Research On Algorithms Of Radar Array Synthesis And Key Parameters Estimation

There is almost 80 years since the birth of radar, and during this period there comes a variety of radar systems. Meanwhile, the function, size, weight, reliability and survivability of radar have changed enormously. There is no doubt that radar is playing a more and more important role in the national economy and military applications. In this thesis, two key techniques in radar signal processing-sparse array synthesis and array parameter estimation is studied. The whole thesis is divided into three parts:The algorithms about sparse array synthesis are discussed in the first part. Firstly, a sparse array synthesis algorithm based on sequential convex optimizations is introduced which is based on power constraint. And the optimization problem can be converted into second order cone program(SOCP) problem which can be solved by SeDuMi. On the basis of the above method, an iterative weighted l1 norm optimization algorithm is proposed which is restrained by a desired waveform over the whole observation angle. Then the optimization problem can be solved by complex derivative in combination with heuristic approach. Simulation results show that, compared with the published sparse array synthesis algorithms, the proposed algorithm can achieve sparser array with shorter array aperture.The DOA estimation based on sparse signal representation is described in the second part. Firstly three common spectral estimation methods are introduced; meanwhile their limitations are pointed out. Then an over-complete set is brought in to transform the common DOA estimation model into the sparse representation model. At the basis of the sparse representation model, l1-SVD method is elaborated in detail. Finally, a unitary transformation matrix is introduced. Then the sparse representation model can be transformed into a model based on real-valued computations. Thus the computational complexity of l1-SVD method can be significantly reduced.The robust parameter estimation algorithms based on Multiple-Input Multiple-Output(MIMO) radar system are introduced in the third part. Firstly MIMO radar system is modeled, meanwhile the Capon and APES methods in MIMO radar system are derived and their performances are analyzed. Secondly MIMO radar with partly calibrated array is considered. A method which can estimate the magnitude and direction of the targets as well as the amplitude and phase of the disturbance of array is proposed. At the same time, how the SNR impacts the method is also analyzed. Finally, the case of MIMO radar with completely mismatched array is considered. And a method named RCB is introduced meanwhile its limitations are analyzed. Then a method called IRCB which is based on RCB is presented. And unlike RCB, IRCB need not know the uncertainty of the manifold vector and the performance is similar to the RCB with the uncertainty of the manifold vector is known prior.