A DOA estimation method for nested arrays based on off-grid sparse Bayesian learning

Abstract

The invention discloses a nested array direction-of-arrival estimation method based on off-grid sparse Bayesian learning. The method comprises: step one, matching filtering is carried out on a narrowband Gaussian signal received by a nested array to obtain a data vector x(t) including DOA information at a t time; step two, with the x(t), a received data covariance matrix R^x under a T snapshot number is calculated and vectorization is carried out on the R^x to obtain a one-dimensional data vector Y^; step three, K^ grid points theta^={theta^i}<K^>i=1 are divided uniformly in a range [-pai / 2,pai / 2], a counting variable I of the number of times for iteration is set to be 1, a variance vector delta and an e angle offset vector beta are initialized, and a measuring matrix phi (theta^, beta) isconstructed; step four, a variance vector theta and an angle deviation value beta in a (K^+1) dimension are updated by using an EM criterion; step five, the grid theta^ is updated by using the beta value obtained at the step four; step six, whether the counting variable I of the number of times for iteration reaches an upper limit L or the delta converges is determined; if not, the counting variable I of the number of times for iteration conforms to a formula: 1=1+1, wherein the beta is equal to 0; updating is carried out on the phi (theta^, beta) by using the updated grid theta ^, and the step four is performed; and step seven, spectral peak searching is carried out on the variance vector delta to obtain angles corresponding to K maximum points, so that a final estimation value of the target angle is obtained.

Description

technical field [0001] The invention belongs to the field of array signal processing, and relates to the estimation of direction of arrival of array signals, in particular to a method for estimation of direction of arrival of non-uniform nested array signals. Background technique [0002] Direction of Arrival (DOA) estimation of multi-narrowband signals using antenna arrays has been widely used in radar, sonar and communication fields. In the past few decades, a large number of effective DOA estimation methods have been proposed. Since the non-uniform array itself has great advantages in increasing the degree of freedom of signal processing, the target angle estimation algorithm based on the nested array has also become a research hotspot. For example, in the literature: P.Pal, P.Vaidyanathan, Nested arrays: A novel approach to array processing with enhanced degrees of freedom, IEEE Transactions on Signal Processing, 58 (8) (2010) 4167-4181, a space-based Smooth multiple s...

AI Technical Summary

Problems solved by technology

However, the traditional algorithm based on subspace processing is easily affected by the signal-to-noise ratio and the number of snapshots. The main disadvantages of the existing DOA estimation method based on sparse Bayesian learning are: the loss of effective array aperture is serious, and the computational complexity is relatively high. high

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