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

摘要/Abstract

摘要：

针对异步和系统误差并存情况下的航迹关联问题, 提出利用航迹序列k近邻区间距离的异步抗差航迹关联算法。定义区间序列与区间点的k近邻区间距离度量, 提出系统误差区间化方法, 通过不等长航迹区间序列间的灰色关联度, 利用经典分配法进行航迹关联判定。与传统算法相比, 对系统误差先验信息的要求低。仿真结果表明, 算法能以较高正确率实现稳定关联, 具有良好的抗差性。算法亦可处理异步不等速率航迹关联问题, 无需时域配准, 具有明显的优势。

关键词: 航迹关联, 抗差关联, k近邻区间距离, 灰色关联度, 区间化

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

中图分类号:

V243.2
TN957

衣晓, 曾睿, 曹昕莹. 利用k近邻区间距离的异步抗差航迹关联算法[J]. 系统工程与电子技术, 2022, 44(5): 1475-1482.

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.

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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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