Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6476
Title: Data-driven cepstral and neural learning of features for robust micro-Doppler classification
Authors: Erol, Barış
Seyfioğlu, Mehmet Saygın
Gürbüz, Sevgi Zübeyde
Amin, Moeness
Keywords: [No Keywords]
Issue Date: 2018
Publisher: Spie-Int Soc Optical Engineering
Source: Conference on Radar Sensor Technology XXII -- APR 16-18, 2018 -- Orlando, FL
Series/Report no.: Proceedings of SPIE
Abstract: Automatic target recognition (ATR) using micro-Doppler analysis is a technique that has been a topic of great research over the past decade, with key applications to border control and security, perimeter defense, and force protection. Patterns in the movements of animals, humans, and drones can all be accomplished through classification of the target's micro-Doppler signature. Typically, classification is based on a set of fixed, pre-defined features extracted from the signature; however, such features can perform poorly under low signal-to-noise ratio (SNR), or when the number and similarity of classes increases. This paper proposes a novel set of data-driven frequency-warped cepstral coefficients (FWCC) for classification of micro-Doppler signatures, and compares performance with that attained from the data-driven features learned in deep neural networks (DNNs). FWCC features are computed by first filtering the discrete Fourier Transform (DFT) of the input signal using a frequency-warped filter bank, and then computing the discrete cosine transform (DCT) of the logarithm. The filter bank is optimized for radar using genetic algorithms (GA) to adjust the spacing, weight, and width of individual filters. For a 11-class case of human activity recognition, it is shown that the proposed data-driven FWCC features yield similar classification accuracy to that of DNNs, and thus provides interesting insights on the benefits of learned features.
URI: https://doi.org/10.1117/12.2304396
https://hdl.handle.net/20.500.11851/6476
ISBN: 978-1-5106-1778-0
ISSN: 0277-786X
1996-756X
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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