Abstract: This paper focuses on optimal sensor deployment for indoor localization with a multi-objective evolutionary algorithm. Our goal is to obtain an algorithm to deploy sensors taking the number of sensors, accuracy and coverage into account. Contrary to most works in the literature, we consider the presence of obstacles in the region of interest (ROI) that can cause occlusions between the target and some sensors. In addition, we aim to obtain all of the Pareto optimal solutions regarding the number of sensors, coverage and accuracy. To deal with a variable number of sensors, we add speciation and structural mutations to the well-known non-dominated sorting genetic algorithm (NSGA-II). Speciation allows one to keep the evolution of sensor sets under control and to apply genetic operators to them so that they compete with other sets of the same size. We show some case studies of the sensor placement of an infrared range-difference indoor positioning system with a fairly complex model of the error of the measurements. The results obtained by our algorithm are compared to sensor placement patterns obtained with random deployment to highlight the relevance of using such a deployment algorithm

En este trabajo hemos investigado la posibilidad de emplear un algoritmo de localización cooperativa (o colaborativa) de personas en entornos interiores, basado en el intercambio de señales de RF con nodos de referencia en la infraestructura, así como señales de RF entre los mismos usuarios. La técnica empleada es un filtro de partículas bayesiano centralizado que procesa una hipótesis sobre la posición conjunta de todos los usuarios; esta hipótesis se va actualizando conforme a los datos de fuerza de señal (RSS) de las señales RF recibidas por cada usuario de los nodos fijos y de los nodos móviles portados por otros usuarios. En la demostración experimental empleamos 7 tags RFID activos fijos y 4 móviles, portados por sendos usuarios, que se desplazan por un edificio del CAR-CSIC. Con este este esquema de posicionamiento, la localización individual no es muy precisa (error mediano de 6.2 m en un edificio de 1600 m2), pero mejora hasta 5.1 m con las medidas cooperativas. El filtro de partículas permite incorporar medidas de PDR (odometría personal), mejorando la precisión de posicionamiento a 4.0 m (individual) y 3.5 m (cooperativa), respectivamente.

En este trabajo se analizan dos equipos comerciales de posicionamiento local tipo UWB. Se analiza la precisión y exactitud de los sistemas en entornos exteriores (sin objetos bloqueantes) y en entornos de interior (con múltiples paredes y mobiliario). Los resultados indican que es posible localizar con gran exactitud en exteriores (hasta 60 metros de distancia con error de menos de 10 cm) y con bastante precisión en interiores (0.5 metros en varias habitaciones). El equipo que obtiene mejor precisión es el Decawave, pero el Bespoon funciona solo un poco peor y tiene un diseño más compacto. Un demostrador de estos equipos se encuentra en nuestras instalaciones CAR-CIC (Arganda del Rey), Madrid. Empresas o centros de investigación que quieran saber más detalles pueden descargarse el pdf aqui: https://doi.org/10.1109/IPIN.2016.7743686 , o un preprint en nuestro servidor local.

This paper presents an acoustic local positioning system (ALPS) suitable for indoor positioning of portable devices such as smartphones or tablets, based on the transmission of high frequency CDMA-coded signals from a fixed network of beacons. The main novelty of the proposed ALPS is its capability to mitigate the effects of multipath propagation by performing an accurate estimation of the Line-of-Sight Time-of-Flights (LOS-TOF) through the Matching Pursuit algorithm. Signal detection, multipath cancellation and positioning estimation all take place within the portable device, which provides a graphical representation of the updated position in less than a second. The performance of the Matching Pursuit algorithm is analyzed in a real scenario and the results show that the proposed method is capable to retrieve the multipath-free System Availability under strong multipath conditions with SNR levels as low as 0dB.

Este trabajo presenta un prototipo de guía inteligente basado en la detección de señales acústicas de alta frecuencia mediante el dispositivo portable del usuario. Se propone el uso de un protocolo de comunicación FSK para etiquetar las diferentes exhibiciones, asegurando a la ver una alta tolerancia al desplazamiento en frecuencia Doppler provocado por el movimiento del usuario. El sistema ha sido implementado en una plataforma basada en Android obteniéndose un conjunto de resultados preliminares que corroboran la viabilidad de la propuesta.

Smart devices are attractive platforms for researchers to collect data coming from several sensors due to their small size, low cost, and the fact that they are already carried routinely by most people. The capability of smart devices to be used as the target of a positioning system has been already demonstrated in previous works. However, most of them rely on a single technology, or they are specific to the environment or user. In this paper we tackle these constraints by presenting a novel seamless positioning system which fuses the sensors information provided by a portable smart device to perform real time location without interruption and independently of the environment the user is moving. We have tested the system with a commercial smart device in an uncalibrated three floor building and its surroundings fusing the GNSS, WiFi and barometer as frequently used sensors, and the microphone and the proximity contactless technologies as occasionally used sensors. The obtained positioning accuracy mainly depends on the indoor path-loss awareness and on the markers density, showing that without using markers but dynamically estimating the path-loss exponents we obtain an error of\2 m for 90 % of cases.

Continual advances in information and communication technologies (ICT) are revolutionizing virtual education and bringing new tools on the market that provide virtual solutions to a range of problems. Nevertheless, nonvirtual experimentation using computer-aided control system design tools is still fundamental for future engineers. This paper describes a control course laboratory element, which is designed to teach electronic control engineering, in which students use a networked control system to steer and drive an electric car prototype. Multidisciplinary in nature, this laboratory allows students to take advantage of their previous academic training, as they model the plant from an open-loop test and contrast the simulated behavior with experimental results obtained on the prototype. A goal in this work was that students, across multiple engineering disciplines, should be able to integrate the knowledge acquired throughout their degree studies. An evaluation of students' grades, and their perception of the course, was conducted; this confirmed the educational benefits of the approach.

This paper focuses on the application of a decision support system based on evolutionary multi-objective optimization for deploying sensors in an indoor localization system. Our methods aim to provide the human expert who works as the sensor resource manager with a full set of Pareto efficient solutions of the sensor placement problem. In our analysis, we use five scalar performance measures as objective functions derived from the covariance matrix of the estimation, namely the trace, determinant, maximum eigenvalue, ratio of maximum and minimum eigenvalues, and the uncertainty in a given direction. We run the multi-objective genetic algorithm to optimize these objectives and obtain the Pareto fronts. The paper includes a detailed explanation of every aspect of the system and an application of the proposed decision support system to an indoor infrared positioning system. Final results show the different placement alternatives according to the objectives and the trade-off between different accuracy performance measures can be clearly seen. This approach contributes to the current state-of-the art in the fact that we point out the problems of optimizing a single accuracy measure and propose using a decision support system that provides the resource manager with a full overview of the set of Pareto efficient solutions considering several accuracy metrics. Since the manager will know all the Pareto optimal solutions before deciding the final sensor placement scheme, this method provides more information than dealing with a single function of the weighted objectives. Additionally, we are able to use this system to optimize objectives obtained from fairly complex functions. On the contrary, recent works that are referenced in this paper need to simplify the localization process to obtain tractable problem formulations.

In order to obtain very precise measurements of the position of agents located at a considerable distance using a sensor system based on position sensitive detectors (PSD), it is necessary to analyze and mitigate the factors that generate substantial errors in the system’s response. These sources of error can be divided into electronic and geometric factors. The former stem from the nature and construction of the PSD as well as the performance, tolerances and electronic response of the system, while the latter are related to the sensor’s optical system. Here, we focus solely on the electrical effects, since the study, analysis and correction of these are a prerequisite for subsequently addressing geometric errors. A simple calibration method is proposed, which considers PSD response, component tolerances, temperature variations, signal frequency used, signal to noise ratio (SNR), suboptimal operational amplifier parameters, and analog to digital converter (ADC) quantitation SNRQ, etc. Following an analysis of these effects and calibration of the sensor, it was possible to correct the errors, thus rendering the effects negligible, as reported in the results section.

This paper presents an application for counting people through a single fixed camera. This system performs the count distinction between input and output of people moving through the supervised area. The counter requires two steps: detection and tracking. The detection is based on finding people's heads through preprocessed image correlation with several circular patterns. Tracking is made through the application of a Kalman filter to determine the trajectory of the candidates. Finally, the system updates the counters based on the direction of the trajectories. Different tests using a set of real video sequences taken from different indoor areas give results ranging between 87% and 98% accuracies depending on the volume of flow of people crossing the counting zone. Problematic situations, such as occlusions, people grouped in different ways, scene luminance changes, etc., were used to validate the performance of the system.