Incremental clustering applied to radar deinterleaving: a parameterized FPGA implementation

Abstract

ICED (Incremental Clustering of Evolving Data) is a novel incremental clustering algorithm designed for data whose characteristics change over time. ICED is an unsupervised clustering technique that assumes no prior knowledge of the incoming data, and supports removing clusters that contain stale data. The user controls the FPGA implementation through a combination of compile time parameters (number of clusters) and run time parameters (distance threshold, fade cycle length). ICED has been applied to a radar application: pulse deinterleaving. ICED is the first implementation of incremental clustering on an FPGA of which we are aware. The implementation runs 39 times faster than an equivalent C implementation on a 3GHz Intel Xeon processor, and is capable of processing radar data in real time.