Maurizio Bocca

Enhancing the accuracy of radio tomographic imaging using channel diversity

Radio tomographic imaging (RTI) is an emerging device-free localization (DFL) technology enabling the localization of people and other objects without requiring them to carry any electronic device. Instead, the RF attenuation field of the deployment area of a wireless network is estimated using the changes in received signal strength (RSS) measured on links of the network. This paper presents the use of channel diversity to improve the localization accuracy of RTI. Two channel selection methods, based on channel packet reception rates (PRRs) and fade levels, are proposed. Experimental evaluations are performed in two different types of environments, and the results show that channel diversity improves localization accuracy by an order of magnitude. People can be located with average error as low as 0.10 m, the lowest DFL location error reported to date. We find that channel fade level is a more important statistic than PRR for RTI channel selection. Using channel diversity, this paper, for the first time, demonstrates that attenuation-based through-wall RTI is possible.

Multiple Target Tracking with RF Sensor Networks

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.

Radio Tomographic Imaging for Ambient Assisted Living

Accurate localization of people in indoor and domestic en- vironments is one of the key requirements for ambient assisted living (AAL) systems. This chapter describes how the received signal strength (RSS) measurements collected by a network of static radio transceivers can be used to localize people without requiring them to wear or carry any radio device. We describe a technique named radio tomographic imaging (RTI), which produces real-time images of the change in the radio propagation field of the monitored area caused by the presence of people. Peoples locations are inferred from the estimated RTI images. We show results from a long-term deployment in a typical single floor, one bedroom apartment. In order to deal with the dynamic nature of the domestic environment, we introduce methods to make the RTI system self-calibrating. Experimental results show that the average localization error of the system is 0.23 m. Moreover, the system is capable of adapt- ing to the changes in the indoor environment, achieving high localization accuracy over an extended period of time.

Breathfinding: A Wireless Network That Monitors and Locates Breathing in a Home

This paper explores using RSS measurements on the links between commercial wireless devices to locate where a breathing person is located and to estimate their breathing rate, in a home, while the person is sitting, lying down, standing, or sleeping. Prior RSS-based device-free localization methods required calibration measurements to be able to locate stationary people, or did not require calibration but only located people who moved. We collect RSS measurements multiple short (3-7 minute) tests and during a longer 66 minute test, and show the location of the breathing person can be estimated, to within about 2 m average error. We describe a detector that distinguishes between sample times during which a person is moving and sample times during which a person is breathing but otherwise motionless. This detector enables removal of motion interference, i.e., RSS changes due to movements other than a persons breathing, and more accurately estimate a persons breathing rate. Being able to locate and monitor a breathing person, without calibration, is important for applications in search and rescue, health care, and security.

A Fade Level-Based Spatial Model for Radio Tomographic Imaging

RSS-based device-free localization (DFL) monitors changes in the received signal strength (RSS) measured by a network of static wireless nodes to locate and track people without requiring them to carry or wear any electronic device. Current models assume that the spatial impact area, i.e., the area in which a person affects a links RSS, has constant size. This paper shows that the spatial impact area varies considerably for each link. Data from extensive experiments are used to derive a spatial weight model that is a function of the fade level, i.e., a measure of whether a link is experiencing destructive or constructive multipath interference, and of the sign of RSS change. In addition, a measurement model is proposed which calculates for each RSS measurement the probability of a person being located inside the derived spatial impact area. An online radio tomographic imaging (RTI) system is described which uses channel diversity and the presented models. Experiments in an open indoor environment, in a typical one-bedroom apartment and in a through-wall scenario are conducted to determine the performance of the proposed system. We demonstrate that the new system is capable of localizing and tracking a person with high accuracy (≤ 0.30 m) in all the environments, without the need to change the model parameters.

Fall detection using RF sensor networks

The number of people aged 65 and over continues to rapidly increase, leading to a greater need for technologies to assist in caring for an aging population. Among these technologies are fall detection systems, since falling is a major concern for the elderly. In this paper we present a method of detecting falls using radio tomographic imaging. A two-level array of RF sensor nodes is deployed around the perimeter of a room, and the shadowing losses in the signals relayed between sensors is used to detect a persons horizontal and vertical position. Training data is used to provide a relationship between the attenutation measured as a function of height and a persons pose, which is then used in a hidden Markov model. During system operation, a forward algorithm estimates the most likely current state at each time. If the time between a standing pose and a lying down pose is too short, the system detects a fall. Using a collected experimental test set, we show that the system can distinguish falls from controlled lying down actions (e.g., sitting on the floor) with 100% reliability and no false alarms.

Dial it in: Rotating RF sensors to enhance radio tomography

A radio tomographic imaging (RTI) system uses the received signal strength (RSS) measured by RF sensors in a static wireless network to localize people in the deployment area, without having them to carry or wear an electronic device. This paper addresses the fact that small-scale changes in the position and orientation of the antenna of each RF sensor can dramatically affect imaging and localization performance of an RTI system. However, the best placement for a sensor is unknown at the time of deployment. Improving performance in a deployed RTI system requires the deployer to iteratively “guess-and-retest”, i.e., pick a sensor to move and then re-run a calibration experiment to determine if the localization performance had improved or degraded. We present an RTI system of servo-nodes, RF sensors equipped with servo motors which autonomously “dial it in”, i.e., change position and orientation to optimize the RSS on links of the network. By doing so, the localization accuracy of the RTI system is quickly improved, without requiring any calibration experiment from the deployer. Experiments conducted in three indoor environments demonstrate that the servo-nodes system reduces localization error on average by 32% compared to a standard RTI system composed of static RF sensors.

RTI Goes Wild : Radio Tomographic Imaging for Outdoor People Detection and Localization

In recent years, Radio frequency (RF) sensor networks have been used to localize people indoor without requiring them to wear invasive electronic devices. These wireless mesh networks, formed by low-power radio transceivers, continuously measure the received signal strength (RSS) of the links. Radio Tomographic Imaging (RTI) is a technique that generates, starting from these RSS measurements, 2D images of the change in the electromagnetic field inside the area covered by the radio transceivers to spot the presence and movements of animates (e.g., people, large animals) or large metallic objects (e.g., cars). Here, we present a RTI system for localizing and tracking people outdoors. Differently than in indoor environments where the RSS does not change significantly with time unless people are found in the monitored area, the outdoor RSS signal is time-variant, e.g., due to rainfall or wind-driven foliage. We present a novel outdoor RTI method that, despite the nonstationary noise introduced in the RSS data by the environment, achieves high localization accuracy and dramatically reduces the energy consumption of the sensing units. Experimental results demonstrate that the system accurately detects and tracks a person in real-time in a large forested area under varying environmental conditions, significantly reducing false positives, localization error and energy consumption compared to state-of-the-art RTI methods.

Joint ultra-wideband and signal strength-based through-building tracking for tactical operations

Accurate device free localization (DFL) based on received signal strength (RSS) measurements requires placement of radio transceivers on all sides of the target area. Accuracy degrades dramatically if sensors do not surround the area. However, law enforcement officers sometimes face situations where it is not possible or practical to place sensors on all sides of the target room or building. For example, for an armed subject barricaded in a motel room, police may be able to place sensors in adjacent rooms, but not in front of the room, where the subject would see them. In this paper, we show that using two ultra-wideband (UWB) impulse radios, in addition to multiple RSS sensors, improves the localization accuracy, particularly on the axis where no sensors are placed (which we call the x-axis). We introduce three methods for combining the RSS and UWB data. By using UWB radios together with RSS sensors, it is still possible to localize a person through walls even when the devices are placed only on two sides of the target area. Including the data from the UWB radios can reduce the localization area of uncertainty by more than 60%.

On the sensitivity of RSS based localization using the log-normal model: An empirical study

The accuracy of radial radio propagation models, e.g. the log-normal path loss model, is severely degraded by the effects of multipath propagation, environmental differences and hardware variability. This has a direct impact on the performance of node localization algorithms that use these models. In this paper, first we study the effect of the environment and hardware variability on the model parameters of the log-normal path loss model. We empirically show that, even in the same environment, the model parameters can vary significantly depending on the nodes used for the calibration. Second, we present node localization results obtained using a maximum likelihood algorithm and evaluate the sensitivity of the algorithm to model parameter changes. Third, we show that the localization results can be improved using an individual model for each node. Using a robot for nodes localization, we report experimental results in three different environments: an open sports hall, a semi-open lobby, and a cluttered office. In corresponding order, accuracy of 0.33 m, 1.07 m and 0.78 m is achieved using individual models.