基于Tri-feature训练的目标与海杂波鉴别算法

doi:10.12305/j.issn.1001-506X.2024.09.05

Abstract

Abstract:

Focusing on the issue of error detection in terminal guidance radar under sea clutter background, research on sea clutter suppression and discrimination is conducted. The target identification and classification algorithm based on Tri-feature training is adopted, and the target amplitude, peak duration range and fluctuation rate are used as feature quantities. The radial basis function (RBF) is selected as the kernel function, which has strong nonlinear mapping ability and can better represent the relationship between data in high-dimensional space. Then, the support vector machine (SVM) target identification and classification classifier design and experimental data verification are carried out. Verified by publicly available measured data, the accuracy rate of the proposed algorithm has reached over 97%. Compared with the traditional template matching recognition method, the target identification and classification algorithm based on Tri-feature training has higher identification accuracy, which proves the effectiveness and progressiveness of the proposed method.

Key words: sea clutter suppression, terminal guidance radar, target classification, support vector machine (SVM)

CLC Number:

TP391

Wei WU, Bing XUE, Dandan LIU. Target and sea clutter identification algorithm based on Tri-feature training[J]. Systems Engineering and Electronics, 2024, 46(9): 2935-2940.

Figures/Tables 8

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Table 1

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Table 4

References 30

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幅度	峰值持续范围	起伏率	判断
8 437.07	5	0.073 5	1
7 947.44	5	0.058 0	1
7 739.83	3	0.026 1	1
5 837.99	3	0.245 7	1
5 496.30	3	0.058 5	1
4 644.88	4	0.154 9	1
4 640.91	5	0.000 9	1
5 329.39	5	0.148 3	1
5 025.35	5	0.057 0	1
6 432.78	5	0.280 1	1
6 483.37	4	0.007 9	1
5 316.07	2	0.180 0	1
5 737.25	4	0.079 2	1
4 846.38	5	0.155 3	1
5 559.16	5	0.147 1	1
5 162.35	5	0.071 4	1
4 921.80	4	0.046 6	1
5 136.60	3	0.043 6	1
6 333.46	5	0.233 0	1
5 287.29	4	0.165 2	1
6 474.44	4	0.224 5	1
5 914.53	5	0.086 5	1
7 025.05	3	0.187 8	1
4 716.10	3	0.328 7	1
7 045.00	2	0.493 8	1
4 885.37	5	0.306 5	1
5 072.60	5	0.038 3	1
4 618.32	4	0.089 6	1
3 323.82	4	0.280 3	1
3 717.93	5	0.118 6	1
6 172.03	4	0.660 1	1
1 768.42	6	0.708 3	0
406.94	5	0.769 9	0
2 308.80	5	0.214 0	0
542.24	4	0.765 1	0
707.29	6	0.304 4	0
1 732.63	7	0.689 8	0
665.26	3	0.616 0	0
526.71	7	0.208 3	0
550.96	5	0.046 0	0
398.71	8	0.276 3	0
501.77	4	0.258 5	0
530.29	6	0.056 8	0
1 032.97	5	0.947 9	0
2 540.42	6	0.685 3	0
800.18	6	0.685 0	0
898.09	7	0.433 0	0
846.05	6	0.057 9	0
535.03	8	0.367 6	0
1 308.84	7	0.691 4	0
1 124.92	6	0.140 5	0
1 454.63	8	0.293 1	0
1 160.54	9	0.202 2	0
960.39	4	0.172 5	0
1 546.57	7	0.610 3	0
487.92	6	0.684 5	0
661.14	9	0.355 0	0
1 533.55	9	0.757 8	0
1 330.59	8	0.132 3	0
554.20	6	0.583 5	0
501.95	6	0.094 3	0
1 658.74	7	0.433 9	0
860.95	7	0.481 0	0

序号	样本总数	实际分类(样本数)		鉴别结果(样本数)		正确率/%
序号	样本总数	舰船	海杂波	舰船	海杂波	正确率/%
1	93	93	0	91	2	97.85
2	141	141	0	140	1	99.29
3	88	0	88	0	88	100.00
4	120	0	120	2	118	98.33

序号	样本总数	实际分类(样本数)		鉴别结果(样本数)		正确率/%
序号	样本总数	舰船	海杂波	舰船	海杂波	正确率/%
1	72	72	0	65	7	90.28
2	134	134	0	124	10	92.54
3	101	0	101	11	90	89.11
4	136	0	136	14	122	91.53

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Target and sea clutter identification algorithm based on Tri-feature training

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