In automotive radar systems, the range and Doppler velocity of vehicles surrounding the radars can be estimated by performing a fast Fourier transform (FFT) on the processed received signals reflected by the vehicles. The trade-off between unambiguity for estimation and resolution in FFT-based estimation methods can be broken with low computational complexity by introducing the Chinese remainder theorem (CRT). However, there are two challenges in CRT-based methods: the additional target association procedure and the error propagation drawback. In this study, a novel multi-waveform radar frame structure is proposed to facilitate the use of the CRT. Based on the frame structure, a corresponding CRT-based target association method is proposed to eliminate ghost targets. Moreover, a generative adversarial neural network (GAN)-based target association method is proposed to further address the error propagation drawback. Simulation results show the robustness of the GAN-based method, with an outstanding performance compared to other rule-based methods, even in severe error scenarios.


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    Title :

    GAN-CRT: A Novel Range-Doppler Estimation Method in Automotive Radar Systems


    Contributors:


    Publication date :

    2020-05-01


    Size :

    344058 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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