FHSS Signal Separation using Constrained Clustering

Frequency Hopping Spread Spectrum (FHSS) signaling is used across many devices operating in both regulated and unregulated bands. In either situation, if there is a malicious device operating within these bands, or more simply a user operating out of the required specifications, the identification and separation of this user is important to insure communication link integrity and interference mitigation. Previous signal separation methods often require difficult to obtain hardware fingerprinting characteristics or rough geolocation estimations. This work will consider the detection based characteristics of FHSS signals in addition to background knowledge that is more freely available as a result of spectrum sensing. From estimates of these hopping characteristics alone, novel source separation with classic clustering algorithms can be performed. Background knowledge derived from temporal properties of received waveforms can improve these clustering methods with the novel application of cannot-link pairwise constraints to signal separation. For equivalent clustering accuracy, constraint-based clustering tolerates higher parameter estimation error, caused by diminishing received signal to noise ratio.