Abstract
Position location estimation in sensor networks is a valuable supplement since it supports the deployment of location-based services. Sensor networks have changing conditions in the environment due to propagation issues, noise and placement of sensors, which represent challenges that position location algorithms must deal with. Accuracy of the location estimation technique is relevant since it allows minimizing positioning error. In indoor environments, propagation issues such as multipath signals, affect adversely the precision of the positioning algorithm. Also, the use of parameters such as time of arrival has a trade-off between the small distances that the signals traverse and the precision of the hardware used to capture such measurements. In this paper, we use received signal strength indicator (RSSI) to estimate the coordinates of individual sensors in an area of study. The RSSI parameter is measured and processed by a set of reference nodes installed in the area. We show that performance of the location estimation algorithm needs additional techniques to obtain improved accuracy rate. We develop additional techniques based on the use of polynomial interpolation and spline functions to balance propagation issues. These techniques help us to implement correcting factors that are used in the propagation model to compensate the RSSI measurements. We use these techniques to show how the positioning error is reduced in the area of study with simulations and measurements using sensors.
Highlights
Localization is a task required for activities that need to know the precise position or location of some object or person; activities like tracking and monitoring of mobile robots for Simultaneous Localization And Mapping (SLAM) and localization of patients or staff in a building, to mention some of them [1,2,3,4]
The cubic spline interpolation is acknowledged by its handling of high-level polynomials to interpolate a function and smooth the values obtained in the section of evidence
When the node of interest obtains a measurement of received signal strength, it uses splines to interpolate on the surface of distances to the references
Summary
Localization is a task required for activities that need to know the precise (or at least an estimation of) position or location of some object or person; activities like tracking and monitoring of mobile robots for Simultaneous Localization And Mapping (SLAM) and localization of patients or staff in a building, to mention some of them [1,2,3,4]. Some tasks are harder to achieve than others, since some of them require a room-level accuracy and others require a high accuracy (typically in the order of few centimeters). That is the case of Vision Based Localization Systems vs Radio Frequency Based Localization. The first kind of systems could have an accuracy of decimeters, while the second kind has typically an accuracy of meters [4,5]. Indoor Localization differs from Outdoor Localization in its complexity.
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