To solve the problem of real-time recognition of large quantities of ISAR data with multi-platform and multi equipment, especially the issue of adaptive feature extraction of ISAR data, an ISAR target recognition system based on artificial intelligence is proposed. The system consists of three arrangements: data layer, recognition analysis layer, and presentation layer. The data layer extracts ISAR data according to different application scenarios; the recognition and analysis layer introduces deep learning algorithm for adaptively feature extraction, model training and optimizing, comprehensive identification and evaluation results analyzing. The presentation layer establishes stable and efficient information service based on the Web service framework and realizes a full cross-platform display of recognition results and feature information. By practice, the system improves the identification speed significantly and achieves real-time recognition of ISAR data, information push, and display across multi-platform, to effectively assist users in decision making and evaluation judgment.
Due to the large dynamic range and low contrast, inverse synthetic aperture radar (ISAR) image is not appropriate for human observation. In order to output and display the target imaging results, a procedure which compresses the dynamic range of the raw images into a lower range is necessary. In this paper, by analyzing the histogram of original ISAR images, the characteristics of ISAR images are investigated. Given the sparse amplitude distribution of original ISAR image and the shortcomings existing in the sparse linear histogram, this paper proposes an ISAR image detail enhancement algorithm (ISARIDE) based on histogram equalization and dynamic range compression. The advantage of the proposed method is that it can retain the target structure information, and improve the visual effect of human eye to target details as soon as possible. The proposed algorithm was tested on the simulated data and real data. The selected target is a flying Boeing 737-800.The results show the validity of the algorithm.