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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Spectral Radon–Fourier Transform for Automotive Radar Applications


    Contributors:
    Longman, Oren (author) / Bilik, Igal (author)


    Publication date :

    2021-04-01


    Size :

    4072455 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Adaptive Radon–Fourier Transform for Weak Radar Target Detection

    Xu, Jia / Yan, Liang / Zhou, Xu et al. | IEEE | 2018





    Fast Implementation of Generalized Radon–Fourier Transform

    Ma, Ben / Zhang, Shunsheng / Jia, Wenkai et al. | IEEE | 2021