English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Traditional aerial target tactical intention recognition is based on a single moment of reasoning, while actual battlefield target tactical intention is realized by a series of actions, so the target state reflects dynamic and temporal variation. To solve this problem, bidirectional propagation and attention mechanisms are introduced based on a gated recurrent unit (GRU) network, and bidirectional gated recurrent units with attention mechanism (BiGRU-Attention) air target tactical intention recognition model is proposed. We use a hierarchical approach to construct the air combat intention characteristic set, encode it into temporal characteristics, encapsulate the decision-maker&#x2019;s experience into labels, learn the deep-level information in the air combat intention characteristic vector through a BiGRU neural network, and use the attention mechanism to adaptively assign network weights, and then place air combat characteristic information with different weights in a softmax function layer for intention recognition. Comparison with a traditional air tactical target intention recognition model and analysis of ablation experiments show that the proposed model effectively improves the tactical intention recognition of air targets.

An Air Target Tactical Intention Recognition Model Based on Bidirectional GRU With Attention Mechanism