Waveform Analysis and Optimization for Radar Coincidence Imaging with Modeling Error

RCI is a novel superresolution staring imaging technique based on the idea of wavefront modulation and temporal-spatial stochastic radiation field. For RCI, the reference matrix should be known accurately, and the imaging performance depends on the incoherence property of the reference matrix. Unfortunately, the modeling error, which degrades the performance significantly, exists generally. In this paper, RCI using frequency-hopping waveforms (FH-RCI) is considered, and a FH code design method aiming to increase the robustness of RCI to modeling error is proposed. First, we derive the upper bound of imaging error for RCI with modeling error and conclude that the condition number of the reference matrix determines the imaging performance. Then the object function for waveform design which minimizes the condition number of the reference matrix is achieved, and the quantum simulated annealing (QSA) is employed to optimize the FH code. Numerical simulations show that the optimized FH code could decrease the condition number of the reference matrix and improve the imaging performance of RCI with modeling error

Radar Coincidence Imaging for Off-Grid Target Using Frequency-Hopping Waveforms

Radar coincidence imaging (RCI) is a high-resolution staring imaging technique without the limitation of the target relative motion. To achieve better imaging performance, sparse reconstruction is commonly used. While its performance is based on the assumption that the scatterers are located at the prediscretized grid-cell centers, otherwise, off-grid emerges and the performance of RCI degrades significantly. In this paper, RCI using frequency-hopping (FH) waveforms is considered. The off-grid effects are analyzed, and the corresponding constrained Cramér-Rao bound (CCRB) is derived based on the mean square error (MSE) of the “oracle” estimator. For off-grid RCI, the process is composed of two stages: grid matching and off-grid error (OGE) calibration, where two-dimension (2D) band-excluded locally optimized orthogonal matching pursuit (BLOOMP) and alternating iteration minimization (AIM) algorithms are proposed, respectively. Unlike traditional sparse recovery methods, BLOOMP realizes the recovery in the refinement grids by overwhelming the shortages of coherent dictionary and is robust to noise and OGE. AIM calibration algorithm adaptively adjusts the OGE and, meanwhile, seeks the optimal target reconstruction result