DopplerGLRTNet for Radar Off-Grid Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914329494814720 |
|---|---|
| author | Rouzoumka, Yadang Alexis Pinsolle, Jean Terreaux, Eugénie Morisseau, Christèle Ovarlez, Jean-Philippe Ren, Chengfang |
| author_facet | Rouzoumka, Yadang Alexis Pinsolle, Jean Terreaux, Eugénie Morisseau, Christèle Ovarlez, Jean-Philippe Ren, Chengfang |
| contents | Off-grid targets whose Doppler (or angle) does not lie on the discrete processing grid can severely degrade classical normalized matched-filter (NMF) detectors: even at high SNR, the detection probability may saturate at operationally relevant low false-alarm rates. A principled remedy is the continuous-parameter GLRT, which maximizes a normalized correlation over the parameter domain; however, dense scanning increases test-time cost and remains sensitive to covariance mismatch through whitening. We propose DopplerGLRTNet, an amortized off-grid GLRT: a lightweight regressor predicts the continuous Doppler within a resolution cell from the whitened observation, and the detector outputs a single GLRT/NMF-like score given by the normalized matched-filter energy at the predicted Doppler. Monte Carlo simulations in Gaussian and compound-Gaussian clutter show that DopplerGLRTNet mitigates off-grid saturation, approaches dense-scan performance at a fraction of its cost, and improves robustness to covariance estimation at the same empirically calibrated Pfa. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13546 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DopplerGLRTNet for Radar Off-Grid Detection Rouzoumka, Yadang Alexis Pinsolle, Jean Terreaux, Eugénie Morisseau, Christèle Ovarlez, Jean-Philippe Ren, Chengfang Signal Processing Off-grid targets whose Doppler (or angle) does not lie on the discrete processing grid can severely degrade classical normalized matched-filter (NMF) detectors: even at high SNR, the detection probability may saturate at operationally relevant low false-alarm rates. A principled remedy is the continuous-parameter GLRT, which maximizes a normalized correlation over the parameter domain; however, dense scanning increases test-time cost and remains sensitive to covariance mismatch through whitening. We propose DopplerGLRTNet, an amortized off-grid GLRT: a lightweight regressor predicts the continuous Doppler within a resolution cell from the whitened observation, and the detector outputs a single GLRT/NMF-like score given by the normalized matched-filter energy at the predicted Doppler. Monte Carlo simulations in Gaussian and compound-Gaussian clutter show that DopplerGLRTNet mitigates off-grid saturation, approaches dense-scan performance at a fraction of its cost, and improves robustness to covariance estimation at the same empirically calibrated Pfa. |
| title | DopplerGLRTNet for Radar Off-Grid Detection |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.13546 |