Study On Parameter Estimation For MIMO Radar

The MIMO radar system which transmits orthogonal signal has great advantages in terms of improving parameter identifiability. The main reason is that it takes full advantage of the signal diversity to form an equivalent array element, which equals to increasing the array aperture. Based on the similarity of the angle estimation model of MIMO radar and array signal model, it is feasible to use the classical spatial spectrum estimation algorithms to estimate the angle in MIMO radar. Compared with the traditional methods, it has some advantages over them to estimate the angle of the targests and overcome some shortcomings of traditional methods based on the sparse signal recovery. This paper studies the angle estimation of targets on the aspect of parameter estimation in MIMO radar system. On the one hand, the traditional spectral estimation algorithms are applied to MIMO radar, On the other hand, the angle estimation is obtained in MIMO radar on perspective of sparse signal recovery.The main work is organized as follows:1. The echo signal model of targets is constructed in MIMO radar system, while the signal processing is elaborated and the equivalent array element is analyzed under different array configuration.2. Three classic algorithms are introduced in detail and utilized to obtain the angle estimation in MIMO radar system. According to the characteristics of array manifold of MIMO radar system, a modified method is studied to calculate autocorrelation matrix of receipt signal, furthermore, the performance of spatial spectrum estimation algorithms is analyzed using the new autocorrelation matrix compared to the old one.3. The sparse signal model in MIMO radar is constructed to carry the angle estimation problem into sparse signal recovery field, furthermore two methods for recovering the sparse signal are specified, such as1??SVD and reiterative estimation algorithm(RIEA). According to characteristics of overcomplete dictionary in MIMO radar system, a reducing-dimension RIEA method is studied to obtain the angle estimation in MIMO radar in which a reducing-dimension matrix is constructed without affecting the targets’ sparsity. To reduce the cost time further, the singular value decomposition(SVD) is combined in the reducing-dimension RIEA method. Thismethod can not only significantly reduce the cost time, but also to restrain the noise to some extent.4. The angle estimation problem is a 2D sparse signal recovery in bistatic MIMO radar, which can be worked out by sovling two 1D sparse signal recovery problems step by step. This method can reduce the cost time dramatically compared to the original algorithm.5. The sparse signal model with mutual coupling, gain and phase error in MIMO radar is constructed. According to the sparse signal model with mutual coupling, the modified1??SVD is developed to obtain the angle estimation accurately in the presence of the mutual coupling. Furthermore the performance of the modified1??SVD algorithm is analyzed in detail. According to the sparse signal with gain and phase errors, the derivation of a modified RIEA algorithm is completed. A detailed analysis of performance of modified RIEA algorithm is accomplished versus amplitude and phase errors.