Automotive radar interference mitigation using a sparse sampling approach

Abstract

Abstract-The application of radar sensors for driver assistance systems and autonomous driving leads to an increasing probability of radar interferences. Those interferences degrade the detection capabilities and can cause sensor blindness. This paper uses a realistic road scenario to address the problems of a common countermeasure that simply removes interferenceaffected parts of time domain radar signals and thereby introduces a gap. The paper solves the problem with the application of a sparse sampling signal recovery algorithm that is also used for compressed sensing problems. It is shown that the signal recovery can clearly overcome the shortcomings of just removing interfered signal parts. In the end of the paper, the applicability of the used algorithm is verified with measured radar data.

An Interference Mitigation Technique for FMCW Radar Using Beat-Frequencies Interpolation in the STFT Domain

“…Among them: 1) zeroing or inverse windowing the interferencecontaminated parts of the signal in the time domain as in [10] and [11]. Inverse windowing the detected interference regions was proposed in [8]; 2) using waveform-diversity and receiver-architecturediversity techniques to avoid the interference (e.g., frequency ramp modulation [12], frequency hopping [13], and [14], digital beam forming for interference suppression [15]; 3) interference reconstruction and cancelation techniques [16]; 4) sparse sampling techniques in [17] (where interference detection is done by monitoring target peakpower threshold levels against the interference-induced noise, then mitigation is done by reconstructing the interference-free signal using a sparse-signal recovery algorithm); and-most recently-in [18]. While zeroing a part of the beat-frequency signal is the simplest interference suppression method, it causes signal phase discontinuity, which results in-after performing the range-compression FFT-target-response broadening in range and high-residual sidelobes.…”

Effort Considerations of Compressed Sensing for Automotive Radar

“…Spending more iterations, e.g., 10 ( ), does not change the spectrum significantly. The advantage to reconstruct the missing samples instead of just setting them to zero is shown in [6]. Fig.…”

“…In one case an interferer disturbed a couple of samples leading to an enhanced noise floor. The affected data are detected and reconstructed as introduced in [6]. In the second example the data rate of the radar signal is reduced as shown in [7] and [8].…”

Synchronous and Asynchronous Radar Interference Mitigation

“…To avoid any ringing artifacts in the processed radar data, the neighborhood of affected samples may be smoothed using a window function [6]. Alternatively one may fill the gaps with sparsity-based methods or interpolation [7]. Note that, all these methods depend on precise detection of interference location and duration.…”