利用k近邻区间距离的异步抗差航迹关联算法

doi:10.12305/j.issn.1001-506X.2022.05.07

Abstract

Abstract:

To solve the problem of track association under the asynchronous and system error, an asynchronous anti-bias track association algorithm based on k-nearest neighbors interval distance of the track sequence is proposed. The k-nearest neighbors interval distance measurement between interval sequence and interval points is defined, the method of the system error interval is put forward, the grey relational degree among the sequences of different track intervals are calculated, and the classical allocation method is used to determine the track-to-track association. Compared with the traditional algorithms, the requirement for prior information on system bias is lower. The simulation results show that the algorithm can achieve stable association with a high accuracy, and good anti-bias performance. The algorithm can also deal with the asynchronous unequal rate track association problem without time domain registration, which has obvious advantages.

Key words: track association, anti-bias association, k-nearest neighbors interval distance, grey relational degree, interval transform

CLC Number:

V243.2
TN957

Xiao YI, Rui ZENG, Xinying CAO. Asynchronous anti-bias track association algorithm by using k-nearest neighbors interval distance[J]. Systems Engineering and Electronics, 2022, 44(5): 1475-1482.

Figures/Tables 10

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Table 1

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References 30

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k的取值	局部节点1, 2航迹区间点数目比n_w^j/n_lⁱ
k的取值	0.5	1	1.5	2	2.5	3	3.5	4	4.5	5
k=2	0.950 1	0.950 2	0.949 5	0.948 5	0.946 8	0.947 7	0.942 0	0.946 7	0.942 4	0.944 1
k=3	0.949 5	0.950 1	0.949 9	0.949 7	0.948 5	0.948 0	0.943 5	0.946 8	0.944 7	0.944 9
k=4	0.949 4	0.950 0	0.949 8	0.949 5	0.949 1	0.949 0	0.948 4	0.947 5	0.949 2	0.947 7
k=5	0.949 0	0.949 8	0.949 7	0.949 4	0.948 8	0.948 0	0.947 8	0.947 0	0.948 4	0.947 4
k=6	0.948 8	0.949 7	0.949 7	0.949 0	0.948 1	0.948 0	0.945 9	0.946 5	0.947 6	0.947 0
k=7	0.948 7	0.949 2	0.949 5	0.948 8	0.947 8	0.947 8	0.945 1	0.946 5	0.945 1	0.944 5

最大系统误差		本文算法(单侧区间化)			本文算法(双侧区间化)			文献[26]算法
测距/m	测角/(°)	E_c	E_e	E_s	E_c	E_e	E_s	E_c	E_e	E_s
500	0.5	0.943 3	0.006 5	0.050 2	0.943 2	0.006 5	0.050 3	0.942 5	0.047 5	0.010 0
1 000	0.5	0.946 3	0.002 2	0.051 5	0.943 0	0.006 0	0.051 0	0.927 6	0.070 4	0.002 0
1 500	0.5	0.948 0	0	0.052 0	0.945 2	0	0.054 8	0.900 0	0.088 1	0.011 9
100	1	0.948 0	0.002 0	0.050 0	0.948 5	0.001 0	0.050 5	0.957 0	0.038 7	0.004 3
100	1.5	0.944 3	0.005 5	0.050 2	0.948 5	0.001 3	0.050 2	0.932 5	0.055 2	0.012 3
100	2	0.944 8	0.004 0	0.051 2	0.939 2	0.010 0	0.050 8	0.928 5	0.051 0	0.020 5
500	1	0.949 8	0	0.050 2	0.949 7	0	0.050 3	0.942 0	0.054 7	0.003 3
1 000	1.5	0.939 3	0.009 5	0.051 2	0.944 0	0.004 0	0.052 0	0.930 7	0.066 0	0.003 3
1 500	2	0.946 0	0	0.054 0	0.936 0	0.012 5	0.051 5	0.880 0	0.107 3	0.012 7

局部节点2传感器开始采样时机时延/s	局部节点1, 2传感器采样频率(采样间隔)/s
	f₁=0.2, t₂=0.3		f₁=0.2, t₂=0.5		f₁=0.2, t₂=0.7
	本文算法	文献[26]算法	本文算法	文献[26]算法	本文算法	文献[26]算法
Δt=0.1	0.942 2	0.917 0	0.938 3	0.909 0	0.932 7	0.887 5
Δt=0.2	0.933 8	0.915 2	0.932 5	0.909 0	0.932 3	0.881 0
Δt=0.3	0.942 7	0.910 0	0.944 2	0.904 7	0.945 2	0.880 2
Δt=0.4	0.934 2	0.906 3	0.928 5	0.903 5	0.934 5	0.865 8
Δt=0.5	0.945 5	0.895 5	0.947 5	0.899 0	0.939 0	0.844 7
Δt=0.6	0.937 3	0.890 3	0.936 8	0.898 5	0.947 0	0.718 7
Δt=0.7	0.936 5	0.889 0	0.942 8	0.888 8	0.935 5	0.710 2

算法类型	文献[26]算法	本文算法
乘法运算量	6mn+m	10mn²+mn
加法运算量	6mn-m	(9n+k-1)mn

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Asynchronous anti-bias track association algorithm by using k-nearest neighbors interval distance

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