基于强化学习自适应时间对应的音频无人机定位
Audio-based UAV Localization with Adaptive Temporal Correspondence via Reinforcement Learning
AI总结：
针对固定音频段长度限制时间对应性的问题，提出基于强化学习自适应音频窗口的无人机定位框架，结合Mamba网络实现低延迟高精度三维定位。
AI中文摘要：
基于音频的定位为反无人机早期预警提供了一种低成本且不受光照影响的传感解决方案。然而，现有方法通常依赖于预定义的固定音频段长度，这限制了时间对应性，并在充分的声学证据与及时定位之间造成权衡。为解决这一问题，我们提出了一种具有自适应时间对应的音频定位框架。首先使用一个探测片段提取紧凑的声学状态，该状态表征观测的可靠性和一致性。在状态的引导下，强化学习控制器动态确定每次定位决策所需的音频窗口大小。随后，选定的音频段由基于Mamba的定位网络处理，该网络具有自适应时间特征调制，用于三维位置估计。大量实验表明，与最先进方法相比，我们的方法在显著降低时间对应延迟的同时实现了具有竞争力的三维定位精度，并在不同场景中表现出强大的泛化能力。
英文摘要：
Audio-based localization provides a low-cost and illumination-independent sensing solution for anti-UAV early warning. However, existing methods typically rely on a predefined fixed audio segment length, which limits temporal correspondence and creates a trade-off between sufficient acoustic evidence and timely localization. To address this issue, we propose an audio-based localization framework with adaptive temporal correspondence. A probe segment is first used to extract a compact acoustic state that characterizes the reliability and consistency of the observation. Guided by the state, a reinforcement learning controller dynamically determines the required audio window size for each localization decision. The selected audio segment is then processed by a Mamba-based localization network with adaptive temporal feature modulation for 3D position estimation. Extensive experiments demonstrate that our method achieves competitive 3D localization accuracy with substantially reduced temporal correspondence latency compared to SOTA methods and exhibits strong generalization across scenarios.
