Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study

The accuracy of indoor wireless localization systems can be substantially enhanced by map-awareness, i.e., by the knowledge of the map of the environment in which localization signals are acquired. In fact, this knowledge can be exploited to cancel out, at least to some extent, the signal degradation due to propagation through physical obstructions, i.e., to the so called non-line-of-sight bias. This result can be achieved by developing novel localization techniques that rely on proper map-aware statistical modelling of the measurements they process. In this manuscript a unified statistical model for the measurements acquired in map-aware localization systems based on time-of-arrival and received signal strength techniques is developed and its experimental validation is illustrated. Finally, the accuracy of the proposed map-aware model is assessed and compared with that offered by its map-unaware counterparts. Our numerical results show that, when the quality of acquired measurements is poor, map-aware modelling can enhance localization accuracy by up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless Communications, 201

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

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

Localisation of mobile nodes in wireless networks with correlated in time measurement noise.

Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated

Statistical Learning Theory for Location Fingerprinting in Wireless LANs

In this paper, techniques and algorithms developed in the framework of statistical learning theory are analyzed and applied to the problem of determining the location of a wireless device by measuring the signal strengths from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no custom hardware is required. The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in the literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques

A Generic Method for the Reliable Calculation of Large- Scale Fading in an Obstacle-Dense Propagation Environment

The aim of this chapter is to summarize and present recent findings in the field of wireless channel mod- eling that provide a new method for the reliable calculation of the statistical parameters of large-scale variations of the average received signal (shadow fading). This algorithm is theoretically based on a path loss estimation model that incorporates losses due to walls and floors. This has been confirmed to be the most precise mathematical tool for average signal strength prediction for various frequencies of interest and propagation environments. The total path loss is estimated as a sum of two independent attenuation processes: free space loss and losses due to obstacles. This solution allows for a direct and reliable calculation of the deviation of the fluctuations of the average received signal in an obstacle- dense environment

Statistical Characterization and Mitigation of NLOS Errors in UWB Localization Systems

In this paper some new experimental results about the statistical characterization of the non-line-of-sight (NLOS) bias affecting time-of-arrival (TOA) estimation in ultrawideband (UWB) wireless localization systems are illustrated. Then, these results are exploited to assess the performance of various maximum-likelihood (ML) based algorithms for joint TOA localization and NLOS bias mitigation. Our numerical results evidence that the accuracy of all the considered algorithms is appreciably influenced by the LOS/NLOS conditions of the propagation environment