End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation

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Abstract

In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation. Instead of training NNs to regress from interfered to clean radar signals as in previous work, we train NNs directly on object detection maps. We do so by performing a continuous relaxation of the cell-averaging constant false alarm rate (CA-CFAR) peak detector, which is a well-established algorithm for object detection using radar. With this new training objective we are able to increase object detection performance by a large margin. Furthermore, we introduce separable convolution kernels to strongly reduce the number of parameters and computational complexity of convolutional NN architectures for radar applications. We validate our contributions with experiments on real-world measurement data and compare them against signal processing interference mitigation methods.
Original languageEnglish
Title of host publication2023 IEEE International Radar Conference (RADAR)
Number of pages6
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Radar Conference: RADAR 2023 - Sydney, Australia
Duration: 6 Nov 202310 Nov 2023

Conference

Conference2023 IEEE International Radar Conference
Abbreviated titleRADAR 2023
Country/TerritoryAustralia
CitySydney
Period6/11/2310/11/23

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