Visual odometry based on camera motion and bidirectional long short-term memory network

Zhao, Zhangzhen; Xing, Bengang; Song, Tao; Liu, Yi; Chen, Bin

doi:10.1117/12.2656772

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Design Of Radar Intent Inference Algorithm Based On Lstm1

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2024

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“…It is very effective for studying the possible behavior of radiation sources in the next moment or a certain period of time in the future. Therefore, We plan to study a multifunctional radar intent inference method based on LSTM [8][9][10][11][12] .…”

Section: Design Of Radar Intent Inference Algorithm Based On Lstmmentioning

confidence: 99%

Design and implementation of intent inference for airborne multifunctional radar based on LSTM

Jin,

Wang,

Feng

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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In order to infer the behavioral intention of airborne multifunctional radar, a working mode transformation model was studied, and an intention inference algorithm was designed based on LSTM. A pattern transformation simulation dataset was constructed, and the intention inference practice of airborne multifunctional radar was carried out. The research results indicate that time length and batch processing rate have a significant impact on the final prediction results. When the timing length is set to 60 and the batch processing rate is set to 20, the prediction accuracy can mostly be maintained above 85%, which lays an important foundation for determining combat actions and countermeasures in actual combat.

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Section: Design Of Radar Intent Inference Algorithm Based On Lstmmentioning

confidence: 99%