Discretization by rasterization is introduced into the method of images (MI) in the context of 3D deterministic radio propagation modeling as a way to exploit spatial coherence of electromagnetic propagation for fine grained parallelism. Traditional algebraic treatment of bounding regions and surfaces is replaced by computer graphics rendering of 3D reflections and double refractions while building the image tree. The visibility of reception points and surfaces is also resolved by shader programs. The proposed rasterization is shown to be of comparable run time to that of the fundamentally parallel shooting and bouncing rays. The rasterization does not affect the signal evaluation backtracking step, thus preserving its advantage over the brute force ray-tracing methods in terms of accuracy. Moreover, the rendering resolution may be scaled back for a given level of scenario detail with only marginal impact on the image tree size. This allows selection of scene optimized execution parameters for faster execution, giving the method a competitive edge. The proposed variant of MI can be run on any GPU that supports real-time 3D graphics.
COBISS.SI-ID: 29665319
Method of images (MI) is one of the oldest methods for radio wave propagation prediction based on the ray-tracing principle. Although the MI was originally restricted to the radio environments with prevailing reflection phenomena, it is also used in indoor scenarios in which through-wall transmission makes significant contribution to the received signal power. Exact handling of propagation paths, either in the form of polyhedra bounding regions or in the form of some other equivalent geometrical description, is usually complemented with the use of visibility trees to contain excessive growth of source images. However, strict visibility trees and double refractions on parallel planes involved in through-wall transmissions are not well-suited. Here we study visibility inaccuracy, which is usually ignored. We propose a source image translation heuristic based on the wall depth, material and field of view. We show that the proposed double refraction modelling improves accuracy of strict visibility trees, which gives a better fit of predicted signal to the theoretically correct solution.
COBISS.SI-ID: 30271015
An increasing number of services and applications are using the user's location for personalization. Accurate localization is crucial for improved energy efficiency of mobile terminals and radio networks, dynamic optimization of radio and network resources, setting up new cognitive wireless networks and enhanced use of the proximity services. The need for tracking and locating persons and belongings in indoor environments is continuously increasing. As the global positioning systems are inadequate for complex indoor and dense urban environments, where a higher precision is usually required, a variety of technologies and algorithms suitable for indoor positioning have been developed. The paper is a comprehensive review of the existing radio localization solutions suitable for indoor environments. In addition to a short description of the radio localization challenges and review of the solutions performance criteria, a brief overview of the suitable wireless technologies is provided. The main focus of the paper is on classification and description of various localization procedures and algorithms. Location determination algorithms based on indirect measurements of distances (triangulation) and scene analysis (fingerprint) are described. Since localization features are also frequently used in energy constrained sensor networks and networks where so-called anchor nodes with known location are relatively sparse, an overview of cooperative localization approaches is also given.
COBISS.SI-ID: 30913063
Indoor localization is one of the key enablers for various application and service areas that rely on precise locations of people, goods, and assets, ranging from home automation and assisted living to increased automation of production and logistic processes and wireless network optimization. Existing solutions provide various levels of precision, which also depends on the complexity of the indoor radio environment. Here we propose two methods for reducing the localization error in indoor non line-of-sight (NLoS) conditions using raw channel impulse response (CIR) information obtained from ultra-wide band radios requiring no prior knowledge about the radio environment. The methods are based on NLoS channel classification and ranging error regression models, both using convolutional neural networks (CNNs) and implemented in the TensorFlow computational framework. We first show that NLoS channel classification using raw CIR data outperforms existing approaches that are based on derived input signal features. We further demonstrate that the predicted NLoS channel state and predicted ranging error information, used in combination with least squares (LS) and weighted LS location estimation algorithms, significantly improve indoor localization performance. We also evaluate the computational performance and suitability of the proposed CNN-based algorithms on various computing platforms with a wide range of different capabilities and show that in a distributed localization system, they can be used on computationally restricted devices.
COBISS.SI-ID: 31291943
Wireless device position tracking has been already thoroughly studied in literature. Most of the studies rely on the presumption that location information is acquired based on range measurements that are performed in a very short period of time. However, in time-division-multiple-access (TDMA) two-way ranging (TWR) ultra-wideband (UWB) wireless localization networks, those ranging measurements are always spread in time by significant time delays. Those delays have negative impact on the tracking performance and the effects of ranging in these systems should be evaluated accordingly. We propose a time-of-flight (ToF) simulation-based approach for indoor tracking algorithm evaluation with a measurement calibration which enables changing the size of TDMA slots and thus observing the tracking performance degradation. A constant velocity movement model with a random curvature of walking path is proposed to simulate the person’s random walking pattern inside the room as naturally as possible. With a proposed simulation framework the impact of individual parameter changes on tracking performance can be evaluated.
COBISS.SI-ID: 31582759