Image fusion for tracking manoeuvring targets

Abstract

It is known that improvements in target tracking can be achieved by using multiple sensors. Most commonly, the individual measurement sequences are merged using a variant of linear algorithms. The approach proposed here differs from the conventional one in that nonlinear methods of data fusion are proposed to account for the peculiarities of the different measurement categories. This technique, called complementary fusion, is illustrated with the problem of tracking an agile target. It is shown that complementary fusion not only leads to higher fidelity tracking, but it also permits the more efficient utilization of the primary sensor.