Spectral Radon-Fourier Transform for Automotive Radar Applications

Fast Fourier transform (FFT) is one of the fundamental signal processing algorithms widely used in radar applications. The Radon-Fourier transform (RFT) can be seen as an FFT generalization that can overcome some of its limitations. This work derives three spectral RFT (SRFT) based approaches to add...

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Bibliographic Details
Published inIEEE transactions on aerospace and electronic systems Vol. 57; no. 2; pp. 1046 - 1056
Main Authors Longman, Oren, Bilik, Igal
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Automotive engineering
Automotive radar
Direction of arrival
Doppler effect
Fast Fourier transformations
Fourier transforms
Radar detection
Radar imaging
Radar signal processing
Radar tracking
Radon
Signal processing
Target detection
Tracking
Transforms
Urban environments
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Summary:Fast Fourier transform (FFT) is one of the fundamental signal processing algorithms widely used in radar applications. The Radon-Fourier transform (RFT) can be seen as an FFT generalization that can overcome some of its limitations. This work derives three spectral RFT (SRFT) based approaches to address major challenges of the multiple-input multiple-output automotive radars. First, two SRFT-based approaches are derived to increase maximal target detection range by mitigation of target migration in range and direction of arrival, jointly, and by multidwell integration processing, which increases the radar coherent integration time without compromising its detection update rate. Next, SRFT-based approach is proposed to address the cluster-to-track association problem that arises in multiple distributed target tracking scenarios that characterize automotive radar operation in dense urban environments.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2020.3038245