Automotive Radar Interference Mitigation Using Adaptive Noise Canceller
Not on the map. Why?
Abstract (arXiv)
Interference among frequency modulated continues wave automotive radars can either increase the noise floor, which occurs in the most cases, or generate a ghost target in rare situations. To address the increment of noise floor due to interference, we proposed a low calculation cost method using adaptive noise canceller to increase the signal-to-interference ratio. In a quadrature receiver, the interference in the positive half of frequency spectrum is correlated to that in the negative half of frequency spectrum, whereas the beat frequencies from real targets are always present in the positive frequency. Thus, we estimated the power of the negative frequency as an indication of interference, and fed the positive frequency and negative frequency components into the primary and reference channel of an adaptive noise canceller, respectively. The least mean square algorithm was used to solve for the optimum filter solution. As a result, both the simulation and experiment showed a good interference mitigation performance.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Nov 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 1.
4 papers.
- 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 58
- 2023Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Reviewcited by 22
- 2022Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigationcited by 6
- 2024RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigationcited by 1
- 2024RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation16 Jul 2024
- 2023Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review8 Dec 2023
- 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review20 Apr 2023
- 2022Resource-efficient Deep Neural Networks for Automotive Radar Interference Mitigation25 Jan 2022