Self-evolutionary radar target detection algorithm based on deep learning

Abstract

The invention relates to a self-evolutionary radar target detection algorithm based on deep learning, and relates to radar signal processing. The self-evolutionary radar target detection algorithm based on deep learning comprises the steps of: performing feature processing of radar data; designing a radar target detection base model; and applying a two-view cooperative training algorithm to modelself-evolution. By adoption of a deep learning method, radar target detection is realized; simultaneously, self-evolution of the capability of the radar target detection model is realized through theprovided two-view cooperative training algorithm; the algorithm is established based on deep learning and machine learning; therefore, the radar identification capability can be improved; the practicability is high; the portability is high; reliable improvement of the detection capability is realized by sufficient utilization of the radar data flow; and requirements of semi-supervised learning lowin human loss can be realized.

Description

technical field [0001] The invention relates to radar signal processing, in particular to a self-evolving radar target detection algorithm based on deep learning. Background technique [0002] Radar target detection technology, as the core technology in the radar field, is of great significance both in the military field and in the civilian field, so it has always been valued by scientific researchers. Traditional radar target detection uses filtering and tracking methods, which may cause serious false alarms and missed detections in the case of low signal-to-noise ratio. At the same time, the traditional radar target detection technology also needs to manually design filters according to the radar signal characteristics, which costs a lot of manual loss. [0003] Deep learning has a powerful ability to express nonlinear features, and can automatically extract key features from a large amount of data. In various recognition tasks, deep learning algorithms are superior to tr...

AI Technical Summary

Problems solved by technology

In addition, due to the influence of weather, radar hardware aging and other factors, radar data will change over time, and the deep learning model trained initially may not be suitable for subsequent data, so it is necessary to use new data to continuously train the model , so that the model continues to evolve as the data changes

The evolution of the model requires a large number of labeled sample data to participate in the model training. Although the radar data is easy to obtain, it requires experts to manually label it, which is very costly. Effectively use these large numbers of unlabeled samples to improve the target detection ability and realize the self-evolution of the classifier model appear very important

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