基于稀疏貝葉斯學(xué)習(xí)的碼元速率估計(jì)
doi: 10.11999/JEIT170906 cstr: 32379.14.JEIT170906
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(西安電子科技大學(xué)電子工程學(xué)院 西安 710071)
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(61201286)，陜西省自然科學(xué)基金(2014JMS304)
Symbol Rate Estimation Based on Sparse Bayesian Learning
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JIN Yan TIAN Tian JI Hongbing
Funds:
The National Natural Science Foundation of China (61201286), The Natural Science Foundation of Shannxi Province (2014JMS304)
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摘要: 現(xiàn)有的相位編碼信號(hào)碼元速率估計(jì)方法在樣本點(diǎn)足夠多的情況下才能準(zhǔn)確估計(jì)出參數(shù)，且算法復(fù)雜度高。針對(duì)此問(wèn)題，該文詳細(xì)分析了BPSK信號(hào)的結(jié)構(gòu)特征，并以此為先驗(yàn)信息對(duì)其循環(huán)自相關(guān)(CA)向量進(jìn)行壓縮采樣，降低了傳統(tǒng)貝葉斯復(fù)數(shù)處理方法的維度。利用壓縮傳感中離散傅里葉變換矩陣的奇偶性，分解傳感矩陣為正弦和余弦變換，分別將CA向量的實(shí)虛部轉(zhuǎn)換到對(duì)應(yīng)變換域測(cè)量，根據(jù)復(fù)數(shù)信號(hào)實(shí)虛部具有相同支撐集這一特點(diǎn)，采用多任務(wù)稀疏貝葉斯重構(gòu)時(shí)延積向量的單邊譜分量，從而估計(jì)出碼元頻率。理論分析和仿真結(jié)果表明，相較于其它基于稀疏貝葉斯學(xué)習(xí)的參數(shù)估計(jì)算法，所提方法在測(cè)量數(shù)量較少的情況下也能準(zhǔn)確估計(jì)出循環(huán)頻率，且算法實(shí)時(shí)性顯著提高。
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關(guān)鍵詞:
- 碼元速率估計(jì) /
- 稀疏貝葉斯學(xué)習(xí) /
- 循環(huán)自相關(guān) /
- 單邊譜
Abstract: Existing methods for symbol rate estimation of phase coded signals require amounts of sensing data, and are of high computational complexity. This paper analyzes the structure characteristics of BPSK signals, which are employed as the prior information for signal compressing and dimensionality reduction. The sensing matrix can be split into sine and cosine component, combined with the Fourier transform parity. According to the fact that the real and imaginary components of a complex value share the same support set, the symbol rate estimation can be obtained, using unilateral spectral of the delay-product vector reconstructed by multi-task Bayesian compressive sensing. Theoretical analysis and simulation results show that compared with other parameter estimation algorithms, the proposed method can reduce the measurements and significantly improve the real-time ability, while keeping the high reconstruction accuracy. -
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