Fast target angle estimation method based on sparse Bayesian learning

Abstract

The invention belongs to the field of array signal processing, and in particular relates to a fast target angle estimation method based on sparse Bayesian learning. The method comprises the followingsteps of S1, performing initialization on the parameters to be estimated of gammaj and sigma0, wherein j is equal to 1,2 to N; S2, quickly obtaining signal posterior probability density functions at each moment by using the AMP algorithm; S3, updating values of the parameters to be estimated of gammaj and sigma0 by using the EM algorithm, wherein j is equal to 1,2 to N; and S4, determining whetherthe update iterative process of the parameters to be estimated converges, returning to the S2 to re-iterate if not, and if so, jumping out of the loop and determining the direction and quantity of the target incoming waves. The method provided by the invention can improve the low signal-to-noise ratio and the multi-objective angle estimation accuracy under small sample conditions, and has the advantages of fast iterative convergence speed and high computational efficiency for estimating the target angle, which can be applied to the real-time multi-objective angle estimation system and has important engineering application value.

Description

technical field [0001] The invention belongs to the field of array signal processing, and in particular relates to a fast target angle estimation method based on sparse Bayesian learning. Background technique [0002] Direction of Arrival Estimation (DOAE) is a very important problem in the field of array signal processing, and related algorithms can be widely used in radar detection, sonar navigation, multi-channel communication and other fields. Most of the traditional target angle estimation algorithms are based on subspace signal flow analysis or belong to the maximum likelihood estimation algorithm. However, these two types of algorithms often have limited resolution and are greatly affected by factors such as the correlation between target signals, the number of samples collected, and the signal-to-noise ratio of the system, so it is difficult to adapt to various complex application environments in practice. [0003] With the introduction of the sparse recovery algori...

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Problems solved by technology

However, since the sparse recovery algorithm needs to perform large-scale matrix operations, the computational complexity of

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