Radio Tomographic Imaging (RTI) uses wireless networks to image the attenuation caused by physical objects. This paper presents a linear model for using received signal strength (RSS) measurements to obtain images of moving objects. Noise models are investigated based on real measurements of a deployed RTI system. Mean-squared error (MSE) bounds on image accuracy are derived, which are used to calculate the accuracy of an RTI system for a given node geometry. The ill-posedness of RTI is discussed, and Tikhonov regularization is used to derive an image estimator. Experimental results of an RTI experiment with 28 nodes deployed around a 441 square foot area are presented. RTI measures signal strengths on many different paths through a medium, similar to radar systems but at radio frequencies. It faces two significant challenges: it only measures signal strength, and RF propagation introduces non-line-of-sight (NLOS) effects. Despite these challenges, RTI has major advantages, such as the ability to penetrate walls, trees, and smoke, and to work in the dark where optical systems fail. RTI has applications in security, rescue, and monitoring systems, as well as in smart homes and buildings. The paper discusses a linear model relating RSS measurements to the change in attenuation in a network area. It investigates statistics for noise in dynamic multipath environments and derives an error bound on image estimation for a given node geometry. The ill-posedness of RTI is discussed, and a regularized solution is derived for obtaining an attenuation image. The paper also describes the setup of an actual RTI experiment, the resultant images, and a discussion of the effect of parameters on the accuracy of the images. The paper presents a model for RTI, including a weight model and noise statistics. It discusses the effect of node density on image accuracy and presents experimental results showing that RTI can accurately image RF attenuation caused by humans in dense wireless networks with inexpensive hardware. The paper also discusses the effect of parameters on image accuracy, including weighting and regularization parameters, and presents error curves showing the importance of choosing appropriate parameters for accurate image reconstruction. The paper concludes that RTI is a promising technology for imaging physical objects in wireless networks and has potential applications in security, rescue, and other areas.Radio Tomographic Imaging (RTI) uses wireless networks to image the attenuation caused by physical objects. This paper presents a linear model for using received signal strength (RSS) measurements to obtain images of moving objects. Noise models are investigated based on real measurements of a deployed RTI system. Mean-squared error (MSE) bounds on image accuracy are derived, which are used to calculate the accuracy of an RTI system for a given node geometry. The ill-posedness of RTI is discussed, and Tikhonov regularization is used to derive an image estimator. Experimental results of an RTI experiment with 28 nodes deployed around a 441 square foot area are presented. RTI measures signal strengths on many different paths through a medium, similar to radar systems but at radio frequencies. It faces two significant challenges: it only measures signal strength, and RF propagation introduces non-line-of-sight (NLOS) effects. Despite these challenges, RTI has major advantages, such as the ability to penetrate walls, trees, and smoke, and to work in the dark where optical systems fail. RTI has applications in security, rescue, and monitoring systems, as well as in smart homes and buildings. The paper discusses a linear model relating RSS measurements to the change in attenuation in a network area. It investigates statistics for noise in dynamic multipath environments and derives an error bound on image estimation for a given node geometry. The ill-posedness of RTI is discussed, and a regularized solution is derived for obtaining an attenuation image. The paper also describes the setup of an actual RTI experiment, the resultant images, and a discussion of the effect of parameters on the accuracy of the images. The paper presents a model for RTI, including a weight model and noise statistics. It discusses the effect of node density on image accuracy and presents experimental results showing that RTI can accurately image RF attenuation caused by humans in dense wireless networks with inexpensive hardware. The paper also discusses the effect of parameters on image accuracy, including weighting and regularization parameters, and presents error curves showing the importance of choosing appropriate parameters for accurate image reconstruction. The paper concludes that RTI is a promising technology for imaging physical objects in wireless networks and has potential applications in security, rescue, and other areas.