Physics-guided deep metric learning with continuous time embeddings for open-world radar pulse de-interleaving

The paper introduces a physics‑guided deep metric learning approach for open‑world radar pulse de‑interleaving, leveraging continuous Time‑of‑Arrival sinusoidal positional encodings to model physical inter‑pulse durations. It builds on a transformer‑based framework, optimizing network parameters solely with Supervised Contrastive learning and employing physics‑based priors—PRI consistency and AoA continuity—for validation and checkpoint selection via unsupervised HDBSCAN clustering. This method aims to improve de‑interleaving performance in dense, contested electromagnetic environments where classical techniques falter.

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