LocDyn：移动水下网络鲁棒分布式定位算法
LocDyn: Robust Distributed Localization for Mobile Underwater Networks
摘要：
如何自定位的大型团队的水下节点只使用嘈杂的距离测量？如何以分布式的方式来做，并将动态纳入问题？如何拒绝离群值并产生可信的位置估计？严格的声学通信信道和地球物理勘测应用的精度要求要求更快、更准确的定位方法。我们接近动态本地化的MAP估计问题，事先编码的动态，我们设计了一个凸松弛方法，利用以前的估计在每个测量采集步骤，该算法收敛在一阶方法的最佳速率。LocDyn分布：不存在负责处理所获取的数据的融合中心，并且对每个节点执行相同的简单计算。LocDyn是准确的：实验证明，一个较小的定位误差比可比的卡尔曼滤波器。LocDyn是强大的：它拒绝离群噪声，而比较方法屈服于定位误差。
英文摘要：
How to self-localize large teams of underwater nodes using only noisy range measurements? How to do it in a distributed way, and incorporating dynamics into the problem? How to reject outliers and produce trustworthy position estimates? The stringent acoustic communication channel and the accuracy needs of our geophysical survey application demand faster and more accurate localization methods. We approach dynamic localization as a MAP estimation problem where the prior encodes dynamics, and we devise a convex relaxation method that takes advantage of previous estimates at each measurement acquisition step; The algorithm converges at an optimal rate for first order methods. LocDyn is distributed: there is no fusion center responsible for processing acquired data and the same simple computations are performed for each node. LocDyn is accurate: experiments attest to a smaller positioning error than a comparable Kalman filter. LocDyn is robust: it rejects outlier noise, while the comparing methods succumb in terms of positioning error.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
