No abstract

The actual paper introduces a concept for localization of mobile nodes in a wireless sensor network. The realized algorithms are characterized by minimal complexity and high robustness even in networks with scarce resources. The implementation on simple, low-power embedded systems is possible without difficulty. An application of the concept for vehicle tracking illustrates the very good performance of the approach. Drahtloses Sensornetzwerk zur robusten Lokalisierung von mobilen Knoten mit minimaler Komplexitä t.Der vorliegende Artikel beschreibt ein Konzept zur Lokalisierungen von Netzwerkknoten in einem drahtlosen Sensornetzwerk. Die realisierten Ortungsalgorithmen zeichnen sich durch geringen Rechenaufwand und große Robustheit auch bei dü nn besetzten Netzwerken aus. Eine Umsetzung auf einfachen, stromsparenden eingebetteten Systemen ist problemlos mö glich. Die Performanz des Verfahrens wird anhand eines Systems zur Fahrzeugortung demonstriert.

No abstract

This paper introduces a generalized framework for positioning mobile nodes in a wireless network. The transformation of the range data measured between pairs of network nodes into a spatial position is a very challenging task if the range measurements are distorted. Typical distortions in wireless ranging systems are caused by an imperfect clock synchronization or by multipath reflection. The positioning method proposed in this paper overcomes problems of the traditional circular or hyperbolic methods and allows for the detection and elimination of distorted measurements. The positioning is done in a non-Markovian manner, which is an advantage over other available methods, especially the Kalman-based positioning methods, since linear assumptions in the dynamics are avoided. Another advantage of the proposed method is that the two tasks positioning and smoothing are carried out separately. Hence, smoothing of the position data, e.g. by means of a Kalman filter, is possible without the well documented problems in conventional methods induced by the usually applied simplifying assumptions of linear dynamics. The very good performance of the presented approach is demonstrated by real life results obtained in industrial environments.