Angle Estimation Of MIMO Radar Under The Condition Of Non-uniform Noise And Mutual Coupling

Multiple-input multiple-output(MIMO)radar,a new radar system has obvious advantage over the traditional phased array radar in performance,which has attracted extensive attention from the scholars at home and abroad in recent years.Angle estimation,as a basic function of radar,is a hot research field of MIMO radar.At present,the angle estimation algorithms of MIMO radar under ideal conditions are matured increasingly.However,these algorithms have poor performance when they are applied to a practical environment.The primary factor is the arrays,the transmitted signal waveforms,the noise in the receiver,etc.do not always meet all ideal conditions.Therefore,researching on the influence of non-ideal factors on angle estimation and exploring available solutions in a practical environment has theoretical and practical significance.In this dissertation,we focused on the angular performance of bi-static MMO radar system when it suffers from the influence of the non-uniform noise and the mutual coupling effect,and proposed several algorithms.The works of this dissertation are given as follows:1.Research on the angle estimation algorithm of MIMO radar in the presence of nonuniform noise.The additional receiver noise may be non-uniform noise due to the external interference and antennas manufacturing technology in the practical environment,which makes the performance of traditional angle estimation algorithms based on the uniform white noise poor or even no longer applicable.Although some iterative algorithms for the non-uniform noise can realize the estimation of angle parameters,they often need to go through several iterations to obtain high angular accuracy,thus the system overhead is increased.In this dissertation,a non-iteration subspace-based(NISB)algorithm is firstly studied and its performance is verified.Secondly,the feasibility of extending this algorithm to the MIMO radar is analyzed,and the performance of which is verified by numerical experiments.Results show that it is better than the non-iterative reduced covariance matrix(RCM)algorithm.Finally,two iterative processing methods are given for the NISB algorithm under the premise of increasing the computational complexity moderately.Experimental results show that the performance of the two iterative processing methods is better than the iterative maximum likelihood subspace estimation(IMLSE)and the iterative least squares subspace estimation(ILSSE).2.Research on the joint estimation problem of angle and mutual coupling coefficient for MIMO radar under the condition of mutual coupling.When the MIMO radar is configured as the type of array antenna,the mutual coupling effect between the elements resulting in an imperfect array manifold matrix.So,the performance of the traditional angle estimation algorithm based on the ideal manifold matrix will degrade or even fail.In such a case,the multiple signals classification(MUSIC)algorithm based on instrumental sensors(IS)can achieve the angle estimation.However,a smaller search step is required for the IS-MUSIC method to improve the angular accuracy,which will increase the computational complexity of that.In addition,this method cannot directly obtain the estimation of mutual coupling coefficients.Based on the local search strategy,combining the IS-MUSIC method and the joint estimation of angle and mutual coupling coefficients(JEAMCC)method who also based on the IS,a new method named IS-MUSIC-JEAMCC method is proposed in this dissertation.The proposed method firstly uses dimension reduction IS-MUSIC algorithm to obtain the initial value of angle estimation with a large search step.Secondly,to compensate for the accuracy loss caused by the first step,doing search with a smaller step in the local search regions that are set around the initial angle values.Finally,the IS-JEAMCC algorithm is used to refine the search in the local areas to obtain high precision estimation of angle and mutual coupling coefficients.The simulation results show that compared with the IS-MUSIC method,the proposed method not only further improves the performance of angle estimation,but also realizes the estimation of mutual coupling coefficients.In addition,compared with IS-JEAMCC,the computational complexity of the proposed method is greatly reduced.3.An angle estimation algorithm based on signal subspace reconstruction and tensor processing is proposed for the non-circular(NC)MIMO radar in the presence of mutual coupling.There is a loss of array aperture due to the truncation of data in the traditional instrumental sensors(IS)method and selection matrix(SM)method,which leads to the decrease of angular measurement accuracy.To extend the array aperture of MIMO radar,the NC signals can be introduced.But there is a high SNR requirement.Considering the dimension of signal can be increased by using tensor analysis to reduce the requirement of high SNR,a new method for the MIMO radar is proposed.Firstly,constructing two selection matrices to eliminate the influence of mutual coupling effect based on the Toeplitz property of mutual coupling matrix,by which the guide matrix is restored to the type of Vander Monde structure.Secondly,reconstructing the array receiving covariance matrix and obtaining the signal subspace of that in the matrix domain based on the signal subspace reconstruction algorithm.Thirdly,the covariance tensor is constructed in the tensor domain and its higher-order singular value decomposition(HOSVD)is performed.The tensor signal subspace is obtained by combining the signal subspace relationship between the matrix domain and the tensor domain.Finally,the initial angle estimation is achieved by using the tensor noise subspace corresponding to the tensor signal subspace,and the final angle estimation is obtained by combining the local search strategy.The experimental results show that compared with other algorithms,such as non-circular estimating signal parameter via rotational invariance techniques(NC-ESPRIT),NC-MUSIC,Unitary Tensor ESPIRT and NC-Unitary Tensor ESPIRT,the proposed algorithm has better performance in root mean square error(RMSE).