Research Of Broadband Beamforming And Beam Optimizing

Beamforming is one important research direction of array signal processing,which has found many applications invarious areas,such as modern radars,sonars,communications,medical diagnosis,treatment and other areas.As the development of all kinds of electronic equipments,the electromagnetic environment becomes more and more complex.Thus the better robustness of beamforming system is an importantresearch direction to suppress noise and interference.In addition,in many applications of array signal processing,the signal source is expected to be a broadband source,so the beamforming systems need highperformance in real-time and widebandfrequency characteristics.It can easily be deduced from the comparing between the binary elements arrays using isotropicelement and another binary arrays using directional element that the arrays can be improve the performance of sidelobe by using the directional element.In addition,uniform circular array(UCA)is fit for fast and broadband beamforming,but its sidelobe frequency characteristics is poor,which lead to the operation bandwidth is narrow.So the directional element can be used in UCA to improve its performance.After that,through the structure of UCA analysis the sidelobe frequency characteristicsdiscovery the poor sidelobe frequency performance of UCA is caused by the non-uniform distribution of the element positions.Then based on the relationship between the radiation function and the desired signal direction,a novel structure of uniform semi-circular arraywhich close half elements whose value of radiation function are zero is proposed to improve the overall performance of UCA with directional element.Based on the analyzing of multipath radio channels,the input signal-to-interference ratio(SIR)is estimated by using the apriori knowledge of direction of arrival(DOA)estimation and the conditionalprobability density function of direction of arrival.It is well known that there is a gap between the interference-plus-noise covariance matrix and thesample covariance matrix.By comparing the robustness of minimum variance distortionless response method and minimum power distortionless response method,it can be found that the gap is a main problem which impairs the robustness of minimum power distortionless response method.And the robustness of minimum power distortionless response method can be improved by using the reconstructed interference-plus-noise covariance matrix to replace thesample covariance matrix to reduce the gap.After that,by analyzing the broaden nulls method,it can be found that there are some wasteful appending of degrees of freedom.Hence,based on the estimation of input SIR,the adaptive beamformingalgorithm with troughs and reconstructed covariance matrix.The proposed method can robustly work in wide input signal-to-noise ratio(SNR)form troughs with less degrees of freedom than broaden nulls method.At last,the Particle Swarm Optimization(PSO)algorithm and Quantum-behaved Particle Swarm Optimization(QPSO)algorithm in Artificial Intelligence(AI)algorithm has the characteristics of parallelism,distribution,randomness,robustness and fast convergence.It also shows good performance in the process of solving the problem of beamforming.Based on the analysis of the convergence process,it is pointed out that the parametersof the fitness function have a great influence on the convergence processof beamforming algorithm based on AI algorithm.On this basis,the fitness function is simply optimized by observing the characteristics of the beamforming process and the convergence speed of the algorithm is improved.After that,the fitness function is optimized by referring to the constraint of the adaptive beamforming,and the practicability of the beamforming algorithm based on the optimized fitness function is improved.