Underwater DOA and SAP estimation method based on fast multi snapshot without inverse sparse bayes

By deriving the relaxed evidence lower bound in complex matrix form for underwater target detection, the high computational cost caused by matrix inversion in sparse Bayesian learning is solved, achieving high-precision and high-speed DOA and SAP estimation, which is suitable for online systems and a wider range of application scenarios.

CN115859012BActive Publication Date: 2026-07-21HARBIN ENG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HARBIN ENG UNIV
Filing Date
2022-11-24
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

In underwater target detection, existing technologies, such as sparse Bayesian learning (SBL), suffer from high computational costs due to matrix inversion, making them difficult to apply to large-scale problems and online systems. Furthermore, the applicability of single-snapshot and multi-snapshot beamforming models in the complex domain is limited.

Method used

We propose an underwater DOA and SAP estimation method based on fast multi-snapshot inverse-free sparse Bayesian. By extending the properties of the smoothing function to the complex matrix form, we derive the relaxed evidence lower bound (relax-ELBO), decouple the relationship between the target source and the measurement matrix, avoid matrix inversion operations, and use the variational distribution of latent variables to approximate the posterior distribution for Bayesian inference.

Benefits of technology

It reduces computational complexity, improves the accuracy and speed of DOA and SAP estimation, enables online applications, and extends to fields such as radar imaging, medical imaging, and seismic imaging.

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Abstract

The application proposes an underwater DOA and SAP estimation method based on fast multi-snapshot non-inverse sparse Bayesian. The method introduces the smooth function property and generalizes to a complex matrix form. Based on the generalized property, the method is introduced into multi-snapshot sparse Bayesian learning, and the matrix form of the relaxation evidence lower bound (relax-ELBO) is derived. Based on the relax-ELBO, the VEM method is used to solve the posterior distribution approximation of the hidden variable. According to the posterior distribution approximation, the DOA and SAP are estimated, the matrix inversion is avoided, the operation speed is greatly improved, and the limitation that the SBL is difficult to be applied in large-scale field and online is overcome.
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