Performance modeling for multisensor tracking and classification
Kuo‐Chu Chang, Eswar Sivaraman, Martin E. Liggins · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Multisensor Fusion allow us to combine information from sensors with different physical characteristics to enhance the understanding of our surroundings and provide the basis for planning and decision-making. Much effort has been made toward the development of building different types of fusion methodologies and architectures. However, it would be desirable if we could estimate the performance of fusion systems before we implement them. This paper presents a performance model to evaluate the multisensor tracking systems where both kinematics and classification components are considered. Secifically, we focus our effort on classification performance prediction by defining the local confusion matrix and global confusion matrix and develop an analytical method to estimate the probability of correct classification over time. Simulation results that support the analytic approaches are also included.