Research On Spatial Spectrum Estimation Algorithm Of UCA In The Presence Of Coherent Sources

Signal incidence angle estimation is an important component of array signal processing and MUSIC spatial spectrum estimation is the mainstream solution for it.MUSIC algorithm uses eigendecomposition to construct orthogonal subspaces which has super-resolution characteristic.The uniform circular array has the advantages of small aperture and the ability to simultaneously estimate the elevation angle and azimuth angle of incident signals.The two-dimensional music spatial spectrum estimation based on uniform circular array combines the advantages of both and is widely used in engineering.In the actual direction-finding processes,the received signals usually cannot meet the ideal signal model.Among them,the coherent sources generated by multipath propagation will lead to the mutual diffusion of the subspace,which makes the spectrum estimation algorithm invalid.Therefore,the direction estimation of coherent sources and its derivation problems are key issues in array signal processing.In this paper,the related problems of spatial spectrum estimation based on the uniform circular array and coherent incident sources are discussed:(1)The decoherence method of spatial spectrum estimation of uniform circular array is discussed,and the realization design of spatial spectrum estimation in the presence of coherent sources is carried out.Firstly,the signal decoherence scheme suitable for uniform circular array is introduced,including model incentive method combining the theory of spatial smoothing and the virtual array translation method.Then based on the selected decoherence algorithm,the software design and implementation of multi-channel spatial spectrum estimation of coherent sources are carried out.(2)The modified eigenvalue method is introduced to improve the source number estimation method.The accurate number of sources is known to be the premise of spatial spectrum subspace division.Considering the divergence of colored noise,this paper introduces the modified eigenvalue method to improve the information theory method and the K-means clustering method based on ratio comparison.Combined with decoherence algorithm,the number of sources in spatial spectrum estimation algorithm can be obtained automatically and stably.(3)An improved spatial spectrum peak search algorithm based on PSO-grid algorithm is proposed to improve the efficiency of the spectral peak searching in the spatial spectrum estimation.The spatial spectrum estimation of uniform circular array needs two-dimensional spectral peak search,and only using the traditional grid traversal method is extremely time-consuming.In this paper,an improved PSO-grid algorithm is proposed,which combines the particle swarm optimization(PSO)algorithm with the grid traversal search method,cause the PSO algorithm has a good ability to search for the extreme value of nonlinear functions.The improved method can solve the problem that PSO is easy to fall into local optimization,and can obtain accurate spectrum peaks in a short time.(4)A method of unequal power signal separation and estimation based on the invariant property of noise subspace is proposed.The spatial spectrum estimation algorithm is designed and implemented based on the equal power incident sources.However,in the actual signal environment,the power of the incident signal may have a large difference.In this case,the strong signal will interfere with the DOA estimation of the close weak signal.Therefore,this paper proposes an improved method for separating and estimating strong and weak signals,and encapsulates the improved method into a sub-module to expand the spatial spectrum estimation of coherent sources.The improved method optimizes the complex calculation flow of the DOA estimation method based on the invariant characteristics of the noise subspace,reduces the time consumption of the implementation process,and can realize the separation and estimation of unequal power signals in the background of coherent sources.