Study On Adaptive Beamforming Algorithm For Large Array Dimensionality Reduction

Large scale phased-array radars contain more and more array elements nowadays.Performing array signal processing algorithm directly at the array element level not only has high hardware cost and difficulties in engineering implementation,but also has a large amount of algorithmic computation,resulting in slow convergence of the algorithm.Therefore,array dimensionality reduction techniques are usually adopted.However,how to subdivide the array to reduce its impact on the performance of the array radar has always been a problem.At the same time,subarray structure will also affect the performance of the array signal processing algorithm,so it will be of great use to study the subarray signal processing technology.This thesis focuses on the above problems,and studies the non-uniform subarray partition technology combined with discrete quantum particle swarm optimization algorithm.The angular tracking measurement method based on subarray level beamforming is analyzed and improved.The main research content of this paper is summarized as follows:Firstly,this thesis beginning with the basic theory of array dimensionality reduction and array signal processing.An array signal model is established,basic concepts such as subarray partitioning and beamforming are introduced,and the reasons for the appearance of grating lobes and grids zeros are analyzed,and subarray adaptive beamforming techniques are studied.The summary of the angular tracking measurement method lays the foundation for the follow-up study based on subarray level beamforming angular tracking measurement method.Secondly,the non-uniform subarray partitioning technology combined with discrete quantum particle swarm optimization algorithm is studied.The subarray division scheme is optimized by using the main-to-side lobe ratio and half-power beam width as fitness functions.There are many kinds of irregular sub-arrays and irregularities if directly optimize the area array,which leads to difficulties in engineering implementation.An improved subarray division algorithm based on discrete quantum particle swarm optimization algorithm is proposed for the above problems.The subarray divided is fewer and more regular by the improved method,which is more beneficial to engineering implementation;Finally,the angular tracking measurement method based on subarray beamforming is further studied.Aiming at the disadvantages of the four-channel adaptive monopulse at subarray level algorithm with high complexity,a phase-comparison angular tracking measurement method for suppressing mainlobe interference is proposed.The simulation results show that the proposed method can suppress mainlobe interference,and have good angular tracking measurement performance,and the algorithm is easy to implement.