Infrared and Laser Engineering, Volume. 51, Issue 4, 20210282(2022)
Target azimuth estimation of synthetic aperture radar image based on block sparse Bayesian learning
Figures & Tables(8)
Fig. 3. Numbers of correctly estimation samples by the proposed algorithm at different estimation precisions
Table 1. Training and test samples of the three MSTAR targets
View tableView in ArticleTable 1. Training and test samples of the three MSTAR targets
Type Depression angle/(°) Target BMP2 BTR70 T72 Training 17 233 (SN_1)232 (SN_2)233(SN_3) 233 (SN_1) 232 (SN_1)231 (SN_2)233 (SN_3) Test 15 195 (SN_1)196 (SN_2)19 6(SN_3) 196 (SN_1) 196 (SN_1)195 (SN_2)191 (SN_3)
Table 2. Azimuth estimation results of the test samples of the three MSTAR targets by the proposed method
View tableView in ArticleTable 2. Azimuth estimation results of the test samples of the three MSTAR targets by the proposed method
Target class Number of samples Number of errors Percentage of correct samples BMP2 (SN_1) 195 1 99.49% BMP2 (SN_2) 196 0 100% BMP2 (SN_3) 196 1 99.50% BTR70 (SN_1) 196 2 98.98% T72 (SN_1) 196 3 98.47% T72 (SN_2) 195 1 99.49% T72 (SN_3) 191 0 100%
Table 3. Results of the proposed method at different estimation precisions
View tableView in ArticleTable 3. Results of the proposed method at different estimation precisions
Number of samples <5° <10° Mean Variance BMP2 587 573 585 2.01 1.85 BTR70 196 192 194 2.05 1.84 T72 582 569 578 2.16 1.77 Total 1365 1334 1357 2.07 1.81
Table 4. Correct estimation percentages of different methods at different estimation precisions
View tableView in ArticleTable 4. Correct estimation percentages of different methods at different estimation precisions
Method type Threshold of error/(°) 2 4 6 8 10 Proposed 76% 88% 97% 99% 99% MER 13% 24% 39% 57% 68% Dominant boundary 55% 82% 93% 97% 99% Sparse representation 64% 80% 93% 98% 99%
Table 5. Time consumption of different methods
View tableView in ArticleTable 5. Time consumption of different methods
Method type Average time consumption/ms Proposed 10.5 MER 45.2 Dominant boundary 40.2 Sparse representation 12.1
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Li You. Target azimuth estimation of synthetic aperture radar image based on block sparse Bayesian learning[J]. Infrared and Laser Engineering, 2022, 51(4): 20210282
Paper Information
Category: Image processing
Received: May. 6, 2021
Accepted: --
Published Online: May. 18, 2022
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