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Cooperative Localization and Navigation最新文献

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Range-Based Navigation Algorithms for Marine Applications 基于距离的船舶导航算法
Pub Date : 2019-08-21 DOI: 10.1201/9780429507229-18
D. Moreno-Salinas, N. Crasta, A. Pascoal, J. Aranda
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引用次数: 1
Precise Pedestrian Positioning by Using Vehicles as Mobile Anchors 利用车辆作为移动锚来精确定位行人
Pub Date : 2019-08-21 DOI: 10.1201/9780429507229-14
Suhua Tang, S. Obana
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引用次数: 0
Motion Control of Multiple AUVs for Simultaneous Mapping and Navigation 多auv同时测绘与导航的运动控制
Pub Date : 2019-08-21 DOI: 10.1201/9780429507229-23
M. Nogueira, F. Pereira
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引用次数: 0
Euclidean Distance Matrix Optimization for Sensor Network Localization * 传感器网络定位的欧氏距离矩阵优化*
Pub Date : 2019-08-21 DOI: 10.1201/9780429507229-5
J. Fliege, H. Qi, N. Xiu
Sensor Network Localization (SNL) is a general framework that generates a set of embedding points in a low-dimensional space so as to preserve given distance information as much as possible. Typical applications include source localization in two or three dimensional space,molecular conformation in three dimensions, graph embedding and data visualization. There are three main difficulties in solving SNL:(i) low-dimensional embedding that gives rise to non-convexity of the problem,coupled with infinitely many local minima;(ii) a large number of lower and upper bounds for certain distances used to improve the embedding quality; and (iii) non-differentiability of some loss functions used to model SNL. There exist a few promising approaches including co-ordinates minimization and semi-definite programming. This survey mainly focus on a recently established approach: Euclidean Distance Matrix (EDM) Optimization. We will give a short but essential introduction how this approach is theoretically well-developed and demonstrate how EDM optimization nicely handles those difficulties through a few widely used loss functions. We also show how regularization terms can be naturally incorporated into EDM optimization. Numerical examples are used to demonstrate the potential of EDM optimization in tackling large scale problems and effect of regularizations.
传感器网络定位(SNL)是一种在低维空间中生成一组嵌入点以尽可能保留给定距离信息的通用框架。典型的应用包括二维或三维空间中的源定位、三维空间中的分子构象、图嵌入和数据可视化。解决SNL的主要困难有三个:(i)低维嵌入会导致问题的非凸性,并伴随着无穷多个局部极小值;(ii)用于提高嵌入质量的一定距离的大量下界和上界;(iii)一些用于SNL建模的损失函数的不可微性。有几种很有前途的方法,包括坐标最小化和半确定规划。这一调查主要集中在最近建立的方法:欧几里得距离矩阵(EDM)优化。我们将简要介绍这种方法在理论上是如何完善的,并演示EDM优化如何通过一些广泛使用的损失函数很好地处理这些困难。我们还展示了如何将正则化项自然地合并到EDM优化中。数值算例说明了电火花加工优化在处理大规模问题中的潜力和正则化的影响。
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引用次数: 5
Cooperative Localization and Navigation for Multiple Autonomous Underwater Vehicles 多自主水下航行器协同定位与导航
Pub Date : 2019-08-21 DOI: 10.1201/9780429507229-26
Lichuan Zhang
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引用次数: 0
期刊
Cooperative Localization and Navigation
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