Compressed sensing based off-grid calibration for microwave imaging with random illuminations under low SNR

Recently, microwave imaging (MWI) based on randomized fields has shown promising potential in practical scenes. However, the off-grid error and low signal-to-noise ratio (SNR) measurements remain a challenge for the imaging process. In this paper, we proposed a compressed sensing (CS) based off-grid calibration method for MWI with random illuminations under low SNR environment. A noisy mismatched imaging model with off-grid error is firstly established. Under the framework of CS, the off-grid error is corrected by genetic algorithm (GA) and singular value decomposition (SVD) is conducted to enhance the SNR of echo signal. In the inversion, the OMP algorithm is used due to its high computational efficiency. At last, the effectiveness of the proposed method is verified based on the numerical data. Compared to the existing algorithms, the proposed approach is able to achieve satisfactory results in both off-grid calibration and noise suppression. Besides, the imaging performance versus the number of measurements, SNR, off-grid level and sparsity is discussed. The proposed method provides a new clue for off-grid MWI in low SNR scenarios.