Three-dimensional supervirtual seismic refraction interferometry: A case study in western Saudi Arabia

Gradually crowded, complex airspace makes it necessary to identify the flight track patterns of interested targets. Existing studies on radar-based target track recognition rarely consider the impact of outliers in the acquired data, which happens very often for small air vehicles such as drones. In addition, the performance achieved with a few labeled track examples has significant room for improvement. We propose a semisupervised target track recognition algorithm based on a semisupervised generative adversarial network (SSGAN) that learns a robust model from a few labeled target track examples with the presence of outliers. Our method identifies and eliminates the outliers in the data set and fills in for the removed data. The proposed method extracts a strong recognition flight feature from the basic flight features and forms the strong recognition flight feature combination (SRFFC) by integrating the advanced flight features. The SRFFC is fed into the SSGAN model to identify target track patterns. Experiments were conducted using simulated data sets. Our results demonstrate that the proposed method achieves a highly competitive target track recognition performance in terms of accuracy, precision, and recall in comparison with the state-of-the-art methods. The minimum accuracy of our proposed method is 97%, which achieves an improvement of 15.7% compared with the state-ofthe-art methods. In addition, our method exhibits great robustness with respect to the number of labeled data and choice of parameters. (c) 2021 SPIE and IS&T [DOI: 10.1117/1.JEI.30.3.031204]

Automated summary

This paper proposes a semi-supervised target track recognition algorithm based on a semi-supervised generative adversarial network (SSGAN) which learns a robust model from a few labeled target track examples and deals with outliers in the data. The method achieves highly competitive target track recognition performance, with a minimum accuracy of 97% and a 15.7% improvement compared to state-of-the-art methods.