Transformer-Based Maneuvering Target Tracking
When tracking maneuvering targets, recurrent neural networks (RNNs), especially long short-term memory (LSTM) networks, are widely applied to sequentially capture the motion states of targets from observations. However, LSTMs can only extract features of trajectories stepwise; thus, their modeling o...
Main Authors: | Guanghui Zhao, Zelin Wang, Yixiong Huang, Huirong Zhang, Xiaojing Ma |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-11-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/22/21/8482 |
Similar Items
-
Detection of Multiple Maneuvering Extended Targets by Three-Dimensional Hough Transform and Multiple Hypothesis Tracking
by: Bo Yan, et al.
Published: (2019-01-01) -
Time Convolutional Network-Based Maneuvering Target Tracking with Azimuth–Doppler Measurement
by: Jianjun Huang, et al.
Published: (2024-01-01) -
Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network
by: Huajun Liu, et al.
Published: (2019-04-01) -
Modeling and Filtering for Tracking Maneuvering Targets
by: Sadiq J. Abou-Loukh
Published: (2009-01-01) -
One Maneuvering Frequency and the Variance Adaptive Filtering Algorithm for Maneuvering Target Tracking
by: Qian Guang-hua, et al.
Published: (2013-06-01)