Research On Geometry Optimization For Sparse Array

The array shape optimization of a sparse array was studied in this thesis. In order toaddress the problem of the array shape optimization of a sparse scalar array under thecondition of the limited array sensor number and array aperture, we firstly derive therelationship between the spatial correlation coefficient of the desired signal and theinterference signal and the output SINR of the LCMV adaptive filter. Subsequently, acomprehensive evaluation function of the spatial adaptive array shape is proposed.Finally, the array shape optimization of the sparse array is achieved based on the PSO(Particle Swarm Optimization) algorithm, and the SA (Simulated Annealing) algorithm.The proposed method can be implemented simply due to the fact that the evaluation ofadaptive performance is transformed into the evaluation of the spatial correlationcoefficient. Furthermore, the proposed method has the advantage of a comprehensiveevaluation since the performance of beamforming at any direction among the range ofscanning beam is considered.On the other hand, in order to address the problem of the array shape optimization ofa sparse polarization sensitive array, we firstly analyze the anti-interference ability ofthe polarization sensitive array when the desired signal and the interference are spatiallyclose. Secondly, we derive the relationship between the spatial-polarization correlationcoefficient of the desired signal and the interference signal and the output SINR of theLCMV adaptive filter. Next, a comprehensive evaluation function of thespatial-polarization adaptive array shape is proposed. Finally, the array shapeoptimization of the sparse polarization sensitive array is realized based on the SA(Simulated Annealing) algorithm.