Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.

https://www.cs.umd.edu/~moustafa/papers/horus_usenix.pdf

The Horus WLAN location determination system

Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

Wireless location refers to the geographic coordinates of a mobile subscriber in cellular or wireless local area network (WLAN) environments. Wireless location finding has emerged as an essential public safety feature of cellular systems in response to an order issued by the Federal Communications Commission (FCC) in 1996. The FCC mandate aims to solve a serious public safety problem caused by the fact that, at present, a large proportion of all 911 calls originate from mobile phones, the location of which cannot be determined with the existing technology. However, many difficulties intrinsic to the wireless environment make meeting the FCC objective challenging. These challenges include channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions. In addition to emergency services, there are many other applications for wireless location technology, including monitoring and tracking for security reasons, location sensitive billing, fraud protection, asset tracking, fleet management, intelligent transportation systems, mobile yellow pages, and even cellular system design and management. This article provides an overview of wireless location challenges and techniques with a special focus on network-based technologies and applications.

Network-based wireless location: challenges faced in developing techniques for accurate wireless location information

We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy.

/pdf/wlan-location-determination-via-clustering-and-probability-2h6idmtc1a.pdf

WLAN location determination via clustering and probability distributions

We estimate the location of a WLAN user based on radio signal strength measurements performed by the user’s mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.

/pdf/a-probabilistic-approach-to-wlan-user-location-estimation-30le1u55mz.pdf

A Probabilistic Approach to WLAN User Location Estimation

A method for estimating a target object properties, including location, in an environment. A topology model indicates permissible locations and transitions and a data model models a location-dependent physical quantity which is observed by the target object's sensing device. Motion models model specific target object types, obeying the permissible locations and transitions. The target object is assigned a set of particles, each having a set of attributes, including location in relation to the topology model. The attributes estimate the target object properties The particles' update cycles include: determining a degree of belief for each particle to estimate the target object properties; determining a weight for each particle based on at least the determined degree of belief and generating new particles for update cycle n+1 in an evolutionary process.

Location determination techniques

A model construction module (MCM) for constructing a probabilistic model (PM) of a wireless environment (RN) in which a target device (T) communicates using signals that have a measurable signal value (x), such as signal strength. The model construction module forms several submodels (611 - 631) of the wireless environment (RN). Each submodel indicates a probability distribution (F1 - F3) for signal values at one or more locations (Q1 - QY) in the wireless environment. The module combines the submodels to a probabilistic model (PM) of the wireless environment (RN), such that the probabilistic model indicates a probability distribution for signal values at several locations in the wireless environment. Alternatively, the model may insert new locations to a single model based on a combination of existing locations. The combination of submodels or existing locations comprises combining the inverse cumulative distribution functions of the submodels or existing locations.

Probabilistic model for a positioning technique

A method and apparatus for using at least one signal quality parameter in an application. A target device makes observations of at least one signal quality parameter in a wireless communication environment. Because there are gross differences between different target devices' observations, there are one or more device models that compensate for the differences. A selection logic selects an optimal device model for each target device. The observations made by the target device are processed with the selected device model and applied to an application, such as a positioning application or network planning.

Applications of signal quality observations

A target device's location is estimated by a location estimation module (LEM) that comprises a probabilistic model (PM) for a plurality of sample points, each of which comprises a sample location and an expected distribution of signal values at that sample point. The location estimation module (LEM) makes a sequence (OS) of observations on, n = 1, 2, 3… , of signal values. Each observation corresponds to a respective location qn along the target device's path. The sequence of observations and the respective location constitute a hidden Markov model. The module estimates the target device's location qn based on the probabilistic model (PM) and the sequence of observations, wherein the sequence of observations comprises one or more future observations on+m for which m is a positive integer. In other words, the target device's location is estimated, at least partially, based on knowledge of its future.

Pauli Misikangas

Papers

A Probabilistic Approach to WLAN User Location Estimation

Location determination techniques

Probabilistic model for a positioning technique

Applications of signal quality observations

Sequence-based positioning technique