Distributed localization of a RF target in NLOS environments

We propose a novel distributed expectation maximization (EM) method for non-cooperative RF device localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of non-line-of-sight signals, the signal path consists of a single reflection between the transmitter and receiver. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference sensor, as well as the angle of arrival of the target's signal. We derive a distributed EM algorithm where each node makes use of its local information to compute summary statistics, and then shares these statistics with its neighbors to improve its estimate of the target localization. Since all the measurements need not be centralized at a single location, the spectrum usage can be significantly reduced. The distributed algorithm also allows for increased robustness of the sensor network in the case of node failures. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements with a network of four nodes, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.Comment: 30 pages, 11 figure

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

Emitter Location Finding using Particle Swarm Optimization

Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error

Evaluation of a geometric positioning algorithm for hybrid wireless networks

International audienceIn this paper, we propose a geometric positioning method for hybrid wireless networks, based on a set membership method. Three common types of radio observables are considered for the position estimation: range, difference of ranges and received power. This paper details how to build geometric constraints from observables, and how to merge them to estimate the position. Given a realistic scenario, Monte Carlo simulation shows that the performance of the proposed method in terms of root mean squared error and cumulative density functions outperforms that of a numerically optimized maximum likelihood

Evaluation of a geometric positioning algorithm for hybrid wireless networks

International audienceIn this paper, we propose a geometric positioning method for hybrid wireless networks, based on a set membership method. Three common types of radio observables are considered for the position estimation: range, difference of ranges and received power. This paper details how to build geometric constraints from observables, and how to merge them to estimate the position. Given a realistic scenario, Monte Carlo simulation shows that the performance of the proposed method in terms of root mean squared error and cumulative density functions outperforms that of a numerically optimized maximum likelihood

Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cram\'{e}r-Rao bound (CRB) on position and rotation angle estimation uncertainty from millimeter wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either line-of- sight, non-line-of-sight, or obstructed-line-of-sight conditions.Comment: The manuscript has been revised, and increased from 27 to 31 pages. Also, Fig.2, Fig. 10 and Table I are adde

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Localization in GPS denied environment

No abstract available