An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters

A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values

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

A Robust Frame of WSN Utilizing Localization Technique

Wireless sensor networks are becoming increasingly popular due to their low cost and wide applicability to support a large number of diverse application areas. Localization of sensor nodes is a fundamental requirement that makes the sensor data meaningful. A wireless sensor network (WSN) consist of spatially distributed autonomous devices using sensors to monitor cooperatively physical or environmental conditions such as temperature, sound, vibration, pressure, motion or pollutants at different locations. The development of wireless sensor networks was originally motivated by a military application like battlefield surveillance. Node localization is required to report the origin of events, assist group querying of sensors, routing and to answer questions on the network coverage. So one of the fundamental challenges in wireless sensor network is node localization. This paper discusses different approaches of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Keywords: Localization, Particle Swarm Optimization, Received Signal Strength, Angle of Arrival

Cooperative Localization with Angular Measurements and Posterior Linearization

The application of cooperative localization in vehicular networks is attractive to improve accuracy and coverage. Conventional distance measurements between vehicles are limited by the need for synchronization and provide no heading information of the vehicle. To address this, we present a cooperative localization algorithm using posterior linearization belief propagation (PLBP) utilizing angle-of-arrival (AoA)-only measurements. Simulation results show that both directional and positional root mean squared error (RMSE) of vehicles can be decreased significantly and converge to a low value in a few iterations. Furthermore, the influence of parameters for the vehicular network, such as vehicle density, communication radius, prior uncertainty and AoA measurements noise, is analyzed.Comment: Submitted for possible publication to an IEEE conferenc

Cooperative localization with angular measurements and posterior linearization

The application of cooperative localization in vehicular networks is attractive to improve accuracy and coverage of the positioning. Conventional distance measurements between vehicles are limited by the need for synchronization and provide no heading information of the vehicle. To address this, we present a cooperative localization algorithm using posterior linearization belief propagation (PLBP) utilizing angle-of-arrival (AoA)-only measurements. Simulation results show that both directional and positional root mean squared error (RMSE) of vehicles can be decreased significantly and converge to a low value in a few iterations. Furthermore, the influence of parameters for the vehicular network, such as vehicle density, communication radius, prior uncertainty, and AoA measurements noise, is analyzed

Bayesian graphical models for indoor localization in MTC deployment scenarios

Abstract. Herein, we propose and assess an iterative Bayesian-based indoor localization system to estimate the position of a target device. We describe the Bayesian network and then build graphical models for various measurement metrics, namely Received Signal Strength (RSS), Time Difference of Arrival (TDOA), and Angle of Arrival (AOA) which are collected by the distributed receivers in the network area. The estimations are carried out by Markov chain Monte Carlo (MCMC) methods which approximates the target’s position using the Bayesian network model and measurements collected by the receivers. We employ an iterative method by using previous estimations of the target’s position as prior knowledge to improve the accuracy of the subsequent estimations, where the prior knowledge is used as the prior distributions of our Bayesian model. In our results, we observe that the proposed iterative localization system improves the performance of the Bayesian TDOA-based localization system by increasing the respective estimate accuracy. Furthermore, we show that the number of measurements collected by the receivers and the selected prior distribution also affect the performance of the proposed iterative mechanism. In fact, the number of measurements increases the accuracy of the mechanism, while its benefit diminishes with more iterations as the mechanism progresses. Regarding the prior distribution, we show that it can lead to good or bad estimations of the target’s position, and therefore, needs to be carefully chosen considering the measurement metric and the mobility of the target node

GPS-free Localization technique using a Wireless Sensor Network

Improvements in localization based technologies have led to a growing business interest in location-based applications and services. Nowadays, locating the physical belonging indoor and outdoor environment become one of the application requirements. Localization is the technique to obtain the location information of objects. The location information of objects can be obtained by using wireless sensor networks in the sensor localization. Global Positioning System (GPS) is one of the device that use wireless networks to obtain location information. However, due to the expensive cost and it is unable to be used in indoor environment, an alternatives need to be figured out. Thus, the aim of this project is to build a reliable and cheaper local positioning system, which can function in indoor and outdoor setting. Literature review on some techniques has been done and from the review, the features, advantage and disadvantage of all techniques discussed are compared. The techniques that have been discussed in this study are time of arrival (TOA), time different of arrival (TDOA), angle of arrival (AOA) and received signal strength indicator (RSSI).Based on the comparison of 4 techniques above, the TOA approach has been selected to be focus further and will be implement in Matlab software for future work. The parameters are varied to compare the performance of node localization. By using the baseline value, the performance comparison is done by varying some parameters which are network area, numbers of anchors, anchors arrangements, and number of run. From the results obtained, four conclusions can be made. First, percentage of accuracy is higher with smaller network area. Second, less number of anchors will increase the percentage error, third, increase in the number of run will yield lower percentage error and the final one is the best topology of anchor are in hexagon arrangement