Publication Type : Conference Paper
Publisher : Symposium on Recent Advances in Communication Theory, Information Theory, Antennas and Propagation.
Source : Symposium on Recent Advances in Communication Theory, Information Theory, Antennas and Propagation(CIAP’17), Manipal University, Karnataka, India, 2017.
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2017
Abstract : In this article, we study Fast Independent Component Analysis (FastICA) algorithm for resolving closely spaced sinusoids embedded in noise. We generated closely spaced sinusoid signals embedded in noise using open source IT++ libraries. The closely spaced sinusoids signals were randomly generated with minimum frequency separation of [0.05 * F s : 0.2 * F s ]. Simulations were performed to resolve closely spaced sinusoids using FFT algorithm with minimum frequency of seperation [0.05 * F s : 0.2 * F s ], but FFT failed to resolve closely spaced sinusoid of such lower frequencies and produced more error for multiple signals and different resolutions. After a through analysis, we studied FastICA to resolve the problem, and has given good results. The performance of FastICA algorithm is evaluated for different sampling rates as well as different number of signal components as done with FFT. It is concluded that FastICA has better performance compared to Fast Fourier Transform (FFT) for resolving multiple closely spaced signals in the presence of Additive White Gaussian Noise (AWGN). The entire simulation results were generated using IT++ libraries.
Cite this Research Publication : N. Saideep, Dr. Dhanesh G. Kurup, and Shikha Tripathi, “Detection of Closely Spaced Sinusoids in Noise using FastICA algorithm”, in Symposium on Recent Advances in Communication Theory, Information Theory, Antennas and Propagation(CIAP’17), Manipal University, Karnataka, India, 2017.