Attention Based Bidirectional Convolutional LSTM for High-Resolution Radio Tomographic Imaging
- Resource Type
- Authors
- Chao-Han Huck Yang; Xiaoli Ma; Songyong Liu; Hongzhuang Wu
- Source
- IEEE Transactions on Circuits and Systems II: Express Briefs. 68:1482-1486
- Subject
- Tomographic reconstruction
Computer science
business.industry
Deep learning
RSS
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
High resolution
020206 networking & telecommunications
02 engineering and technology
computer.file_format
010501 environmental sciences
Sensor fusion
01 natural sciences
Data modeling
0202 electrical engineering, electronic engineering, information engineering
Computer vision
Tomography
Artificial intelligence
Electrical and Electronic Engineering
business
Wireless sensor network
computer
0105 earth and related environmental sciences
- Language
- ISSN
- 1558-3791
1549-7747
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.