Polarization DOA Estimation Based On Niche Intelligent Algorithm

With the wide application of space direction finding technology in electronic countermeasures,aviation and navigation,measurement and control,etc.,the space spectrum direction finding technology with high precision and high resolution has been developed rapidly.After decades of research by scholars at home and abroad,the spatial spectrum estimation theory and technology of traditional omnidirectional arrays have become increasingly mature.With the rise of polarization-sensitive arrays,polarization arrays have become a hot issue for scholars in recent years due to their characteristics of obtaining signal spatial information and signal polarization information.In this thesis,the number of sources estimation method,real-time algorithm,coherent signal decoherence,and array error correction problems in the polarization-sensitive array spatial spectrum estimation are proposed,and corresponding solutions are proposed.The main research work of the full text is summarized as follows:The polarization and characterization of electromagnetic waves are introduced and the polarization-sensitive array receiving model is defined,including the array element structure,array structure,narrowband signal model and noise model.Based on this model,the calculation principle of the polarization MUSIC algorithm and its traditional dimensionality reduction estimation algorithm is deduced in detail,and the simulation is implemented.Aiming at the problem of estimating the number of information sources,data fitting and clustering are improved to the traditional Gerschgorin's disk estimation.Faced with the complete failure of the information theory criterion in the color noise environment,and the Gerschgorin's disk estimation is suitable for the color noise,but the estimation performance is poor,the Gerschgorin's disk is improved,and the Gerschgorin's disk radius is data fitting through a specific function.And further use clustering algorithm for classification.The improved method is simulated and analyzed,and the results show that the method improves the estimation accuracy when the signal-to-noise ratio is low,and at the same time avoids the difficult parameter selection problem of the traditional discriminant method.Aiming at the problem that the space spectrum search calculation of the polarization MUSIC algorithm is too large,the niche genetic algorithm and the particle swarm algorithm are modified and improved,and the iterative termination condition of the spectrum function search is defined.The existing niche genetic algorithm is restricted by the mutation mechanism and is only based on the maximum number of iterations to determine the termination problem.Heuristic mutation is improved and the iteration termination condition is defined.At the same time,the niche idea is applied to the particle swarm algorithm.,Further improve the calculation speed.Finally,performance analysis shows the feasibility and superiority of the improved niche genetic algorithm and particle swarm algorithm in spatial spectrum search.Aiming at the general weakness of subspace algorithms such as MUSIC that it is difficult to identify multiple coherent signals,research on decoherence algorithms is carried out.Utilizing the moving characteristics of the array generally mounted on the moving platform,the array moving decoherence algorithm is applied to estimate the coherent signal,and the sampling interval parameter and the moving number parameter selection rules of the array movement are clarified through the principle explanation and simulation analysis.Based on the realization of the above algorithm,the error model construction and error impact analysis of the array error existing in practical applications are carried out,and the channel amplitude and phase error,mutual coupling error and joint error correction are realized through the WF algorithm and the RARE algorithm.