一種新的基于稀疏表示的寬帶信號(hào)DOA估計(jì)方法
doi: 10.11999/JEIT150423 cstr: 32379.14.JEIT150423
基金項(xiàng)目:
國(guó)家重點(diǎn)實(shí)驗(yàn)室基金(914XXX1002)和中央高?;究蒲袠I(yè)務(wù)費(fèi)(JB140213)
A Novel Method of DOA Estimation for Wideband Signals Based on Sparse Representation
Funds:
The National Foundation for Key Laboratory (914XXX1002)
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摘要: 該文提出一種基于稀疏表示的寬帶信號(hào)波達(dá)方向(DOA)估計(jì)方法,解決稀疏表示方法在寬帶信號(hào)DOA估計(jì)中由于基矩陣維數(shù)過(guò)大而使算法存儲(chǔ)量和重構(gòu)計(jì)算量大的問(wèn)題。用單一頻點(diǎn)的基矩陣代替頻率和角度聯(lián)合構(gòu)建的基矩陣,使基矩陣的列數(shù)僅相當(dāng)于一個(gè)頻點(diǎn)處冗余基矩陣的列數(shù),大大降低了稀疏重構(gòu)方法的存儲(chǔ)量和計(jì)算量。該方法首先對(duì)各頻點(diǎn)的頻域數(shù)據(jù)進(jìn)行聚焦處理,將不同頻率的數(shù)據(jù)堆疊到參考頻率上并建立參考頻率處的基矩陣,然后建立聚焦后的稀疏表示模型進(jìn)行DOA估計(jì),并采用奇異值分解進(jìn)一步降低算法的運(yùn)算量,最后給出殘差門限的選擇方法。該算法不僅適用于非相關(guān)信號(hào),也可直接處理相關(guān)信號(hào)而不需要任何的去相關(guān)運(yùn)算,且具有高的檢測(cè)概率和估計(jì)精度,仿真實(shí)驗(yàn)和分析驗(yàn)證了該方法的有效性。
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關(guān)鍵詞:
- 波達(dá)方向估計(jì) /
- 稀疏表示 /
- 寬帶信號(hào) /
- 相關(guān)信號(hào)
Abstract: A novel wideband signals Direction-Of-Arrival (DOA) estimation method based on sparse representation is proposed. This algorithm can reduce the storage and calculation of the traditional sparse representation methods in wideband signals process, which is caused by the large dimension of base matrix. The over-complete dictionary is constructed by using one-frequency to replace the 2D combination of frequency and angle. The column number of constructed dictionary only equals to that of single-frequency redundant dictionary. The proposed method first adopts focused thought to stack the different frequency data to the reference frequency and founds the redundant dictionary with a single frequency. Then, a sparse recovery model is established to obtain the DOA estimations, which are coming from following the focus process. At the same time, the Singular Value Decomposition (SVD) is used to summarize each frequency to reduce computation burden further. Finally, an automatic selection criterion for the regularization parameter involved in the proposed approach is introduced. The proposed algorithm can effectively distinguish the correlative signals without any decorrelation processing, and it has higher accuracy and detection possibility. The experiment results indicate that the proposed method is effective to estimate the DOA of wideband signals. -
Rbsamen Michael and Pesavento Marius. Maximally robust capon beamformer[J]. IEEE Transactions on Signal Processing, 2014, 62(7): 1834-1849. Rangarao K V and Venkatanarasimhan S. Gold-MUSIC: a variation on MUSIC to accurately determine peaks of the spectrum[J]. IEEE Transactions on Antennas and Propagation, 2013, 61(4): 2263-2268. Steinwandt J, Roemer F, and Haardt M. Performance analysis of ESPRIT-type algorithms for non-circular sources[C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, 2013: 3986-3990. Hu N, Ye Z, Xu D, et al.. A sparse recovery algorithm for DOA estimation using weighted subspace fitting[J]. Signal Processing, 2012, 92(10): 2566-2570. Yin Ji-hao and Chen Tian-qi. Direction-of-arrival estimation using a sparse representation of array covariance vectors[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4489-4493. 沈志博, 董春曦, 黃龍, 等. 基于壓縮感知的寬頻段二維DOA估計(jì)算法[J]. 電子與信息學(xué)報(bào), 2014, 36(12): 2935-2941. Shen Zhi-bo, Dong Chun-xi, Huang Long, et al.. Broadband 2-D DOA estimation based on compressed sensing[J]. Journal of Electronics Information Technology, 2014, 36(12): 2935-2941. 林波, 張?jiān)鲚x, 朱炬波. 基于壓縮感知的DOA估計(jì)稀疏化模型與性能分析[J]. 電子與信息學(xué)報(bào), 2014, 36(3): 589-594. Lin Bo, Zhang Zeng-hui, and Zhu Ju-bo. Sparsity model and performance analysis of DOA estimation with compressive sensing[J]. Journal of Electronics Information Technology, 2014, 36(3): 589-594. Malioutov D M, ?etin M, and Willsky A S. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010-3022. Liu Zi-cheng, Wang Xue-lei, Zhao Guang-hui, et al.. Wideband DOA estimation based on sparse representation-an extension of L1-SVD in wideband cases[C]. IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC), Kunming, 2013: 1-4. He Zhen-qing, Shi Zhi-ping, Huang Lei, et al.. Underdetermined DOA estimation for wideband signals using robust sparse covariance fitting[J]. IEEE Signal Processing Letters, 2015, 22(4): 435-439. 李鵬飛, 張旻, 鐘子發(fā). 基于空頻域稀疏表示的寬頻段DOA估計(jì)[J]. 電子與信息學(xué)報(bào), 2012, 34(2): 404-409. Li Peng-fei, Zhang Min, and Zhong Zi-fa. Broadband DOA estimation based on sparse representation in spatial frequency domain[J]. Journal of Electronics Information Technology, 2012, 34(2): 404-409. Zhao Guang-hui, Liu Zi-cheng, Lin Jie, et al.. Wideband DOA estimation based on sparse representation in 2-D frequency domain[J]. IEEE Sensors Journal, 2015, 15(1): 227-233. Han K and Nehorai A. Wideband Gaussian source processing using a linear nested array[J]. IEEE Signal Processing Letters, 2013, 20(11): 1110-1113. Kumar D S, Hinduja I S, and Mani V V. DOA estimation of IR-UWB signals using coherent signal processing[C]. IEEE 10th International Colloquium on Signal Processing Its Application (CSPA), Malaysia, 2014: 288-291. Hung H and Kaveh M. Focusing matrices for coherent signal-subspace processing[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1988, 36(8): 1272-1281. Hu N, Xu X, and Ye Zhong-fu. DOA estimation for wideband signals based on sparse signal reconstruction using prolate spheroidal wave functions[J]. Signal Processing, 2014, 96(5): 395-400. Liu Zhang-meng and Huang Zhi-tao. Direction-of-arrival estimation of wideband signals via covariance matrix sparse representation[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4256-4270. -
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