Abstract:

A novel framework of high precision Bayesian inverse synthetic aperture radar (ISAR) imaging based on Bayesian statistics is proposed. The model of ISAR imaging is constructed in Bayesian formalism and statistic parameters are estimated accurately with data-driven in process. Then high precision ISAR imaging could be achieved by realizing adaptive ISAR image representation. Specifically, the novelty of the algorithm lies in its high robustness: phase adjustment is accomplished by solving an optimization problem, regardless of the formation of phase errors. Besides, the algorithm takes high capability of de-noise. Hence, the well-focused image could be achieved in low signal-to-noise ratio (SNR). Finally, high efficiency could be ensured with fast Fourier transform/inversed fast Fourier transform (FFT/IFFT) and matrix Hadamard multiplication operations by converting Bayesian statistics into optimization. The experimental results using measured data confirm the validation of the proposal.