RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

A ranging method with IEEE 802.11 data frames for indoor localization

IEEE 802.11 networks constitute a suitable infrastructure for accurate indoor positioning. However, existing approaches based on fingerprinting present drawbacks that make them not suitable for most of applications. This paper presents an innovative TOA-based ranging technique over IEEE 802.11 networks intended to be the essential step of an indoors location system. This approach is based on round trip time measurements using standard IEEE 802.11 link layer frames and a statistical post-processing to mitigate the noise of the measurements. A prototype has been implemented in order to assess the validity and evaluate the performance of the proposed technique. First results show ranging accuracies of less than one meter of error in LOS situations

Loss Diagnosis and Indoor Position Location System based on IEEE 802.11 WLANs

Wireless local area networks (WLANs) have been widely deployed to provide short range broadband communications. Due to the fast evolvement of IEEE 802.11 based WLAN standards and various relevant applications, many research efforts have been focused on the optimization of WLAN data rate, power and channel utilization efficiency. On the other hand, many emerging applications based on WLANs have been introduced. Indoor position location (IPL) system is one of such applications which turns IEEE 802.11 from a wireless communications infrastructure into a position location network. This thesis mainly focuses on data transmission rate enhancement techniques and the development of IEEE 802.11 WLAN based IPL system with improved locationing accuracy. In IEEE 802.11 systems, rate adaptation algorithms (RAAs) are employed to improve transmission efficiency by choosing an appropriate modulation and coding scheme accord­ ing to point-to-point channel conditions. However, due to the resource-sharing nature of WLANs, co-channel interferences and frame collisions cannot be avoided, which further complicates the wireless environment and makes the RAA design a more challenging task. As WLAN performance depends on many dynamic factors such as multipath fading and co-channel interferences, differentiating the cause of performance degradation such as frame losses, which is known as loss diagnosis techniques, is essential for performance enhance­ ments of existing rate adaptation schemes. In this thesis, we propose a fast and reliable collision detection scheme for frame loss diagnosis in IEEE 802.11 WLANs. Collisions are detected by tracking changes of the signal-to-interference-and-noise-ratio (SINR) in IEEE 802.11 WLANs with a nonparametric order-based cumulative sum (CUSUM) algorithm for rapid loss diagnosis. Numerical simulations are conducted to evaluate the effectiveness of the proposed collision detection scheme. The other aspect of this thesis is the investigation of an IEEE 802.11 WLAN based IPL system. WLAN based IPL systems have received increasing attentions due to their variety of potential applications. Instead of relying on dedicated locationing networks and devices, IEEE 802.11 WLAN based IPL systems utilize widely deployed IEEE 802.11 WLAN infrastructures and standardized wireless stations to determine the position of a target station in indoor environments. iii Abstract In this thesis, a WLAN protocol-based distance measurement technique is investigated, which takes advantages of existing IEEE 802.11 data/ACK frame exchange sequences. In the proposed distance measurement technique, neither dedicated hardware nor hardware modifications is required. Thus it can be easily integrated into off-the-shelf commercial, inexpensive WLAN stations for IPL system implementation. Field test results confirm the efficacy of the proposed protocol-based distance measurement technique. Furthermore, a preliminary IPL system based on the proposed method is also developed to evaluate the feasibility of the proposed technique in realistic indoor wireless environments

Identification and Mitigation of NLOS based on Channel Information Rules for Indoor UWB Localization

Indoor localization is an emerging technology that can be utilized for developing products and services for commercial usage, public safety, military applications and so forth. Commercially it can be applied to track children, people with special needs, help navigate blind people, locate equipment, mobile robots, etc. The objective of this thesis is to enable an indoor mobile vehicle to determine its location and thereby making it capable of autonomous localization under Non-light of sight (NLOS) conditions. The solution developed is based on Ultra Wideband (UWB) based Indoor Positioning System (IPS) in the building. The proposed method increases robustness, scalability, and accuracy of location. The out of the box system of DecaWave TREK1000 provides tag tracking features but has no method to detect and mitigate location inaccuracies due to the multipath effect from physical obstacles found in an indoor environment. This NLOS condition causes ranges to be positively biased, hence the wrong location is reported. Our approach to deal with the NLOS problem is based on the use of Rules Classifier, which is based on channel information. Once better range readings are achieved, approximate location is calculated based on Time of Flight (TOF). Moreover, the proposed rule based IPS can be easily implemented on hardware due to the low complexity. The measurement results, which was obtained using the proposed mitigation algorithm, show considerable improvements in the accuracy of the location estimation which can be used in different IPS applications requiring centimeter level precision. The performance of the proposed algorithm is evaluated experimentally using an indoor positioning platform in a laboratory environment, and is shown to be significantly better than conventional approaches. The maximum positioning error is reduced to 15 cm for NLOS using both an offline and real time tracking algorithm extended from the proposed approach

Application of Channel Modeling for Indoor Localization Using TOA and RSS

Recently considerable attention has been paid to indoor geolocation using wireless local area networks (WLAN) and wireless personal area networks (WPAN) devices. As more applications using these technologies are emerging in the market, the need for accurate and reliable localization increases. In response to this need, a number of technologies and associated algorithms have been introduced in the literature. These algorithms resolve the location either by using estimated distances between a mobile station (MS) and at least three reference points (via triangulation) or pattern recognition through radio frequency (RF) fingerprinting. Since RF fingerprinting, which requires on site measurements is a time consuming process, it is ideal to replace this procedure with the results obtained from radio channel modeling techniques. Localization algorithms either use the received signal strength (RSS) or time of arrival (TOA) of the received signal as their localization metric. TOA based systems are sensitive to the available bandwidth, and also to the occurrence of undetected direct path (UDP) channel conditions, while RSS based systems are less sensitive to the bandwidth and more resilient to UDP conditions. Therefore, the comparative performance evaluation of different positioning systems is a multifaceted and challenging problem. This dissertation demonstrates the viability of radio channel modeling techniques to eliminate the costly fingerprinting process in pattern recognition algorithms by introducing novel ray tracing (RT) assisted RSS and TOA based algorithms. Two sets of empirical data obtained by radio channel measurements are used to create a baseline for comparative performance evaluation of localization algorithms. The first database is obtained by WiFi RSS measurements in the first floor of the Atwater Kent laboratory; an academic building on the campus of WPI; and the other by ultra wideband (UWB) channel measurements in the third floor of the same building. Using the results of measurement campaign, we specifically analyze the comparative behavior of TOA- and RSS-based indoor localization algorithms employing triangulation or pattern recognition with different bandwidths adopted in WLAN and WPAN systems. Finally, we introduce a new RT assisted hybrid RSS-TOA based algorithm which employs neural networks. The resulting algorithm demonstrates a superior performance compared to the conventional RSS and TOA based algorithms in wideband systems

Joint received signal strength, angle-of-arrival, and time-of-flight positioning

This paper presents a software positioning framework that is able to jointly use measured values of three parameters: the received signal strength, the angle-of-arrival, and the time-of-flight of the wireless signals. Based on experimentally determined measurement accuracies of these three parameters, results of a realistic simulation scenario are presented. It is shown that for the given configuration, angle-of-arrival and received signal strength measurements benefit from a hybrid system that combines both. Thanks to their higher accuracy, time-of-flight systems perform significantly better, and obtain less added value from a combination with the other two parameters