A Study Of Adaptive Beamforming Algorithm Based On Multiple Complex Interference

As the array size grows more and more,in order to reduce the computational complexity,it is necessary to reduce the dimension of the large array and introduce the problem of sub-array adaptive beam forming.Basing on large array.This thesis mainly studies how to reduce the complexity of the algorithm of interference suppression,improve the freedom of interference suppression,and suppress side lobe and main lobe interference.Since interference suppression is a prerequisite condition for effective target detection,kinds of adaptive beam forming techniques have a good suppression effect on non-coherent side lobe interference.Duing to the coherence of the signal with the side lobe interference,an effective method is needed for suppressing interference.In addition,when the interference enters into the main beam,conventional adaptive beam forming method can be used to form deep null in the interference direction.But the main beam will be serious offset and distortion,making the side lobes significantly raise that impact on the target detection.Basing on those,to the planar phased array and the Nested array,Adaptive beam forming algorithms and phase measurement algorithms are focused in this thesis.As shown in the following steps.1.Adaptive beam forming algorithms and sum-difference tracking algorithms are studied for the Nested array.Firstly,aim at the layout of two level Nested array.At the condition of receiving data covariance matrix pulled into column vector by using Khatri-Rao product,the thesis introduces how to reconstruct receiving data covariance matrix on virtual ULA by special matrix reconstruction.By using the covariance matrix of the received data of the virtual array,we can carry on the following criterion constraint and get the weight of the adaptive beam forming.Then,by interpolating the data,the receiving data of the original two level Nested array can be virtualized as the received data of the virtual array.Then the weights of sum-difference beam interference suppression are calculated and the adaptive single pulse beam is formed.Finally,using the monopulse sum-difference beam forming after Interference suppression achieves accurate tracking.2.We propose a dimension-separated toeplitz method for adaptive beam forming in the presence of coherent interference to achieve the effect of suppressing coherent side lobe interference.In the thesis,combining the dimension-separated technique and matrix toeplitz method,plane array can be divided into azimuth and elevation dimensions.In this case,the received data can be divided into elevation dimension and azimuth dimension.The covariance matrix of each dimension using toeplitz method can remove coherent,so as to achieve the effect of coherent interference suppression.Because of the dimension-separated,this method reduces the complexity of the algorithm and can effectively suppress coherent interference.3.An improved algorithm for suppressing main lobe and side lobe jamming at sub-array level five channels and DOA estimation are studied.In the original sum-difference,and double difference channel are added to an auxiliary channel.Using the MSE criterion,the thesis proposes an improved adaptive monopulse algorithm at five channel of sub-array level.For the large scale planar phase array,the dimensionality reduction is realized by the sub-array division.After the dimensionality reduction,the sub-array level difference beam forming and the auxiliary beam forming are performed.The weights of the interference suppression are obtained by the MSE criterion constraint.Interference suppression of main lobe & side lobe can be achieved by using dimension-separated technology.Simulation results and performance analysis show that this algorithm has good interference suppression effect and high accuracy of DOA estimation.