Air target potential threat degree prediction method and system based on SSA-BP

Abstract

The invention provides an air target potential threat degree prediction method and system based on SSA-BP, and the method comprises the steps: carrying out the information collection of a target, and obtaining the real-time feature data of the air target; predicting to obtain future state data of the air target; identifying emotional information of our pilot at the current moment; constructing a threat degree data set of the air target in two-level fusion of the emotion of the pilot and the features of the air target, and carrying out quantification and normalization processing to obtain a standard target feature-emotion data set; and optimizing a BP neural network structure through a sparrow search algorithm, carrying out fusion analysis on future state data of the target and emotion information of a commander, and obtaining the threat degree of the target at the future moment from the situation levels of the two parties. The efficient threat degree prediction method is constructed from the aspects of two parties, and the method is simple, high in tracking speed, high in interactivity, easy to implement and capable of effectively coping with interference of aerial targets.

Description

technical field [0001] The invention relates to the field of target detection and recognition, in particular to a prediction scheme for potential threat degree of air targets based on a sparrow search algorithm (SparrowSearch Algorithm, SSA) optimized BP (Back Propagation) neural network. Background technique [0002] Target potential threat prediction is the most basic link to realize situation assessment, and it is the potential damage capability of air targets relative to our aircraft. Aerial targets such as birds and drones may pose threats, and the threats to our side must be considered comprehensively based on speed, position, altitude, etc. To solve this problem, the main methods currently used include fuzzy analysis, evidence reasoning, support vector machine, component analysis, multi-attribute decision-making, neural network and other methods, and considerable research results have been obtained. Although there are many methods for assessing the threat level of a ...

AI Technical Summary

Problems solved by technology

Compared with the situation where the two sides have little disparity in strength, in the possible situation where the enemy is strong and we are weak, even if the state of the target remains unchanged, its threat to our side will increase significantly, that is, a "relative imbalance" will appear, resulting in The estimated target threat value cannot effectively provide a reference for situation assessment

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