Radar Coincidence Imaging Based on Adaptive Frame-iteration Compressive Sensing

The radar coincidence imaging (RCI) doesn’t rely on relative motion between target and radar, but its super-resolution characteristics requires a large sample size of the radiation fields. In the actual operation, transmitting too many signals at once not only requires large antenna array, but also easily causes aliasing. Thus the model of adaptive frame-iteration compressive sensing (AFCS) was proposed in this paper. Compared to the traditional antenna array, MIMO antenna array [ ] transmits signals independently and ensures low enough correlations between every array element. Based on the spatial multiplexing characteristics of MIMO antenna array, in each iteration-frame the randomly selected array elements transmit incoherent signals, and the scattering coefficients of target plane can be obtained by correlation processing of the echo signal and the reference signal. Moreover, according to the distribution of scattering coefficients, we can combine frame-iteration and compressive sensing to realize super-resolution imaging. Numerical simulation results demonstrate that the proposed model is feasible.