基于Bessel先驗快速稀疏貝葉斯學習的互質(zhì)陣列DOA估計

馮明月; 何明浩; 陳昌孝; 韓俊

doi:10.11999/JEIT170951

基于Bessel先驗快速稀疏貝葉斯學習的互質(zhì)陣列DOA估計

doi: 10.11999/JEIT170951 cstr: 32379.14.JEIT170951

(空軍預警學院武漢 430019)

基金項目:

國家自然科學基金(61703430)，湖北省自然科學基金(2016CFB288)

詳細信息

作者簡介:
馮明月：男，1988年生，博士生，研究方向為電子對抗信息處理. 何明浩：男，1963年生，教授，博士生導師，研究方向為電子對抗信息處理. 陳昌孝：男，1982年生，博士，研究方向為電子對抗信息處理. 韓俊：男，1983年生，博士，研究方向為電子對抗信息處理、雷達信號處理.

中圖分類號: TN911.7
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- 文章訪問數(shù): 1512
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-10-16
- 修回日期: 2018-01-16
- 刊出日期: 2018-07-19

DOA Estimation for Co-prime Array Based on Fast Sparse Bayesian Learning Using Bessel Priors

FENG Mingyue HE Minghao CHEN Changxiao HAN Jun

Funds:

The National Natural Science Foundation of China (61703430), The Natural Science Foundation of Hubei Province (2016CFB288)

摘要

摘要: 為提高低采樣點條件下互質(zhì)陣列DOA估計精度，該文提出基于Bessel先驗快速稀疏貝葉斯學習算法。該方法針對互質(zhì)陣列輸出的多采樣點復數(shù)數(shù)據(jù)，首先構(gòu)建了基于Bessel先驗的多量測分層模型；其次推導了模型所涉超參數(shù)的對數(shù)似然函數(shù)，根據(jù)最大似然估計準則得到了超參數(shù)的迭代公式；最后提出了快速實現(xiàn)方案，提高了運算效率。仿真結(jié)果表明，該方法不依賴先驗信息，在低采樣點條件下具有更高的DOA估計精度和分辨率，能夠?qū)ο喔尚盘栠M行高精度DOA估計，并具有較高的運算效率。此外，該文探究了虛擬陣列擴展與互質(zhì)陣列測向自由度擴展間的關聯(lián)，為后續(xù)陣列誤差條件下互質(zhì)陣列DOA研究估計提供參考。
- 互質(zhì)陣列 /
- DOA估計 /
- 稀疏貝葉斯學習 /
- 貝塞爾先驗 /
- 低采樣點
Abstract: In order to improve DOA estimation accuracy of co-prime array while the number of snapshots is small, a novel fast Sparse Bayesian Learning (SBL) algorithm using Bessel priors is proposed. Focusing on the multi-snapshots complex output data of coprime array, a multiple measurement vectors hierarchical model based on Bessel priors is firstly built. Then the log-likelihood function of model hyperparameters is derived, and the iterative formulas of hyperparameters are derived based on the criterion of maximum likelihood estimation. Finally, a fast implementation scheme is developed in order to improve the computation efficiency. Simulation experiments show that the proposed algorithm is independent on prior information. Under the condition of small number of snapshots, higher DOA estimation accuracy and resolution of uncorrelated and correlated signals can be achieved with proper computational efficiency. Further more, the necessity between virtual array extension and DOA estimation freedom of co-prime array is explored, which provides reference to DOA estimation for co-prime array under array perturbation conditions.
- Co-prime array、Direction Of Arrival (DOA) estimation、Sparse Bayesian Learning (SBL)、Bessel priors、Small number of snapshots /

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參考文獻(19)

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