发表机构
Department of Electrical Engineering and Electronics, School of Engineering, University of Liverpool; Jiangsu JITRI Tsingunited Intelligent Control Technology Co., Ltd.; School of Computer Science and Informatics, University of Liverpool; ARIES Research Centre, Polytechnic School, Universidad Antonio de Nebrija（利物浦大学工程学院电气工程与电子系; 江苏集萃清联智控科技有限公司; 利物浦大学计算机科学与信息学院; 安东尼奥·德·内布里哈大学理工学院ARIES研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对延迟声学定位下的UUV导航，提出压缩延迟信息投影（CDIP）方法，通过投影延迟校正避免回退重放，在1.5秒延迟下将RMSE从1.062米降至0.456米，同时大幅降低计算成本。
AI 中文摘要
延迟的声学定位数据包约束了历史导航状态，但当前时刻的更新却将它们与不匹配的状态进行比较，而精确的回退/重放则会重新执行其间估计器的历史。本文提出了压缩延迟信息投影（CDIP），这是一种因果的15状态误差状态卡尔曼滤波器（ESKF）处理方法，用于延迟声学无人水下航行器（UUV）导航。CDIP保留源时刻快照以及历史到当前的互协方差，然后将延迟的源时刻声学校正直接投影到当前状态，而无需完全回退/重放。精确的固定滞后回退/重放乱序测量（OOSM）处理作为高保真精度参考。在固定1.5秒声学延迟且无中断的154次可用配对记录中，CDIP将平均轨迹位置均方根误差（RMSE）从基线的1.062米降低至0.456米（57.1%）。其0.456米的平均值比重放平均值0.451米高1.03%，而其测得的每次更新平均运行时间降低了99.2%（约127倍）。一项单独的预先声明的扫描覆盖六个固定延迟，每个延迟有30次配对记录，以及一项有真值支持的9维一致性分析，界定了这一解释。额外的针对性实验表明，在50-300秒的声学中断期间，轨迹精度接近重放，同时保持亚毫秒级的更新成本。因此，CDIP提供了一种紧凑的延迟信息处理方法，在所评估的配置下具有经验性的精度-计算权衡；证据并未确立相对于重放的统计等效性或非劣效性。
英文摘要
Delayed acoustic positioning packets constrain historical navigation states, but a current-time update evaluates them against a mismatched state, whereas exact rewind/replay re-executes the intervening estimator history. This paper introduces compressed delayed-information projection (CDIP), a causal 15-state error-state Kalman filter (ESKF) treatment for delayed-acoustic unmanned underwater vehicle (UUV) navigation. CDIP retains a source-epoch snapshot and the historical-to-current cross-covariance, then projects the delayed source-epoch acoustic correction directly to the current state without full rewind/replay. Exact fixed-lag rewind/replay out-of-sequence-measurement (OOSM) processing serves as a high-fidelity accuracy reference. In 154 usable paired recordings at a fixed 1.5-s acoustic delay without an outage, CDIP reduced mean trajectory-position root-mean-square error (RMSE) from 1.062 m for the baseline to 0.456 m (57.1%). Its 0.456-m mean was 1.03% higher than the 0.451-m replay mean, while its measured mean per-update runtime was 99.2% lower (approximately 127-fold). A separate predeclared sweep across six fixed delays, with 30 paired recordings per delay, and a truth-supported 9-D consistency analysis bound the interpretation. Additional targeted experiments showed near-replay trajectory accuracy across 50-300-s acoustic outages while preserving sub-millisecond update cost. CDIP therefore provides a compact delayed-information treatment with an empirical accuracy-computation trade-off under the evaluated configuration; the evidence does not establish statistical equivalence or non-inferiority relative to replay.
Comments42 pages, 7 figures, 5 tables
