Dissertations / Theses on the topic 'Multiple Odor Sources Localization'

Speech communication is gaining in popularity in many different contexts as technology evolves. With the introduction of mobile electronic devices such as cell phones and laptops, and fixed electronic devices such as video and teleconferencing systems, more people are communicating which leads to an increasing demand for new services and better speech quality. Methods to enhance speech recorded by microphones often operate blindly without prior knowledge of the signals. With the addition of multiple microphones to allow for spatial filtering, many blind speech enhancement methods have to operate blindly also in the spatial domain. When attempting to improve the quality of spoken communication it is often necessary to be able to reliably determine the location of the speakers. A dedicated source localization method on top of the speech enhancement methods can assist the speech enhancement method by providing the spatial information about the sources. This thesis addresses the problem of speech-source localization, with a focus on the problem of localization in the presence of multiple concurrent speech sources. The primary work consists of methods to estimate the direction of arrival of multiple concurrent speech sources from an array of sensors and a method to correct the ambiguities when estimating the spatial locations of multiple speech sources from multiple arrays of sensors. The thesis also improves the well-known SRP-based methods with higher-order statistics, and presents an analysis of how the SRP-PHAT performs when the sensor array geometry is not fully calibrated. The thesis is concluded by two envelope-domain-based methods for tonal pattern detection and tonal disturbance detection and cancelation which can be useful to further increase the usability of the proposed localization methods. The main contribution of the thesis is a complete methodology to spatially locate multiple speech sources in enclosed environments. New methods and improvements to the combined solution are presented for the direction-of-arrival estimation, the location estimation and the location ambiguity correction, as well as a sensor array calibration sensitivity analysis.

Em vista da tentência de se criarem intefaces entre humanos e máquinas que cada vez mais permitam meios simples de interação, é natural que sejam realizadas pesquisas em técnicas que procuram simular o meio mais convencional de comunicação que os humanos usam: a fala. No sistema auditivo humano, a voz é automaticamente processada pelo cérebro de modo efetivo e fácil, também comumente auxiliada por informações visuais, como movimentação labial e localizacão dos locutores. Este processamento realizado pelo cérebro inclui dois componentes importantes que a comunicação baseada em fala requere: Detecção de Atividade de Voz (Voice Activity Detection - VAD) e Localização de Fontes Sonoras (Sound Source Localization - SSL). Consequentemente, VAD e SSL também servem como ferramentas mandatórias de pré-processamento em aplicações de Interfaces Humano-Computador (Human Computer Interface - HCI), como no caso de reconhecimento automático de voz e identificação de locutor. Entretanto, VAD e SSL ainda são problemas desafiadores quando se lidando com cenários acústicos realísticos, particularmente na presença de ruído, reverberação e locutores simultâneos. Neste trabalho, são propostas abordagens para tratar tais problemas, para os casos de uma e múltiplas fontes sonoras, através do uso de informações audiovisuais, explorando-se variadas maneiras de se fundir as modalidades de áudio e vídeo. Este trabalho também emprega um arranjo de microfones para o processamento de som, o qual permite que as informações espaciais dos sinais acústicos sejam exploradas através do algoritmo estado-da-arte SRP (Steered Response Power). Por consequência adicional, uma eficiente implementação em GPU do SRP foi desenvolvida, possibilitando processamento em tempo real do algoritmo. Os experimentos realizados mostram uma acurácia média de 95% ao se efetuar VAD de até três locutores simultâneos, e um erro médio de 10cm ao se localizar tais locutores.<br>Given the tendency of creating interfaces between human and machines that increasingly allow simple ways of interaction, it is only natural that research effort is put into techniques that seek to simulate the most conventional mean of communication humans use: the speech. In the human auditory system, voice is automatically processed by the brain in an effortless and effective way, also commonly aided by visual cues, such as mouth movement and location of the speakers. This processing done by the brain includes two important components that speech-based communication require: Voice Activity Detection (VAD) and Sound Source Localization (SSL). Consequently, VAD and SSL also serve as mandatory preprocessing tools for high-end Human Computer Interface (HCI) applications in a computing environment, as the case of automatic speech recognition and speaker identification. However, VAD and SSL are still challenging problems when dealing with realistic acoustic scenarios, particularly in the presence of noise, reverberation and multiple simultaneous speakers. In this work we propose some approaches for tackling these problems using audiovisual information, both for the single source and the competing sources scenario, exploiting distinct ways of fusing the audio and video modalities. Our work also employs a microphone array for the audio processing, which allows the spatial information of the acoustic signals to be explored through the stateof- the art method Steered Response Power (SRP). As an additional consequence, a very fast GPU version of the SRP is developed, so that real-time processing is achieved. Our experiments show an average accuracy of 95% when performing VAD of up to three simultaneous speakers and an average error of 10cm when locating such speakers.

Disertační práce se zabývá lokalizací zdrojů zvuku a akustickým zoomem. Hlavním cílem této práce je navrhnout systém s akustickým zoomem, který přiblíží zvuk jednoho mluvčího mezi skupinou mluvčích, a to i když mluví současně. Tento systém je kompatibilní s technikou prostorového zvuku. Hlavní přínosy disertační práce jsou následující: 1. Návrh metody pro odhad více směrů přicházejícího zvuku. 2. Návrh metody pro akustické zoomování pomocí DirAC. 3. Návrh kombinovaného systému pomocí předchozích kroků, který může být použit v telekonferencích.

Odour localisation is the problem of finding the source of an odour or other volatile chemical. It promises many valuable practical and humanitarian applications. Most localisation methods require a robot to reactively track an odour plume along its entire length. This approach is time consuming and may be not be possible in a cluttered indoor environment, where airflow tends to form sectors of circulating airflow. Such environments may be encountered in crawl-ways under floors, roof cavities, mines, caves, tree-canopies, air-ducts, sewers or tunnel systems. Operation in these places is important for such applications as search and rescue and locating the sources of toxic chemicals in an industrial setting. This thesis addresses odour localisation in this class of environments. The solution consists of a sense-map-plan-act style control scheme (and low level behaviour based controller) with two main stages. Firstly, the airflow in the environment is modelled using naive physics rules which are encapsulated into an algorithm named a Naive Reasoning Machine. It was used in preference to conventional methods as it is fast, does not require boundary conditions, and most importantly, provides approximate solutions to the degree of accuracy required for the task, with analogical data structures that are readily useful to a reasoning algorithm. Secondly, a reasoning algorithm navigates the robot to specific target locations that are determined with a physical map, the airflow map, and knowledge of odour dispersal. Sensor measurements at the target positions provide information regarding the likelihood that odour was emitted from potential odour source locations. The target positions and their traversal are determined so that all the potential odour source sites are accounted for. The core method provides values corresponding to the confidence that the odour source is located in a given region. A second search stage exploiting vision is then used to locate the specific location of the odour source within the predicted region. This comprises the second part of a bi-modal, two-stage search, with each stage exploiting complementary sensing modalities. Single hypothesis airflow modelling faces limitations due to the fact that large differences between airflow topologies are predicted for only small variations in a physical map. This is due to uncertainties in the map and approximations in the modelling process. Furthermore, there are uncertainties regarding the flow direction through inlet/outlet ducts. A method is presented for dealing with these uncertainties, by generating multiple airflow hypotheses. As the robot performs odour localisation, airflow in the environment is measured and used to adjust the confidences of the hypotheses using Bayesian inference. The best hypothesis is then selected, which allows the completion of the localisation task. This method improves the robustness of odour localisation in the presence of uncertainties, making it possible where the single hypothesis method would fail. It also demonstrates the potential for integrating naive physics into a statistical framework. Extensive experimental results are presented to support the methods described above.

L'objectif général de cette thèse était de proposer une solution de localisation en intérieur à la fois simple et capable de surmonter les défis de la propagation dans les environnements en intérieur. Pour ce faire, un système de localisation basé sur la méthode des signatures et adoptant le temps d'arrivée du signal de l'émetteur au récepteur comme signature, a été proposé. Le système présente deux architectures différentes, une première orientée privée utilisant la méthode d'accès multiple à répartition par code et une deuxième centralisée basée sur la méthode d'accès multiple à répartition dans le temps. Le système calcule la position de l'objet d'intérêt par la méthode de noyau. Une comparaison expérimentale entre le système à architecture orientée privée et un système de localisation sonore déjà existant et basé sur la méthode de trilatération, a permis de confirmer les résultats trouvés dans le cas de la localisation par ondes radiofréquences. Cependant, nos expérimentations étaient les premières à montrer l'effet de la réverbération sur les approches de la localisation acoustique. Dans un second lieu, un système de localisation basé sur la technique de retournement temporel, permettant une localisation simultanée de sources avec différentes précisions, a été testé par simulations en faisant varier le nombre de sources. Ce système a été ensuite validé par expérimentations. Dans la dernière partie de notre étude, nous nous sommes intéressés à la réduction de l'audibilité du signal utile à la localisation par recours à la psycho-acoustique. Un filtre défini à partir du seuil d'audition absolu a été appliqué au signal de localisation. Nos résultats ont montré une amélioration de la précision de localisation comparé au système de localisation sans modèle psycho-acoustique et ce grâce à l'utilisation d'un filtre adapté au modèle psycho-acoustique à la réception. Par ailleurs, l'écoute du signal après application du modèle psycho-acoustique a montré une réduction significative de son audibilité comparée à celle du signal original<br>The objective of this PhD is to propose a location solution that should be simple and robust to multipath that characterizes the indoor environments. First, a location system that exploits the time domain of channel parameters has been proposed. The system adopts the time of arrival of the path of maximum amplitude as a signature and estimates the target position through nonparametric kernel regression. The system was evaluated in experiments for two main configurations: a privacy-oriented configuration with code-division multiple-access operation and a centralized configuration with time-division multiple-access operation. A comparison between our privacy-oriented system and another acoustic location system based on code-division multiple-access operation and lateration method confirms the results found in radiofrequency-based localization. However, our experiments are the first to demonstrate the detrimental effect that reverberation has on acoustic localization approaches. Second, a location system based on time reversal technique and able to localize simultaneously sources with different location precisions has been tested through simulations for different values of the number of sources. The system has then been validated by experiments. Finally, we have been interested in reducing the audibility of the localization signal through psycho-acoustics. A filter, set from the absolute threshold of hearing, is then applied to the signal. Our results showed an improvement in precision, when compared to the location system without psychoacoustic model, thanks to the use of matched filter at the receiver. Moreover, we have noticed a significant reduction in the audibility of the filtered signal compared to that of the original signal

The recent increasing threat of chemical weapons technologies has highlighted the need for superior detection of hazardous emission sources. One promising area of technological development is odour source detection using plume-tracing mobile robots, which have the potential to detect emissions released by sources such as fire, toxic gas and explosives, without endangering human life during the detection process. Aims of this proposed research project are to investigate the use of multiple mobile robots to locate a dangerous odour source in realistic environments. The realistic environments presented in this study are two city-like environments and a complicated indoor building environment. These two kinds of environments are considered to have a very close relationship with human life. The research methods for this project include two aspects which are simulation and experimental validation. A simulation framework for this study was constructed using combined CFD and MATLAB techniques. The control strategies for plume-tracing mobile robots were developed and tested in the simulation framework. A prototype plume-tracing mobile robot was physically developed for the purpose of real world experimental validation. A series of experiments proved that the control strategies developed in the simulation framework was applicable to a real plume-tracing robot; on the other hand, the experiments also proved that the developed simulation framework was capable of reflecting real world plume-tracing scenarios. The contributions to the plume-tracing research field achieved in this research project are: (a) a novel simulation framework using combined CFD and MATLAB techniques for plume tracing research was developed and it is believed that the framework is helpful for the researchers in plume-tracing research field; (b) a small size plume-tracing mobile robot was fabricated. This small robot has the capability of tracing odour plumes in complicated wind-varying environments. The capability of this kind of robot is considered to be a new input into this research field. (c) a supervisory approach was proposed in this study and the developed supervisory programs coordinated multiple robots to locate an odour source in realistic environments effectively.<br>Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 2010

碩士<br>國立中正大學<br>電機工程所<br>95<br>Nowadays, many people have some or little degradations on their hearing capabilities no matter what their ages and lives are. In some cases, the medical treatment can successfully recover their hearing functions. However, in some cases, the hearing-aid devices embedded in ears are required to improve their hearing. These hearing-aid devices have the same purpose of amplifying signals and suppressing noises. In this project, the microphone array is developed to localize and enhance sound for supporting the hearing-aid device. To correctly locate sound position, the Time Domain Cross Correlation (TDDC) scheme is employed to obtain the delays among four microphones. Additionally, the configuration of four microphones is explored to understand the relationship between derived sound position’s deviations and position arrangement of four microphones. Based on the signals recorded from these four microphones, the theoretical foundation is constructed and used to obtain the sound position. After determining sound position, the delay and sum beamformer using the Wiener filter is utilized to suppress noises and to generate a good mono sound. This mono sound after noise suppression is used to generate two-channel 3D sound by using the Head Related Transform Function (HRTF). According to the derived sound location, the mono sound is convoluted with data from the HRTF library to yield two 3D sounds that can enable users perceive like the real sound event. In the practical applications, the human head usually rotates to track the interested event. Additionally, the sounds respective to the human ears change their incident directions. Hence, the proposed microphone array system is further to compensate the effect of head rotation to become a useful system. After one-year development, we successfully develop the microphone array system that can be widely used in the hearing-aid products to benefit human being.

碩士<br>國立臺北科技大學<br>電機工程系<br>107<br>Considering that the environment is noisy and multiple people are making sounds at the same time, it is still difficult for human ears and even today's voice assistants to identify the exact location or content of the sound. Therefore, this study hopes to estimate the horizontal angle, elevation angle and distance of multiple sound sources by means of the proposed three-dimensional multi-source localization system combined with microphone array technology. Then we can obtain the precise position of each sound source in space. In this thesis, a planar annular array of four microphones is used to construct a three-dimensional multi-source localization system, whereby the two-dimensional coordinate positioning algorithm can be estimated in real time. The partial algorithm for estimating the elevation angle is more complicated and takes a long processing time. Hence, offline estimation for elevation angle is currently used. In this paper, the limitation of single planar array for distance location is solved in the form of adding a dual array. In addition, the improved beamforming method is proposed to solve the three-dimensional positioning problem using planar arrays. The overall system architecture uses PCSF (Perpendicular Cross-Spectra Fusion), MP (Matching Pursuit), Triangulation and Beamforming as the core algorithms to estimate multi-source coordinates in three-dimensional space. Under the premise of Single-Source Zone, this paper converts audio into Time-Frequency Domain data, and then uses PCSF and MP algorithms to estimate multi-source horizontal angle. The next step is to obtain the two-dimensional sound source coordinates using triangulation method. Finally, we estimate the multi-source elevation angles with the improved beamforming method to determine the multi-source three-dimensional coordinates.

碩士<br>國立交通大學<br>電機與控制工程系所<br>97<br>This work proposes a method that is able to simultaneously localize a mobile robot and unknown number of multiple sound sources in the environment. The reason of using sound sources as the landmarks in SLAM algorithm is presented. Several DOA estimation methods are described and a combinational one is used for real time application. After knowing the DOA information, a bearings-only SLAM (simultaneous localization and mapping) algorithm is introduced in detail, which contains the theoretical structure of Bayes filter. The estimated DOAs are known as the bearings information in the algorithm. As source signals are not persistent and there is no identification of the signal content, data association is unknown which is solved using particle filter. Modifications of the algorithm are made for real time application. Experimental results are presented to verify the effectiveness of the proposed approaches.

Multimodal function optimization generally focuses on algorithms to find either a local optimum or the global optimum while avoiding local optima. However, there is another class of optimization problems which have the objective of finding multiple optima with either equal or unequal function values. The knowledge of multiple local and global optima has several advantages such as obtaining an insight into the function landscape and selecting an alternative solution when dynamic nature of constraints in the search space makes a previous optimum solution infeasible to implement. Applications include identification of multiple signal sources like sound, heat, light and leaks in pressurized systems, hazardous plumes/aerosols resulting from nuclear/ chemical spills, fire-origins in forest fires and hazardous chemical discharge in water bodies, oil spills, deep-sea hydrothermal vent plumes, etc. Signals such as sound, light, and other electromagnetic radiations propagate in the form of a wave. Therefore, the nominal source profile that spreads in the environment can be represented as a multimodal function and hence, the problem of localizing their respective origins can be modeled as optimization of multimodal functions. Multimodality in a search and optimization problem gives rise to several attractors and thereby presents a challenge to any optimization algorithm in terms of finding global optimum solutions. However, the problem is compounded when multiple (global and local) optima are sought. This thesis develops a novel glowworm swarm optimization (GSO) algorithm for simultaneous capture of multiple optima of multimodal functions. The algorithm shares some features with the ant-colony optimization (ACO) and particle swarm optimization (PSO) algorithms, but with several significant differences. The agents in the GSO algorithm are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the function-profile values at their current locations into a luciferin value and broadcast the same to other agents in their neighborhood. The glowworm depends on a variable local decision domain, which is bounded above by a circular sensor range, to identify its neighbors and compute its movements. Each glowworm selects a neighbor that has a luciferin value more than its own, using a probabilistic mechanism, and moves toward it. That is, they are attracted to neighbors that glow brighter. These movements that are based only on local information enable the swarm of glowworms to partition into disjoint subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple optima (not necessarily equal) of a given multimodal function. Natural glowworms primarily use the bioluminescent light to signal other individuals of the same species for reproduction and to attract prey. The general idea in the GSO algorithm is similar in these aspects in the sense that glowworm agents are assumed to be attracted to move toward other glowworm agents that have brighter luminescence (higher luciferin value). We present the development of the GSO algorithm in terms of its working principle, various algorithmic phases, and evolution of the algorithm from the first version of the algorithm to its present form. Two major phases ¡ splitting of the agent swarm into disjoint subgroups and local convergence of agents in each subgroup to peak locations ¡ are identified at the group level of the algorithm and theoretical performance results related to the latter phase are obtained for a simplified GSO model. Performance of the GSO algorithm against a large class of benchmark multimodal functions is demonstrated through simulation experiments. We categorize the various constants of the algorithm into algorithmic constants and parameters. We show in simulations that fixed values of the algorithmic constants work well for a large class of problems and only two parameters have some influence on algorithmic performance. We also study the performance of the algorithm in the presence of noise. Simulations show that the algorithm exhibits good performance in the presence of fairly high noise levels. We observe graceful degradation only with significant increase in levels of measurement noise. A comparison with the gradient based algorithm reveals the superiority of the GSO algorithm in coping with uncertainty. We conduct embodied robot simulations, by using a multi-robot-simulator called Player/Stage that provides realistic sensor and actuator models, in order to assess the GSO algorithm's suitability for multiple source localization tasks. Next, we extend this work to collective robotics experiments. For this purpose, we use a set of four wheeled robots that are endowed with the capabilities required to implement the various behavioral primitives of the GSO algorithm. We present an experiment where two robots use the GSO algorithm to localize a light source. We discuss an application of GSO to ubiquitous computing based environments. In particular, we propose a hazard-sensing environment using a heterogeneous swarm that consists of stationary agents and mobile agents. The agents deployed in the environment implement a modification of the GSO algorithm. In a graph of mini mum number of mobile agents required for 100% source-capture as a function of the number of stationary agents, we show that deployment of the stationary agents in a grid configuration leads to multiple phase-transitions in the heterogeneous swarm behavior. Finally, we use the GSO algorithm to address the problem of pursuit of multiple mobile signal sources. For the case where the positions of the pursuers and the moving source are collinear, we present a theoretical result that provides an upper bound on the relative speed of the mobile source below which the agents succeed in pursuing the source. We use several simulation scenarios to demonstrate the ecacy of the algorithm in pursuing mobile signal sources. In the case where the positions of the pursuers and the moving source are non-collinear, we use numerical experiments to determine an upper bound on the relative speed of the mobile source below which the pursuers succeed in pursuing the source.

Multimodal function optimization generally focuses on algorithms to find either a local optimum or the global optimum while avoiding local optima. However, there is another class of optimization problems which have the objective of finding multiple optima with either equal or unequal function values. The knowledge of multiple local and global optima has several advantages such as obtaining an insight into the function landscape and selecting an alternative solution when dynamic nature of constraints in the search space makes a previous optimum solution infeasible to implement. Applications include identification of multiple signal sources like sound, heat, light and leaks in pressurized systems, hazardous plumes/aerosols resulting from nuclear/ chemical spills, fire-origins in forest fires and hazardous chemical discharge in water bodies, oil spills, deep-sea hydrothermal vent plumes, etc. Signals such as sound, light, and other electromagnetic radiations propagate in the form of a wave. Therefore, the nominal source profile that spreads in the environment can be represented as a multimodal function and hence, the problem of localizing their respective origins can be modeled as optimization of multimodal functions. Multimodality in a search and optimization problem gives rise to several attractors and thereby presents a challenge to any optimization algorithm in terms of finding global optimum solutions. However, the problem is compounded when multiple (global and local) optima are sought. This thesis develops a novel glowworm swarm optimization (GSO) algorithm for simultaneous capture of multiple optima of multimodal functions. The algorithm shares some features with the ant-colony optimization (ACO) and particle swarm optimization (PSO) algorithms, but with several significant differences. The agents in the GSO algorithm are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the function-profile values at their current locations into a luciferin value and broadcast the same to other agents in their neighborhood. The glowworm depends on a variable local decision domain, which is bounded above by a circular sensor range, to identify its neighbors and compute its movements. Each glowworm selects a neighbor that has a luciferin value more than its own, using a probabilistic mechanism, and moves toward it. That is, they are attracted to neighbors that glow brighter. These movements that are based only on local information enable the swarm of glowworms to partition into disjoint subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple optima (not necessarily equal) of a given multimodal function. Natural glowworms primarily use the bioluminescent light to signal other individuals of the same species for reproduction and to attract prey. The general idea in the GSO algorithm is similar in these aspects in the sense that glowworm agents are assumed to be attracted to move toward other glowworm agents that have brighter luminescence (higher luciferin value). We present the development of the GSO algorithm in terms of its working principle, various algorithmic phases, and evolution of the algorithm from the first version of the algorithm to its present form. Two major phases ¡ splitting of the agent swarm into disjoint subgroups and local convergence of agents in each subgroup to peak locations ¡ are identified at the group level of the algorithm and theoretical performance results related to the latter phase are obtained for a simplified GSO model. Performance of the GSO algorithm against a large class of benchmark multimodal functions is demonstrated through simulation experiments. We categorize the various constants of the algorithm into algorithmic constants and parameters. We show in simulations that fixed values of the algorithmic constants work well for a large class of problems and only two parameters have some influence on algorithmic performance. We also study the performance of the algorithm in the presence of noise. Simulations show that the algorithm exhibits good performance in the presence of fairly high noise levels. We observe graceful degradation only with significant increase in levels of measurement noise. A comparison with the gradient based algorithm reveals the superiority of the GSO algorithm in coping with uncertainty. We conduct embodied robot simulations, by using a multi-robot-simulator called Player/Stage that provides realistic sensor and actuator models, in order to assess the GSO algorithm's suitability for multiple source localization tasks. Next, we extend this work to collective robotics experiments. For this purpose, we use a set of four wheeled robots that are endowed with the capabilities required to implement the various behavioral primitives of the GSO algorithm. We present an experiment where two robots use the GSO algorithm to localize a light source. We discuss an application of GSO to ubiquitous computing based environments. In particular, we propose a hazard-sensing environment using a heterogeneous swarm that consists of stationary agents and mobile agents. The agents deployed in the environment implement a modification of the GSO algorithm. In a graph of mini mum number of mobile agents required for 100% source-capture as a function of the number of stationary agents, we show that deployment of the stationary agents in a grid configuration leads to multiple phase-transitions in the heterogeneous swarm behavior. Finally, we use the GSO algorithm to address the problem of pursuit of multiple mobile signal sources. For the case where the positions of the pursuers and the moving source are collinear, we present a theoretical result that provides an upper bound on the relative speed of the mobile source below which the agents succeed in pursuing the source. We use several simulation scenarios to demonstrate the ecacy of the algorithm in pursuing mobile signal sources. In the case where the positions of the pursuers and the moving source are non-collinear, we use numerical experiments to determine an upper bound on the relative speed of the mobile source below which the pursuers succeed in pursuing the source.

Στην παρούσα εργασία περιγράφεται μια νέα προσέγγιση στο πρόβλημα του εντοπισμού θέσης πολλαπλών πηγών στα ασύρματα δίκτυα αισθητήρων. Ο αλγόριθμος που προτείνεται βασίζεται σε μια εναλλακτική ερμηνεία της πληροφορίας που παρέχεται από το δίκτυο (υπό τη μορφή της λαμβανόμενης ισχύος του σήματος από κάθε κόμβο- αισθητήρα). Στα πλαίσια της εργασίας διεξάχθηκαν εκτενή πειράματα για την αξιολόγηση της μεθόδου όσον αφορά στην απόδοση, συναρτήσει διαφόρων παραμέτρων του προβλήματος.<br>This work describes a new approach to the energy-based multiple source localization problem in wireless sensor networks. The algorithm that is proposed in the present work is based on a alternative interpretation of the information that is provided by the network (under the form of received signal strength in each sensor-node). Extensive simulations have been conducted to characterize the performance of this method under various parameters.

Με τα δίκτυα αισθητήρων μπορούμε να παρακολουθούμε το περιβάλλον και να εξάγουμε χρήσιμη πληροφορία με αυτόματο τρόπο. Τα τελευταία χρόνια, λόγω και της ανάπτυξης κατάλληλων ολοκληρωμένων κυκλωμάτων, έχουν εμφανιστεί κόμβοι αισθητήρων σε πολύ μικρό μέγεθος. Αυτοί οι κόμβοι έχουν την δυνατότητα να επεξεργάζονται δεδομένα, να επικοινωνούν μεταξύ τους και να περιέχουν περισσότερα από ένα είδη αισθητήρων. Η συγκεκριμένη εργασία ασχολείται με δίκτυα τυχαία διασκορπισμένων αισθητήρων. Το πρόβλημα που μελετήθηκε είναι ο εντοπισμός της θέσης πολλαπλών πηγών από το δίκτυο. Οι πηγές εκπέμπουν ευρείας ζώνης σήματα που μοντελοποιούνται ως διαδικασίες AR. Η τεχνική λειτουργεί με έναν σειριακό τρόπο. Επιλέγει μια πηγή, εκτιμά τις διαφορές χρόνων άφιξης του σήματός της και υπολογίζει την θέση της πηγής χρησιμοποιώντας το κριτήριο ελαχίστων τετραγώνων. Στην συνέχεια, ακυρώνει το σήμα της πηγής από τα σήματα που έχουν λάβει οι κόμβοι του δικτύου και η όλη διαδικασία ξεκινάει από την αρχή. Παρουσιάζονται πειραματικά αποτελέσματα που δείχνουν την επιτυχή λειτουργία της στην περίπτωση που υπάρχει στην περιοχή του δικτύου μια, δυο ή τρεις πηγές.<br>Sensor networks are used for monitoring an environment and extracting useful information in an automated way. In recent years, mostly because of the development of suitable integrated circuits, sensor nodes, in small sizes, have emerged. These nodes are capable of processing data, communicating with each other and multi-modal sensing. The thesis is concerned with ad-hoc sensor networks. The problem, that is tackled, is the estimation of position of sources in a multi-source environment. The signals, that are emitted, are modelled as AR processes. The proposed method works in a serial manner. Firstly, one of the sources is selected and the time differences of arrival among the sensor nodes are computed. Then, the position of the source is estimated using the least squares criterion. Finally, the signal of the source is cancelled from the sensor nodes’ received signals and the whole procedure starts over. Experimental results show the functionality of the method when one, two or three sources are present in the environment.