基于稀疏貝葉斯學(xué)習(xí)的低空測(cè)角算法

張永順; 葛啟超; 丁姍姍; 郭藝奪

doi:10.11999/JEIT151319

基于稀疏貝葉斯學(xué)習(xí)的低空測(cè)角算法

doi: 10.11999/JEIT151319 cstr: 32379.14.JEIT151319

2.
(空軍工程大學(xué)防空反導(dǎo)學(xué)院西安 710051) ②(信息感知技術(shù)協(xié)同創(chuàng)新中心西安 710077)

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61372033, 61501501)

計(jì)量
- 文章訪問數(shù): 1454
- HTML全文瀏覽量: 124
- PDF下載量: 343
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-11-25
- 修回日期: 2016-04-18
- 刊出日期: 2016-09-19

Low-angle Estimation Method via Sparse Bayesian Learning

2.
(Air and Missile Defense College, Air Force Engineering University, Xi&rsquo

Funds:

The National Natural Science Fouudation of China (61372033, 61501501)

摘要

摘要: 為解決米波雷達(dá)低空測(cè)角的精度問題，該文結(jié)合稀疏貝葉斯學(xué)習(xí)方法，利用相鄰快拍稀疏結(jié)構(gòu)的相似性，將多觀測(cè)向量模型通過Kronecker積變換成具有塊稀疏結(jié)構(gòu)的單觀測(cè)向量模型，同時(shí)通過矩陣變換解決了貝葉斯準(zhǔn)則在復(fù)數(shù)域中的應(yīng)用。通過稀疏貝葉斯學(xué)習(xí)的不斷迭代恢復(fù)出了信號(hào)在感知矩陣下的系數(shù)矩陣，得到了信源的角度信息。仿真實(shí)驗(yàn)驗(yàn)證了該方法相對(duì)于廣義MUSIC和M-FOCUSS算法具有更好的性能，并且分析了快拍數(shù)變化對(duì)算法性能的影響。
- 米波雷達(dá) /
- 多徑 /
- 壓縮感知 /
- 稀疏貝葉斯學(xué)習(xí) /
- 多觀測(cè)向量
Abstract: In order to improve the accuracy of low-angle estimation in meter-wave radars, combined with sparse Bayesian learning, this paper makes use of the Kronecker product and the similarity of the sparse structure between adjacent snapshots to transform the multiple measurement vector model into a single measurement vector model. The angle of the source is obtained by the coefficient matrix of the sensing matrix related to signal and the coefficient matrix is recovered by the continuous iteration in sparse Bayesian learning. Simulation experiments show that the proposed method has better performance than the generalized MUSIC algorithm and M-FOCUSS algorithm, the influence on algorithm performance caused by the snapshot change is obtained.
- Meter-wave radars /
- Multipath /
- Compressive sensing /
- Sparse Bayesian learning /
- Multiple measurement vector

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參考文獻(xiàn)(27)

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