Development of Multi-functional Control Architecture for Multisensor Surveillance Systems

Abstract

Multisensor approach is often utilized in modern surveillance systems because of its abilities to provide complementary and overlapping coverage on targets. In order to generate target tracks and estimates, the sensor data need to be fused. While a centralized and hierarchical processing approach is theoretically optimal, there are significant advantages in decentralizing the fusion operations over multiple processing nodes. This paper discusses decentralized and heterarchical control architectures, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on fusions and estimates. A decentralized multisensor data fusion and estimation algorithm with nonlinear information filter were developed for each sensor node for effective information gathering, filtering and estimation along the desired trajectory. The dynamic systems were mathematically modelled and simulated. The simulation results show that the developed architecture satisfies stochastic stability criteria, manifests excellent tracking and filtering properties than the convectional architecture.