Abstract
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.
Highlights
Radar coincidence imaging (RCI) is a novel staring imaging technique without the limitation of the target relative motion [1, 2]
We focus on the waveform design for FH-RCI with modeling error, since the modeling error generally exists, for example, gainphase error [2, 14], off-grid error [15, 16, 22], and array position error [23]
This paper considers the FH-RCI with modeling error and focuses on FH code design method
Summary
Radar coincidence imaging (RCI) is a novel staring imaging technique without the limitation of the target relative motion [1, 2]. The sparse recovery accuracy is determined by the correlations between the columns of the dictionary matrix [4]; minimizing the coherence measure ensures theoretical guarantee for sparse support recovery of signals with potentially higher sparsity level Based on this conclusion, the CS radar waveform design was investigated by minimizing the cross correlations between different target responses [17]. Based on the sparse recovery model, Gogineni considered the optimal waveform design of MIMO radar by reducing the block coherence measure of the sensing matrix and selecting the FH codes of all the transmitters [4]. Results of numerical simulations show our proposed FH code design method optimizes the reference matrix and improves the imaging performance by reducing the condition number.
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