Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance.
We introduce SCORE-LM, which couples a small scatterer-conditioned operator-response encoder (SCORE) to an adapted local language model.
SCORE combines self-referenced complex trajectories, physical descriptors, and a selective state-space branch, with source-only self-supervision and directional fault inference.
Performance Achievements
On eight capture-excluded Rad-R fault recordings, it achieves state-of-the-art performance within the evaluated nine-model comparison: 88.39% mean capture recall and 88.20% four-fault macro-F1 at ten frames.
Its 39,520 radar inference coefficients are 119.7 times fewer than RadrNet-DS-CI's, while recall is 15.56 percentage points higher than this strongest competitor.
In a separate low-label protocol, SCORE reaches 71.58% recall with one labeled source window per class.
Compact Diagnosis and Maintenance Guidance
A nonlinear projector converts four frozen fault similarities into five soft tokens, linking compact diagnosis to class-conditioned maintenance guidance.
Language Adaptation
On 75 development questions covering 24 radar windows, language adaptation raises correct-fault answers from 45 to 62 (60.0% to 82.7%) relative to removing the co-trained adapters, while retaining the same projector.
SCORE-LM thus combines a compact radar specialist with a language interface for communicating fault-specific inspection guidance.