This paper presents a linear model for radio tomographic imaging (RTI), which uses received signal strength (RSS) measurements to image the attenuation caused by moving objects in wireless networks. The authors investigate noise models based on real measurements and derive mean-squared error (MSE) bounds to assess image accuracy. They discuss the ill-posedness of RTI and apply Tikhonov regularization to derive an image estimator. Experimental results from an RTI experiment with 28 nodes deployed in a 441 square foot area are presented, demonstrating the effectiveness of the method in imaging human presence and movement. The paper also explores the impact of node density and parameter settings on image accuracy, highlighting the importance of balancing regularization and weighting parameters to achieve optimal results.This paper presents a linear model for radio tomographic imaging (RTI), which uses received signal strength (RSS) measurements to image the attenuation caused by moving objects in wireless networks. The authors investigate noise models based on real measurements and derive mean-squared error (MSE) bounds to assess image accuracy. They discuss the ill-posedness of RTI and apply Tikhonov regularization to derive an image estimator. Experimental results from an RTI experiment with 28 nodes deployed in a 441 square foot area are presented, demonstrating the effectiveness of the method in imaging human presence and movement. The paper also explores the impact of node density and parameter settings on image accuracy, highlighting the importance of balancing regularization and weighting parameters to achieve optimal results.