Nayef Alsindi

Measurement and Modeling of Ultrawideband TOA-Based Ranging in Indoor Multipath Environments

In this paper, we present the results of the measurement and modeling of ultrawideband (UWB) time of arrival (TOA)-based ranging in different indoor multipath environments. We provide a 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 direct path (DP) and evaluate the path loss behavior in the former case, which is important for indoor geolocation coverage characterization. The frequency-domain measurements were conducted, with a nominal frequency of 4.5 GHz with two different bandwidths, i.e., 500 MHz and 3 GHz. The parameters of the ranging error probability distributions and path loss models are provided for different environments (e.g., an old office, a modern office, a house, and a manufacturing floor) and different ranging scenarios [e.g., indoor to indoor (ITI), outdoor to indoor (OTI), and roof to indoor (RTI)].

Performance of TOA estimation algorithms in different indoor multipath conditions

Using TOA to determine the distance between the transmitter and the receiver is the most popular technique for accurate indoor positioning. Accuracy of measuring the distance using TOA is sensitive to the bandwidth of the system and the multipath condition between the wireless terminal and the access point. The behavior of the distance measurement error using TOA techniques in LOS and OLOS indoor environments are substantially different. In general, as the bandwidth increases the distance measurement error decreases. However, for the so called undetected direct path (UDP) conditions the system exhibits substantially high distance measurement errors that can not be eliminated with the increase in the bandwidth of the system. In this paper we provide an analysis of the behavior of super-resolution and traditional TOA estimation algorithms in LOS, OLOS and UDP conditions in indoor areas. The analysis is based on the frequency domain measurements of the indoor radio channel propagations in several indoor areas with special attention to the UDP conditions.

Analysis of Time of Arrival Estimation Using Wideband Measurements of Indoor Radio Propagations

Using time of arrival (TOA) to determine the distance between the transmitter and the receiver is the most popular technique for accurate indoor positioning. The accuracy of measuring the distance using this method is sensitive to the bandwidth of the system and the multipath condition between the wireless terminal and the access point. In general, as the bandwidth increases beyond a certain value, it is expected that the measured TOA error approaches zero. However, for the so-called undetected direct path (UDP) conditions, the system exhibits substantially high distance measurement errors that cannot be eliminated with the increase in the bandwidth of the system. In this paper, we provide an analysis of the behavior of superresolution and traditional TOA estimation algorithms in line-of-sight (LOS), non-LOS, and UDP conditions in indoor areas. The analysis is based on wideband frequency- domain measurements of the indoor radio channel propagations in several indoor areas, with special attention to the UDP conditions.

UDP identification and error mitigation in toa-based indoor localization systems using neural network architecture

Time-of-Arrival (ToA) based localization has attracted considerable attention for solving the very complex and challenging problem of indoor localization, mainly due to its fine range estimation process. However, ToA-based localization systems are very vulnerable to the blockage of the direct path (DP) and occurrence of undetected direct path (UDP) conditions. Erroneous detection of other multipath components as DP, which corresponds to the true distance between transmitter and receiver, introduces substantial ranging and localization error into ToA-based systems. Therefore, in order to enable robust and accurate ToA-based indoor localization, it is important to identify and mitigate occurrence of DP blockage. In this paper we present two methodologies to identify and mitigate the UDP conditions in indoor environments. We first introduce our identification technique which utilizes the statistics of radio propagation channel metrics along with binary hypothesis testing and then we introduce our novel identification technique which integrates the same statistics into a neural network architecture. We analyze each approach and the effects of neural network parameters on the accuracy of the localization system. We also compare the results of the two approaches in a sample indoor environment using both real-time measurement and ray tracing simulation. The identification metrics are extracted from wideband frequency-domain measurements conducted in a typical office building with a system bandwidth of 500 MHz, centered around 1 GHz. Then we show that with the knowledge of the channel condition, it is possible to improve the localization performance by mitigating those UDP-induced ranging errors. Finally, we compare the standard deviation of localization error of traditional localization system and UDP identification-enhanced localization system with their respective lower bound.

Cooperative localization bounds for indoor 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 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. The performance of its ranging and cooperative localization capabilities in dense indoor multipath environments, however, 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 paper, based on empirical models of UWB TOA-based Outdoor-to-Indoor (OTI) and Indoor-to-Indoor (ITI) ranging, we derive and analyze cooperative localization bounds for WSNs in different indoor multipath environments: residential, manufacturing floor, old office and modern office buildings. 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.

On the accuracy of RF positioning in multi-Capsule endoscopy

In this paper, we derive and analyze cooperative localization bounds for endoscopic wireless capsule as it passes through the human gastrointestin (GI) tract. We derive the Cramer-Rao lower bound (CRLB) variance limits on location estimators which use measured received signal strength(RSS). Using a three-dimension human body model from a full wave simulation software and log-normal models for RSS propagation from implant organs to body surface, we calculate bounds on location estimators in three digestive organs: stomach, small intestine and large intestine. We provide analysis of the factors affecting localization accuracy including various organ environments, external sensor array topology and number of pills in cooperation. The simulation results show that the number of receiver sensors on body surface has more influence on the accuracy of localization than the number of pills in cooperation inside the GI tract.

A Novel Cooperative Localization Algorithm for Indoor Sensor Networks

Recently, node localization for multi-hop sensor networks has attracted considerable attention. In these networks, error propagation provides a serious challenge to algorithm development and accuracy of final location estimates. In this paper we introduce a novel computationally efficient distributed algorithm, cooperative localization with optimum quality of estimate (CEOQ) which takes advantage of the behavior of the channel to provide accurate indoor positioning. This algorithm uses the quality of ranging and positioning estimates to provide practical and accurate results and more importantly reduce error propagation substantially. Using UWB measurements and modeling of the ranging error in a typical office building we compare the performance of this cooperative localization algorithm with a non-channel based algorithm for indoor ad-hoc sensor environments

UWB Channel Measurements for Accurate Indoor Localization

Recently, indoor localization has attracted considerable attention. More importantly, indoor channel measurements and models are very essential to accurate characterization of the ranging error for military applications. This paper provides the results of UWB measurements and modeling performed for indoor geolocation applications. The measurement campaign took place in the Worcester, MA in a modern office building, a manufacturing floor, a residential house, and an old office building. A total of 2934 wideband measurements at frequency band of 3-8 GHz were collected in the four sites. Measurements were divided into indoor-to-indoor, outdoor-to-indoor, and roof-to-indoor conditions with two different polarity of the mobile antenna representing an upright soldier and a soldier lying on the ground. The models developed from the measurements represent a number of propagation scenarios for different areas in each set of measurements. In this paper we provide novel path-loss models of the first-detected path (FDP) and the total power which is essential for localization applications. In addition ranging error models are also provided which characterizes the behavior of the direct-path (DP) and its relationship to the error. All the models are presented for two different bandwidths of 500 MHz and 3 GHz.

Indoor Geolocation Distance Error Modeling using UWB Channel Measurements

In this paper we introduce a model for the distance error measured from the estimated time of arrival (TOA) of the direct path (DP) in a typical multipath indoor environment. We use the results of our ultra-wideband (UWB) measurement database in a sample office environment. To begin modeling, first we separate the causes of the error into multipath and undetected direct path (UDP), and then we model them separately considering the variation of bandwidth of the system. We show that the behavior of the distance error consists of two parts; one that is from multipath, and the other one from UDP. Both errors can be modeled as Gaussian, so the final distance error is a mixture of two Gaussian distributions. We also related the statistics of the distributions to the bandwidth of the system

An Error Propagation Aware Algorithm for Precise Cooperative Indoor Localization

Recently, node localization for sensor networks has attracted considerable attention for military application. Despite the recent proposals, the relationship between the channel behavior and the performance analysis of cooperative algorithms has not been addressed. The assumptions about the statistics of the ranging error used in the literature are either too general or overly optimistic. Additionally when sensors collaborate to localize each other, there is no attempt to characterize the error in the position of the nodes. This lack of error propagation-awareness can degrade the performance of an algorithm and create divergence problems. In this paper we first introduce detailed modeling of channel propagation in indoor environments in the form of novel empirical path loss (PL) and distance measurement error (DME) models developed from the results of UWB channel measurements. Then we integrate these models in developing error propagation aware (EPA) precise cooperative localization algorithm that tracks the extent of the position error in each sensor node and its overall effect on subsequent multi-laterations. Finally we compare the algorithm against the Cramer-Rao lower bound (CRLB)