187 research outputs found

    Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study

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    The accuracy of indoor wireless localization systems can be substantially enhanced by map-awareness, i.e., by the knowledge of the map of the environment in which localization signals are acquired. In fact, this knowledge can be exploited to cancel out, at least to some extent, the signal degradation due to propagation through physical obstructions, i.e., to the so called non-line-of-sight bias. This result can be achieved by developing novel localization techniques that rely on proper map-aware statistical modelling of the measurements they process. In this manuscript a unified statistical model for the measurements acquired in map-aware localization systems based on time-of-arrival and received signal strength techniques is developed and its experimental validation is illustrated. Finally, the accuracy of the proposed map-aware model is assessed and compared with that offered by its map-unaware counterparts. Our numerical results show that, when the quality of acquired measurements is poor, map-aware modelling can enhance localization accuracy by up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless Communications, 201

    Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates

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    This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features. Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps. We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on \textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table

    Single Antenna Anchor-Free UWB Positioning based on Multipath Propagation

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    Radio based localization and tracking usually require multiple receivers/transmitters or a known floor plan. This paper presents a method for anchor free indoor positioning based on single antenna ultra wideband (UWB) measurements. By using time of arrival information from multipath propagation components stemming from scatterers with different, but unknown, positions we estimate the movement of the receiver as well as the angle of arrival of the considered multipath components. Experiments are shown for real indoor data measured in a lecture room with promising results. Simultaneous estimation of both receiver motion, transmitter and scatterer positions is performed using an factorization based approach followed by non-linear least squares optimization. A RANSAC approach to automatic matching of data has also been implemented and tested. The resulting reconstruction is compared to ground truth motion as given by the antenna positioner. The resulting accuracy is in the order of one cm

    A self-calibrating system for finger tracking using sound waves

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    In this thesis a system for tracking the fingers of a user using sound waves is developed. The proposed solution is to attach a small speaker to each finger and then have a number of microphones placed ad hoc around a computer monitor listening to the speakers. The system should then be able to track the positions of the fingers so that the coordinates can be mapped to the computer monitor and be used for human-computer interfacing. The thesis focuses on the proof-of-concept of the system. The system pipeline consists of three parts: signal processing, system self-calibration and real-time sound source tracking. In the signal processing step four different signal methods are constructed and evaluated. It is shown that multiple signals can be used in parallel. The signal method with the best performance uses a number of dampened sine waves stacked on top of each other, with each sound wave having a different frequency within a specified frequency band. The goal was to use ultrasound frequency bands for the system but experimenting showed that they gave rise to a lot of aliasing, thus rendering the higher frequency bands unusable. The second step, the system self-calibration, aims to do a scene reconstruction to find the positions of the microphones and the sound source path using only the received signal transmissions. First the time-difference of arrival (TDOA) values are estimated using robust techniques centred around a GCC-PHAT. The time offsets are then estimated in order to convert the TDOA problem into a time-of-arrival (TOA) problem so that the positions of the receivers and sound events can be calculated. Finally a "virtual screen" is fitted to the sound source path to be used for coordinate projection. The scene reconstruction was successful in 80 % of the test cases, in the sense that it managed to estimate the spatial positions at all. The estimates for the microphones had errors of 11.8 +/- 5 centimetres on average for the successful test cases, which is worse than the results presented in previous research. However, the best test case outperformed the results of another paper. The newly developed and implemented technique for finding the virtual screen was far from robust and only found a reasonable virtual screen in 12.5 % of the test cases. In the third step the sound events were estimated, one sound event at a time, using the SRP-PHAT method with the CFRC improvement. Unfortunate choices of the search volumes made the calculations very computationally heavy. The results were comparable to those of the system self-calibration when using the same data and the estimated microphone positions

    Taylor series method in TDOA approach for indoor positioning system

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    Localisation technologies have always remained in the limelight of positioning-science as researchers have ever shown keen interest to know the exact positions of things. Ultrasonic sensors are mainly used for localisation of mobile robots since they provide high accuracy. This paper presents Taylor Series Method in Time Difference of Arrival approach using ultrasonic sensors.Signals are send from the sensors periodically.The time difference of arrival of signals from the ultrasonic sensors is used by the receiver unit to estimate the location of the mobile unit. The equations formed by using Time Difference of Approach are solved using Taylor Series Method. Taylor Series Method provides a more accurate result since they give less error compared to other methods and they ignore the measurement errors

    Ambient Sound-Based Collaborative Localization of Indeterministic Devices

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    Localization is essential in wireless sensor networks. To our knowledge, no prior work has utilized low-cost devices for collaborative localization based on only ambient sound, without the support of local infrastructure. The reason may be the fact that most low-cost devices are indeterministic and suffer from uncertain input latencies. This uncertainty makes accurate localization challenging. Therefore, we present a collaborative localization algorithm (Cooperative Localization on Android with ambient Sound Sources (CLASS)) that simultaneously localizes the position of indeterministic devices and ambient sound sources without local infrastructure. The CLASS algorithm deals with the uncertainty by splitting the devices into subsets so that outliers can be removed from the time difference of arrival values and localization results. Since Android is indeterministic, we select Android devices to evaluate our approach. The algorithm is evaluated with an outdoor experiment and achieves a mean Root Mean Square Error (RMSE) of 2.18 m with a standard deviation of 0.22 m. Estimated directions towards the sound sources have a mean RMSE of 17.5 ° and a standard deviation of 2.3 °. These results show that it is feasible to simultaneously achieve a relative positioning of both devices and sound sources with sufficient accuracy, even when using non-deterministic devices and platforms, such as Android

    Computer Vision without Vision : Methods and Applications of Radio and Audio Based SLAM

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    The central problem of this thesis is estimating receiver-sender node positions from measured receiver-sender distances or equivalent measurements. This problem arises in many applications such as microphone array calibration, radio antenna array calibration, mapping and positioning using ultra-wideband and mapping and positioning using round-trip-time measurements between mobile phones and Wi-Fi-units. Previous research has explored some of these problems, creating minimal solvers for instance, but these solutions lack real world implementation. Due to the nature of using different media, finding reliable receiver-sender distances is tough, with many of the measurements being erroneous or to a worse extent missing. Therefore in this thesis, we explore using minimal solvers to create robust solutions, that encompass small erroneous measurements and work around missing and grossly erroneous measurements.This thesis focuses mainly on Time-of-Arrival measurements using radio technologies such as Two-way-Ranging in Ultra-Wideband and a new IEEE standard 802.11mc found on many WiFi modules. The methods investigated, also related to Computer Vision problems such as Stucture-from-Motion. As part of this thesis, a range of new commercial radio technologies are characterised in terms of ranging in real world enviroments. In doing so, we have shown how these technologies can be used as a more accurate alternative to the Global Positioning System in indoor enviroments. Further to these solutions, more methods are proposed for large scale problems when multiple users will collect the data, commonly known as Big Data. For these cases, more data is not always better, so a method is proposed to try find the relevant data to calibrate large systems

    Acoustic localization of people in reverberant environments using deep learning techniques

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    La localización de las personas a partir de información acústica es cada vez más importante en aplicaciones del mundo real como la seguridad, la vigilancia y la interacción entre personas y robots. En muchos casos, es necesario localizar con precisión personas u objetos en función del sonido que generan, especialmente en entornos ruidosos y reverberantes en los que los métodos de localización tradicionales pueden fallar, o en escenarios en los que los métodos basados en análisis de vídeo no son factibles por no disponer de ese tipo de sensores o por la existencia de oclusiones relevantes. Por ejemplo, en seguridad y vigilancia, la capacidad de localizar con precisión una fuente de sonido puede ayudar a identificar posibles amenazas o intrusos. En entornos sanitarios, la localización acústica puede utilizarse para controlar los movimientos y actividades de los pacientes, especialmente los que tienen problemas de movilidad. En la interacción entre personas y robots, los robots equipados con capacidades de localización acústica pueden percibir y responder mejor a su entorno, lo que permite interacciones más naturales e intuitivas con los humanos. Por lo tanto, el desarrollo de sistemas de localización acústica precisos y robustos utilizando técnicas avanzadas como el aprendizaje profundo es de gran importancia práctica. Es por esto que en esta tesis doctoral se aborda dicho problema en tres líneas de investigación fundamentales: (i) El diseño de un sistema extremo a extremo (end-to-end) basado en redes neuronales capaz de mejorar las tasas de localización de sistemas ya existentes en el estado del arte. (ii) El diseño de un sistema capaz de localizar a uno o varios hablantes simultáneos en entornos con características y con geometrías de arrays de sensores diferentes sin necesidad de re-entrenar. (iii) El diseño de sistemas capaces de refinar los mapas de potencia acústica necesarios para localizar a las fuentes acústicas para conseguir una mejor localización posterior. A la hora de evaluar la consecución de dichos objetivos se han utilizado diversas bases de datos realistas con características diferentes, donde las personas involucradas en las escenas pueden actuar sin ningún tipo de restricción. Todos los sistemas propuestos han sido evaluados bajo las mismas condiciones consiguiendo superar en términos de error de localización a los sistemas actuales del estado del arte

    Localization using Distance Geometry : Minimal Solvers and Robust Methods for Sensor Network Self-Calibration

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    In this thesis, we focus on the problem of estimating receiver and sender node positions given some form of distance measurements between them. This kind of localization problem has several applications, e.g., global and indoor positioning, sensor network calibration, molecular conformations, data visualization, graph embedding, and robot kinematics. More concretely, this thesis makes contributions in three different areas.First, we present a method for simultaneously registering and merging maps. The merging problem occurs when multiple maps of an area have been constructed and need to be combined into a single representation. If there are no absolute references and the maps are in different coordinate systems, they also need to be registered. In the second part, we construct robust methods for sensor network self-calibration using both Time of Arrival (TOA) and Time Difference of Arrival (TDOA) measurements. One of the difficulties is that corrupt measurements, so-called outliers, are present and should be excluded from the model fitting. To achieve this, we use hypothesis-and-test frameworks together with minimal solvers, resulting in methods that are robust to noise, outliers, and missing data. Several new minimal solvers are introduced to accommodate a range of receiver and sender configurations in 2D and 3D space. These solvers are formulated as polynomial equation systems which are solvedusing methods from algebraic geometry.In the third part, we focus specifically on the problems of trilateration and multilateration, and we present a method that approximates the Maximum Likelihood (ML) estimator for different noise distributions. The proposed approach reduces to an eigendecomposition problem for which there are good solvers. This results in a method that is faster and more numerically stable than the state-of-the-art, while still being easy to implement. Furthermore, we present a robust trilateration method that incorporates a motion model. This enables the removal of outliers in the distance measurements at the same time as drift in the motion model is canceled
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