<title>Improved observable operator model for joint target tracking and classification</title>

Abstract

The Observable Operator Model (OOM) approach have been proposed as a better alternative to the Hidden Markov Model (HMM). However the basic modeling of OOMs assume that the data is generated by some discrete state variable which can take on one of several values which is unreasonable for most classification problems. Main limitation of existing OOM classification is that they require substantial training data, assumed to be similar to the data on which the algorithm is tested. In many applications the target is observed from multiple target-sensor orientations (or aspects), and the underlying feature information is highly aspect dependant and continuous variable. The multi-aspect target classification method presented based on continuous-valued Observable Operator Model (OOM), from which a full posterior distribution of a target class is inferred. It is possible to extend a discrete OOM as a continuous-valued OOM using a membership function. Further, predefined set of classes were used in training based joint target tracking and classification methods. These methods perform poorly, when new target present in the surveillance region which is not in the available class-set. In order to overcome this shortage, we propose an online training algorithm for OOM, which identifies new incoming target classes and add them into the available class-set. As the number of target class increases with the online learning procedure, there is a need for an adaptive class-set selection in order to reduce computational cost. An adaptive class-set approach for joint target tracking and classification is formulated via hypotheses testing, which reduces computation cost compared to calculating OOM likelihood for each target class. Simulation results are given to demonstrates the merits of continuous-valued Observable Operator Method (OOM) for target classification over discrete OOM, advantages of online training OOM and the efficiency of class-set adaptation algorithm.