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Proceedings Paper

Data-driven cepstral and neural learning of features for robust micro-Doppler classification
Author(s): Baris Erol; Mehmet Saygin Seyfioglu; Sevgi Zubeyde Gurbuz; Moeness Amin
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Paper 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.

Paper Details

Date Published: 4 May 2018
PDF: 8 pages
Proc. SPIE 10633, Radar Sensor Technology XXII, 106330J (4 May 2018); doi: 10.1117/12.2304396
Show Author Affiliations
Baris Erol, Villanova Univ. (United States)
Mehmet Saygin Seyfioglu, TOBB Univ. of Economics and Technology (Turkey)
Sevgi Zubeyde Gurbuz, The Univ. of Alabama (United States)
Moeness Amin, Villanova Univ. (United States)

Published in SPIE Proceedings Vol. 10633:
Radar Sensor Technology XXII
Kenneth I. Ranney; Armin Doerry, Editor(s)

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