Small Language Model enabled Autonomous agent for Language-Conditioned Cognitive Radar

Abstract:Modern radar systems require adapting their processing strategies in response to changing interference, clutter, and data availability. This paper introduces a framework for a small language model (SLM)-driven autonomous agent designed for language-conditioned cognitive radar, functioning as an intelligent controller for a suite of array signal processing tools. Given a natural-language command, the agent extracts radar-operation-related cues, selects an appropriate sequence of signal-processing methods, configures parameters, and invokes executable tools for numerical computation. Experiments with a synthetic uniform linear array (ULA) radar demonstrate that, given a natural-language command, the agent performs meaningful algorithm selection across diverse scenarios for sidelobe control, jammer suppression, multiple-null beamforming, coherent-source handling, and low-snapshot direction-of-arrival (DOA) estimation. Ablation results show that radar-specific prompting and physics-grounded tool execution are both required for reliable decisions and hallucination-free numerical results.

Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)

arXiv:2608.11596 [eess.SP]

(or arXiv:2608.11596v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2608.11596 Focus to learn more arXiv-issued DOI via DataCite

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