Deinterleaving pulse trains with DBSCAN and FART

Detta är en Uppsats för yrkesexamina på avancerad nivå från Uppsala universitet/Avdelningen för systemteknik

Sammanfattning: Studying radar pulses and looking for certain patterns is critical in order to assess the threat level of the environment around an antenna. In order to study the electromagnetic pulses emitted from a certain radar, one must first register and identify these pulses. Usually there are several active transmitters in anenvironment and an antenna will register pulses from various sources. In order to study the different pulse trains, the registered pulses first have to be sorted sothat all pulses that are transmitted from one source are grouped together. This project aims to solve this problem, using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and compare the results with those obtained by Fuzzy Adaptive Resonance Theory (FART). We aim to further dig into these methods and map out how factors such as feature selection and training time affects the results. A solution based on the DBSCAN method is proposed which allows online clustering of new points introduced to the system. The methods are implemented and tested on simulated data. The data consists of pulse trains from simulated transmitters with unique behaviors. The deployed methods are then tested varying the parameters of the models as well as the number of pulse trains they are asked to deinterleave. The results when applying the models are then evaluated using the adjusted Rand index (ARI). The results indicate that in most cases using all possible data (in this case the angle of arrival, radio frequency, pulse width and amplitudes of the pulses) generate the best results. Rescaling the data further improves the result and tuning the parameters shows that the models work well when increasing the number of emitters. The results also indicate that the DBSCAN method can be used to get accurate estimates of the number of emitters transmitting. The online DBSCAN generates a higher ARI than FART on the simulated data set but has a higher worst case computational cost.

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