RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

A Hardware Platform for Communication and Localization Performance Evaluation of Devices inside the Human Body

Body area networks (BAN) is a technology gaining widespread attention for application in medical examination, monitoring and emergency therapy. The basic concept of BAN is monitoring a set of sensors on or inside the human body which enable transfer of vital parameters between the patient´s location and the physician in charge. As body area network has certain characteristics, which impose new demands on performance evaluation of systems for wireless access and localization for medical sensors. However, real-time performance evaluation and localization in wireless body area networks is extremely challenging due to the unfeasibility of experimenting with actual devices inside the human body. Thus, we see a need for a real-time hardware platform, and this thesis addressed this need. In this thesis, we introduced a unique hardware platform for performance evaluation of body area wireless access and in-body localization. This hardware platform utilizes a wideband multipath channel simulator, the Elektrobit PROPSimâ„¢ C8, and a typical medical implantable device, the Zarlink ZL70101 Advanced Development Kit. For simulation of BAN channels, we adopt the channel model defined for the Medical Implant Communication Service (MICS) band. Packet Reception Rate (PRR) is analyzed as the criteria to evaluate the performance of wireless access. Several body area propagation scenarios simulated using this hardware platform are validated, compared and analyzed. We show that among three modulations, two forms of 2FSK and 4FSK. The one with lowest raw data rate achieves best PRR, in other word, best wireless access performance. We also show that the channel model inside the human body predicts better wireless access performance than through the human body. For in-body localization, we focus on a Received Signal Strength (RSS) based localization algorithm. An improved maximum likelihood algorithm is introduced and applied. A number of points along the propagation path in the small intestine are studied and compared. Localization error is analyzed for different sensor positions. We also compared our error result with the Cramèr- Rao lower bound (CRLB), shows that our localization algorithm has acceptable performance. We evaluate multiple medical sensors as device under test with our hardware platform, yielding satisfactory localization performance

Bayesian algorithms for mobile terminal positioning in outdoor wireless environments

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Development and Demonstration of a TDOA-Based GNSS Interference Signal Localization System

Background theory, a reference design, and demonstration results are given for a Global Navigation Satellite System (GNSS) interference localization system comprising a distributed radio-frequency sensor network that simultaneously locates multiple interference sources by measuring their signals’ time difference of arrival (TDOA) between pairs of nodes in the network. The end-to-end solution offered here draws from previous work in single-emitter group delay estimation, very long baseline interferometry, subspace-based estimation, radar, and passive geolocation. Synchronization and automatic localization of sensor nodes is achieved through a tightly-coupled receiver architecture that enables phase-coherent and synchronous sampling of the interference signals and so-called reference signals which carry timing and positioning information. Signal and crosscorrelation models are developed and implemented in a simulator. Multiple-emitter subspace-based TDOA estimation techniques are developed as well as emitter identification and localization algorithms. Simulator performance is compared to the CramérRao lower bound for single-emitter TDOA precision. Results are given for a test exercise in which the system accurately locates emitters broadcasting in the amateur radio band in Austin, TX.Aerospace Engineering and Engineering Mechanic

using ripe atlas for geolocating ip infrastructure

The vast majority of studies on IP geolocation focuses on localizing the end-users, and little attention has been devoted to localizing the elements of the Internet infrastructure, i.e., the routers and servers that make the Internet work. In this paper, we study the maximum theoretical accuracy that can be achieved by a geolocation approach aimed at geolocating the Internet infrastructure. In particular, we study the effects on localization accuracy produced by the position of landmarks and by the strategy followed for their enrollment. We compare two main approaches: the first is more centralized and controlled, and uses well-connected machines belonging to the infrastructure as landmarks; the second is more distributed and scalable and is based on landmarks positioned at the edge of the network. The study is based on an extensive set of measurements collected using the RIPE Atlas platform. The results show that the uniform and widespread diffusion of landmarks can be as important as their measurement accuracy. The study is carried out at both the worldwide and regional scale, including regions that were scarcely observed in the past. The results highlight that the geographical characteristics of the Internet paths are dependent on the considered region, thus suggesting the use of specifically calibrated models. Finally, the study shows that geolocating IP infrastructure with active measurements is feasible in terms of precision and scalability of the overall system

MmWave V2V Localization in MU-MIMO Hybrid Beamforming

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in a number of vehicles reduces the Cramr-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters

mmWave V2V Localization in MU-MIMO Hybrid Beamforming

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in a number of vehicles reduces the Cramr-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters