PCA Assisted DTCWT Denoising for Improved DOA Estimation of Closely Spaced and Coherent Signals

• Autorzy: Ganage Dharmendra , Ravinder Yerram

Identyfikatory

DOI

10.24425/ijet.2019.126290

• Języki publikacji: EN

Abstrakty

EN

Performance of standard Direction of Arrival (DOA) estimation techniques degraded under real-time signal conditions. The classical algorithms are Multiple Signal Classification (MUSIC), and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT). There are many signal conditions hamper on its performance, such as closely spaced and coherent signals caused due to the multipath propagations of signals results in a decrease of the signal to noise ratio (SNR) of the received signal. In this paper, a novel DOA estimation technique named CW-PCA MUSIC is proposed using Principal Component Analysis (PCA) to threshold the nearby correlated wavelet coefficients of Dual-Tree Complex Wavelet transform (DTCWT) for denoising the signals before applying to MUSIC algorithm. The proposed technique improves the detection performance under closely spaced, and coherent signals with relatively low SNR conditions. Also, this method requires fewer snapshots, and less antenna array elements compared with standard MUSIC and wavelet-based DOA estimation algorithms.

Słowa kluczowe

EN

DOA estimation; signal denoising; closely spaced; coherent signal; adaptive antenna array; antenna elements

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2019
• Tom: Vol. 65, No. 1
• Strony: 111--117
• Opis fizyczny: Bibliogr. 18 poz., wykr., rys.

Twórcy

autor

Ganage Dharmendra

• Department of Electronics and Telecommunication, Sinhgad College of Engineering, Savitribai Phule Pune University, Pune, India

autor

Ravinder Yerram

• Department of Electronics and Telecommunication, Pune Institute of Computer Technology, Savitribai Phule Pune University, Pune, India

Bibliografia

• [1] J. C. Liberti and T. S. Rappaport, “Smart antennas for wireless communications: IS-95 and third generation CDMA applications,” Prentice Hall, New Jersey, 1999, pp. 253-284.
• [2] R. O. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, Mar. 1986, pp. 276-280. DOI: 10.1109/TAP.1986.1143830.
• [3] R. Roy and T. Kailath, “ESPRIT-Estimation of signal parameter via rotational invariance techniques,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 37, no. 7, Jul. 1989, pp. 984-995. DOI: 10.1109/29.32276.
• [4] L. Gan and X. Luo, “Direction-of-arrival estimation for uncorrelated and coherent signals in the presence of multipath propagation,” IET Microwaves, Antennas and Propagation, vol. 7, no. 9, Mar. 2013, pp. 746-753. DOI: 10.1049/iet-map.2012.0659.
• [5] Z. Ye, Y. Zhang, X. Xu, and C. Liu, “Direction of arrival estimation for uncorrelated and coherent signals with uniform linear array”, IET Radar, Sonar and Navigation, vol. 3, no. 2, 2009, pp. 144-154. DOI: 10.1049/iet-rsn:20080041.
• [6] N. C. Pramod and G. V. Anand, “Nonlinear wavelet denoising for DOA estimation by music”, in Proc. IEEE Int. Conf. Signal Processing & Communications (SPCOM 04), Dec. 2004, pp. 388-392. DOI: 10.1109/SPCOM.2004.1458487.
• [7] X. Mao and H. Pan, “An improved DOA estimation algorithm based on wavelet operator, "Journal of Communications”, vol. 1, no. 12, Dec. 2013, pp. 839-844. doi: 10.12720/jcm.8.12.839-844.
• [8] Jianfeng Liu, “State space based method for the DOA estimation by the forward-backward data matrix using small snapshots”, International Journal of Electronics and Telecommunications, vol. 63, no. 3, 2017, pp. 315-322. DOI: 10.1515/eletel-2017-0042.
• [9] G. Chen and W. Zhu, “Signal denoising using neighboring dual-tree complex wavelet coefficients”, IET Signal Processing, vol. 6, no. 2, 2012, pp. 143-147. DOI: 10.1049/iet-spr.2010.0262.
• [10] N. G. Kingsbury, “Complex wavelets for shift invariant analysis and filtering of signals,” Journal of Applied and Computational Harmonic Analysis, vol. 10, no. 3, May 2001, pp. 234-253. DOI: 10.1006/acha.2000.0343
• [11] I. Selesnick, R. Baraniuk, and N. Kingsbury, “The dual-tree complex wavelet transform”, IEEE Signal Processing Magazine, vol. 22, no. 6, Nov. 2005, pp. 123-151. DOI: 10.1109/MSP.2005.1550194.
• [12] M. K. Lakshmanan and H. Nikookar, “A review of wavelets for digital wireless communication”, Wireless Personal Communications, vol. 37, 2006, pp. 387-420. DOI: 10.1007/s11277-006-9077-y.
• [13] X. Yu, W. Liang, L. Zhang, H. Jin, and J. Qiu, “Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline”, Mechanical Systems and Signal Processing, vol. 72–73, 2016, pp. 266-285. DOI: 10.1016/j.ymssp.2015.10.034.
• [14] Varsha and P. Basu, “An improved dual tree complex wavelet transform based image denoising using GCV thresholding”, in Proc. IEEE Int. Conf. Computational Systems and Communications (ICCSC 14), Trivandrum, India, Dec. 2014, pp. 133-138. DOI: 10.1109/COMPSC.2014.7032635.
• [15] F. Wang and Z. Ji, “Application of the dual-tree complex wavelet transform in biomedical signal denoising”, Bio-Medical Materials and Engineering, vol. 24, 2014, pp. 109-115. DOI: 10.3233/BME-130790.
• [16] R. Yang and M. Ren, “Wavelet denoising using principal component analysis”, Expert Systems with Applications, vol. 38, 2011, pp. 1073-1076. DOI: 10.1016/j.eswa.2010.07.069.
• [17] S. Bacchelli and S. Papi, “Image denoising using principal component analysis in the wavelet domain”, Journal of Computational and Applied Mathematics, vol. 189, 2006, pp. 606-621. DOI: 10.1016/j.cam.2005.04.030.
• [18] D. L. Donoho, “De-Noising by soft-thresholding”, IEEE Transactions on Information Theory, vol. 41, no. 3, May 1995, pp. 613-627. DOI: 10.1109/18.382009.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).