发表机构
IT University of Copenhagen（哥本哈根信息技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无预部署应答器的被动AUV定位，提出基于IMU去扭曲的FP-MUSIC算法，在波浪扰动下显著降低定位误差，并将中等海况下2米信标分离精度从45%提升至75%。
AI 中文摘要
在没有预先部署海底应答器或无法直接访问车载传感器的情况下，对自主水下航行器进行定位仍然是一个核心挑战。我们提出了一种接收端被动的三维定位与空间映射系统，该系统利用单个配备水听器阵列和惯性测量单元（IMU）的浮标。核心难点在于，表面波浪运动会引起六自由度（6-DOF）扰动，导致阵列在快照之间旋转，从而降低传统子空间处理的性能。我们通过引入一种定点迭代多信号分类算法（FP-MUSIC）来解决这一问题，该算法利用IMU测量值在到达方向估计之前对快照协方差进行去扭曲处理。此外，我们采用子空间投影宽带匹配滤波器来解析信标距离，并利用功率不对称性进行独立的前后识别。在模拟海况下的评估表明，与未补偿的方法相比，FP-MUSIC显著降低了定位误差，并在波浪引起的运动下保持了稳健的三维跟踪和航行器姿态估计。在中等海况下，FP-MUSIC将2米信标分离精度从约45%提高到75%。
英文摘要
Localizing an autonomous underwater vehicle without pre-deployed seabed transponders, or direct access to onboard vehicle sensors remains a core challenge. We present a receiver-passive 3-D localization and spatial mapping system utilizing a single floating surface buoy equipped with a hydrophone array and an inertial measurement unit (IMU). The central difficulty is that surface wave motion induces six-degree-of-freedom (6-DOF) perturbations that rotate the array between snapshots, degrading conventional subspace processing. We resolve this by introducing a fixed-point iterative MUltiple SIgnal Classification algorithm (FP-MUSIC) that uses IMU measurements to de-warp snapshot covariances prior to direction-of-arrival estimation. Furthermore, we employ a subspace-projected wideband matched filter to resolve beacon ranges and use power asymmetry for independent front-back identification. Evaluations across simulated sea states demonstrate that FP-MUSIC substantially reduces localization error relative to uncompensated methods and sustains robust 3-D tracking and vehicle orientation estimation under wave-induced motion. At moderate sea state, FP-MUSIC increases the 2-m beacon-separation accuracy from approximately 45% to 75%.
