@inproceedings{overdevest_deep_2024,

title = {Deep Unfolding for Sparse Distance Recovery in PMCW MIMO Automotive Radar: 21st European Radar Conference, EuRAD 2024},

author = {J. Overdevest and J. Ji and A. G. C. Koppelaar and A. Pandharipande and H. J. W. Belt and R. J. G. Van Sloun},

url = {https://www.scopus.com/pages/publications/85210804447},

doi = {10.23919/EuRAD61604.2024.10734898},

year  = {2024},

date = {2024-11-01},

urldate = {2024-11-01},

booktitle = {2024 21st European Radar Conference, EuRAD 2024},

pages = {31–34},

abstract = {Phase-Modulated Continuous Wave (PMCW) radars have attracted significant attention due to advances in mm-wave technology, waveform design, and digital signal processing. The de facto technique for distance estimation in PMCW radar receivers is matched filtering with a bank of correlators. There is an inherent trade-off between support for multiple antennas (MIMO support), sequences with good correlation properties and the maximum achievable unambiguous range/velocity. An important challenge in PMCW MIMO radars is to design receivers that result in low sidelobe levels in the range domain, for a given choice of sequences. In this paper, we propose a novel range processing scheme by formulating an optimization problem with l1-norm regularization that promotes sparse distance estimates. To solve this, we propose deep unfolded FISTA and ADMM algorithms for distance sidelobe suppression and restoration of the orthogonality of the transmitted codewords. We show that the proposed method achieves better dynamic range compared to the traditional matched filtering approach.},

note = {Publisher: Institute of Electrical and Electronics Engineers},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wei_score-based_2024,

title = {Score-based Generative Modeling for Interference Mitigation in Automotive FMCW Radar: 21st European Radar Conference, EuRAD 2024},

author = {X. Wei and J. Overdevest and J. Li and J. Youn and S. Ravindran and R. J. G. Van Sloun},

url = {https://www.scopus.com/pages/publications/85210853997},

doi = {10.23919/EuRAD61604.2024.10734954},

year  = {2024},

date = {2024-11-01},

urldate = {2024-11-01},

booktitle = {2024 21st European Radar Conference, EuRAD 2024},

pages = {27–30},

abstract = {Automotive radar interference is a growing problem as automotive radars proliferate in advanced driver assistance systems and autonomous driving. Numerous studies have been proposed to address interference mitigation based on hand-crafted priors, like sparsity-based techniques, or through purely data-driven approaches. However, their effectiveness is often compromised when these representations fail to accurately reflect the statistical characteristics of the interfering radar parameters in dynamic scenarios. In this work, we propose a new method that treats interference mitigation as a source separation problem. We leverage score-based generative networks to explicitly learn the interfering radar parameters. These learned parameters are subsequently combined with Maximum-A-posteriori estimation, allowing for an algorithm with enhanced performance. We demonstrate that our algorithm outperforms the baselines in signal-To-noise ratio.},

note = {Publisher: Institute of Electrical and Electronics Engineers},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{youn_interference_2024,

title = {Interference Mitigation Evaluation Methodology for Automotive Radar},

author = {J. Youn and J. Li and R. Wu and J. Overdevest},

url = {https://ieeexplore.ieee.org/document/10734960},

doi = {10.23919/EuRAD61604.2024.10734960},

year  = {2024},

date = {2024-09-01},

urldate = {2024-09-01},

booktitle = {2024 21st European Radar Conference (EuRAD)},

pages = {115–118},

abstract = {Interference mitigation is crucial to restore the degraded detection performance of interfered radars. For developing advanced interference mitigation methods, a well-defined evaluation methodology is required to assess the performance of different methods thoroughly and appropriately. In this paper, we propose to evaluate interference mitigation methods by measuring detection performance and signal-to-noise ratio in the range-Doppler domain. We especially suggest measuring the performance analytically without involving any detector but based on the estimated probability density functions of the target and background to isolate the effect of the detector in the performance evaluation. The time-domain thresholding and time-frequency domain thresholding methods are compared to validate the proposed methodology on simulated data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{de_oliveira_fusion_nodate,

title = {Fusion Model Using a Neural Network and MLE for a Single Snapshot DOA Estimation with Imperfection Mitigation textbar Request PDF},

author = {M. L. L. De Oliveira and M. J. G. Bekooij},

url = {https://www.researchgate.net/publication/376925649_Fusion_Model_Using_a_Neural_Network_and_MLE_for_a_Single_Snapshot_DOA_Estimation_with_Imperfection_Mitigation},

doi = {10.1109/RADAR54928.2023.10371066},

year  = {2023},

date = {2023-10-01},

urldate = {2023-10-01},

booktitle = {ResearchGate},

abstract = {Request PDF textbar On Nov 6, 2023, Marcio L. Lima De Oliveira and others published Fusion Model Using a Neural Network and MLE for a Single Snapshot DOA Estimation with Imperfection Mitigation textbar Find, read and cite all the research you need on ResearchGate},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{overdevest_signal_2023,

title = {Signal Reconstruction for FMCW Radar Interference Mitigation Using Deep Unfolding: 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023},

author = {J. Overdevest and A. G. C. Koppelaar and M. J. G. Bekooij and J. Youn and R. J. G. Sloun},

url = {https://www.scopus.com/pages/publications/86000372404},

doi = {10.1109/ICASSP49357.2023.10096297},

year  = {2023},

date = {2023-05-01},

urldate = {2023-05-01},

booktitle = {ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

abstract = {Removal of frequency-modulated continuous wave (FMCW) interference by zeroing corrupted samples causes significant distortions and peak power losses in the range-Doppler map. Existing methods aim to diminish these distortions by utilizing data from one dimension to reconstruct the corrupted samples, which do not perform well when a large number of samples are interfered and have difficulty recovering weak target signals.In this paper, model-based deep learning interference mitigation algorithms, called ALISTA and ALFISTA, are presented that reduce these artifacts by leveraging the full integration gain using all uncorrupted fast-time and slow-time samples. Simulations with 50% corrupted samples show that target peak power loss and velocity peak-to-sidelobe ratio (VPSR) with a 20-layer ALFISTA improves with 5.5 and 9.6 dB compared to zeroing. Furthermore, significant improvements in precision and recall are observed, even when large amounts (50-80%) of samples are missing.},

note = {Publisher: Institute of Electrical and Electronics Engineers},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}