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Attention Based Bidirectional Convolutional LSTM for High-Resolution Radio Tomographic Imaging

Radio tomographic imaging (RTI) is a technique for imaging the environment by using the received signal strength (RSS) measurements from a wireless sensor network. This brief considers the data fusion for a sequence of RSS measurements, and proposes an attention based bidirectional convolutional long short-term memory (LSTM) based deep learning method to achieve the high-resolution RTI. A time sequence of tomographic images of the dynamic environment can be obtained efficiently by employing the developed RTI system. The effectiveness of the presented method is demonstrated by the simulation examples.