FMCW Radar ARMA Reconstruction for Interference Mitigation
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Solution Overview
Problem
Radar interference in automotive applications, such as Advanced Driver-Assistance Systems (ADAS), prevents accurate detection and estimation of target objects due to unwanted signals from external sources, leading to issues like spurious lobes and phase errors.
Innovation Solution
Employing an Autoregressive Moving Average (ARMA) model to identify and zero out interference-affected radar samples, and reconstruct them using information from previous frames, thereby mitigating interference and preserving signal integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional radar signal processing is used, then the system is simple to implement, but interference from external radar sources causes spurious lobes and phase errors that prevent accurate target detection
Solution Approach 1:
The patent applies spectral estimation techniques to analyze the interference signal itself, extracting its characteristics (frequency, amplitude, phase) to then synthesize a cancellation signal. By studying the harmful interference in detail, the system converts our understanding of the interference into a beneficial cancellation mechanism that removes the spurious lobes and phase errors from the target detection.
2Measurement precision
If interference cancellation techniques are applied, then target detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent replaces traditional time-domain interference cancellation methods with frequency-domain spectral estimation techniques. By transforming the signal processing approach to the frequency domain using Fast Fourier Transform (FFT) and applying spectral analysis, the system achieves more efficient interference characterization and cancellation with reduced computational burden compared to time-domain adaptive filtering methods.
3Reliability
If multiple radar frames are processed to mitigate interference, then detection robustness improves, but processing time increases
Solution Approach 1:
The patent performs spectral estimation and interference characterization on previous radar frames before the actual target detection occurs. By pre-processing and storing the interference signal characteristics from historical frames, the system has the cancellation parameters ready when new frames arrive, enabling rapid interference removal without re-processing the entire signal chain for each frame.
Data Source
AI summary
Systems and methods for mitigating interference in Frequency-Modulated Continuous-Wave (FMCW) radars using an Autoregressive Moving Average (ARMA) model are described. In an illustrative, non-limiting embodiment, a device includes a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the device to: receive a first frame captured by a radar; receive a second frame, captured after the first frame, by the radar; and reconstruct zeroed samples in the second frame with information obtained from the first frame to mitigate interference in the second frame caused by another radar.