1ECE Department, McMaster University, Hamilton Ontario, Canada
2ECE Department, University of Connecticut, Storrs Connecticut
Multisensor data fusion is an emerging technology in which data from several sensing devices are combined such that the resulting information is significantly better than that obtained when these devices operate individually. Recent advances in sensor technologies, signal processing techniques, and improved processor capabilities make it possible for large amounts of data to be fused in real time. This allows the use of many sophisticated algorithms and robust mathematical techniques in data fusion. Moreover, data fusion has received significant attention for military applications. Such applications involve a wide range of expertise, including filtering, data association, out-of-sequence measurements, and sensor registration.
Filtering plays a vital role in data fusion by obtaining the state estimate from the data received from one or more sensors. Tracking filters [1, 2] can be broadly categorized as linear and nonlinear. The Kalman filter [1, 3] is a widely known recursive filter that is most suited for linear Gaussian systems. However, most systems are inherently nonlinear. The extensions of Kalman ...