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• W3022374198 abstract "The network localization problem with convex and non-convex distance constraints may be modeled as a nonlinear optimization problem. In the existing localization techniques, the non-convex distance constraints are either removed or relaxed into convex constraints to use the convex optimization methods like semi-definite programming, least square approximation, etc. for solving the problem. We use the nonlinear Lagrangian technique for non-convex optimization to convert the localization problem with noisy distance measurements to a root finding problem of a single variable continuous function without any modification of these constraints. This problem is then solved using an iterative method. In this iterative method, the computation of the functional values involves a finite mini-max problem. We use smoothing gradient method to compute the functional value. The proposed iterative method converges to a solution of the network localization problem with a desired label of accuracy in real time." @default.
• W3022374198 created "2020-05-13" @default.
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• W3022374198 date "2020-08-01" @default.
• W3022374198 modified "2023-09-27" @default.
• W3022374198 title "Solution of network localization problem with noisy distances and its convergence" @default.
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• W3022374198 doi "https://doi.org/10.1016/j.adhoc.2020.102166" @default.
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