基于空域稀疏性的自適應(yīng)頻譜檢測(cè)算法

于宏毅; 程標(biāo); 胡赟鵬; 沈智翔

doi:10.11999/JEIT151030

基于空域稀疏性的自適應(yīng)頻譜檢測(cè)算法

doi: 10.11999/JEIT151030

基金項(xiàng)目:

國家自然科學(xué)基金(61501517)

計(jì)量
- 文章訪問數(shù): 1291
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-09-10
- 修回日期: 2016-01-22
- 刊出日期: 2016-07-19

Adaptive Spectrum Detection Algorithm Based on Spatial Sparsity

Funds:

The National Natural Science Foundation of China (61501517)

摘要

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。
- 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.
- Spectrum detection /
- Sparse Bayesian Learning (SBL) /
- Constant false-alarm rate /
- Adaptive threshold

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參考文獻(xiàn)(26)

DIKMESE S, SOFOTASIOS P C, IHALAINEN T, et al. Efficient energy detection methods for spectrum sensing under non-flat spectral characteristics[J]. IEEE Journal on Selected Areas in Communications, 2015, 33(5): 755-770. doi: 10.1109/JSAC.2014.2361074.

馬彬, 方源, 謝顯中. 一種主用戶隨機(jī)到達(dá)情況下改進(jìn)的循環(huán)平穩(wěn)特征檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2015, 37(7): 1531-1537. doi: 10.11999/JEIT141283.

MA Bin, FANG Yuan, and XIE Xianzhong. Improved cyclostationary spectrum sensing scheme for primary users randomly arriving[J]. Journal of Electronics Information Technology, 2015, 37(7): 1531-1537. doi: 10.11999/JEIT 141283.

ZHANG Xinzhi, CHAI Rong, and GAO Feifei. Matched filter based spectrum sensing and power level detection for cognitive radio network[C]. IEEE Global Conference on Signal and Information Processing. Atlanta, USA, 2014: 1267-1270.

WILCOX D, TSAKALAKI E, KORTUN A, et al. On spatial domain cognitive radio using single-radio parasitic antenna arrays[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(3): 571-580. doi: 10.1109/JSAC. 2013.130321.

趙曉暉, 李曉燕. 認(rèn)知無線電中基于陣列天線和協(xié)方差矩陣的頻譜感知算法[J]. 電子與信息學(xué)報(bào), 2014, 36(7): 1693-1698. doi: 10.3724/SP.J.1146.2013.01057.

ZHAO Xiaohui and LI Xiaoyan. Spectrum sensing algorithm in cognitive radio based on array antenna and covariance matrix[J]. Journal of Electronics Information Technology, 2014, 36(7): 1693-1698. doi: 10.3724/SP.J.1146.2013.01057.

馬啟明, 王宣銀, 杜栓平. 基于頻譜幅度起伏特性的微弱信號(hào)檢測(cè)方法研究[J]. 電子與信息學(xué)報(bào), 2008, 30(11): 2642-2645.

MA Qiming, WANG Xuanyin, and DU Shuanping. Research of the method for the weak signal detection based on the amplitude fluctuation property of the frequency spectrum[J]. Journal of Electronics Information Technology, 2008, 30(11): 2642-2645.

劉暢, SYED S A, 張銳, 等. 基于空間譜的多天線盲頻譜感知算法[J]. 通信學(xué)報(bào), 2015, 36(4): 119-128. doi: 10.11959/j.issn. 1000-436x.2015087.

LIU Chang, SYED S A, ZHANG Rui, et al. Spatial spectrum based blind spectrum sensing for multi-antenna cognitive radio system[J]. Journal on Communication, 2015, 36(4): 119-128. doi: 10.11959/j.issn.1000-436x.2015087.

左加闊, 陶文鳳, 包永強(qiáng), 等. 多跳認(rèn)知水聲通信中的分布式稀疏頻譜檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2013, 35(10): 2359-2364. doi: 10.3724/SP.J.1146.2013.00042.

ZUO Jiakuo, TAO Wenfeng, BAO Yongqiang, et al. Distributed sparse spectrum detection in multihop cognitive underwater acoustict communication networks[J]. Journal of Electronics Information Technology, 2013, 35(10): 2359-2364. doi: 10.3724/SP.J.1146.2013.00042.

HUANG Dinhwa, WU Sauhsuan, WU Wenrong, et al. Cooperative radio source positioning and power map reconstruction: a sparse bayesian learning approach[J]. IEEE Transactions on Vehicular Technology, 2015, 64(6): 2318-2332. doi: 10.1109/TVT.2014.2345738.

LEI Chuan, ZHANG Jun, and GAO Qiang. Detection of unknown and arbitrary sparse signals against noise[J]. IET Signal Processing, 2014, 8(2): 146-157. doi: 10.1049/iet-spr. 2011.0125.

WACHOWSKI N and AZIMI M R. Detection and classification of nonstationary transient signals using sparse approximations and bayesian networks[J]. IEEE/ACM Transactions on Audio Speech and Language Processing, 2014, 22(12): 1750-1764. doi: 10.1109/TASLP.2014.2348913.

PARIS S, MARY D, and FERRARI A. Detection tests using sparse models, with application to hyperspectral data[J]. IEEE Transactions on Signal Processing, 2013, 61(6): 1481-1494. doi: 10.1109/TSP.2013.2238533.

ZHOU Yifeng, YIP P C, and LEUNG H. Tracking the direction-of-arrival of multiple moving targets by passive arrays: algorithm[J]. IEEE Transactions on Signal Processing, 1999, 47(10): 2655-2666.

WIPF D P and RAO B D. An empirical bayesian strategy for solving the simultaneous sparse approximation problem[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3704-3716. doi: 10.1109/TSP.2007.894265.

WIPF D P, RAO B D, and NAGARAJAN S. Latent variable Bayesian models for promoting sparsity[J]. IEEE Transactions on Information Theory, 2011, 57(9): 6236-6255. doi: 10.1109/TIT.2011.2162174.

KELLY E J and FORSYTHE K M. Adaptive detection and parameter estimation for multidimensional signal models[R]. Lexington: Lincoln Lab, 1989.

TIPPING M E. Sparse bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, 1(3): 211-244.

劉寅, 吳順君, 吳明宇, 等. 基于空域稀疏性的寬帶DOA估計(jì)[J]. 航空學(xué)報(bào), 2012, 33(11): 2028-2038.

LIU Yin, WU Shunjun, WU Mingyu, et al. Wideband DOA estimation based on spatial sparseness[J]. Acta Aeronautica et Astronautica Sinica, 2012, 33(11): 2028-2038.

LIU Zhangmeng, HUANG Zhitao, and ZHOU Yiyu. Direction-of-Arrival estimation of wideband signals via covariance matrix sparse representation[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4256-4270. doi: 10.1109/TSP.2011.2159214.

KAY S M and GABRIEL J R. An invariance property of the generalized likelihood ratio test[J]. IEEE Signal Processing Letters, 2003, 10(12): 352-355. doi: 10.1109/LSP.2003. 818865.

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