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| Citation: | DING Zihang, XIE Junwei, and WANG Bo. Missing covariance matrix recovery with the FDA-MIMO radar using deep learning method[J]. Journal of Radars, 2023, 12(5): 1112–1124. doi: 10.12000/JR23002
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Missing Covariance Matrix Recovery with the FDA-MIMO Radar Using Deep Learning Method
doi: 10.12000/JR23002
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Abstract
The realization of anti-jamming technologies via beamforming for applications in Frequency-Diverse Arrays and Multiple-Input and Multiple-Output (FDA-MIMO) radar is a field that is undergoing intensive research. However, because of limitations in hardware systems, such as component aging and storage device capacity, the signal covariance matrix data computed by the receiver system may be missing. To mitigate the impact of the missing covariance matrix data on the performance of the beamforming algorithm, we have proposed a covariance matrix data recovery method for FDA-MIMO radar based on deep learning and constructed a two-stage framework based on missing covariance matrix recovery-adaptive beamforming. Furthermore, a learning framework based on this two-stage framework and the Generative Adversarial Network (GAN) is constructed, which is mainly composed of a discriminator (D) and a generator (G). G is primarily used to output complete matrix data, while D is used to judge whether this data is real or filled. The entire network closes the gap between the samples generated by G and the distribution of the real data via a confrontation between D and G, consequently leading to the missing data of the covariance matrix being recovered. In addition, considering that the covariance matrix data is complex, two independent networks are constructed to train the real and imaginary parts of the matrix data. Finally, the numerical experiment results reveal that the difference in the root square mean error levels between the real and recovery data is 0.01 in magnitude. -
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References
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- Figure 1. The framework of dual channel GAN network
- Figure 2. The structure of D and G network
- Figure 3. The grayscale image of the real part of covariance matrix (single interference)
- Figure 4. The grayscale image of the imaginary part of covariance matrix (single interference)
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Figure 5. RMSE performance versus training process (different
$ \varepsilon $ - Figure 6. RMSE performance versus training process (different k)
- Figure 7. FDA-MIMO radar beampattern based on different covariance matrices (single interference)
- Figure 8. The grayscale image of the real part of covariance matrix (multiple interferences)
- Figure 9. The grayscale image of the imaginary part of covariance matrix (multiple interferences)
- Figure 10. FDA-MIMO radar beampattern based on different covariance matrices (multiple interferences)