Mohamed Khalaf-Allah

Abstract: This paper investigates a Global Positioning system (GPS)-free positioning method for mobile units (MUs) in outdoor wireless environments by using the Bayesian filtering formulation. The procedure utilizes simulated inertial measurements, cell-ID of the serving base station, and pre-determined locations grouped according to cell antennas radio coverage in the experimentation area. The developed algorithm makes no assumptions on the initial position of the MU. However, the algorithm takes some time to converge. Experiments show the range of inertial measurement errors that would maintain reliable location information with accuracy comparable to GPS positioning.

Abstract: The mobile terminal positioning problem is categorized into three different types according to the availability of (1) initial accurate location information and (2) motion measurement data.Location estimation refers to the mobile positioning problem when both the initial location and motion measurement data are not available. If both are available, the positioning problem is referred to as position tracking. When only motion measurements are available, the problem is known as global localization. These positioning problems were solved within the Bayesian filtering framework. Filter derivation and implementation algorithms are provided with emphasis on the mapping approach. The radio maps of the experimental area have been created by a 3D deterministic radio propagation tool with a grid resolution of 5�m. Real-world experimentation was conducted in a GSM network deployed in a semiurban environment in order to investigate the performance of the different positioning algorithms.

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Abstract: With this paper we present the integration of a 3D laser range sensor into a Monte Carlo Localization (MCL) system. Having the detailed environment perception of the 3D sensor, robust localization in difficult indoor scenes is possible. The presented localization system is able to handle moving people and other unmapped obstacles. At the same time it is possible to use a simplified worldmodel that consists of 2D walls only. This kind of map is easy to generate and independent of displaced furniture. Following the description of the localization system, realworld experiments on position tracking and global localization will demonstrate the functionality of our approach.

Abstract: Localization is a key problem for mobile robot autonomy. It is the problem of determin-ing a robot�s pose from noisy sensor data. MCL algorithms represent a robot�s belief about its location by a set of weighted hypotheses (samples), which approximate the posterior distribution under a common Bayesian formulation of the localization prob-lem. This work presents a self-localization system for mobile robots based on odometry data, 3D laser perception and a line-feature map of the environment. MCL is utilized to fuse data from the different sensors and the given map in order to generate an accurate location estimate. This localization system has been implemented on an experimental robot platform and tested in a real world indoor environment. Furthermore, intensive simulation studies have been conducted. Real world tests and simulation results confirm robustness and reliability of the developed localization system.

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Abstract: The ability to reliably and cheaply localize mobile terminals will allow users to understand and utilize the what, where and when of the surrounding physical world. Therefore, mobile terminal location information will open novel application opportunities in many areas. The mobile terminal positioning problem is categorized into three different types according to the availability of (1) initial accurate location information and (2) motion measurement data. Location estimation refers to the mobile positioning problem when both the initial location and motion measurement data are not available. If both are available, the positioning problem is referred to as position tracking. When only motion measurements are available the problem is known as global localization. These positioning problems were solved within the Bayesian filtering framework in order to work under a common theoretical context. Filter derivation and implementation algorithms are provided with emphasis on the radio mapping approach. The radio maps of the experimental area have been created by a 3D deterministic radio propagation tool with a grid resolution of 5 m. Real-world experiments were conducted in a GSM network, deployed in a semi-urban environment, in order to investigate the performance of the different positioning algorithms. A method is proposed to compute the Cramér-Rao lower bound (CRLB) in order to asses the performance of the received signal strength (RSS) based location estimation algorithm (database correlation method). The fingerprinting databases are usually constructed using complex 3D radio propagation prediction tools. Thus, the RSS-location mapping function is neither continuous nor differentiable everywhere as required by the Cramér-Rao bound calculations. The key approach is reconstructing the fingerprinting database using an empirical path loss formula that sufficiently characterizes the wireless propagation environment of the test area. The Cramér-Rao lower bound is derived and calculated for the reconstructed database in the experimental area. Furthermore, the posterior Cramér-Rao lower bound (PCRLB) is derived and computed in order to asses the performance of the position tracking algorithm.