用LSTM预测变速度目标，实现精准跟踪与环绕
Bearing-Only Tracking and Circumnavigation of a Fast Time-Varied Velocity Target Utilising an LSTM
- 采用LSTM模型预测目标位置和变速运动状态
- 在多种变速场景下误差显著低于传统方法
- 适用于模拟真实系统运动的非完整双积分模型
仅依靠方位测量进行单个或多个智能体对目标的跟踪、定位及环绕，是控制领域的重要挑战。以往研究多针对静止或匀速运动目标，而对时变速度目标的精确追踪仍属开放问题。本文提出一种基于长短期记忆网络（LSTM）的估计算法，用于预测目标的位置与速度，并设计了相应的控制策略。在多种时变速度场景下，该方法相比已有估计与环绕方案，展现出更低的控制误差与估计误差。此外，实验验证了该方法在模拟真实系统动力学（双积分非完整系统）的目标跟踪中的有效性。
原文摘要 · Abstract (English)
Bearing-only tracking, localisation, and circumnavigation is a problem in which a single or a group of agents attempts to track a target while circumnavigating it at a fixed distance using only bearing measurements. While previous studies have addressed scenarios involving stationary targets or those moving with an unknown constant velocity, the challenge of accurately tracking a target moving with a time-varying velocity remains open. This paper presents an approach utilising a Long Short-Term Memory (LSTM) based estimator for predicting the target's position and velocity. We also introduce a corresponding control strategy. When evaluated against previously proposed estimation and circumnavigation approaches, our approach demonstrates significantly lower control and estimation errors across various time-varying velocity scenarios. Additionally, we illustrate the effectiveness of the proposed method in tracking targets with a double integrator nonholonomic system dynamics that mimic real-world systems.
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