Wireless Localization

Reliable indoor navigation of mobile robots has been a popular research topic in recent years. GPS systems used for outdoor mobile robot navigation can not be used indoor (warehouse, hospital or other buildings) because it requires an unobstructed view of the sky. Therefore a specially designed indoor localization system for mobile robot is needed. This project aims to develop a reliable position and heading angle estimator for real time indoor localization of mobile robots. Two different techniques have been developed and each consisted of three different sensor modules based on infrared sensing, calibrated odometry and calibrated gyroscope. Integration of these three sensor modules is achieved by applying the real time Kalman filter which provides filtered and reliable information of a mobile robot's current location and orientation relative to its environment. Extensive experimental results are provided to demonstrate its improvement over conventional methods like dead reckon...

This letter addresses the hybrid range/angle-based target localization problem in a cooperative 3-D wireless sensor network where no central processor is available. Due to battery exhaust over time, sensors' transmit powers are assumed different and unknown. Range and angle measurements are drawn from the received signal strength and angle-of-arrival models, respectively. By exploiting the measurement models, we derive a novel local-estimator by which each target updates its own estimate, based on the least squares criterion. Second-order cone relaxation technique is then applied to approximately solve the attained problem due to its non-convex nature. Our simulation results show that the proposed algorithm efficiently solves the localization problem.

Wireless network is a network set up by using radio signal frequency to communicate among computers and other network devices that are not connected by cables of any kind. These network mainly used for communication and data exchange. However, it is also being used to acquired one’s or device’s position with certain accuracy. For outdoor, Global Positioning System (GPS) is being used by peoples to navigate to any places in the world and also used in rocket or guided missile system as well. In case of indoor positioning or localization, GPS system is not possible due to the satellite signal is being block and therefore weak. Bluetooth Low Energy (BLE) is one of many solutions to cover this problem. BLE indoor localizationtechnology able to obtain the position byutilizing the Receive Signal Strength Index (RSSI) which is then used in positioning algorithm such as fingerprinting or trilateration. But there is limitation with theaccuracy of this systemmainly due to unstable RSSI. There ...

In wireless positioning systems, the transmitter's power is usually fixed. In this paper, we explore the use of varying transmission powers to increase the performance of a wireless localization system. To this extent, we have designed a robot positioning system based on wireless motes. Our motes use an inexpensive, low-power sub-1-GHz system-on-chip (CC1110) working in the 433-MHz ISM band. Our localization algorithm is based on a particle filter and infers the robot position by: (1) comparing the power received with the expected one; and (2) integrating the robot displacement. We demonstrate that the use of transmitters that vary their transmission power over time improves the performance of the wireless positioning system significantly, with respect to a system that uses fixed power transmitters. This opens the door for applications where the robot can localize itself actively by requesting the transmitters to change their power in real time.

Indoor localisation techniques in multi-floor environments are emerging for location based service applications. Developing an accurate location determination and timeefficient technique is crucial for online location estimation of the multi-floor localisation system. The localisation accuracy and computational complexity of the localisation system mainly relies on the performance of the algorithms embedded with the system. Unfortunately, existing algorithms are either time-consuming or inaccurate for simultaneous determination of floor and horizontal locations in multifloor environment. This thesis proposes an improved multi-floor localisation technique by integrating three important elements of the system; radio map fingerprint database optimisation, floor or vertical localisation, and horizontal localisation. The main focus of this work is to extend the kernel density approach and implement multi-class machine learning classifiers to improve the localisation accuracy and processing time of the each and overall elements of the proposed technique.

In typical urban wireless channels with large delay spreads, the conventional TDoA (Time Difference of Arrival)-based mobile-WiMAX (Worldwide Interoperability for Microwave Access) positioning systems hardly satisfy the FCC (Federal Communications Commission)'s E-911 (Enhanced-911) detection criteria. In this paper, we propose a simple yet very effective TDoA-based position tracking algorithm for mobile-WiMAX systems when the user moves into GPS (Global Positioning System)-shadowed areas such as indoors or dense urban environments.

In this paper, we propose a new approach based on convex optimization to address the received signal strength (RSS)-based cooperative localization problem in wireless sensor networks (WSNs). By using iterative procedures and measurements between two adjacent nodes in the network exclusively, each target node determines its own position locally. The localization problem is formulated using the maximum likelihood (ML) criterion, since ML-based solutions have the property of being asymptotically efficient. To overcome the non-convexity of the ML optimization problem, we employ the appropriate convex relaxation technique leading to second-order cone programming (SOCP). Additionally, a simple heuristic approach for improving the convergence of the proposed scheme for the case when the transmit power is known is introduced. Furthermore, we provide details about the computational complexity and energy consumption of the considered approaches. Our simulation results show that the proposed approach outperforms the existing ones in terms of the estimation accuracy for more than 1.5 m. Moreover, the new approach requires a lower number of iterations to converge, and consequently, it is likely to preserve energy in all presented scenarios, in comparison to the state-of-the-art approaches.

This letter addresses the hybrid range/angle-based target localization problem in a cooperative 3-D wireless sensor network where no central processor is available. Due to battery exhaust over time, sensors' transmit powers are assumed different and unknown. Range and angle measurements are drawn from the received signal strength and angle-of-arrival models, respectively. By exploiting the measurement models, we derive a novel local-estimator by which each target updates its own estimate, based on the least squares criterion. Second-order cone relaxation technique is then applied to approximately solve the attained problem due to its non-convex nature. Our simulation results show that the proposed algorithm efficiently solves the localization problem.

In this paper, we propose a new approach based on convex optimization to address the received signal strength (RSS)-based cooperative localization problem in wireless sensor networks (WSNs). By using iterative procedures and measurements between two adjacent nodes in the network exclusively, each target node determines its own position locally. The localization problem is formulated using the maximum likelihood (ML) criterion, since ML-based solutions have the property of being asymptotically efficient. To overcome the non-convexity of the ML optimization problem, we employ the appropriate convex relaxation technique leading to second-order cone programming (SOCP). Additionally, a simple heuristic approach for improving the convergence of the proposed scheme for the case when the transmit power is known is introduced. Furthermore, we provide details about the computational complexity and energy consumption of the considered approaches. Our simulation results show that the proposed approach outperforms the existing ones in terms of the estimation accuracy for more than 1.5 m. Moreover, the new approach requires a lower number of iterations to converge, and consequently, it is likely to preserve energy in all presented scenarios, in comparison to the state-of-the-art approaches.

This paper presents a brief overview of recently developed algorithms for target localization and tracking using combined angle of arrival (AoA) and received signal strength (RSS) observations. Due to the non-linearity and non-convexity of the problem at hand, our main focus here is on the estimators that solve the localization problem by transforming it into more suitable frameworks. In other words, these estimators share the main objective in which the original problem is approximated by another (possibly convex) one whose solution can be readily found. Different convex relaxation techniques are described, and the paper presents recent advancement of the techniques in the sense of both computational complexity and localization accuracy.

This work revises existing solutions for a problem of target localization in wireless sensor networks (WSNs), utilizing integrated measurements, namely received signal strength (RSS) and angle of arrival (AoA). The problem of RSS/AoA-based target localization became very popular in the research community recently, owing to its great applicability potential and relatively low implementation cost. Therefore, here, a comprehensive study of the state-of-the-art (SoA) solutions and their detailed analysis is presented. The beginning of this work starts by considering the SoA approaches based on convex relaxation techniques (more computationally complex in general), and it goes through other (less computationally complex) approaches, as well, such as the ones based on the generalized trust region sub-problems framework and linear least squares. Furthermore, a detailed analysis of the computational complexity of each solution is reviewed. Furthermore, an extensive set of simulation results...

This work revises existing solutions for a problem of target localization in wireless sensor networks (WSNs), utilizing integrated measurements, namely received signal strength (RSS) and angle of arrival (AoA). The problem of RSS/AoA-based target localization became very popular in the research community recently, owing to its great applicability potential and relatively low implementation cost. Therefore, here, a comprehensive study of the state-of-the-art (SoA) solutions and their detailed analysis is presented. The beginning of this work starts by considering the SoA approaches based on convex relaxation techniques (more computationally complex in general), and it goes through other (less computationally complex) approaches, as well, such as the ones based on the generalized trust region sub-problems framework and linear least squares. Furthermore, a detailed analysis of the computational complexity of each solution is reviewed. Furthermore, an extensive set of simulation results...

In this paper, we propose and investigate a hybrid positioning data fusion technique for heterogeneous networks in critical transmission scenarios. The focus is on two scenarios: the small indoor scenario combining Wi-Fi and cellular systems and the small-to-mid-scale scenario composed of one located Mobile Terminal (MT) and one anchor node (AN). More specifically, we investigate the effect of the availability of three metrics i.e. the time of arrival (ToA), the angle of arrival (AoA), and the received signal strength-based fingerprint (RSS) on the positioning accuracy when the number of ANs is less than three. To combine these measurements, we use a 2-level unscented Kalman Filter (UKF) in conjunction with some advanced clustering techniques based on genetic algorithms. Simulation results show that the proposed hybrid data fusion technique outperforms the techniques presented in the literature independently of the transmission conditions.

The time for reflections and visions about the Fourth Generation of Wireless Mobile Communication Systems (4G) is getting closer to the X hour, therefore the research community has to finally declare how 4G will really look like. Besides the no-boundaries visions, in this paper we have a look into the 4G crystal ball, attempting to define the forthcoming system fusing both the user and the technology perspectives.

In this paper, we propose an innovative solution for positioning determination in fourth generation (4G) wireless networks by introducing the Ad-Coop Positioning System (ACPS). The ACPS is supported by a hybrid cellular ad-hoc architecture, where the cellular network has a centralised control over the ad-hoc connections among pairs of mobiles. Specifically, peer-to-peer (P2P) communications are exploited in a mesh fashion within cellular-established clusters for cooperation-aided localisation purposes (from that, the word ad-coop is derived). The major contribution of this work is the design of the data fusion method for the ACPS which appropriately combines and weights the long-and short-range location information in a nonlinear least square (NLLS) minimisation procedure. The numerical results shown in the paper demonstrate that the ACPS enhances the location estimation accuracy with respect to conventional hybrid positioning techniques in cellular networks. ‡ ‡ In practice, the standard deviation of the measurement errors depends on the chip rate, the propagation environment and the home BS antenna parameters. It can be estimated based on the SNR and its typical values for various propagation conditions .

To overcome the shortcomings of conventional cellular positioning, a novel cooperative location algorithm that uses the available peer-to-peer communication between the mobile terminals (MTs) is proposed. The main idea behind the proposed approach is to incorporate the long-and short-range location information to improve the estimation of the MT's coordinates. Since short-range communications among MTs are characterized by high line-of-sight (LOS) probability, an improved spring-model-based cooperative location method can be exploited to provide low-cost improvement for cellular-based location in the non-line-ofsight (NLOS) environments.