A Threat Assessment Method for Air Combat Targets Based on Standardized Fully Connected Residual Networks

Abstract

The invention discloses an air combat target threat assessment method based on a standardized full connection residual network, belonging to the field of battlefield situation assessment. First, carry out the simulation experiment to mark the data, build the training set and test set and store them in the CSV file, and then build a standardized fully connected residual network under the TensorFlow database, including building a graph for reading CSV file data, residual network layer and standardized full connection Residual network diagram, and finally create a TensorFlow session, train the network model and test it, analyze the network performance, and verify the model. The invention solves the problem of inaccurate evaluation results caused by lack of self-learning reasoning ability of large sample data in other air combat target threat assessment methods, and can self-learn the distribution of input data, dig out the laws hidden in the data, and train a good model Air combat target threats can be accurately assessed. The present invention is mainly used for (but not limited to) 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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