基于GPS的堆叠串行LSTM组合神经网络目标跟踪方法
DOI:
https://doi.org/10.52810/FAAI.2024.002Keywords:
轨迹估计, 循环神经网络, GPS, 滤波算法, LSTM, 堆叠串行结构Abstract
机动目标轨迹估计广泛应用于无人驾驶、拦截导弹等领域。由于机动目标的运动特性的不确定性、传感器精度低的问题，轨迹估计一直是一个开放研究问题和一项有挑战性的工作。本文提出了目标运动特性不确情况下，一种基于深度 LSTM 神经网络的轨迹估计方法。该网络有两个具有堆叠串行关系的 LSTM 网络组成，其中，一个 LSTM 网络用于预测运动状态，另一个网络用于更新状态。与经典的基于机动模型的 Kalman 滤波器相比，本文的方法基于网络学习、无需对运动特性和传感器特性进行建模。实验结果表明，该方法可以在目标运动具有未知和不确定性的情况下，有效提升轨迹的估计性能。
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