DOA Estimation And Gain-phase Error Correction For High Frequency Ground Wave Radar Arrays

With its advantages of over-the-horizon,wide range and all-weather real-time monitoring,high-frequency ground-wave radar has been vigorously developed in the field of marine environmental monitoring in the past four decades.In practical applications,due to various factors and complex background environment,the amplitude phase response of antenna channels to incoming wave signals from various directions is inconsistent,which will lead to performance failure when estimating the direction of Boda using various directional algorithms.Therefore,it is important to study the array amplitude phase error correction method to reduce the impact of the error on the directional algorithm,as well as to improve the directional performance.In this paper,various existing error correction methods and the classical direction of arrival(DOA)estimation method are investigated in depth.The advantages and disadvantages of the existing algorithms are analyzed through simulations,and the sparse Bayesian learning method in compressive sensing theory is introduced to investigate DOA estimateion and amplitude phase error correction.The analysis of simulated and measured data shows that the sparse reconstruction-like DOA estimation method has good performance and high angular resolution under the conditions of low signal-to-noise ratio and small snapshot number.The main research of this paper is as follows.1)Theoretical study of array signal processing.Based on the array received signal model,the principle of DOA estimation and three classical DOA estimation algorithms are introduced,and the advantages and disadvantages of the algorithms and the influence of the array amplitude and phase errors on the performance of the algorithms are analyzed by simulation.The theory of array error correction and three commonly used error correction methods are also introduced,and the performance of the algorithms is compared by simul--ation.2)DOA estimation method research.Firstly,the principle of sparse Bayesian learning DOA estimation is introduced,and then an improved de-gridding DOA estimation method based on sparse Bayesian learning is proposed for the quantization error problem caused by grid division after array sparsification,and DOA estimation of spatially tight neighboring signals,and the effectiveness of the method is analyzed by simulation comparison.3)Research on the self-correction method of array amplitude phase error.Based on the error self-correction theory and the principle of signal subspace orthogonality,a sparse Bayesian learning-based amplitude phase error self-correction method is proposed to realize the joint estimation of DOA and amplitude phase error correction coefficients,and the algorithm rate is improved by dimensionality reduction optimization of the data.Finally,the effectiveness of the algorithm is verified by simulation and analysis of real measurement data.