RSS-based wireless LAN indoor localization and tracking using deep architectures

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA

An Optimized Propagation Model based on Measurement Data for Indoor Environments, Journal of Telecommunications and Information Technology, 2018, nr 2

Propagation is an essential factor ensuring good coverage of wireless communications systems. Propagation models are used to predict losses in the path between transmitter and receiver nodes. They are usually defined for general conditions. Therefore, their results are not always adapted to the behavior of real signals in a specific environment. The main goal of this work is to propose a new model adjusting the loss coefficients based on empirical data, which can be applied in an indoor university campus environment. The Oneslope, Log-distance and ITU models are described to provide a mathematical base. An extensive measurement campaign is performed based on a strict methodology considering different cases in typical indoor scenarios. New loss parameter values are defined to adjust the mathematical model to the behavior of real signals in the campus environment. The experimental results show that the model proposed offers an attenuation average error of 2.5% with respect to the losses measured. In addition, comparison of the proposed model with existing solutions shows that it decreases the average error significantly for all scenarios under evaluation

Super-Resolution TOA Estimation with Diversity Techniques for Indoor Geolocation Applications

Recently, there are great interests in the location-based applications and the location-awareness of mobile wireless systems in indoor areas, which require accurate location estimation in indoor environments. The traditional geolocation systems such as the GPS are not designed for indoor applications, and cannot provide accurate location estimation in indoor environments. Therefore, there is a need for new location finding techniques and systems for indoor geolocation applications. In this thesis, a wide variety of technical aspects and challenging issues involved in the design and performance evaluation of indoor geolocation systems are presented first. Then the TOA estimation techniques are studied in details for use in indoor multipath channels, including the maximum-likelihood technique, the MUSIC super-resolution technique, and diversity techniques as well as various issues involved in the practical implementation. It is shown that due to the complexity of indoor radio propagation channels, dramatically large estimation errors may occur with the traditional techniques, and the super-resolution techniques can significantly improve the performance of the TOA estimation in indoor environments. Also, diversity techniques, especially the frequency-diversity with the CMDCS, can further improve the performance of the super-resolution techniques

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems

The world around us is getting more connected with each day passing by – new portable devices employing wireless connections to various networks wherever one might be. Locationaware computing has become an important bit of telecommunication services and industry. For this reason, the research efforts on new and improved localisation algorithms are constantly being performed. Thus far, the satellite positioning systems have achieved highest popularity and penetration regarding the global position estimation. In spite the numerous investigations aimed at enabling these systems to equally procure the position in both indoor and outdoor environments, this is still a task to be completed. This research work presented herein aimed at improving the state-of-the-art positioning techniques through the use of two highly popular mobile communication systems: WLAN and public land mobile networks. These systems already have widely deployed network structures (coverage) and a vast number of (inexpensive) mobile clients, so using them for additional, positioning purposes is rational and logical. First, the positioning in WLAN systems was analysed and elaborated. The indoor test-bed, used for verifying the models’ performances, covered almost 10,000m2 area. It has been chosen carefully so that the positioning could be thoroughly explored. The measurement campaigns performed therein covered the whole of test-bed environment and gave insight into location dependent parameters available in WLAN networks. Further analysis of the data lead to developing of positioning models based on ANNs. The best single ANN model obtained 9.26m average distance error and 7.75m median distance error. The novel positioning model structure, consisting of cascade-connected ANNs, improved those results to 8.14m and 4.57m, respectively. To adequately compare the proposed techniques with other, well-known research techniques, the environment positioning error parameter was introduced. This parameter enables to take the size of the test environment into account when comparing the accuracy of the indoor positioning techniques. Concerning the PLMN positioning, in-depth analysis of available system parameters and signalling protocols produced a positioning algorithm, capable of fusing the system received signal strength parameters received from multiple systems and multiple operators. Knowing that most of the areas are covered by signals from more than one network operator and even more than one system from one operator, it becomes easy to note the great practical value of this novel algorithm. On the other hand, an extensive drive-test measurement campaign, covering more than 600km in the central areas of Belgrade, was performed. Using this algorithm and applying the single ANN models to the recorded measurements, a 59m average distance error and 50m median distance error were obtained. Moreover, the positioning in indoor environment was verified and the degradation of performances, due to the crossenvironment model use, was reported: 105m average distance error and 101m median distance error. When applying the new, cascade-connected ANN structure model, distance errors were reduced to 26m and 2m, for the average and median distance errors, respectively. The obtained positioning accuracy was shown to be good enough for the implementation of a broad scope of location based services by using the existing and deployed, commonly available, infrastructure

Geometry-based radio channel modeling : propagation analysis and concept development

In order to fully exploit the potential that the multiple-input multiple-output (MIMO) technology can provide for the novel radio communication applications, knowledge of the radio channel is necessary. For instance, signal processing algorithms or network coverage planning are tasks that are vitally dependent on the characteristics of the radio channel in which the system is desired to operate. However, since it is both time-consuming and expensive to measure all the envisioned usage scenarios, accurate and easy-to-use channel models are essential in many stages of the system development. This thesis aims at improving the quality of the geometry-based stochastic MIMO channel models (GSCMs). First, an overview of the existing MIMO channel models is given including a detailed description of the principles of the models using the geometry-based approach. In addition, the shortages of the current GSCMs are discussed in order to motivate the work of the thesis on their part. The main achievements of this thesis are the following. First of all, as compulsory background work, a measurement-based ray tracer (MBRT) was developed in order to facilitate detailed analysis of the radio channel measurements. With the help of the MBRT, channel model parameters for GSCMs were extracted from measurement data gathered in various indoor environments. In addition, the characteristics of the so called dense multipath components (DMC) were comprehensively studied, and as a result, a method to include the DMC to the GSCMs was developed. Finally, issues related to multi-link MIMO channel modeling were addressed. First and foremost, the propagation phenomena that are important in multi-link scenarios were studied. Based on the analyses, an approach to extend current GSCMs to fully support simulations of multi-link scenarios was invented. Many of the outcomes of this thesis have been directly applied in the COST 2100 MIMO channel model

Radio frequency channel characterization for energy harvesting in factory environments

This thesis presents ambient energy data obtained from a measurement campaign carried out at an automobile plant. At the automobile plant, ambient light, ambient temperature and ambient radio frequency were measured during the day time over two days. The measurement results showed that ambient light generated the highest DC power. For plant and operation managers at the automobile plant, the measurement data can be used in system design considerations for future energy harvesting wireless sensor nodes at the plant. In addition, wideband measurements obtained from a machine workshop are presented in this thesis. The power delay profile of the wireless channel was obtained by using a frequency domain channel sounding technique. The measurements were compared with an equivalent ray tracing model in order to validate the suitability of the commercial propagation software used in this work. Furthermore, a novel technique for mathematically recreating the time dispersion created by factory inventory in a radio frequency channel is discussed. As a wireless receiver design parameter, delay spread characterizes the amplitude and phase response of the radio channel. In wireless sensor devices, this becomes paramount, as it determines the complexity of the receiver. In reality, it is sometimes difficult to obtain full detail floor plans of factories for deterministic modelling or carry out spot measurements during building construction. As a result, radio provision may be suboptimal. The method presented in this thesis is based on 3-D fractal geometry. By employing the fractal overlaying algorithm presented, metallic objects can be placed on a floor plan so as to obtain similar radio frequency channel effects. The environment created using the fractal approach was used to estimate the amount of energy a harvesting device can accumulate in a University machine workshop space