Jin Xue-Bo, Chen Wei, Ma Hui-Jun, Kong Jian-Lei, Su Ting-Li, Bai Yu-Ting
Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China.
China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China.
Sensors (Basel). 2023 Oct 23;23(20):8650. doi: 10.3390/s23208650.
GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.
基于全球定位系统（GPS）的机动目标定位与跟踪是自动驾驶的一个关键方面，在导航、交通运输、自动驾驶车辆及其他领域有着广泛应用。经典的跟踪方法采用具有精确系统参数的卡尔曼滤波器来估计状态。然而，由于机动目标的复杂运动和未知的传感器特性，难以对其不确定性进行建模。此外，GPS数据常常包含未知的有色噪声，使得获取准确的系统参数具有挑战性，这可能会降低经典方法的性能。为解决这些问题，我们提出一种基于卡尔曼滤波器的状态估计方法，该方法不需要预定义参数，而是使用注意力学习。我们使用带有长短期记忆（LSTM）网络的Transformer编码器来提取动态特征，并基于注意力学习模块的输出，使用期望最大化（EM）算法在线估计系统模型参数。最后，卡尔曼滤波器利用学习到的系统参数、动力学和测量特征来计算动态状态估计。基于GPS仿真数据和北京地理生活车辆GPS轨迹数据集，实验结果表明，我们的方法在估计精度上优于经典方法和纯无模型网络估计方法，为实际的机动目标跟踪应用提供了一种有效的解决方案。
