ArticleSensors (Basel, Switzerland)2022
Adaptive Data Fusion Method of Multisensors Based on LSTM-GWFA Hybrid Model for Tracking Dynamic Targets.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- A Hybrid Simulation-Physical Data-Driven Framework for Occupant Injury Prediction in Vehicle Underbody Structures.Sensors (Basel, Switzerland) · 2026Article
- Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight.Sensors (Basel, Switzerland) · 2023Article
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4 authors.
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Abstract
In preparation for the battlefields of the future, using unmanned aerial vehicles (UAV) loaded with multisensors to track dynamic targets has become the research focus in recent years. According to the air combat tracking scenarios and traditional multisensor weighted fusion algorithms, this paper contains designs of a new data fusion method using a global Kalman filter and LSTM prediction measurement variance, which uses an adaptive truncation mechanism to determine the optimal weights. The method considers the temporal correlation of the measured data and introduces a detection mechanism for maneuvering of targets. Numerical simulation results show the accuracy of the algorithm can be improved about 66% by training 871 flight data. Based on a mature refitted civil wing UAV platform, the field experiments verified the data fusion method for tracking dynamic target is effective, stable, and has generalization ability.
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