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We propose hybrid methods for localization in wireless sensor networks fusingnoisy range measurements with angular information (extracted from video).Compared with conventional methods that rely on a single sensed variable, thismay pave the way for improved localization accuracy and robustness. We addressboth 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 wepropose a single nonlinear least-squares cost function, fusing both angular andrange measurements. We then relax the problem to obtain an estimate of theoptimal positions. This contrasts with other hybrid approaches that alternatethe execution of localization algorithms for each type of measurementseparately, to progressively refine the position estimates. Single-sourcelocalization uses a semidefinite relaxation to obtain a one-shot matrixsolution from which the source position is derived via factorization. Networklocalization uses a different approach where sensor coordinates are retained asoptimization variables, and the relaxed cost function is efficiently minimizedusing fast iterations based on Nesterov's optimal method. Further, an automatedcalibration procedure is developed to express range and angular information,obtained by different devices, possibly deployed at different locations, in asingle consistent coordinate system. This drastically reduces the need formanual calibration that would otherwise negatively impact the practicalusability of hybrid range/video localization systems. We develop and test, bothin simulation and experimentally, the new hybrid localization algorithms, whichnot only overcome the limitations of previous fusing approaches but alsocompare favorably to state-of-the-art methods, outperforming them in somescenarios.

FLORIS and CLORIS: Hybrid Source and Network Localization Based on Ranges and Video