Ad hoc solutions for tracking and providing navigation support to emergency response teams is an important and safetycritical challenge. We propose a navigation system based on a combination of foot-mounted inertial sensors and ultrasound beacons. We evaluate experimentally the performance of our dead reckoning system in different environments and for different trail topologies. The inherent drift observed in dead reckoning is addressed by deploying ultrasound beacons as landmarks. We study through simulations the use of the proposed approach in guiding a person along a defined path.Simulation results show that satisfactory guidance performance is achieved despite noisy ultrasound measurements, magnetic interference and uncertainty in ultrasound node locations. The models used for the simulations are based on experimental data and the authors' experience with actual sensors. The simulation results will be used to inform future development of a full real time system.

In this paper we identify the common techniques and technologies that are enabling location identification in a ubiquitous computing environment. We also address the important parameters for evaluating such systems. Through this survey, we explore the current trends in commercial products and research in the area of localization. Although localization is an old concept, further research is needed to make it really usable for ubiquitous computing. Therefore, we indicate future research directions and address localization in the framework of our Smart Surroundings project.

In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis confirms our claim that 'signal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the move'. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of WLAN signal's RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements in a diverse set of conditions (indoors in different buildings and outdoors-city center, parking lot, university campus etc.,) and tested against the same data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support.

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This paper presents two algorithms, non-linear regression and Kalman filtering, that fuse heterogeneous data (pseudorange and angle-of-arrival) from an ultra-wideband positioning system. The performance of both the algorithms is evaluated using real data from two deployments, for both static and dynamic scenarios. We also consider the effectiveness of the proposed algorithms for systems with reduced infrastructure (lower deployment density), and for lower-complexity sensing platforms which are only capable of providing either pseudorange or angle-of-arrival.