A supervised deep feedforward neural network (SDFNN)-based image reconstruction algorithm for radio tomographic imaging

Radio tomographic imaging (RTI) is an emerging imaging technique that utilizes the shadowing losses on links between multiple pairs of wireless nodes within the sensing area to estimate the attenuation of physical objects. By using an image reconstruction algorithm, the attenuations caused by the physical objects will be transformed into a tomographic image. The tomographic image provides information about the shape, size and position of an object. However, the process of reconstructing a tomographic image from the RSS measurements is an ill-posed inverse problem, meaning that a small number of errors or variations in measurements will lead to a significant impact on the image quality. The existing linear inverse solvers provide fast reconstruction, but the imaging results is non-satisfactory and inaccurate. On the other hand, the nonlinear inverse solvers produce a higher quality image but are computationally expensive. Studies of applying deep learning technique and neural networks in tomographic reconstructions to solve the ill-posed inverse problems have emerged in recent years. However, to the best of our knowledge, the studies conducted in solving the inverse problem of RTI system using deep learning technique are rare. Therefore, a supervised deep feedforward neural network (SDFNN)-based image reconstruction algorithm for the RTI system is explored in this study to determine the feasibility of deep learning technique in reconstructing a tomographic image using RSS measurements only

Estimating Single and Multiple Target Locations Using K-Means Clustering with Radio Tomographic Imaging in Wireless Sensor Networks

Geolocation involves using data from a sensor network to assess and estimate the location of a moving or stationary target. Received Signal Strength (RSS), Angle of Arrival (AoA), and/or Time Difference of Arrival (TDoA) measurements can be used to estimate target location in sensor networks. Radio Tomographic Imaging (RTI) is an emerging Device-Free Localization (DFL) concept that utilizes the RSS values of a Wireless Sensor Network (WSN) to geolocate stationary or moving target(s). The WSN is set up around the Area of Interest (AoI) and the target of interest, which can be a person or object. The target inside the AoI creates a shadowing loss between each link being obstructed by the target. This research focuses on position estimation of single and multiple targets inside a RTI network. This research applies K-means clustering to localize one or more targets. K-means clustering is an algorithm that has been used in data mining applications such as machine learning applications, pattern recognition, hyper-spectral imagery, artificial intelligence, crowd analysis, and Multiple Target Tracking (MTT)

Doctor of Philosophy

dissertationLocation information of people is valuable for many applications including logistics, healthcare, security and smart facilities. This dissertation focuses on localization of people in wireless sensor networks using radio frequency (RF) signals, speci cally received signal strength (RSS) measurements. A static sensor network can make RSS measurements of the signal from a transmitting badge that a person wears in order to locate the badge. We call this kind of localization method radio device localization. Since the human body causes RSS changes between pairwise sensor nodes of a static network, we can also use RSS measurements from pairwise nodes of a network to locate people, even if they are not carrying any radio device. We call this device-free localization (DFL). The rst contribution of this dissertation is to radio device localization. The human body has a major e ect on the antenna gain pattern of the transmitting badge that the person is wearing, however, existing r