Air combat target threat assessment method based on standardized full-connection residual network

Abstract

The invention discloses an air combat target threat assessment method based on a standardized full-connection residual network, and belongs to the field of battlefield situation assessment. Firstly, asimulation experiment is carried out to mark data; constructing a training set and a test set and storing the training set and the test set in a CSV file; secondly, constructing a standardized full-connection residual error network under a TensorFlow database, including constructing a graph for reading CSV file data, a residual error network layer and a standardized full-connection residual errornetwork graph, and finally creating a TensorFlow session, training a network model, testing, analyzing network performance and verifying the model. According to the method, the problem of inaccurateevaluation result caused by lack of self-learning reasoning capability for large sample data in other air combat target threat evaluation methods is solved, distribution of input data can be self-learned, rules hidden in the data can be mined, and the trained model can accurately evaluate the air combat target threat. The battlefield situation assessment method is mainly used for (but not limitedto) battlefield situation assessment.

Description

technical field [0001] The invention belongs to the field of air battlefield situation analysis, in particular to an air combat target threat assessment method based on a standardized fully connected residual network. Background technique [0002] Air combat target threat assessment, as an important auxiliary means of air combat, is an important basis for pilots to dominate the air combat situation and achieve quick victory in air combat. It mainly uses the enemy aircraft situation information obtained by our side, combined with expert experience and mathematical theory to evaluate enemy aircraft The lethality and the degree of threat to our aircraft. Accurate assessment of the threat level of air combat targets can provide pilots with a reliable basis for decision-making, realize rapid attacks on targets with high threat levels, and improve the combat efficiency and survival probability of our aircraft. [0003] At present, fully mining the situational information and laws...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem of inaccurate evaluation results caused by the lack of self-learning reasoning ability for large sample data in the air battlefield target threat assessment method, the present invention proposes an air combat target threat assessment method based on a standardized fully connected residual network based on deep learning

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