Offline Direction Clustering of Overlapping Radar Pulses from Homogeneous Emitters

Abstract: 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.