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Solution of network localization problem with noisy distances and its convergence
Ad Hoc Networks ( IF 3.490 ) Pub Date : 2020-05-03 , DOI: 10.1016/j.adhoc.2020.102166
Ananya Saha; Buddhadeb Sau

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.
更新日期:2020-05-03

 

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