Re-Excitable Response Representations from a Single-Waveform Radar Observation

Pulsed radar measurements are intrinsically conditioned on the transmitted waveform, and the same scene can therefore produce substantially different observations under different excitation conditions. We study radar re-excitation: predicting how a scene observed once under a source waveform can be measured under another known waveform, without physical re-acquisition. The central challenge is that the source may not adequately capture response directions that become important under the query waveform, so source consistency alone is insufficient for reliable prediction. We address this ambiguity with a Re-Excitable Response Representation constructed from a single source observation. The representation anchors measurement-supported content to a source-evidence response, organizes unresolved content through shared propagation trajectories and compact event-aligned responses, and performs observability-conditioned closed-form completion with structural intervention restricted to source-weak directions. The completed response can subsequently be re-excited under known waveform queries without requiring target-waveform scene observations. Across comprehensive synthetic benchmarks and real-world tests, our method achieves the lowest MSE and highest SSIM among six physics-guided and learning-based baselines. Re-excited measurements further enable two frozen inversion models to approach native-waveform performance, demonstrating a reusable interface between varying sensing waveforms and downstream physical inference.