Optimal nonlinear filtering for track-before-detect in IR image sequences

The 3D matched filter proposed by Reed et al. and its generalizations provide a powerful processing technique for detecting moving low observable targets. This technique is a centerpiece of various track-before-detect (TBD) systems. However, the 3D matched filter was designed for constant velocity targets and its applicability to more complicated patterns of target dynamics is not obvious. In this paper the 3D matched filter and BAVF are extended to the case of switching multiple models of target dynamics. We demonstrate that the 3D matched filtering can be cast into a general framework of optimal spatio-temporal nonlinear filtering for hidden Markov models. A robust and computationally efficient Bayesian algorithm for detection and tracking of low observable agile targets in IR Search and Track (IRST) systems is presented. The proposed algorithm is fully sequential. It facilitates optimal fusion of sensor measurements and prior information regarding possible threats. The algorithm is implemented as a TBD subsystem for IRST, however the general methodology is equally applicable for other imaging sensors.