Network-centric architectures are defined by the complete absence of a traditional central data fusion site and also, in general, a central communication facility. Instead, the data fusion is performed at each network node and these nodes communicate on a strictly point-to-point basis. The network topology, which may be dynamic, is assumed to be unknown. These governing constraints imply a fault-tolerant, scalable, and modular system. However, such systems are prone to possible inconsistent fused estimates as a consequence of the well-known rumor propagation problem. The algorithmic challenge is to combat this problem without sacrificing the aforementioned benefits. This has led to the formulation of a technique known as Covariance Intersection (CI). Most recently, CI has been integrated with the Kalman filter to produce the Split CI algorithm - a general solution to decentralised data fusion in arbitrary communication networks. These algorithms have not yet been evaluated outside of a limited simulation environment. The purpose of this paper is to present a study of their relative performance in a hardware-based decentralised sensor network system.
The paper will describe a number of indoor experiments that involve tracking a ground target by means of multiple, networked, wall-mounted cameras. High precision ground truth target positions are available from a laser-tracking device. The experiments will evaluate Kalman, CI, and Split CI algorithm performance - measured in terms of consistency, convergence and accuracy - with respect to a range of static and dynamic network topologies.