Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Statistics for Spatial Data.

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

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.

Locating the nodes: cooperative localization in wireless sensor networks

National Institute of Standards and Technology における超伝導研究及び生活

The ability of a receiver to determine when a transmitter has changed location is important for energy conservation in wireless sensor networks, for physical security of radio-tagged objects, and for wireless network security in detection of replication attacks. In this paper, we propose using a measured temporal link signature to uniquely identify the link between a transmitter and a receiver. When the transmitter changes location, or if an attacker at a different location assumes the identity of the transmitter, the proposed link distinction algorithm reliably detects the change in the physical channel. This detection can be performed at a single receiver or collaboratively by multiple receivers. We record over 9,000 link signatures at different locations and over time to demonstrate that our method significantly increases the detection rate and reduces the false alarm rate, in comparison to existing methods.

/pdf/robust-location-distinction-using-temporal-link-signatures-2ry3km3lfi.pdf

Robust location distinction using temporal link signatures

Locating the Nodes

RF sensor networks are wireless networks that can localize and track people (or targets) without needing them to carry or wear any electronic device. They use the change in the received signal strength (RSS) of the links due to the movements of people to infer their locations. In this paper, we consider real-time multiple target tracking with RF sensor networks. We apply radio tomographic imaging (RTI), which generates images of the change in the propagation field, as if they were frames of a video. Our RTI method uses RSS measurements on multiple frequency channels on each link, combining them with a fade level-based weighted average. We introduce methods, inspired by machine vision and adapted to the peculiarities of RTI, that enable accurate and real-time multiple target tracking. Several tests are performed in an open environment, a one-bedroom apartment, and a cluttered office environment. The results demonstrate that the system is capable of accurately tracking in real-time up to four targets in cluttered indoor environments, even when their trajectories intersect multiple times, without mis-estimating the number of targets found in the monitored area. The highest average tracking error measured in the tests is 0.45 m with two targets, 0.46 m with three targets, and 0.55 m with four targets.

/pdf/multiple-target-tracking-with-rf-sensor-networks-4qrixr1yg1.pdf

Multiple Target Tracking with RF Sensor Networks

A system for locating a number of devices (112-130) by measuring signals transmitted between known location devices (112-118, 134-138, 214-218, 224-228) and unknown location devices (120-130, 222), and signals transmitted between pairs of unknown location devices (120-130,222), entering signal measurements into a graph function that includes a number of first sub-expressions, a number of which include signal measurement prediction sub-expressions, and have extrema when a predicted signal measurement is equal to an actual signal measurement, and optimizing the graph function.

Method and apparatus for location estimation

A technique for intra-piconet location determination and tomography is described. This technique uses received signal strength indicator (RSSI) values in conjunction with transmitted power levels to determine the relative location of each device within a small network employing frequency hopped spread spectrum transmission. In addition to the location determination properties of the invention, the geometry of the devices in the network, as well as the path loss information between pairs of devices, may be used to infer the location of absorbers and reflectors within the piconet. This absorption and reflection information may be used in creating the piconet tomography. The approach described in this specification may be applied in conjunction with the Bluetooth wireless Personal Area Network (PAN) specification to determine device locations, mitigate the effects of multi-path, and perform indoor location and security functions, and other application functions requiring cost-effective location determination.

Intra-piconet location determination and tomography

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