Research On Array Multi-Parameter Estimation Algorithms Via Compressed Sening Parallel Factor

Array signal processing is an important branch in the field of modern signal processing,which utilizes the sensor array to receive the signal.Compared with the traditional single directional sensors,sensor arrays have the advantsges of more flexible beam control,higher signal gain,stronger interference suppression ability and higher spatial resolution.An important issue of array signals processing is the parameters estimation,including the estimation of direction of arrival(DOA),frequency and polarimetric parameters.Parallel factor analysis is a common method for parameter estimation in array signal processing.Usually,the storage space requirement and computational complexity of this method can be reduced by combinging with compressed sening.Multi-parameter estimation algorithms via compressed sensing parallel factor framework are investigated in this paper,which has scientific significance and practical values.The main work in this paper is summarized as follows.1)A compressed sensing trilinear decomposition-based algorithm is studied for the two-dimensional angle estimation algorithm of uniform rectangle array.The algorithm needs no spectrum peak searching,and achieves automatically paired two-dimensional angle estimation.Owing to compression,the algorithm has lower computational complexity.Simulation results verify that the algorithm has close angle estimation performance to the conventional trilinear model-based algorithm,and it outperforms the estimation of signal parameters via rotational invariance techniques(ESPRIT)algorithm.2)A compressed sensing trilinear decomposition-based algorithm is proposed for joint direction of arrival(DOA)and frequency estimation of narrow-band signals with linear array.Comparison of the computational complexity between the proposed algorithm and the conventional trilinear model-basded algorithm is presented in the paper,which verifys that the proposed alfgorithm has lower computational complexity.Besides,Cramer-Rao bounds(CRBs)of the angle and frequency estimation are derived.The DOA and frequency estimation performance of the proposed algorithm is very close to that of the conventional PARAFAC algorithm,and better than that of ESPRIT algorithm and the propagator method(PM).Furthermore,the proposed algorithm can achieve automatically paired DOA and frequency estimation.Besides,it is applicable for both uniform and non-uniform linear arrays.3)A compressed sensing quadrilinear decomposition-based algorithm is proposed for direction of departure(DOD)and DOA estimation for bistatic MIMO radar with electromagnetic vector sensors.In this algorithm,the received data is firstly arranged into a quadrilinear model and then it is compressed according to the compressed sensing theory.Then quadrilinear decomposition is conducted on the compressed quadrilinear data model via the quadrilinear alternating least square algorithm and finally obtain the automatically paired angle estimates with sparsity.Owing to compression,the proposed algorithm has smaller storage requirement and lower computational complexity than the conventional quadrilinear decomposition-based algorithm.The algorithm has higher angle estimation accuracy than ESPRIT algorithm and its estimation performance is close to that of the conventional quadrilinear decomposition-based algorithm.The CRB of angle estimation is also dereived.