Distributed Passive Radar Imaging Techniques Based On Sparse Spatial Spectrum

Great challenges and urgent requirements have been taken place in the field of traditional radar imaging,while the development of radar network forming,illumination of opportunity and signal processing techniques have brought distributed passive radar imaging into focus of recent literature.Distributd passive radar system benefits from the advantages of low system cost,high imging accuracy and great anti-interference capacity,however,it still has a long way to go,several topics have been addressed in this work based on the deduction of the mathematical model of the distributed passive radar system:1.First of all,characteristics of the spatial spectrum of the received sigal by distributed passive radar have been studied.The mathematical model of distributed passive radar imaging is then built,and further analysised spatial spectrum where few transmitter and receiver is deployed,which lead to our adoption of Compressive Sensing based distributed passive radar imaging algorithms.2.Secondly,disposition method of the transmitters and receivers is then proposed,regarding to an objective function of Peak Signal to Noise Ratio,resolution and the correlation of the columns within reconstruction matrix,and Genetic algorithm as the optimization algorithm.Implementation details is also given.Simulation has demonstrated a tremendous increatment of imaging performance,resolution and algorithm effictiveness with our proposed optimized receiver disposition.3.And then,in order to promote the compatibility and imaging accuracy of the distributed passive imaging system,a framework consisting of sparse representation of the received multi-snapshot radar signal covariance matrix,Sparse Bayesian Learning based reconstruction algorithm has been built,and the determination of the regular parameter is also clarified.Experiments have validated exceptional effectiveness of our proposed framework over traditional OMP based imaging method,and sparse representation of covariance matrix based algorithm obtained a lower reconstruction error under the condition of low singal to noise ratio compared with direct sparse representation of signals based method.4.Finally,distributed passive radar imaging method involving off-grid issue is ellucidated.Different kinds of approximation is examined to further analyzing the off-grid issue in the distributed passive radar system,and method based on Taylor expansion and searching along the opposite direction of the gradient is constructed to address the actual location of the scatterers,which is our so-called Parameter Perturbation OMP based off-grid distributed passive radar imaging method.Metric of KLD is adopted to assess the accuracy of reconstructed image.Simulations have been carried out to verify the performance of our proposed method,which exhibit a success of our proposed algorithm to some extent,and a lower reconstruction error with respect to signal noise ratio,level of off-grid disturbation and size of grids compared with OMP.