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

Authors

  • Dharmendra Gokuldas Ganage Assistant Professor, Department of Electronics & Telecommunication, Sinhgad College of Engineering, Savitribai Phule Pune University, Pune, India
  • Yerram Ravinder Professor and Head, Department of Electronics & Telecommunication, Pune Institute of Computer Technology, Savitribai Phule Pune University, Pune, India

Abstract

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.

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Published

2019-02-16

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Section

Digital Signal Processing