Algorithm Simulation of Multi-targets Track Correlation Based on Spectral Feature

doi:10.16182/j.issn1004731x.joss.24-0417

Abstract

Abstract:

In order to solve the problems of multi-targets track correlation in dense scenes, a method of track sequential real-time processing for multi-source track correlation system modelingis proposed. By calculating the absolute and relative position of the spectral features,the unified correlation matrix can be defined based on fuzzy decision theory, and the multi-target track correlation can be realized. Numerical simulations have shown the effectiveness of the track correlation algorithm on the basis of spectral features. Especially, the accuracy of correlation is much larger than that of the nearest-neighbor distance algorithm under the condition of dense target environment with systematic error. Meanwhile, the computational efficiency is much higher than that of the algorithm based on triangular topology, which proves this algorithm is more suitable for real-time processing systems.

Key words: track correlation, topology structure, spectral feature, fuzzy decision, sequential processing

CLC Number:

TP391.9

Ding Zhenping, Guo Huidong. Algorithm Simulation of Multi-targets Track Correlation Based on Spectral Feature[J]. Journal of System Simulation, 2025, 37(9): 2387-2396.

Figures/Tables 12

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目标编号	雷达A探测位置/m	雷达B探测位置/m
1	(14 876.7,19 749.4)	(15 178.7,19 662.9)
2	(14 647.3,19 277.8)	(14 952.9,19 141.9)
3	(15 236.6,19 269.6)	(15 497.5,19 204.7)
4	(15 477.9,19 757.1)	(15 750.9,19 691.5)
5	(15 829.2,19 266.0)	(16 085.2,19 213.4)
6	(15 029.0,17 076.9)	(15 332.8,17 029.4)
7	(15 056.3,17 439.5)	(15 378.9,17 363.8)

综合目标	第1次关联	第2次关联	第3次关联
1	a1	a1	a1、b1
2	a2	a2	a2、b2
3	a3	a3、b2	a3、b3
4	a4	a4、b1	a4、b4
5	a5	a5、b3	a5
6	a6	a6	a6
7	a7	a7	a7
8	b1	—	—
9	b2	—	—

目标编号	航向/(°)	航向误差/%	航速/(m/s)	航速误差/%
1	180.1	6.2×10^-2	15.1	50.8×10^-2
2	180.3	17.2×10^-2	14.9	36.1×10^-2
3	180.3	19.3×10^-2	14.9	80.0×10^-2
4	180.2	10.0×10^-2	15.0	28.7×10^-2
5	180.0	0.1×10^-2	15.0	31.5×10^-2
6	238.6	57.8×10^-2	9.8	176.2×10^-2
7	305.9	3.8×10^-2	12.9	110.4×10^-2