2018/01/24 by Beatriz Quintino Ferreira, João Paulo Gomes, Ferreira, Beatriz Quintino +5
Engineering · #FOS: Electrical engineering #FOS: Mathematics #Indoor and Outdoor Localization Technologies #Optimization and Control (math.OC) #Robotics and Sensor-Based Localization #Sparse and Compressive Sensing Techniques #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1801.08155
openalex publication_date 2018/01/24 · openalex created_date 2022/10/01 · openalex updated_date 2026/08/01
We propose hybrid methods for localization in wireless sensor networks fusing\nnoisy range measurements with angular information (extracted from video).\nCompared with conventional methods that rely on a single sensed variable, this\nmay pave the way for improved localization accuracy and robustness. We address\nboth the single-source and network (i.e., cooperative multiple-source)\nlocalization paradigms, solving them via optimization of a convex surrogate.\nThe formulations for hybrid localization are unified in the sense that we\npropose a single nonlinear least-squares cost function, fusing both angular and\nrange measurements. We then relax the problem to obtain an estimate of the\noptimal positions. This contrasts with other hybrid approaches that alternate\nthe execution of localization algorithms for each type of measurement\nseparately, to progressively refine the position estimates. Single-source\nlocalization uses a semidefinite relaxation to obtain a one-shot matrix\nsolution from which the source position is derived via factorization. Network\nlocalization uses a different approach where sensor coordinates are retained as\noptimization variables, and the relaxed cost function is efficiently minimized\nusing fast iterations based on Nesterov's optimal method. Further, an automated\ncalibration procedure is developed to express range and angular information,\nobtained by different devices, possibly deployed at different locations, in a\nsingle consistent coordinate system. This drastically reduces the need for\nmanual calibration that would otherwise negatively impact the practical\nusability of hybrid range/video localization systems. We develop and test, both\nin simulation and experimentally, the new hybrid localization algorithms, which\nnot only overcome the limitations of previous fusing approaches but also\ncompare favorably to state-of-the-art methods, outperforming them in some\nscenarios.\n