用声学信号与卡尔曼滤波融合，提升水下机器人定位精度和断联后搜寻效率。
Predictive Modeling in AUV Navigation: A Perspective from Kalman Filtering
- 通过多浮标声信号时差测量，结合卡尔曼滤波实现状态估计。
- 断联后预测运动轨迹并量化不确定性，定位精度显著优于仅用时差方法。
- 可区分继续航行与推进故障，指导资源精准投放至最可能恢复区域。
本文提出一种面向安全的自主水下航行器（AUV）导航框架，通过固定浮标网络接收AUV发出的声信号，计算到达时间差（TDOA）作为位置观测值。这些观测值与基于卡尔曼滤波的预测模型融合，获得连续且抗噪的状态估计。相比仅使用TDOA的基线方法，该框架在定位精度和轨迹稳定性方面均有显著提升。除实时跟踪外，系统还能在通信中断后预测航行器后续运动，并显式建模不确定性增长。搜索模块可区分持续航行与推进故障，使搜救资源能集中部署于最可能的恢复区域。该框架通过融合多浮标声学数据与卡尔曼滤波及不确定性传播，确保通信中断期间导航精度与可靠搜寻区域界定。
原文摘要 · Abstract (English)
We present a safety-oriented framework for autonomous underwater vehicles (AUVs) that improves localization accuracy, enhances trajectory prediction, and supports efficient search operations during communication loss. Acoustic signals emitted by the AUV are detected by a network of fixed buoys, which compute Time-Difference-of-Arrival (TDOA) range-difference measurements serving as position observations. These observations are subsequently fused with a Kalman-based prediction model to obtain continuous, noise-robust state estimates. The combined method achieves significantly better localization precision and trajectory stability than TDOA-only baselines. Beyond real-time tracking, our framework offers targeted search-and-recovery capability by predicting post-disconnection motion and explicitly modeling uncertainty growth. The search module differentiates between continued navigation and propulsion failure, allowing search resources to be deployed toward the most probable recovery region. Our framework fuses multi-buoy acoustic data with Kalman filtering and uncertainty propagation to maintain navigation accuracy and yield robust search-region definitions during communication loss.
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