Observable operator model-based joint target tracking and classification

Abstract

In this paper, a new joint target tracking and classification technique based on Observable Operator Models (OOM) is considered. The OOM approach, which has been proposed as a better alternative to the Hidden Markov Model (HMM), is used to model the stochastic process of target classification. These OOMs afford both mathematical simplicity and algorithmic efficiency compared to HMM. Conventional classification techniques use only the feature information from target signatures. The proposed OOM based classification technique incorporates the target-to-sensor orientation together with a sequence of feature information from multiple sensors. The target-to-sensor orientation evolves over time and the target aspect is important in determining the target classes. The multi-aspect classification is modeled using OOM to handle unknown target orientation. This algorithm exploits the inter-dependency of target state and the target class, which improves both the state estimates and classification of each target. Measurement ambiguity is present in both kinematic and feature measurement and therefore, the OOM based classifier is integrated into the multiframe data association framework that is used to resolve measurement origin uncertainties. This technique enables one to overcome ambiguity in feature measurements while improving track purity. A two dimensional example demonstrates the merits of the proposed OOM based joint target tracking and classification algorithm.