A multi-sensor inference and data fusion method for tracking small, manoeuvrable maritime craft in cluttered regionsy
Daniel E. Clark, Dave Miller, E. H. Amadou Gning, Simon Julier · 2013
We present an inference and data fusion method for tracking maritime Fast Inshore Attack Craft (FIACs) using multiple sensors. The scenario addressed encompasses littoral, counter-piracy and maritime constabulary operations. The problem space is characterised by mixed sensor modalities, non-stationary and spatially-varying non-Gaussian clutter, intermittent observations and a high false alarm rate. Our method combines the Probability Hypothesis Density (PHD) lter for multi-target Bayesian inference with Generalised Covariance Intersection (GCI) for decentralised data fusion. We outline the development and testing of our solution using electro-optical and radar observations of marine trac in the Solent. These data are complemented by ground truth positional data of marine trac including high-frequency positional estimates of two representative FIACs. Our system has been deployed both oine and in real time. We carry out a number of experiments designed to show the ecacy of the algorithms in representative scenarios. The performance of our algorithms is quantied using multi-target inference metrics. We show that the combination of PHD and GCI has many advantages over traditional inference and fusion methods, particularly in cluttered environments.