Joint DOA and Non-circular Phase Estimation of Non-circular Signals for Antenna Arrays: Block Sparse Bayesian Learning Method

Fuente: arXiv
Salvato in:
Dettagli Bibliografici
Autori principali: Shen, Zihan, Li, Jiaqi, Dong, Xudong, Zhang, Xiaofei
Natura: Preprint
Pubblicazione: 2026
Soggetti:
Accesso online:
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
_version_ 1866908763830616064
author Shen, Zihan
Li, Jiaqi
Dong, Xudong
Zhang, Xiaofei
author_facet Shen, Zihan
Li, Jiaqi
Dong, Xudong
Zhang, Xiaofei
contents This letter proposes a block sparse Bayesian learning (BSBL) algorithm of non-circular (NC) signals for direction-of-arrival (DOA) estimation, which is suitable for arbitrary unknown NC phases. The block sparse NC signal representation model is constructed through a permutation strategy, capturing the available intra-block structure information to enhance recovery performance. After that, we create the sparse probability model and derive the cost function under BSBL framework. Finally, the fast marginal likelihood maximum (FMLM) algorithm is introduced, enabling the rapid implementation of signal recovery by the addition and removal of basis functions. Simulation results demonstrate the effectiveness and the superior performance of our proposed method.
format Preprint
id arxiv_https___arxiv_org_abs_2601_09148
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Joint DOA and Non-circular Phase Estimation of Non-circular Signals for Antenna Arrays: Block Sparse Bayesian Learning Method
Shen, Zihan
Li, Jiaqi
Dong, Xudong
Zhang, Xiaofei
Signal Processing
This letter proposes a block sparse Bayesian learning (BSBL) algorithm of non-circular (NC) signals for direction-of-arrival (DOA) estimation, which is suitable for arbitrary unknown NC phases. The block sparse NC signal representation model is constructed through a permutation strategy, capturing the available intra-block structure information to enhance recovery performance. After that, we create the sparse probability model and derive the cost function under BSBL framework. Finally, the fast marginal likelihood maximum (FMLM) algorithm is introduced, enabling the rapid implementation of signal recovery by the addition and removal of basis functions. Simulation results demonstrate the effectiveness and the superior performance of our proposed method.
title Joint DOA and Non-circular Phase Estimation of Non-circular Signals for Antenna Arrays: Block Sparse Bayesian Learning Method
topic Signal Processing
url https://arxiv.org/abs/2601.09148