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

Abstract

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.