ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
On the optimal performance of collaborative position location
IEEE Transactions on Wireless Communications
Particle filtering: the need for speed
EURASIP Journal on Advances in Signal Processing
Digital Signal Processing
Square-root unscented Kalman filtering-based localization and tracking in the Internet of Things
Personal and Ubiquitous Computing
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The ad hoc network localization problem deals with estimating the geographical location of all nodes in an ad hoc network focusing on those nodes that do not have a direct way (e.g., GPS) to determine their own location. Proposed solutions to the ad hoc localization problem assume that nodes are capable of measuring received signal strength (RSSI) and/or are able to do coarse (sectoring) or fine signal angle of arrival (AoA) measurements. Existing algorithms exploit different aspects of such sensory data to provide either better localization accuracy or higher localization coverage. However, there is a need for a framework that could benefit from the interactions of nodes with mixed types of sensors. In this paper, we study the behavior of RSSI and AoA sensory data in the context of the ad hoc localization problem using both geometric analysis and computer simulations. We show which type of sensor is better suited for what type of network scenario. We study how nodes using either, both, or none of these sensors could coexist in the same localization framework. We then provide a general particle filtering framework, the first of its kind, that allows heterogeneity in the types of sensory data to solve the localization problem. We show that when compared to localization scenarios where only one type of sensor is used, our framework provides significantly better localization results. Furthermore, our framework not only provides a location estimate for each non-anchor, but it also provides an implicit confidence measure as to how accurate this estimate is. This confidence measure enables nodes to further improve on their location estimates using a local, iterative one-hop simple message exchange without having to rely on synchronized multi-phase operations like in traditional multilateration methods.