This thesis investigates out-of-distribution recognition for time-series data of pulsedradar signals. The classifier is a naive Bayesian classifier based on Gaussian mixturemodels and Dirichlet process mixture models. In the mixture models, we model thedistribution of three pulse features in the time series, namely radio-frequency in thepulse, duration of the pulse, and pulse repetition interval which is the time betweenpulses. We found that simple thresholds on the likelihood can effectively determine ifsamples are out-of-distribution or belong to one of the classes trained on. In addition,we present a simple method that can be used for deinterleaving/pulse classification andshow that it can robustly classify 100 interleaved signals and simultaneously determineif pulses are out-of-distribution. / Det här examensarbetet undersöker hur en maskininlärnings-modell kan anpassas för attkänna igen när pulserade radar-signaler inte tillhör samma fördelning som modellen är tränadmed men också känna igen om signalen tillhör en tidigare känd klass. Klassifieringsmodellensom används här är en naiv Bayesiansk klassifierare som använder sig av Gaussian mixturemodels och Dirichlet Process mixture models. Modellen skapar en fördelning av tidsseriedatan för pulserade radar-signaler och specifikt för frekvensen av varje puls, pulsens längd och tiden till nästa puls. Genom att sätta gränser i sannolikheten av varje puls eller sannolikhetenav en sekvens kan vi känna igen om datan är okänd eller tillhör en tidigare känd klass.Vi presenterar även en enkel metod för att klassifiera specifika pulser i sammanhang närflera signaler överlappar och att metoden kan användas för att robust avgöra om pulser ärokända.

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.

In this study, emitter identification techniques have been investigated and a schema has been proposed to solve the emitter identification problem in Electronic Warfare systems. Clustering technique, histogram based deinterleaving techniques and a continuous wavelet transform based deinterleaving technique have been reviewed. A receiver simulator software has been developed to test the performance of these techniques and to compare them against each other. To compensate the disadvantages of these techniques, a schema utilizing the beneficial points of them has been developed. With the modifications proposed a resultant schema has been obtained. Proposed schema uses clustering and deinterleaving together with other proposed modifications. Tests made through out this study have shown that this usage improves performance of emitter identification system. Hence, proposed schema can be used to identify the emitters in real EW systems.

In this work, a new deinterleaving algorithm that can be used as a part of an ESM system and its implementation by using an FPGA is studied. The function of the implemented algorithm is interpreting the complex electromagnetic military field in order to detect and determine different RADARs and their types by using incoming RADAR pulses and their PDWs. It is assumed that RADAR signals in the space are received clearly and PDW of each pulse is generated as an input to the implemented algorithm system. Clustering analysis and a new interpreting process is used to deinterleave the RADAR pulses. In order to implement the algorithm, FPGA is used for achieving a faster and more efficient system. Comparison of the new algorithm and the previous deinterleaving studies is done. The simulation results are shown and discussed in detail.

Within the defence industry, it is essential to be aware of threats in the environment. A potential threat can be detected by identifying certain types of emitters in the surroundings that are typically used in the enemies’ systems. An emitter’s type can be identified by having a receiver measuring radar pulses in the environment and analysing the pulses transmitted from that specific emitter. As several emitters usually transmit pulses in an environment, the receiver measures pulses from all of these emitters. In order to analyse the pulses from only one emitter, the pulses must be sorted into groups based on what emitter they are transmitted from. This sorting can for instance be performed by considering similarities and differences in the pulses’ features. This thesis investigates whether the change in the pulses’ Angle of Arrival (AOA) over time can be used for sorting the pulses. Such an approach can be useful in scenarios where signals from homogeneous emitters, that are similar in their features, need to be distinguished. In addition, by taking the change in AOA into consideration, rather than relying on the AOA itself, the approach has the potential of separating signals from emitters that overlap with respect to the AOA over time at some time step. A multiple-step clustering algorithm which is adapted from Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used for the pulse sorting. The algorithm is primarily evaluated in testing scenarios including homogeneous emitters whose pulses overlap with respect to the AOA at some time step. The goal is to divide the pulses into groups depending on what emitter they are transmitted from. The pulses involved in an overlap are typically not distinguishable and they should therefore not be assigned to any cluster. Signals received before and after an overlap are allowed to belong to different clusters even if they are from the same emitter. The algorithm was able to cluster signals properly and to identify the overlapping signals in testing scenarios where the emitters were placed in specific patterns. The performance worsened as the emitters were allowed to have any position and the number of emitters increased, which can imply that the algorithm performs poorly when the emitters are closely located. In order to determine whether, or to what extent, this approach is suitable for pulse sorting, the algorithm should be further evaluated in more testing scenarios. / Inom försvarsindustrin är det grundläggande att vara medveten om hot i ens omgivning. Ett möjligt hot kan upptäckas genom att identifiera särskilda typer av emittrar i omgivningen som brukar användas i en fiendes system. Genom att med en mottagare mäta radarpulser i omgivningen och sedan analysera en särskild emitters pulser kan denna emitters typ identifieras. I en omgivning är det normalt ett flertal emittrar som sänder ut signaler vilket gör att mottagaren mäter flera emittrars pulser samtidigt. För att kunna analysera pulserna från endast en särskild emitter måste pulserna sorteras i grupper baserat på vilken emitter de kommer ifrån. Sorteringen kan exempelvis baseras på likheter och skillnader mellan signalernas egenskaper. Detta projekt undersöker huruvida pulser kan sorteras baserat på förändringen i pulsernas ankomstvinkel över tid. Denna metod kan vara användbar då signaler från homogena emittrar ska separeras då dessa signaler har liknande egenskaper. Genom att göra sorteringen baserad på ankomstvinkelns förändring över tid, istället för att endast kolla på ankomstvinkeln, är det även möjligt att skilja på signaler vars ankomstvinklar överlappar vid något tillfälle över tid. En klustringsalgoritm uppbyggd i flera steg används för pulssorteringen. Denna algoritm är i grunden baserad på principerna från Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Algoritmen är huvudsakligen evaluerad på testscenarios med homogena emittrar vars pulsers ankomstvinkel överlappar vid något tillfälle. Målet är att dela in pulser i grupper efter vilken emitter de kommer ifrån. Pulser involverade i ett överlapp är normalt inte möjliga att särskilja och dessa pulser ska därför inte tillhöra något kluster. Signaler som mottages före och efter ett överlapp är tillåtna att höra till olika kluster även om de kommer från samma emitter. Algoritmen lyckades utföra klustringen och identifiera överlappande signaler i testscenarion då emittrarna placerats i särskilda mönster. Algoritmens prestanda försämrades då emittrarna tilläts ha godtyckliga positioner och antalet emittrar ökade. Detta kan innebära att metoden fungerar sämre när emittrarna är placerade nära varandra. För att avgöra huruvida denna metod är lämplig för pulssortering bör metoden utvärderas i flera testscenarion.

This thesis deals with an autonomous single-channel deinterleaving. An autonomous single-channel deinterleaving is a separation of the received sequence of impulses from more than one emitter to sequences of impulses from one emitter without a human assistance. Methods used for deinterleaving could be divided into single-parameter and multiple-parameter methods according to the number of parameters used for separation. This thesis primarily deals with multi-parameter methods. As appropriate methods for an autonomous single-channel deinterleaving DBSCAN and variational bayes methods were chosen. Selected methods were adjusted for deinterleaving and implemented in programming language Python. Their efficiency is examined on simulated and real data.