Abstract
Considering the high complexity of the UAV life detection radar signal and the large amount of data, it is difficult to distinguish the detection target quickly and intelligently. This paper proposes an identification and classification method for life detection radar based on Markov Transition Field and convolutional neural network (CNN). In this paper, the radar echo is down-sampled and processed, and then the one-dimensional data is converted into two-dimensional images by Markov Transition Field. Finally, the feature extraction of the two-dimensional image is performed through the CNN. The analysis of the measured data shows that the average recognition accuracy of the proposed model reaches 95%. It is better than spectrum graph processing and other time series processing methods such as GAF (Gramian Angular Field). It is based on the research on human identification methods of life detection radar provides the basis.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
