Dictionary mismatch clutter space-time spectrum estimation method based on sparse Bayesian learning

Abstract

The invention discloses a dictionary mismatch clutter space-time spectrum estimation method based on sparse Bayesian learning. The method comprises the steps of establishing a dynamic space-time steering vector dictionary containing mismatch errors; carrying out dictionary mismatch error expectation maximization iterative estimation; establishing a clutter sparse recovery model after dictionary mismatch error compensation; and performing clutter space-time power spectrum sparse Bayesian estimation in the presence of dictionary mismatch. The estimation method provided by the invention comprisesthe steps of firstly, establishing a space-time dynamic dictionary model by utilizing two-dimensional Taylor series, then constructing a Bayesian sparse recovery model by taking a dictionary mismatcherror as a to-be-estimated hyper-parameter, correcting a space-time steering vector dictionary by utilizing a mismatch error estimation value, and finally, performing clutter covariance matrix sparserecovery by using the corrected space-time steering vector dictionary, and further calculating a clutter space-time spectrum. The method has the advantage of high clutter spectrum sparse recovery precision in the presence of dictionary mismatch.

Description

technical field [0001] The invention belongs to the field of airborne array radar space-time adaptive processing clutter suppression, in particular to a dictionary mismatch clutter space-time spectrum estimation method based on sparse Bayesian learning. Background technique [0002] Space-Time Adaptive Processing (STAP) is an effective method for airborne array radar to suppress clutter. The clutter suppression performance of STAP mainly depends on the estimation accuracy of the clutter-plus-noise Covariance Matrix (CCM). The traditional STAP method uses a statistical estimation method to obtain the estimated value of CCM. If it is necessary to ensure that the output signal-to-clutter ratio does not drop by more than 3dB compared with the optimal value, at least two times the number of system degrees of freedom (Independent Identically Distributed, IId ) clutter samples. However, in practical systems, when the clutter is non-stationary or non-uniform, it is difficult to ob...

AI Technical Summary

Problems solved by technology

BAI proposes a method based on Orthogonal Matching Pursuit (OMP), which solves the dictionary mismatch problem by finding a dictionary vector that matches the real model through the gradient descent method, but the performance of the OMP method is highly dependent on parameter selection.

But so far there is no report on the estimation method of dictionary mismatch clutter space-time spectrum based on sparse Bayesian learning

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