A New Asymmetric Track Filtering Algorithm Based on TCN-ResGRU-MHA

Guardado en:

Detalles Bibliográficos
Publicado en:	Symmetry vol. 17, no. 12 (2025), p. 2094-2121
Autor principal:	Wu Hanbao
Otros Autores:	Yang, Yonggang, Chen, Wei, Wang, Yizhi
Publicado:	MDPI AG
Materias:	Parallel processing Accuracy Deep learning Weapons Radar tracking Artificial neural networks Task complexity Tracking systems Polar coordinates Architecture Tracking Machine learning Time series Asymmetry Propagation Embedded systems Coordinate transformations Latitude Root-mean-square errors Noise reduction Sensors Neural networks Moving targets Effectiveness Target detection Situational awareness Random noise Algorithms Hybrid structures Kalman filters Real time Filtration Euclidean geometry Cartesian coordinates
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Descripción
Resumen:	Modern target tracking systems rely on radar as a sensor to detect targets and generate raw track points. These raw track points are affected by the radar’s own noise and the asymmetric non-Gaussian noise resulting from the nonlinear transformation from polar coordinates to Cartesian coordinates. Without effective processing, such data cannot directly support highly reliable situational awareness, early warning decisions, or weapon guidance. Track filtering, as a core component of target tracking, plays an irreplaceable foundational role in achieving real-time, accurate, and stable estimation of moving target states. Traditional deep learning filtering algorithms struggle with capturing long-term dependencies in high-dimensional spaces, often exhibiting high computational complexity, slow response to transient signals, and compromised noise suppression due to their inherent architectural asymmetries. In order to address these issues and balance the model’s high accuracy, strong real-time performance, and robustness, a new trajectory filtering algorithm based on a temporal convolutional network (TCN), Residual Gated Recurrent Unit (ResGRU), and multi-head attention (MHA) is proposed. The TCN-ResGRU-MHA hybrid structure we propose combines the parallel processing advantages and detail-capturing ability of a TCN with the residual learning capability of a ResGRU, and introduces the MHA mechanism to achieve adaptive weighting of high-dimensional features. Using the root mean square error (RMSE) and Euclidean distance to evaluate the model effect, the experimental results show that the RMSE of TCN-ResGRU-MHA is 27.4621 (m) lower than CNN-GRU, which is an improvement of 15.99% in the complex scene of high latitude, and the distance is 37.906 (m) lower than CNN-GRU, which is an improvement of 18.65%. These results demonstrate its effectiveness in filtering and tracking tasks in high-latitude complex scenarios.
ISSN:	2073-8994
DOI:	10.3390/sym17122094
Fuente:	Engineering Database