FLORIS 和 CLORIS：基于范围和视频的混合源和网络定位
FLORIS and CLORIS: Hybrid Source and Network Localization Based on Ranges and Video
AI总结：
本文提出融合噪声范围测量与角信息的混合定位方法，通过凸优化解决单源和网络定位问题，提高定位精度与鲁棒性。
摘要：
本文提出融合噪声范围测量与角信息的混合定位方法，通过凸优化解决单源和网络定位问题，提高定位精度与鲁棒性。
英文摘要：
We propose hybrid methods for localization in wireless sensor networks fusing noisy range measurements with angular information (extracted from video). Compared with conventional methods that rely on a single sensed variable, this may pave the way for improved localization accuracy and robustness. We address both the single-source and network (i.e., cooperative multiple-source) localization paradigms, solving them via optimization of a convex surrogate. The formulations for hybrid localization are unified in the sense that we propose a single nonlinear least-squares cost function, fusing both angular and range measurements. We then relax the problem to obtain an estimate of the optimal positions. This contrasts with other hybrid approaches that alternate the execution of localization algorithms for each type of measurement separately, to progressively refine the position estimates. Single-source localization uses a semidefinite relaxation to obtain a one-shot matrix solution from which the source position is derived via factorization. Network localization uses a different approach where sensor coordinates are retained as optimization variables, and the relaxed cost function is efficiently minimized using fast iterations based on Nesterov's optimal method. Further, an automated calibration procedure is developed to express range and angular information, obtained by different devices, possibly deployed at different locations, in a single consistent coordinate system. This drastically reduces the need for manual calibration that would otherwise negatively impact the practical usability of hybrid range/video localization systems. We develop and test, both in simulation and experimentally, the new hybrid localization algorithms, which not only overcome the limitations of previous fusing approaches but also compare favorably to state-of-the-art methods, outperforming them in some scenarios.
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。
