On the Multiangle Discrete Fractional Fourier Transform

The efficiently computed multiangle centered discrete fractional Fourier transform (MA-CDFRFT) [1] has proven as a useful tool for time-frequency analysis; however, its scope is limited to the centered discrete fractional Fourier transform (CDFRFT). Meanwhile, extensive research on the standard DFRFT has lead to a better understanding of this transform as well as numerous possible choices for eigenvectors for implementation. In this letter we present a simple adaptation of the MA-CDFRFT which allows us to efficiently compute its standard counterpart, which we call the multiangle DFRFT (MA-DFRFT). Furthermore, we formalize the symmetries inherent to the MA-CDFRFT and MA-DFRFT to halve the number of FFTs needed to compute these transforms, paving the way for applications in resource constrained environments.

FMCW Radar Interference Mitigation based on the Fractional Fourier Transform

In this paper, we propose a novel method for frequency modulated continuous wave (FMCW) radar mutual interference mitigation based on the discrete fractional Fourier transform (DFrFT). Interference chirps are detected and mitigated by compression and zeroing in the fractional domain. We provide an efficient implementation that can deal with multiple interferers, where we perform consecutive DFrFTs utilizing its angle-additivity property. For that purpose, we generalize and reduce the computational complexity of the multi-angle centered discrete fractional Fourier transform [1]. Our algorithm is designed to be simple and fast such that it can be implemented in hardware. We evaluate our algorithm on a synthetic I/Q-modulated dataset and outperform reference methods in terms of the mean squared error, signal-to-interference-plus-noise ratio, error vector magnitude, true positive rate, false alarm rate and F1-score.

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

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.