Adaptive Mean Ridgelet Transform Filtering for Detecting Signal and Comparison of Algorithm's Implements

Abstract:

For solving the problem which the performance of detection was reduced in the low signal to noise ratio (SNR) using Wigner-Ville Hough transform (WHT), the method of XWVD adaptive mean Ridgelet transform filtering (XWVD-M-FRIT) was proposed. In this method, due to the power distribution of signal is different from noise or reverberation in time-frequency domain, so designed adaptive axial mean filter, then using Ridgelet transform filtering to restrain noise or reverberation. At last, it is to detect the signal using Hough transform. The results of real and simulation experiments showed, compared with WHT, in the low SNR the new method is feasible to restrain noise or reverberation in time-frequency domain for improving the performance of signal detection. furthermore, the performance of varying implement of adaptive mean and Ridgelet transform filtering were compared.

• Periodical: Applied Mechanics and Materials (Volumes 719-720)
• Pages: 1171-1176
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.719-720.1171
• Online since: January 2015
• Authors: Guang Ping Zhu*, Hui Sun
• Keywords: Detection, Reverberation, Ridgelet Transform, Signal to Noise Ratio, WVD

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