基于迁移学习的雷达杂波幅度统计模型选择

doi:10.12305/j.issn.1001-506X.2022.08.09

Abstract

Abstract:

In the process of target detection and recognition, the selection of a radar clutter amplitude statistical model is an important step. In order to improve the accuracy of clutter amplitude statistical model selection, based on the adaptive sample distribution, a weighted rebalance distribution adaptation transfer learning method is proposed to realize the information transfer from simulation data to the measured sea clutter IPIX data. Compared with the existing algorithms, the experimental results show that the improved algorithm can achieve better classification accuracy, and the verification results on the transfer learning public dataset Office-Caltech10 also show the universality of the algorithm.

Key words: radar clutter, IPIX date, transfer learning, weighted rebalance distribution adaptation

CLC Number:

TN957.51

Liru YANG, Yongxiang LIU, Wei YANG. Radar clutter amplitude statistical model selection based on transfer learning[J]. Systems Engineering and Electronics, 2022, 44(8): 2457-2467.

Figures/Tables 11

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References 36

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类型	对数正态		韦布尔		K		瑞利
类型	σ	μ	q	p	v	a	σ
标签	1		2		3		4
图 1(a)	0.553	-0.505	0.984	7.523	1.317	1.617	1.003
图 1(b)	0.569	-0.718	0.999	2.466	1.946	1.483	0.812

任务类型	迁移任务	1NN	TCA+1NN	W-BDA+1NN	W-RBDA+1NN
均匀样本	Xs80→Xt360(小样本)	48.89	71.94	82.22	88.89
	Xs200→ Xt360(小样本)	49.72	72.22	91.39	93.61
	Xs400 →Xt360(等量样本)	53.89	71.11	89.44	90.56
	Xs1200 → Xt360(大样本)	51.67	72.22	90.28	91.11
非均匀样本	Xs80 → Xt200	33.50	56.00	84.50	87.00
	Xs200 →Xt200	34.00	56.00	86.00	91.00
	Xs400 →Xt200	37.00	57.50	80.00	88.00
	Xs1200→Xt200	33.50	58.50	84.50	92.50

样本	对数正态	韦布尔	K	瑞利	标签
1	0.004 4	0.000 8	0.000 4	0.003 0	3
2	0.008 0	0.036 6	0.023 6	0.066 8	1
3	0.013 2	0.002 4	0.188 9	0.017 1	2
4	0.209 2	0.023 9	2.727 8	0.006 7	4

样本	标签	对数正态	韦布尔	K	瑞利
1NN	1	0.044 4	0.016 2	0.424 0	0.067 6
	2	0.183 7	0.012 2	2.092 0	0.052 0
	3	0.003 0	0.000 4	0.000 2	0.002 7
	4	0.047 9	0.006 0	0.602 8	0.146 6
TCA	1	0.007 2	0.022 7	0.028 6	0.107 5
	2	0.195 8	0.012 9	2.257 7	0.059 7
	3	0.052 5	0.005 3	0.480 4	0.014 1
	4	0.026 2	0.006 1	0.183 3	0.045 7
W-BDA	1	0.060 0	0.022 7	0.644 8	0.096 5
	2	0.084 3	0.004 8	1.015 3	0.107 0
	3	0.025 9	0.005 2	0.167 6	0.018 2
	4	0.281 0	0.019 8	3.185 2	0.013 2
W-RBDA	1	0.012 7	0.022 5	0.081 4	0.106 2
	2	0.115 0	0.006 3	1.375 1	0.101 0
	3	0.048 8	0.004 1	0.440 8	0.011 6
	4	0.269 8	0.020 1	3.047 6	0.011 1

迁移任务	W-BDA+1NN	W-RBDA+1NN	迁移任务	W-BDA+1NN	W-RBDA+1NN
C→A	45.30	48.23	W→C	35.00	35.80
C→W	39.66	40.00	W→A	38.62	39.04
C→D	50.96	50.96	W→D	92.36	92.36
A→C	40.69	40.69	D→C	33.75	34.19
A→W	41.69	41.69	D→A	36.53	36.95
A→D	39.49	40.13	D→W	90.51	90.51

Radar clutter amplitude statistical model selection based on transfer learning

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