Topics in matrix analysis
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Location errors in wireless embedded sensor networks: sources, models, and effects on applications
ACM SIGMOBILE Mobile Computing and Communications Review
An Analysis of Error Inducing Parameters in Multihop Sensor Node Localization
IEEE Transactions on Mobile Computing
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
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Wireless systems already provide time delay and signal strength measurements and the future may see antenna arrays that provide directional information. All these may be used for positioning. Although the statistical accuracy of different positioning methods is well studied, the systematic error effects, which arise, for example, from errors in sensor (node) location, network synchronization, or the path loss model, are not. This study fills this gap providing a unified error-propagation-law-based tool to analyze measurement and systematic error effects. The considered positioning systems, which are compared based on the developed framework, are the hyperbolic (time-delay-based), direction finding (DF), received signal strength (RSS), and relative RSS (RRSS) location systems. The obtained analytical results verify our intuitive expectations; the hyperbolic methods are sensitive to errors in network synchronization, RRRS methods to channel modelling errors, whereas DF methods are rather insensitive to systematic errors. However, the bias of DF methods is at its largest if the sensor location error is perpendicular to the line joining the sensor and the source. If the methods are compared based on overall accuracy, hyperbolic methods may be preferred in large sized networks, whereas the DF and RRSS methods may provide better accuracy in small sized networks. However, RRSS systems require a dense network in order to provide reliable results.