Distributed ISAR technique has the potential to increase the cross-range resolution by exploiting multi-channel echoes from distributed virtual equivalent sensors. In the existing imaging approaches, the echoes acquired from different sensors are rearranged into an equivalent single-channel ISAR signal. Then, the missing data between the observation angles of any two adjacent sensors is restored by interpolation. However, the interpolation method can be very inaccurate when the gap is large or the signal-to-noise (SNR) of echoes is low. In this paper, we discuss sparse representation of distributed ISAR echoes since the scattering field of the target is usually composed of only a limited number of strong scattering centres, representing strong spatial sparsity. Then, by using sparse algorithm (Orthogonal Matching Pursuit algorithm, OMP), the positions and amplitudes of the scattering points in every range bin can be recovered and the final ISAR image with high cross-range resolution can be obtained. Results show the effectiveness of the proposed method.
Fault Diagnosis of Rolling Bearing Based on Adaptive Block Forward and Backward Stagewise Orthogonal Matching Pursuit Algorithm