Improving Radio Tomographic Imaging Accuracy by Attention Augmented Optimization Technique

Radio tomographic imaging (RTI) has become a popular approach to reconstruct spatial loss fields (SLFs) in an area covered by a wireless network based on received signal strength (RSS) measurements. SLF images quantify the attenuation rate of the radio-frequency waves at each location in the network. The attenuation for the propagation path can be modeled as the 2-dimensional integral of SLF scaled by a weight function, which is the foundation of RTI techniques and makes the SLF reconstruction possible. In recent years, many methods, including machine-learning-based schemes, have been proposed to achieve more accurate SLF estimates. In this letter, we develop an attention neural network-augmented optimization SLF estimation scheme by taking advantage of deep learning and the traditional RTI technique. Our proposed method achieves the best reconstruction performance among the existing approaches.