Off-grid sparse Bayesian DOA estimation method based on layered synthesis Lasso prior model

Abstract

The invention relates to an off-grid sparse Bayesian DOA estimation method based on a layered synthesis Lasso prior model. The method is characterized in that firstly, a sparse signal model is established according to the characteristics of sparse distribution of incident signals in a spatial domain; secondly, probability hypothesis of each variable in the model is performed to construct an HSL probability model, a variational inference algorithm is adopted to obtain an updating formula of unknown parameters in the probability model, after giving an unknown parameter initial value to the probability model, output data of an array is processed in an iterative updating manner, an estimated value of a model parameter is obtained through calculation, and finally a DOA estimated value is calculated by using the obtained parameter estimated value and adopting a one-dimensional search method.

Description

technical field [0001] The invention belongs to the field of array signal processing, in particular to an off-grid sparse Bayesian DOA estimation method based on a layered composite Lasso prior model. Background technique [0002] In the field of direction of arrival (DOA, Direction of Arrival) estimation, the sparse Bayesian learning (SBL, Sparse Bayesian Learning) class estimation method belongs to the sparse reconstruction class estimation method, which has super-resolution, low signal-to-noise ratio and small The estimation results under the conditions of the number of snapshots are better, and there is no need to preset complex model parameters. Compared with the classical subspace-like method MUSIC and the norm-optimized sparse reconstruction method L1-SVD, the SBL-like estimation method has better estimation performance, and has received extensive attention and research in recent years. The SBL estimation method converts the DOA estimation problem into a sparse signa...

AI Technical Summary

Problems solved by technology

Although the probability model based on Gaussian prior can describe the statistical properties of sparse signals, it is not optimal

In addition, it is noticed that the actual signal incident angle may not fall on the preset airspace angle grid points. In order to obtain accurate DOA estimation, an off-grid error is introduced when establishing a sparse signal model (that is, the real incident angle deviates from the preset Angle value of the angle grid point), and estimate the off-grid error in the subsequent parameter estimation, such as the OGSBI method (Z.Yang, L.Xie, and C.Zhang, Off-grid direction of arrival estimation using sparse Bayesian inference, in IEEE Trans.Signal Process, 2013; 61(1):38-43) and SURE-IR method (J.Fang, F.Wang, Y.Shen, H.Li, and R.S.Blum, Super-resolutioncompressed sensing for line spectral estimation: an iterative reweighted approach, in IEEE Trans.Signal Process., 2016; 64(18):649-4662), but the signal prior distributions used by these two methods are not statistically sparse enough, so they are close to the resolution space There are performance limitations when the source

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