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Impact Factor
Chinese Journal of Information Fusion, 2024, Volume 1, Issue 1: 50-62

Free Access | Research Article | 08 June 2024
1 School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
* Corresponding author: Xuebo Jin, email: [email protected]
Received: 22 March 2024, Accepted: 01 June 2024, Published: 08 June 2024  

Abstract
Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed here does not need to model the motion characteristics and sensor characteristics. It can achieve high-performance tracking by learning historical data dynamics and sensor characteristics. Experimental results show that this method can effectively improve the trajectory estimation performance when the target motion is unknown and uncertain.

Graphical Abstract
GPS Tracking Based on Stacked-Serial LSTM Network

Keywords
Trajectory Estimation
Recurrent Neural Network
GPS
Filtering Algorithm
LSTM
Stacked Serial Structure

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Cite This Article
APA Style
Jin, X., Liu, S., Kong, J., Bai, Y., Su, T., & Ma, H. (2024). GPS Tracking Based on Stacked-Serial LSTM Network. Chinese Journal of Information Fusion, 1(1), 50–62. https://doi.org/10.62762/CJIF.2024.361889

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