Positioning and Tracking in Asynchronous Wireless Sensor Networks

This thesis deals with the problem of locating mobile nodes in anasynchronous wireless communication network, i.e., a wireless network of mobileterminals where some or all nodes do not have access to a global time reference.A strong focus lies on reducing the complexity associated with straight forwardclassical algorithms of node coordinate estimation. The thesis is based on anumber of previously published papers, listed in the introductory chapter.Two complexity reducing data preprocessing methods are presented. Bothpreprocessors achieve a complexity reduction through a cancellation of unknownclock-offsets from the estimation problem. Based on a concept of invariantpreprocessors, we show how the individual unknown clock-offsets at some or allnodes in the network may be discarded from the estimation problem, without anydegradation of the asymptotic performance bounds of the positioning problem.We further present two, fully distributed, sub-optimal positioning algorithmsthat operate on a set of asynchronous delay measurements. The first, called thekernel algorithm, reduces complexity by a divide and conquer approach. Thesecond algorithm is based on a mechanical analogy of the positioning problem. Weevaluate the performance of both algorithms, in terms of the mean-squaredpositioning error, by computer simulation. The performance of the kernelalgorithm is found to lie on the order of the delay measurement accuracy, whilethe second algorithm is shown to attain the Cram\ue9r-Rao lower bound under a setof reasonable assumptions.In the last part of the thesis, a novel tracking filter is proposed to reducethe complexity associated with tracking maneuvering objects in a wirelessnetwork. The tracking filter is based on a classical Kalman filter, but usesadditional information, supplied by the tracked node, to aid in the trackingprocess. One drawback associated with this type of approach to tracking is thepossibility of an unstable filter. We argue that the implementation can be maderobust using very simple alterations. Further, we argue that the classicalmean-squared-error performance measure is not fully appropriate for delaysensitive applications, and introduce a novel performance measure called thetime margin measure, suitable for evaluation of tracking algorithms that operateunder latency constraints. We discuss the merits of our proposed trackingfilter, with respect to this new performance measure, as compared to a classicalKalman implementation