Constrained Clustering for Frequency Hopping Spread Spectrum Signal Separation

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 this user important to insure communication link integrity and interference mitigation.

The identification of a user involves the grouping of that users signal transmissions, and the separation of those transmission from transmissions of other users in a shared frequency band.

Traditional signal separation methods often require difficult to obtain hardware fingerprinting characteristics or approximate geo-location estimates.

This work will consider the characteristics of FHSS signals that can be extracted directly from signal detection.

From estimates of these hopping characteristics, novel source separation with classic clustering algorithms can be performed.

Background knowledge derived from the time domain representation 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 (SNR), for example.

Additionally, prior work does not fully cover the implications of detecting and estimating FHSS signals using image segmentation on a Time-Frequency (TF) waterfall.

This work will compare several methods of FHSS signal detection, and discuss how each method may effect estimation accuracy and signal separation quality.

The use of constraint-based clustering is shown to provide higher clustering accuracy, resulting in more reliable separation and identification of active users in comparison to traditional clustering methods.