基于互相关协方差矩阵的改进多重信号分类高分辨波达方位估计方法

毛琳琳; 张群飞; 黄建国; 史文涛; 韩晶

doi:10.11999/JEIT141208

基于互相关协方差矩阵的改进多重信号分类高分辨波达方位估计方法

doi: 10.11999/JEIT141208

基金项目:

国家自然科学基金(61271415)

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出版历程
- 收稿日期: 2014-09-17
- 修回日期: 2015-04-16
- 刊出日期: 2015-08-19

Improved Multiple Signal Classification Algorithm for Direction of Arrival Estimation Based on Covariance Matrix of Cross-correlation

摘要

摘要: 针对经典高分辨波达方位(DOA)估计方法在低信噪比下分辨性能较差的问题，该文提出一种适用于主动探测系统的基于互相关矩阵的改进多重信号分类(MUSIC)高分辨方位估计方法(I-MUSIC)。该方法首先利用主动声呐发射信号已知的特性，将发射信号与阵元接收信号进行互相关，利用互相关序列形成新的空域协方差矩阵，再进行特征分解。理论分析表明，互相关处理在抑制噪声的同时保留了阵元之间的相位信息，可以得到比MUSIC方法更准确的子空间划分，进而提高低信噪比方位估计性能。在此基础上，提出一种基于相关时间门限的改进MUSIC高分辨方位估计(T-MUSIC)方法，通过对互相关序列设置时间门限进一步提高方位估计信噪比。仿真结果表明，与MUSIC方法相比，I-MUSIC与T-MUSIC可以分别使低信噪比时的估计性能提高3 dB和6 dB，相应平均估计误差分别为原方法的77%和53%。在阵元间接收噪声存在相关性时，T-MUSIC与I-MUSIC方法相比可获得8 dB的估计增益，估计性能更优。I-MUSIC与T-MUSIC应用于多目标主动探测，可大幅提高探测系统在低信噪比下的方位估计性能。
- 信号处理 /
- 波达方位估计 /
- 互相关 /
- 协方差矩阵 /
- 多重信号分类
Abstract: In view of the poor performance of traditional Direction of Arrival (DOA) methods at low signal-to-noise ratios, an improved MUltiple SIgnal Classification (MUSIC) algorithm for DOA estimation applied to active detection system based on covariance matrix decomposition of cross-correlation (I-MUSIC) is proposed. Exploiting the transmission feature of active sonar, cross-correlation sequence between the transmitted signal and the array output is formulated. The spatial covariance matrix is then constructed from the sequence. Then matrix decomposition is implemented over the new spatial covariance matrix to estimate the DOA. It is proved that cross-correlation can suppress noise while preserving the phase information between array elements, which facilitate the subspace separation at low SNRs. Furthermore, another novel method based on correlation Time threshold (T-MUSIC) is proposed to further improve the DOA performance. Simulation results indicate that I-MUSIC and T-MUSIC can obtain a performance gain of 3 dB and 6 dB, with the estimate error being 77% and 53% of the original method respectively. Due to data selection via time threshold, T-MUSIC is not appreciably affected by noise, and thus outperforms IM-MUISC for 8 dB at low SNRs. I-MUSIC and T-MUSIC can improve the DOA performance at low SNRs significantly if applied to active multi-target detection system.
- Signal processing /
- Direction of Arrival (DOA) estimation /
- Cross-correlation /
- Covariance matrix /
- MUltiple SIgnal Classification (MUSIC)

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参考文献(23)

Cheng Q, Lei H, and So H C. Improved unitary root-MUSIC for DOA estimation based on pseudo-noise resampling[J]. IEEE Signal Processing Letters, 2014, 21(2): 140-144.

Zeng W, So C and Lei H. lp-MUSIC: Robust direction-of- arrival estimator for impulsive noise environments[J]. IEEE Transactions on Signal Processing, 2013, 61(17): 4296-4308.

Vincent F, Besson O, and Chaumette E. Approximate maximum likelihood direction of arrival estimation for two closely spaced sources[C]. Proceedings of the 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), St. Martin, France, 2013: 320-323.

Heidenreich P and Zoubir M. Fast maximum likelihood DOA estimation in the two-target case with applications to automotive radar[J]. Signal Processing, 2013, 93(12): 3400-3409.

Lee Y, Hudson E, and Yao K. Acoustic DOA estimation: an approximate maximum likelihood approach[J]. IEEE Systems Journal, 2014, 8(1): 131-141.

Park S, Choi H, Yang W, et al.. Direction of arrival estimation using weighted subspace fitting with unknown number of signal sources[C]. Proceedings of the 11th International Conference on Advanced Communication Technology, Piscataway, USA, 2009: 2295-2298.

Wang H, Kay S, and Saha S. An importance sampling maximum likelihood direction of arrival estimator[J]. IEEE Transactions on Signal Processing, 2008, 56(10): 5082-5092.

Li X and Huang J. Bayesian high resolution DOA estimator based on importance sampling[C]. Proceedings of IEEE Oceans 2005, Washington, D.C., USA, 2005, 1: 611-615.

Shi W, Huang J, and Hou Y. Fast DOA estimation algorithm for MIMO sonar based on ant colony optimization[J]. Journal of Systems Engineering and Electronics, 2012, 23(2): 173-178.

Yan F G, Jin M, and Qiao X L. Source localization based on symmetrical MUSIC and its statistical performance analysis[J]. Science China Information Sciences, 2013, 56(6): 1-13.

Di C, Elio D, and Giovanni J. Wideband source localization by space-time MUSIC subspace estimation[C]. Proceedings of 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), Trieste, Italy, 2013: 331-336.

Choi W and Sarkar K. Minimum norm property for the sum of the adaptive weights for a direct data domain least squares algorithm[J]. IEEE Transactions on Antennas and Propagation, 2006, 54(3): 1045-1050.

Rangarao 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.

Yan F, Jin M, and Qiao X. Low-complexity DOA estimation based on compressed MUSIC and its performance analysis[J]. IEEE Transactions on Signal Processing, 2013, 61(8): 1915-1930.

Reddy V, Ng B, and Khong A. Insights into MUSIC-like algorithm[J]. IEEE Transactions on Signal Processing, 2013, 61(10): 2551-2556.

Ying Z and Boon P. MUSIC-like DOA estimation without estimating the number of sources[J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1668-1676.

Azaria M and Hertz D. Time delay estimation by generalized cross- correlation methods[J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1984, 32(2): 280-285.

Benesty J, Jingdong C, and Yiteng H. Time-delay estimation via linear interpolation and cross correlation[J]. IEEE Transactions on Speech and Audio Processing, 2004, 12(5): 509-519.

Pertil? P, Korhonen T, and Visa A. Measurement combination for acoustic source localization in a room environment[J]. EURASIP Journal on Audio, Speech, and Music Processing, 2008, 3: 1-14.

Dermatas S, Fakotakis D, and Kokkinakis K. Fast endpoint detection algorithm for isolated word recognition in office environment[C]. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Toronto, Canada, 1991：733-736.

郭胜楠, 崔慧娟, 唐昆. 低信噪比下基于短时谱估计的语音增强[J]. 清华大学学报(自然科学版), 2010, 50(1): 149-152.

Guo Sheng-nan, Cui Hui-juan, and Tang Kun. Speech enhancement based on short time spectral amplitude estimates in low SNR[J]. Journal of Tsinghua University (Science and Technology), 2010, 50(1): 149-152.

李志舜. 鱼雷自导信号与信息处理[M]. 西安: 西北工业大学出版社, 2004: 138-144.

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