Unsupervised deep basis pursuit based resolution enhancement for forward looking MIMO SAR imaging
Nowadays, radar-based image reconstruction is becoming important in higher level automated driving, especially for all weather conditions. In this article, we present an unsupervised deep learning method for forward looking multiple-input multiple-output synthetic aperture radar (FL-MIMO SAR) to enhance the angular resolution.We present mathematical analysis for the composite antenna pattern generated by FL-MIMO SAR as well as image reconstruction with deep learning for FL-MIMO SAR. We present a computationally efficient deep basis pursuit (DBP) method to solve for convolutional neural network (CNN) with unsupervised learning (i.e., without ground truth) and present modified backprojection algorithm to reconstruct SAR image with enhanced angular resolution. We present experimental results to verify our proposed methodology and compare the performance with compressed sensing-based backprojection algorithm on both simulation and real data.
History
Refereed
- Yes
Volume
59Issue number
6Page range
9080-9093Publication title
IEEE Transactions on Aerospace and Electronic SystemsISSN
0018-9251External DOI
Publisher
Institute of Electrical and Electronics Engineers (IEEE)File version
- Published version
Official URL
Affiliated with
- School of Engineering and The Built Environment Outputs