Automotive Radar Signal Processing and Interference Mitigation
Summary
Automotive radar systems have become a cornerstone of advanced driver-assistance and autonomous driving technologies, offering robust object detection and velocity estimation under diverse environmental conditions. Modern systems predominantly employ frequency-modulated continuous wave (FMCW) waveforms combined with multiple-input multiple-output (MIMO) antenna arrays to extract range, velocity and angular information via fast Fourier transform and beamforming. As the density of radars on roads increases, mutual interference and intentional jamming pose critical challenges: they can generate false alarms, mask genuine targets and degrade detection range. To address these issues, signal-processing strategies have evolved from simple time-domain gating and threshold adjustment to sophisticated approaches such as sparsity-based reconstruction, adaptive filtering, signal classification and machine-learning-driven parameter estimation. These methods seek to identify interference signatures, isolate or reconstruct corrupted segments of the echo signal and preserve target information while maintaining real-time performance on automotive-grade hardware. The global imperative for safer mobility and the proliferation of radar-based sensors in urban and rural environments underscore the need for scalable, low-cost interference mitigation solutions that integrate seamlessly with existing sensor-fusion architectures.
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Automotive Radar Signal Processing and Interference Mitigation publication trend
The graph below shows the total number of articles in automotive radar signal processing and interference mitigation across all publications each year (not limited to Nature Index journals).
Technical terms
Frequency Modulated Continuous Wave (FMCW) radar: A radar modality in which the transmitted frequency is varied linearly over time, enabling range measurement from the beat frequency between transmit and receive signals.
Moving Target Indicator (MTI): A processing technique that differentiates moving targets from stationary clutter by exploiting Doppler shifts, often using high-pass filtering or phase cancellation.
Autoregressive (AR) model: A statistical model that represents each sample of a time series as a linear combination of previous samples, used here to predict and replace corrupted radar signal segments.
Sparsity-based restoration: A signal-recovery approach that enforces a solution with only a few nonzero components, formulating interference mitigation as an optimisation problem with ℓ1-norm regularisation.
References
- An MTI-Like Approach for Interference Mitigation in FMCW Radar Systems. IEEE Transactions on Aerospace and Electronic Systems (2023).
- Incoherent Interference Detection and Mitigation for Millimeter-Wave FMCW Radars. Remote Sensing (2022).
- Autoregressive Model-Based Signal Reconstruction for Automotive Radar Interference Mitigation. IEEE Sensors Journal (2020).
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