Improved Target Detection and Feature Extraction using a Complex-Valued Adaptive Sinc Filter on Radar Time Domain Data

Stadelmayer T, Santra A, Stadelmayer M, Weigel R, Lurz F (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

2012 PROCEEDINGS OF THE 20TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)

Publisher: EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP

City/Town: KESSARIANI

Pages Range: 1745-1749

Conference Proceedings Title: 29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021)

Event location: , ELECTR NETWORK

DOI: 10.23919/EUSIPCO54536.2021.9616250

Abstract

In state-of-art radar classification tasks the raw time domain ADC data is transformed to frequency domain wherein the target is detected. Feature data, such as the micro-Doppler signature, is extracted for classification of e.g. a performed gesture or a person based on its gait. However, the transformation into frequency domain using short-time Fourier transform resulting in erroneous target detection due to superposition of target components thus leading to sub-optimal feature for subsequent classification task. Thus, in this paper we propose a target feature extraction approach that operates directly on 2D time domain radar data by using a complex-valued adaptive 2D sine filter. The proposed approach tracks the target's slow-time and fast-time frequencies by a regulation loop, which progressively adjusts the filter's center frequency to the target location. We demonstrate that the proposed approach extracts better features in both single- and multi-target scenarios, leading to improved classification accuracy in gait classification problems using radars. Furthermore, the proposed time domain feature extraction approach facilitates the use of a parametric neural network that works directly on time domain data.

Authors with CRIS profile

Thomas Stadelmayer Lehrstuhl für Technische Elektronik (LTE) Robert Weigel Lehrstuhl für Technische Elektronik (LTE)

Involved external institutions

Infineon Technologies AG DE Germany (DE) Johannes Kepler Universität (JKU) Linz AT Austria (AT) Technische Universität Hamburg-Harburg (TUHH) DE Germany (DE)

How to cite

APA:

Stadelmayer, T., Santra, A., Stadelmayer, M., Weigel, R., & Lurz, F. (2021). Improved Target Detection and Feature Extraction using a Complex-Valued Adaptive Sinc Filter on Radar Time Domain Data. In 29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021) (pp. 1745-1749). , ELECTR NETWORK: KESSARIANI: EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP.

MLA:

Stadelmayer, Thomas, et al. "Improved Target Detection and Feature Extraction using a Complex-Valued Adaptive Sinc Filter on Radar Time Domain Data." Proceedings of the 29th European Signal Processing Conference (EUSIPCO), , ELECTR NETWORK KESSARIANI: EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP, 2021. 1745-1749.

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