A Markov Model for Dynamic Behavior of Toa-Based Ranging in Indoor Localization

The existence of undetected direct path ( UDP) conditions causes occurrence of unexpected large random ranging errors which pose a serious challenge to precise indoor localization using time of arrival ( ToA). Therefore, analysis of the behavior of the ranging error is essential for the design of precise ToA-based indoor localization systems. In this paper, we propose a novel analytical framework for the analysis of the dynamic spatial variations of ranging error observed by a mobile user based on an application of Markov chain. the model relegates the behavior of ranging error into four main categories associated with four states of the Markov process. the parameters of distributions of ranging error in each Markov state are extracted from empirical data collected from a measurement calibrated ray tracing ( RT) algorithm simulating a typical office environment. the analytical derivation of parameters of the Markov model employs the existing path loss models for the first detected path and total multipath received power in the same office environment. Results of simulated errors from the Markov model and actual errors from empirical data show close agreement

Indoor Geo-location And Tracking Of Mobile Autonomous Robot

The field of robotics has always been one of fascination right from the day of Terminator. Even though we still do not have robots that can actually replicate human action and intelligence, progress is being made in the right direction. Robotic applications range from defense to civilian, in public safety and fire fighting. With the increase in urban-warfare robot tracking inside buildings and in cities form a very important application. The numerous applications range from munitions tracking to replacing soldiers for reconnaissance information. Fire fighters use robots for survey of the affected area. Tracking robots has been limited to the local area under consideration. Decision making is inhibited due to limited local knowledge and approximations have to be made. An effective decision making would involve tracking the robot in earth co-ordinates such as latitude and longitude. GPS signal provides us sufficient and reliable data for such decision making. The main drawback of using GPS is that it is unavailable indoors and also there is signal attenuation outdoors. Indoor geolocation forms the basis of tracking robots inside buildings and other places where GPS signals are unavailable. Indoor geolocation has traditionally been the field of wireless networks using techniques such as low frequency RF signals and ultra-wideband antennas. In this thesis we propose a novel method for achieving geolocation and enable tracking. Geolocation and tracking are achieved by a combination of Gyroscope and encoders together referred to as the Inertial Navigation System (INS). Gyroscopes have been widely used in aerospace applications for stabilizing aircrafts. In our case we use gyroscope as means of determining the heading of the robot. Further, commands can be sent to the robot when it is off balance or off-track. Sensors are inherently error prone; hence the process of geolocation is complicated and limited by the imperfect mathematical modeling of input noise. We make use of Kalman Filter for processing erroneous sensor data, as it provides us a robust and stable algorithm. The error characteristics of the sensors are input to the Kalman Filter and filtered data is obtained. We have performed a large set of experiments, both indoors and outdoors to test the reliability of the system. In outdoors we have used the GPS signal to aid the INS measurements. When indoors we utilize the last known position and extrapolate to obtain the GPS co-ordinates

Sensitivity Analysis for Measurements of Multipath Parameters Pertinent to TOA based Indoor Geolocation

Recently, indoor geolocation technologies has been attracting tremendous attention. For indoor environments, the fine time resolution of ultra-wideband (UWB) signals enables the potential of accurate distance measurement of the direct path (DP) between a number of reference sources and the people or assets of interest. However, Once the DP is not available or is shadowed, substantial errors will be introduced into the ranging measurements, leading to large localization errors when measurements are combined from multiple sources. The measurement accuracy in undetected direct path (UDP) conditions can be improved in some cases by exploiting the geolocation information contained in the indirect path measurements. Therefore, the dynamic spatial behavior of paths is an important issue for positioning techniques based on TOA of indirect paths. The objectives of this thesis are twofold. The first is to analyze the sensitivity of TOA estimation techniques based on TOA of the direct path. we studied the effect of distance, bandwidth and multipath environment on the accuracy of various TOA estimation techniques. The second is to study the sensitivity of multipath parameters pertinent to TOA estimation techniques based on the TOA of the indirect paths. We mainly looked into the effect of distance, bandwidth, threshold for picking paths, and multipath environment on the number of multipath components(MPCs) and path persistency. Our results are based on data from a new measurement campaign conducted on the 3rd floor of AK laboratory. For the TOA estimation techniques based on DP, the line of sight (LOS) scenario provides greatest accuracy and these TOA estimation techniques are most sensitive to bandwidth availability in obstructed line of sight (OLOS) scenario. All the TOA estimation algorithms perform poorly in the UDP scenario although the use of higher bandwidth can reduce the ranging error to some extent. Based on our processed results, The proposal for selecting the appropriate TOA estimation technique with certain constrains is given. The sensitivity study of multipath parameters pertinent to indirect-path-based TOA estimation techniques shows that the number of MPCs is very sensitive to the threshold for picking paths and to the noise threshold. It generally decreases as the distance increase while larger bandwidth always resolves more MPCs. The multipath components behave more persistently in line of sight (LOS) and obstructed line of sight (OLOS) scenarios than in UDP scenarios, and the use of larger bandwidth and higher threshold for picking paths also result in more persistent paths

Modeling the Behavior of Multipath Components Pertinent to Indoor Geolocation

Recently, a number of empirical models have been introduced in the literature for the behavior of direct path used in the design of algorithms for RF based indoor geolocation. Frequent absence of direct path has been a major burden on the performance of these algorithms directing researchers to discover algorithms using multipath diversity. However, there is no reliable model for the behavior of multipath components pertinent to precise indoor geolocation. In this dissertation, we first examine the absence of direct path by statistical analysis of empirical data. Then we show how the concept of path persistency can be exploited to obtain accurate ranging using multipath diversity. We analyze the effects of building architecture on the multipath structure by demonstrating the effects of wall length and wall density on the path persistency. Finally, we introduce a comprehensive model for the spatial behavior of multipath components. We use statistical analysis of empirical data obtained by a measurement calibrated ray-tracing tool to model the time-of- arrival, angle-of-arrival and path gains. The relationship between the transmitter-receiver separation and the number of paths are also incorporated in our model. In addition, principles of ray optics are applied to explain the spatial evolution of path gains, time-of-arrival and angle-of-arrival of individual multipath components as a mobile terminal moves inside a typical indoor environment. We also use statistical modeling for the persistency and birth/death rate of the paths

Indoor Cooperative Localization for Ultra Wideband Wireless Sensor Networks

In recent years there has been growing interest in ad-hoc and wireless sensor networks (WSNs) for a variety of indoor applications. Localization information in these networks is an enabling technology and in some applications it is the main sought after parameter. The cooperative localization performance of WSNs is ultimately constrained by the behavior of the utilized ranging technology in dense cluttered indoor environments. Recently, ultra-wideband (UWB) Time-of-Arrival (TOA) based ranging has exhibited potential due to its large bandwidth and high time resolution. However, the performance of its ranging and cooperative localization capabilities in dense indoor multipath environments needs to be further investigated. Of main concern is the high probability of non-line of sight (NLOS) and Direct Path (DP) blockage between sensor nodes, which biases the TOA estimation and degrades the localization performance. In this dissertation, we first present the results of measurement and modeling of UWB TOA-based ranging in different indoor multipath environments. We provide detailed characterization of the spatial behavior of ranging, where we focus on the statistics of the ranging error in the presence and absence of the DP and evaluate the pathloss behavior in the former case which is important for indoor geolocation coverage characterization. Parameters of the ranging error probability distributions and pathloss models are provided for different environments: traditional office, modern office, residential and manufacturing floor; and different ranging scenarios: indoor-to-indoor (ITI), outdoor-to-indoor (OTI) and roof-to-indoor (RTI). Based on the developed empirical models of UWB TOA-based OTI and ITI ranging, we derive and analyze cooperative localization bounds for WSNs in the different indoor multipath environments. First, we highlight the need for cooperative localization in indoor applications. Then we provide comprehensive analysis of the factors affecting localization accuracy such as network and ranging model parameters. Finally we introduce a novel distributed cooperative localization algorithm for indoor WSNs. The Cooperative LOcalization with Quality of estimation (CLOQ) algorithm integrates and disseminates the quality of the TOA ranging and position information in order to improve the localization performance for the entire WSN. The algorithm has the ability to reduce the effects of the cluttered indoor environments by identifying and mitigating the associated ranging errors. In addition the information regarding the integrity of the position estimate is further incorporated in the iterative distributed localization process which further reduces error escalation in the network. The simulation results of CLOQ algorithm are then compared against the derived G-CRLB, which shows substantial improvements in the localization performance

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

Distance Measurement Error Modeling for Time-of-Arrival Based Indoor Geolocation

In spite of major research initiatives by DARPA and other research organizations, precise indoor geolocation still remains as a challenge facing the research community. The core of this challenge is to understand the cause of large ranging errors in estimating the time of arrival (TOA) of the direct path between the transmitter and the receiver. Results of wideband measurement in variety of indoor areas reveal that large ranging errors are caused by severe multipath conditions and frequent occurrence of undetected direct path (UDP) situations. Empirical models for the behavior of the ranging error, which we refer to as the distance measurement error (DME), its relation to the distance between the transmitter and the receiver and the bandwidth of the system is needed for development of localization algorithms for precise indoor geolocation. The main objective of this dissertation is to design a direct empirical model for the behavior of the DME. In order to achieve this objective we provide a framework for modeling of DME, which relates the error to the distance between the transmitter and the receiver and bandwidth of the system. Using this framework we first designed a set of preliminary models for the behavior of the DME based on the CWINS proprietary measurement calibrated ray-tracing simulation tool. Then, we collected a database of 2934 UWB channel impulse response measurements at 3-8GHz in four different buildings to incorporate a variety of building materials and architectures. This database was used for the design of more in depth and realistic models for the behavior of the DME. The DME is divided into two components, Multipath-DME (MDME) and UDP-DME (UDME). Based on the empirical data, models for the behavior of each of these components are developed. These models reflect the sensitivity to bandwidth and show that by increasing the bandwidth MDME decreases. However in UDME the behavior is complicated. At first it reduces as we increase the bandwidth but after a certain bandwidth it starts to increase. In addition to these models through an analysis on direct path power versus the total power the average probability of having a UDP was calculated

Target Tracking in Confined Environments with Uncertain Sensor Positions

To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and/or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms, and provides robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201