دورية أكاديمية
Research on High Robustness Underwater Target Estimation Method Based on Variational Sparse Bayesian Inference
العنوان: | Research on High Robustness Underwater Target Estimation Method Based on Variational Sparse Bayesian Inference |
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المؤلفون: | Libin Du, Huming Li, Lei Wang, Xu Lin, Zhichao Lv |
المصدر: | Remote Sensing, Vol 15, Iss 13, p 3222 (2023) |
بيانات النشر: | MDPI AG, 2023. |
سنة النشر: | 2023 |
المجموعة: | LCC:Science |
مصطلحات موضوعية: | array signal processing, sparse Bayesian learning, direction estimation, pulse noise, Science |
الوصف: | Pulse noise (such as glacier fracturing and offshore pile driving), commonly seen in the marine environment, seriously affects the performance of Direction-of-Arrival (DOA) estimation methods in sonar systems. To address this issue, this paper proposes a high robustness underwater target estimation method based on variational sparse Bayesian inference by studying and analyzing the sparse prior assumption characteristics of signals. This method models pulse noise to build an observation signal, completes the derivation of the conditional distribution of the observed variables and the prior distribution of the sparse signals, and combines Variational Bayes (VB) theory to approximate the posterior distribution, thereby obtaining the recovered signal of the sparse signals and reducing the impact of pulse noise on the estimation system. Our simulation results showed that the proposed method achieved higher estimation accuracy than traditional methods in both single and multiple snapshot scenarios and has practical potential. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 15133222 2072-4292 |
Relation: | https://www.mdpi.com/2072-4292/15/13/3222; https://doaj.org/toc/2072-4292 |
DOI: | 10.3390/rs15133222 |
URL الوصول: | https://doaj.org/article/2e45cb20372944f5a814cb39fe958ae1 |
رقم الأكسشن: | edsdoj.2e45cb20372944f5a814cb39fe958ae1 |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 15133222 20724292 |
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DOI: | 10.3390/rs15133222 |