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

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%.Comment: 9 pages, conference submissio

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.Comment: 9 page

Device-free Localization using Received Signal Strength Measurements in Radio Frequency Network

Device-free localization (DFL) based on the received signal strength (RSS) measurements of radio frequency (RF)links is the method using RSS variation due to the presence of the target to localize the target without attaching any device. The majority of DFL methods utilize the fact the link will experience great attenuation when obstructed. Thus that localization accuracy depends on the model which describes the relationship between RSS loss caused by obstruction and the position of the target. The existing models is too rough to explain some phenomenon observed in the experiment measurements. In this paper, we propose a new model based on diffraction theory in which the target is modeled as a cylinder instead of a point mass. The proposed model can will greatly fits the experiment measurements and well explain the cases like link crossing and walking along the link line. Because the measurement model is nonlinear, particle filtering tracing is used to recursively give the approximate Bayesian estimation of the position. The posterior Cramer-Rao lower bound (PCRLB) of proposed tracking method is also derived. The results of field experiments with 8 radio sensors and a monitored area of 3.5m 3.5m show that the tracking error of proposed model is improved by at least 36 percent in the single target case and 25 percent in the two targets case compared to other models.Comment: This paper has been withdrawn by the author due to some mistake

Comparison of Methods for Radio Position of Non-Emitting Dismounts

Radio Tomographic Imaging (RTI) is a form of Device Free Passive Localization (DFPL) that utilizes the Received Signal Strength (RSS) values from a collection of wireless transceivers to produce an image in order to localize a subject within a Wireless Sensor Network (WSN). Radio Mapping is another form of DFPL that can utilize the same RSS values from a WSN to localize a subject by comparing recent values to a set of calibration data. RTI and Radio Mapping have never been directly compared to one another as a means of localization within a WSN. The goal of this research is to compare using TelosB mote devices these approaches in a side-by-side manner. A real world WSN was constructed and both RTI and Radio Mapping methodologies were applied to identical data sets with the results compared and discussed. Initial results show that both methodologies have inherent advantages and disadvantages respective to one another; Radio Mapping performs significantly better in WSNs with a low number of transceivers being 100% accurate within the bounds of this experimentation, while RTI has significantly more simple calibration procedures