Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity
Funds:
The National Natural Science Foundation of China (61501517)
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摘要: 現(xiàn)有的頻譜檢測(cè)算法沒有充分利用信號(hào)在角度維的稀疏性質(zhì)。該文根據(jù)角度維的稀疏特性建立信號(hào)模型,通過稀疏貝葉斯學(xué)習(xí)(Sparse Bayesian Learning, SBL)算法解決稀疏信號(hào)的重構(gòu)問題,并在迭代過程中引入二元假設(shè)檢驗(yàn)思想,推導(dǎo)出一種自適應(yīng)門限的選取策略,把傳統(tǒng)的重構(gòu)算法轉(zhuǎn)化為一個(gè)針對(duì)不同來波方向的信號(hào)檢測(cè)問題。該算法能夠在恒虛警概率下對(duì)多信號(hào)進(jìn)行全盲檢測(cè),同時(shí)實(shí)現(xiàn)信號(hào)來波方向的精確估計(jì)。實(shí)驗(yàn)結(jié)果證明,自適應(yīng)判決方法能夠有效地提高稀疏重構(gòu)算法的重構(gòu)精度,降低運(yùn)算復(fù)雜度,參數(shù)估計(jì)精度和信號(hào)檢測(cè)性能相比于現(xiàn)有算法得到明顯的提升。
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關(guān)鍵詞:
- 頻譜檢測(cè) /
- 稀疏貝葉斯學(xué)習(xí) /
- 恒虛警概率 /
- 自適應(yīng)門限
Abstract: The existing spectrum detection method can not take full advantage of angle dimension. To sense the spectrum more comprehensively, the signal model is established based on the sparsity of angle dimension. The reconstruction result can be derived by Sparse Bayesian Learning (SBL) algorithm. By integrating the binary probability hypothesis into iterative procedure of SBL, a decision test combined with adaptive threshold is derived. The proposed pruning step can accept the active components of the model, and transform the sparse recovery into a detection problem for signals from different angles. Therefore, the algorithm can sense the spectrum blindly with constant false-alarm rate as well as estimate the accurate angle of each incident signal. Numerical simulation results verify that adaptive threshold can improve reconstruction accuracy with low computing cost. Moreover, the proposed algorithm can achieve better estimation and detection performance than previous algorithms. -
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