Unmanned Aerial Vehicles(UAV) swarm is a rapidly developing field, and with it comes the need to identify the swarm based on observations. The problem of trajectory clustering is put forward in the identification of UAV swarms, especially modularized UAV swarms. We propose a new method of Network Integrated trajectory clustering(NIT) to solve the trajectory clustering problem in a fast-changing and chaotic environment which requires a quick response, fault tolerance, and accuracy. The experiment results prove the flexibility and adaptability of the NIT method towards various demands and multi-dimensional data. Moreover, the algorithm proposed based on the method shows priority over the other three trajectory clustering methods(DTW, Fréchet distance, GMM) on the accuracy, and fault tolerance in clustering swarm trajectories. The method raised in this paper is an innovation to both multi-agent systems identification and trajectory clustering methods.

A fuzzy feature of weather radar echo was introduced as the measurement of horizontal texture of echo image, and the local mean of the second lowest level was taken as the measurement of vertical echo scale. An algorithm of weather radar base reflectivity noise filtering was proposed based on Support Vector Machine (SVM). The results show that the echoes of non-precipitation such as ground clutter (GC), clear sky, abnormal propagation (AP) can be removed effectively, especially for the mixing echoes of precipitation and non-precipitation coexisted together, the algorithm works pretty well to identify the two kinds of echoes that mean different weathers.2008 Congress on Image and Signal Processing 978-0-7695-3119-9/08 $25.00

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