基于k近邻平均距离的异步航迹关联算法

doi:10.12305/j.issn.1001-506X.2022.11.27

Abstract

Abstract:

Aiming at the complex problem of local node track-to-track association under asynchronous and unequal rates, an asynchronous track-to-track direct association algorithm based on the k means distance of the nearest neighbors is proposed. First, the k means distance calculation rule of the nearest neighbor between different track sequences is given. Then the gray relational degree among different track sequences are calculated, the classical allocation method is used to determine the track-to-track association. The algorithm does not need time domain registration, and it avoids the accumulation of estimation error propagation. The simulation data show that the algorithm has high correct correlation rate and low time consumption. Asynchronous factors such as inconsistency of sampling period and startup time of local nodes have no obvious influence on the algorithm, and the algorithm is not affected by the change of noise distribution form and targets number.

Key words: track-to-track association, asynchronous track, k means distance of nearest neighbors, grey relational degree, multidimensional allocation

CLC Number:

V243.2
TN957

Xiao YI, Rui ZENG. Asynchronous track-to-track association algorithm based on k means distance of nearest neighbors[J]. Systems Engineering and Electronics, 2022, 44(11): 3515-3521.

Figures/Tables 12

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References 17

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算法性能	k近邻平均距离中k的取值选取
算法性能	k=2	k=3	k=4	k=5	k=6	k=7
耗时/s	5.288 9	5.314 2	5.378 4	5.410 6	5.445 6	5.473 2

k近邻平均距离中k的取值选取	局部节点1、2采样周期t₁、t₂(时间间隔)比
k近邻平均距离中k的取值选取	t₁∶t₂=0.5	t₁∶t₂=1	t₁∶t₂=1.5	t₁∶t₂=2	t₁∶t₂=2.5	t₁∶t₂=3	t₁∶t₂=3.5	t₁∶t₂=4	t₁∶t₂=4.5	t₁∶t₂=5
k=2	0.979 7	0.979 8	0.979 1	0.978 4	0.978 3	0.977 5	0.971 5	0.976 5	0.972 4	0.973 9
k=3	0.979 2	0.979 8	0.979 4	0.979 7	0.978 5	0.977 8	0.973 5	0.976 4	0.973 6	0.974 9
k=4	0.979 2	0.979 6	0.979 3	0.979 5	0.978 9	0.978 6	0.977 9	0.977 4	0.978 0	0.977 2
k=5	0.979 1	0.979 4	0.979 2	0.979 3	0.977 6	0.978 0	0.975 7	0.976 9	0.978 7	0.977 5
k=6	0.978 8	0.979 6	0.979 2	0.979 3	0.977 8	0.977 5	0.974 7	0.976 5	0.977 5	0.976 8
k=7	0.978 3	0.979 1	0.978 7	0.978 8	0.976 8	0.977 6	0.977 5	0.976 4	0.974 6	0.974 3

局部节点1、2开机时机差/s	局部节点1、2采样周期(时间间隔)比
局部节点1、2开机时机差/s	t₁∶t₂=0.5	t₁∶t₂=0.52	t₁∶t₂=0.55
0.1	0.978 8	0.979 0	0.979 8
0.2	0.978 7	0.979 2	0.979 5
0.3	0.979 3	0.978 8	0.979 4

参数	噪声分布形式
参数	高斯分布	瑞利分布	指数分布	均匀分布
正确关联率	0.979 2	0.979 7	0.979 8	0.979 6

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Asynchronous track-to-track association algorithm based on k means distance of nearest neighbors

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