University of Twente Student Theses


Ranging and localisation error mitigation in indoor obstructed direct path conditions

Op 't Land, Sjoerd (2011) Ranging and localisation error mitigation in indoor obstructed direct path conditions.

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Abstract:This master thesis is part of the ‘Localisation in Smart Dust Sensor Networks’ project. Smart dust is the future vision of having many small, light, cheap, dependable, long-lasting, biodegradable network nodes that can even be carried by the wind. The ability of these network nodes to localise themselves is crucial to many applications. Lateration with Ultra-Wideband (UWB) Time of Flight (ToF) range (distance) measurements is widely regarded as the method of choice for localisation in smart dust networks. In practice, the performance of this localisation technique is impaired by Obstructed Direct Paths (ODPs). An obstruction delays or removes the detectable radio path, causing the real distance to be overestimated. These positively biased range measurements, in turn, cause localisation errors. In this thesis, we perform a survey of known ODP detection techniques, some of which are chiefly tested in simulation. All reviewed techniques consist in evaluating features: functions of one measured channel impulse response. Then we design a measurement set-up with a state-of-the-art UWB transceiver and physical obstacles, to test the known ODP detection techniques. By combining the features from each technique, we are able to estimate both the bias and the precision of each range measurement. Using this information, we can discard distance measurements that appear to be imprecise. This generally improves the localisation accuracy if the geometry (the spatial arrangement of nodes) is reasonable; if the geometry is bad, the localisation accuracy worsens slightly. Collaterally, we propose a new improvement on the existing leading edge detection, yielding a ranging accuracy in line-of-sight (LOS) conditions of 6cm mean absolute error, where the existing leading edge detection yields 8 cm accuracy.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
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