Target Tracking in Decentralised Networks with Bandwidth Limitations
Abstract: The number and the size of sensor networks, e.g., used for monitoring of public places, are steadily increasing, introducing new demands on the algorithms used to process the collected measurements. The straightforward solution is centralised fusion, where all measurements are sent to a common node where all estimation is performed. This can be shown to be optimal, but it is resource intensive, scales poorly, and is sensitive to communication and sensor node failure. The alternative is to perform decentralised fusion, where the computations are spread out in the network. Distributing the computation results in an algorithm that scales better with the size of the network and that can be more robust to hardware failure. The price of decentralisation is that it is more difficult to provide optimal estimates. Hence, a decentralised method needs to be designed to maximise scaling and robustness while minimising the performance loss. This MSc thesis studies tree aspects of the design of decentralised networks: the network topology, communication schemes, and methods to fuse the estimates from different sensor nodes. Results are obtained using simulations of a network consisting of radar sensors, where the quality of the estimates are compared(the root mean square error, RMSE) and the consistency of the estimates (the normalised estimation error squared, NEES). Based on the simulation, it is recommended that a 2-tree network topology should be used, and that estimates should be communicated throughout the network using an algorithm that allows information to propagate. This is achieved by sending information in two steps. The first step is to let the nodes send information to their neighbours with a certain frequency, after which a fusion is performed. The second step is to let the nodes indirectly forward the information they receive by sending the result of the fusion. This second step is not performed every time information is received, but rather at an interval, e.g., every fifth time. Furthermore, 3 sub-optimal methods to fuse possibly correlated estimates are evaluated: Covariance Intersection, Safe Fusion, and Inverse Covariance Intersection. The outcome is to recommend using Inverse Covariance Intersection.
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