Estimation and cancellation of interferences in automotive radar signals

Abstract

Radar sensors become typical components for automotive driver assistance systems and autonomous driving. The widespread use of this sensor type leads to an increasing risk of unwanted interferences. Those interferences can severely degrade the detection performance and cause sensor blindness. To overcome this problem, this paper describes a method to estimate and remove interfering signals in data of chirp sequence modulated radars. The estimation is derived theoretically and applied on simulation and measurement data.

Phenomenology of automotive radar interference

“…However, as stated by the authors, the accuracy of the proposed techniques in Bechter and Waldschmidt [27] and Bechter et al. [28] requires high calculation efforts and largely depends on the interference amplitude and the number of interfered samples: the higher the amplitude and the larger number of samples affected by interference, the better the accuracy of interference parameter estimation is. Here, an extra signal processing chain in the receiver with wideband LPF2 and STFT is proposed at the output of the mixer to analyse the spectrogram across the entire spectrum to extract a different set of interference parameters such as the start frequency, bandwidth, sweep time, chirp rate and idle time.…”

Radar Sensors for Autonomous Driving: Modulation Schemes and Interference Mitigation

“…However, zeroing requires a robust detection and raises additional frequency components; these artifacts can be reduced by introducing a window function at the time domain discontinuities [78] or a prediction of the missing data with autoregressive models [81]. It is also possible to estimate and afterwards cancel the interference component [82] from the time signals. This does not suppress the interference completely, but it does not introduce artifacts in the spectrum.…”

A DNN Autoencoder for Automotive Radar Interference Mitigation

“…Various signal processing techniques have been proposed to address the problem of mutual interference [6][7][8][9][10][11][12][13][14] in the frequency-modulated continuous wave (FMCW) radar or the chirp sequence (CS) radar [15]. Besides the traditional signal processing approaches, recent research results have shown that deep learning approaches can be used to solve this problem [16][17][18].…”