Development of a Model and Localization Algorithm for Received Signal Strength-Based Geolocation

Location-Based Services (LBS), also called geolocation, have become increasingly popular in the past decades. They have several uses ranging from assisting emergency personnel, military reconnaissance and applications in social media. In geolocation a group of sensors estimate the location of transmitters using position and Radio Frequency (RF) information. A review of the literature revealed that a majority of the Received Signal Strength (RSS) techniques used made erroneous assumptions about the distribution or ignored effects of multiple transmitters, noise and multiple antennas. Further, the corresponding algorithms are often mathematically complex and computationally expensive. To address the issues this dissertation focused on RSS models which account for external factors effects and algorithms that are more efficient and accurate

A Real-Time Laboratory Testbed For Evaluating Localization Performance Of WIFI RFID Technologies

A realistic comparative performance evaluation of indoor Geolocation systems is a complex and challenging problem facing the research community. This is due to the fact that performance of these systems depends on the statistical variations of the fading multipath characteristics of the wireless channel, the density and distribution of the access points in the area, and the number of the training points used by the positioning algorithm. This problem, in particular, becomes more challenging when we address RFID devices, because the RFID tags and the positioning algorithm are implemented in two separate devices. In this thesis, we have designed and implemented a testbed for comparative performance evaluation of RFID localization systems in a controlled and repeatable laboratory environment. The testbed consists of a real-time RF channel simulator, several WiFi 802.11 access points, commercial RFID tags, and a laptop loaded with the positioning algorithm and its associated user interface. In the real-time channel simulator the fading multipath characteristics of the wireless channel between the access points and the RFID tags is modeled by a modified site-specific IEEE 802.11 channel model which combines this model with the correlation model of shadow fading existing in the literature. The testbed is first used to compare the performance of the modified IEEE 802.11 channel model and the Ray Tracing channel model previously reported in the literature. Then, the testbed with the new channel model is used for comparative performance evaluation of two different WiFi RFID devices

On the Usage of Geolocation-Aware Spectrum Measurements for Incumbent Location and Transmit Power Detection

© 2017 IEEE. Determining the geographical area that needs to be excluded due to incumbent activity is critical to realize high spectral utilization in spectrum sharing networks. This can be achieved by estimating the incumbent location and transmit power. However, keeping the hardware complexity of sensing nodes to a minimum and scalability are critical for spectrum sharing applications with commercial intent. We present a discrete-space l1-norm minimization solution based on geolocation-aware energy detection measurements. In practice, the accuracy of geolocation tagging is limited. We capture the impact as a basis mismatch and derive the necessary condition that needs to be satisfied for successful detection of multiple incumbents' location and transmit power. We find the upper bound for the probability of eliminating the impact of limited geolocation tagging accuracy in a lognormal shadow fading environment, which is applicable to all generic I1-norm minimization techniques. We propose an algorithm based on orthogonal matching pursuit that decreases the residual in each iteration by allowing a selected set of basis vectors to rotate in a controlled manner. Numerical evaluation of the proposed algorithm in a Licensed Shared Access (LSA) network shows a significant improvement in the probability of missed detection and false alarm

Localization in Spatially Correlated Shadow-Fading Environment

Στην διπλωματική αυτή εργασία ενδιαφέρομαστε για Received Signal Strength (RSS) localization λόγω της ενγενης απλοτιτας του οπου ο καθε δεκτης μετρα ισχυ. Η διατριβή παρουσιάζει τόσο θεωρητικα όσο και πειραματικά αποτελέσματα. Στην αρχη κατασκευαζεται και παρουσιάζεται ένα νέο θεωρητικό όριο για το πρόβλημα εντοπισμού μίας πηγής σε χωρικα συσχέτισομενο περιβαλον, με χρησει conditional measurments και στη συνέχεια να χρησιμοποιείτε για την αξιολόγηση των επιδόσεων. Επιπλέον, παρουσιάζονται ορισμένα θεωρητικά αποτελέσματα στο πιο δύσκολο πρόβλημα του εντοπισμού πολλαπλών πηγών και πάλι για την περίπτωση του χωτικα συσχετιζομενου shadow fading περιβαλοντος. Αυτά τα αποτελέσματα δεν χρεισιμοποιουν contitionla measurments, αλλά δείχνουν πώς η απόδοση σχετιζετε με τον αριθμό των δεκτων, των αριθμό των άγνωστων πηγων, και ο συντελεστής συσχέτισης του περιβάλλοντος. Επιπλέον, δύο πειραματικές εκστρατειες εσωτερικου χωρου περιλαμβάνονται στην παρούσα διατριβή, και στις δύο χρησιμοποιηθηκε η OpenAirInterface (OAI) πλατφόρμα. Ο κύριος στόχος των εκστρατειών ήταν 1) να εξακριβώσει η ύπαρξη χωρικης συσχετισεις του shadow fading για εσοτερικο χωρο και 2) να χρησιμοποιούν ad-hoc τεχνικές και αλγόριθμοι, προκειμένου να επιτευχθεί κάποιο όφελος από την χωρική συσχέτιση υποθέτοντας γνώση contitional measurments.In this thesis we are interested on Received Signal Strength (RSS) localization due to its simplicity as every radio measures power. Thesis presents both theoretical as well as experimental results. We derive and present a new theoretical bound for the single-source localization problem that takes spatial- correlation as well as conditional measurements into account and then uses it to assess performance. Furthermore, presents some theoretical results in the more challenging multi-source localization problem again for the case of correlated shadow-fading environments. These results did not assume prior knowledge of conditional measurements, but show how the localization performance scales with respect to the number of sensors, the number of unknown sources, and the correlation coefficient of the environment. Additionally, two indoor experimental campaigns are included in this thesis, both of them used the OpenAirInterface (OAI) platform. The main target of the campaigns was 1) to verify the existence of shadow-fading in the indoor environment and 2) to use ad-hoc techniques in our localization algorithms in order achieve some gain from spatial correlation assuming knowledge of conditional measurements.

Cramer-Rao Bounds for Joint RSS/DoA-Based Primary-User Localization in Cognitive Radio Networks

Knowledge about the location of licensed primary-users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we consider the achievable accuracy of PU localization algorithms that jointly utilize received-signal-strength (RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only localization algorithms separately and assume DoA estimation error variance is a fixed constant or rather independent of RSS. We derive the CRB for joint RSS/DoA-based PU localization algorithms based on the mathematical model of DoA estimation error variance as a function of RSS, for a given CR placement. The bound is compared with practical localization algorithms and the impact of several key parameters, such as number of nodes, number of antennas and samples, channel shadowing variance and correlation distance, on the achievable accuracy are thoroughly analyzed and discussed. We also derive the closed-form asymptotic CRB for uniform random CR placement, and perform theoretical and numerical studies on the required number of CRs such that the asymptotic CRB tightly approximates the numerical integration of the CRB for a given placement.Comment: 20 pages, 11 figures, 1 table, submitted to IEEE Transactions on Wireless Communication

Indoor Cooperative Localization for Ultra Wideband Wireless Sensor Networks

In recent years there has been growing interest in ad-hoc and wireless sensor networks (WSNs) for a variety of indoor applications. Localization information in these networks is an enabling technology and in some applications it is the main sought after parameter. The cooperative localization performance of WSNs is ultimately constrained by the behavior of the utilized ranging technology in dense cluttered indoor environments. Recently, ultra-wideband (UWB) Time-of-Arrival (TOA) based ranging has exhibited potential due to its large bandwidth and high time resolution. However, the performance of its ranging and cooperative localization capabilities in dense indoor multipath environments needs to be further investigated. Of main concern is the high probability of non-line of sight (NLOS) and Direct Path (DP) blockage between sensor nodes, which biases the TOA estimation and degrades the localization performance. In this dissertation, we first present the results of measurement and modeling of UWB TOA-based ranging in different indoor multipath environments. We provide detailed characterization of the spatial behavior of ranging, where we focus on the statistics of the ranging error in the presence and absence of the DP and evaluate the pathloss behavior in the former case which is important for indoor geolocation coverage characterization. Parameters of the ranging error probability distributions and pathloss models are provided for different environments: traditional office, modern office, residential and manufacturing floor; and different ranging scenarios: indoor-to-indoor (ITI), outdoor-to-indoor (OTI) and roof-to-indoor (RTI). Based on the developed empirical models of UWB TOA-based OTI and ITI ranging, we derive and analyze cooperative localization bounds for WSNs in the different indoor multipath environments. First, we highlight the need for cooperative localization in indoor applications. Then we provide comprehensive analysis of the factors affecting localization accuracy such as network and ranging model parameters. Finally we introduce a novel distributed cooperative localization algorithm for indoor WSNs. The Cooperative LOcalization with Quality of estimation (CLOQ) algorithm integrates and disseminates the quality of the TOA ranging and position information in order to improve the localization performance for the entire WSN. The algorithm has the ability to reduce the effects of the cluttered indoor environments by identifying and mitigating the associated ranging errors. In addition the information regarding the integrity of the position estimate is further incorporated in the iterative distributed localization process which further reduces error escalation in the network. The simulation results of CLOQ algorithm are then compared against the derived G-CRLB, which shows substantial improvements in the localization performance

Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks

Geolocation involves using data from a sensor network to assess and estimate the location of a moving or stationary target. Received Signal Strength (RSS), Angle of Arrival (AoA), and/or Time Difference of Arrival (TDoA) measurements can be used to estimate target location in sensor networks. Radio Tomographic Imaging (RTI) is an emerging Device-Free Localization (DFL) concept that utilizes the RSS values of a Wireless Sensor Network (WSN) to geolocate stationary or moving target(s). The WSN is set up around the Area of Interest (AoI) and the target of interest, which can be a person or object. The target inside the AoI creates a shadowing loss between each link being obstructed by the target. This research focuses on position estimation of single and multiple targets inside a RTI network. This research applies K-means clustering to localize one or more targets. K-means clustering is an algorithm that has been used in data mining applications such as machine learning applications, pattern recognition, hyper-spectral imagery, artificial intelligence, crowd analysis, and Multiple Target Tracking (MTT)

Advanced Wireless Localisation Methods Dealing with Incomplete Measurements

Positioning techniques have become an essential part of modern engineering, and the improvement in computing devices brings great potential for more advanced and complicated algorithms. This thesis first studies the existing radio signal based positioning techniques and then presents three developed methods in the sense of dealing with incomplete data. Firstly, on the basis of received signal strength (RSS) location fingerprinting techniques, the Kriging interpolation methods are applied to generate complete fingerprint databases of denser reference locations from sparse or incomplete data sets, as a solution of reducing the workload and cost of offline data collection. Secondly, with incomplete knowledge of shadowing correlation, a new approach of Bayesian inference on RSS based multiple target localisation is proposed taking advantage of the inverse Wishart conjugate prior. The MCMC method (Metropolis-within-Gibbs) and the maximum a posterior (MAP) / maximum likelihood (ML) method are then considered to produce target location estimates. Thirdly, a new information fusion approach is developed for the time difference of arrival (TDOF) and frequency difference of arrival (FDOA) based dual-satellite geolocation system, as a solution to the unknown time and frequency offsets. All proposed methods are studied and validated through simulations. Result analyses and future work directions are discussed

Super-Resolution TOA Estimation with Diversity Techniques for Indoor Geolocation Applications

Recently, there are great interests in the location-based applications and the location-awareness of mobile wireless systems in indoor areas, which require accurate location estimation in indoor environments. The traditional geolocation systems such as the GPS are not designed for indoor applications, and cannot provide accurate location estimation in indoor environments. Therefore, there is a need for new location finding techniques and systems for indoor geolocation applications. In this thesis, a wide variety of technical aspects and challenging issues involved in the design and performance evaluation of indoor geolocation systems are presented first. Then the TOA estimation techniques are studied in details for use in indoor multipath channels, including the maximum-likelihood technique, the MUSIC super-resolution technique, and diversity techniques as well as various issues involved in the practical implementation. It is shown that due to the complexity of indoor radio propagation channels, dramatically large estimation errors may occur with the traditional techniques, and the super-resolution techniques can significantly improve the performance of the TOA estimation in indoor environments. Also, diversity techniques, especially the frequency-diversity with the CMDCS, can further improve the performance of the super-resolution techniques

Hybrid RSS-RTT Localization Scheme for Indoor Wireless Networks

[EN]Nowadays, a variety of information related to the distance between two wireless devices can be easily obtained. This paper presents a hybrid localization scheme that combines received signal strength (RSS) and round-trip time (RTT) information with the aim of improving the previous localization schemes. The hybrid localization scheme is based on an RSS ranging technique that uses RTT ranging estimates as constraints among other heuristic constraints. Once distances have been well estimated, the position of the mobile station (MS) to be located is estimated using a new robust least-squared multilateration (RLSM) technique that combines the RSS and RTT ranging estimates mitigating the negative effect of outliers. The hybrid localization scheme coupled with simulations and measurements demonstrates that it outperforms the conventional RSS-based and RTT-based localization schemes, without using either a tracking technique or a previous calibration stage of the environment.Dirección General de Telecomunicaciones de la Consejería de Fomento de Castilla y Leó