Spectral Radon-Fourier Transform for Automotive Radar Applications

Oren Longman; Igal Bilik

doi:10.1109/TAES.2020.3038245

Spectral Radon-Fourier Transform for Automotive Radar Applications

Oren Longman, Igal Bilik

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

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.

Original language	English
Article number	9261109
Pages (from-to)	1046-1056
Number of pages	11
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	57
Issue number	2
DOIs	https://doi.org/10.1109/TAES.2020.3038245
State	Published - 1 Apr 2021
Externally published	Yes

ASJC Scopus subject areas

Aerospace Engineering
Electrical and Electronic Engineering

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10.1109/TAES.2020.3038245

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abstract = "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.",

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AB - 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.

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Spectral Radon-Fourier Transform for Automotive Radar Applications

Abstract

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