Deinterleaving RADAR emitters with optimal transport distances

TL;DR

This paper presents an unsupervised approach combining clustering and optimal transport distances to deinterleave complex RADAR signals, improving emitter identification in electronic intelligence.

Contribution

It introduces a novel hierarchical clustering method based on optimal transport distances for deinterleaving RADAR pulses, handling complex emitter behaviors.

Findings

Effective deinterleaving of complex RADAR signals demonstrated on simulated data

Hierarchical clustering with optimal transport outperforms traditional methods

Method handles complex emitter behaviors with high accuracy

Abstract

Detection and identification of emitters provide vital information for defensive strategies in electronic intelligence. Based on a received signal containing pulses from an unknown number of emitters, this paper introduces an unsupervised methodology for deinterleaving RADAR signals based on a combination of clustering algorithms and optimal transport distances. The first step involves separating the pulses with a clustering algorithm under the constraint that the pulses of two different emitters cannot belong to the same cluster. Then, as the emitters exhibit complex behavior and can be represented by several clusters, we propose a hierarchical clustering algorithm based on an optimal transport distance to merge these clusters. A variant is also developed, capable of handling more complex signals. Finally, the proposed methodology is evaluated on simulated data provided through a realistic simulator. Results show that the proposed methods are capable of deinterleaving complex RADAR signals.