Auto-localization algorithm for local positioning systems

This paper studies the problem of determining the position of beacon nodes in Local Positioning Systems (LPSs), for which there are no inter-beacon distance measurements available and neither the mobile node nor any of the stationary nodes have positioning or odometry information. The common solution is implemented using a mobile node capable of measuring its distance to the stationary beacon nodes within a sensing radius. Many authors have implemented heuristic methods based on optimization algorithms to solve the problem. However, such methods require a good initial estimation of the node positions in order to find the correct solution. In this paper we present a new method to calculate the inter-beacon distances, and hence the beacons positions, based in the linearization of the trilateration equations into a closed-form solution which does not require any approximate initial estimation. The simulations and field evaluations show a good estimation of the beacon node positions.

Error estimation for the linearized auto-localization algorithm

The Linearized Auto-Localization (LAL) algorithm estimates the position of beacon nodes in Local Positioning Systems (LPSs), using only the distance measurements to a mobile node whose position is also unknown. The LAL algorithm calculates the inter-beacon distances, used for the estimation of the beacons’ positions, from the linearized trilateration equations. In this paper we propose a method to estimate the propagation of the errors of the inter-beacon distances obtained with the LAL algorithm, based on a first order Taylor approximation of the equations. Since the method depends on such approximation, a confidence parameter τ is defined to measure the reliability of the estimated error. Field evaluations showed that by applying this information to an improved weighted-based auto-localization algorithm (WLAL), the standard deviation of the inter-beacon distances can be improved by more than 30% on average with respect to the original LAL method.

Aplicación de auto-corrección continua para practicas de análisis de estructuras

The adaptation to the European Higher Educational area is an opportunity to incorporate on-line methods of education assessment in the field of the "Structural Analysis". This paper promotes the intensive use of MOODLE platform of the Technical University of Madrid, to implement the Information and Communication Technology program in this area. This article summarizes the educational experience achieved during the last courses though the use of a new software tool for continuous self-testing by the students. Individualized test are achieved by randomized variable data for each student. Several benefits for the educational purposes have arisen. Firstly, a considerable speeding of the evaluation process, and more than this, a massive input by the students. Certainly, the use of this tool can be very useful for the learning process and therefore for the improvement of the student’s skills and abilities.