{k/k-1}$	系统航迹在k时刻的预测协方差	基于上一时刻
输出变量	$\hat {\boldsymbol{x}}_{k/k}$	融合后在k时刻的状态估计	−
输出变量	$ {\boldsymbol{P}}_{k/k}$	融合后在k时刻的误差协方差	−

Enhanced hierarchical fusion algorithm based on adaptive maneuvering frequency

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序号	传感器	探测距离/km	精度/m	刷新率/s	探测频率
1	SC-P5000	5~10	≤8	≤2	2.4 GHz/5.8 GHz
2	SC-P5000+	5~10	≤6	≤2	900 MHz/1.4 GHz/ 2.4 GHz/5.8 GHz
3	SC-P8000	8~10	≤4	≤2	800 MHz—1400 MHz/ 2.4 GHz/5.2 GHz/5.8 GHz
4	SC-R3000	3	≤25	≤1	5.2 GHz/5.8 GHz/12.5 GHz

序号	信息源航迹	RMSE/m	系统误差/m
1	SC-P8000	3.98	3.83
2	SC-P5000+	5.23	5.11
3	SC-P5000	7.35	7.26
4	SC-R3000	18.15	17.27

滤波	融合
滤波	Weight	CC	BSC	HF
CKF	4.83	3.16	2.91	2.83
α-CKF	3.75	2.88	2.67	2.51

实验轮次	SC-P8000	SC-P5000+	SC-P5000	SC-R3000
1	2.8799	1.8152	3.0494	17.2718
2	2.8799	1.8152	3.0494	22.4782
3	3.9354	1.8152	3.0494	17.2718
4	2.8799	1.8152	6.6097	17.2718
5	2.8799	5.9925	3.0494	17.2718
6	3.9354	5.9925	6.6097	22.4782