A Super-resolution-CFAR-Adaptive Reconstruction Algorithm for Range and Velocity Parameter of Frequency Agile Radar
A super-resolution-constant false alarm(CFAR)-adaptive reconstruction algorithm suitable for sparse processing models in the range velocity dimension is proposed to address the problem of target parameter estimation and strong deception interference suppression in frequency agile radar.This algorithm achieves target search,range velocity parameter estimation and deception in-terference suppression.The distance velocity super-resolution spectrum based on quantized lattice points achieves the matching of target distance velocity parameters with dictionary elements,and uses the CFAR algorithm to perform two-dimensional search on the distance velocity plane to achieve target detection and distance velocity parameter estimation.The adaptive reconstruction cancella-tion method is used to subtract the estimated target components from the observation vector matrix.During the iteration process,the two-dimensional spectral noise subspace dimension is updated,and the distance velocity spectrum,CFAR,and adaptive cancella-tion are cyclically used to achieve the remaining target estimation.By using multi-objective estimation and cancellation during each iteration process,the problem of weak target parameter estimation under strong deception interference has been solved.An alterna-tive approach to solve the underdetermined equation of the sparse representation is offered in the range and velocity dimensions for agile frequency radar.
frequency agilitysparse processingsuper resolutiondeception interference suppression
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