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.