Dynamic airborne sensor resource management for ground moving target tracking and classification

In this paper we describe an algorithm for dynamic sensor resource management (SRM) of a multimode sensor for tracking and classification of ground moving targets. The sensor is an airborne mechanically steered radar that operates in ground moving target indicator (GMTI) and high range resolution (HRR) modes. The SRM problem is cast into an optimization framework with a multiobjective criteria, namely, minimize tracking kinematic and target classification uncertainties where Markov models and decision trees are utilized, respectively, to dynamically capture (at the frame revisit rate) the tracking and classification information state. The SRM algorithm derives a sensor collection schedule on a frame by frame basis utilizing tracking updates from a GMTI multiple hypothesis tracker (MHT) and classification updates from an HRR 1D automatic target recognition (ATR) algorithm. Preliminary results based on a simulated ground target scenario are presented demonstrating the inherent trade-offs between the two radar modes and the multiobjective SRM criteria