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- Title
Intelligent Tracking Method for Aerial Maneuvering Target Based on Unscented Kalman Filter.
- Authors
Dong, Yunlong; Li, Weiqi; Li, Dongxue; Liu, Chao; Xue, Wei
- Abstract
This paper constructs a nonlinear iterative filtering framework based on a neural network prediction model. It uses recurrent neural networks (RNNs) to achieve accurate regression of complex maneuvering target dynamic models and integrates them into the nonlinear iterative filtering system via Unscented Transformation (UT). In constructing the neural network prediction model, the Temporal Convolutional Network (TCN) modules that capture long-term dependencies and the Long Short-Term Memory (LSTM) modules that selectively forget non-essential information were utilized to achieve accurate regression of the maneuvering models. When embedding the neural network prediction model, this paper proposes a method for extracting Sigma points using the UT transformation by 'unfolding' multi-sequence vectors and explores design techniques for the time sliding window length of recurrent neural networks. Ultimately, an intelligent tracking algorithm based on unscented filtering, called TCN-LSTM-UKF, was developed, effectively addressing the difficulties of constructing models and transition delays under high-maneuvering conditions and significantly improving the tracking performance of highly maneuvering targets.
- Subjects
TRACKING algorithms; DESIGN techniques; DYNAMIC models; REGRESSION analysis; RECURRENT neural networks
- Publication
Remote Sensing, 2024, Vol 16, Issue 17, p3301
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs16173301