Sensor Network Localization with Noisy Distance Measurements

Explore sensor network localization techniques in this lecture from the Fields Institute's mini-symposium on Sensor Network Localization and Dynamical Distance Geometry. Delve into the challenges of determining sensor node positions using noisy distance measurements and a small number of anchor nodes with known locations. Examine a multiplicative noise model and associated least-squares problem. Learn about obtaining initial positions through Euclidean distance matrix (EDM) problems solved with semidefinite programming and anchor position alignment. Investigate methods for achieving low-rank solutions by manipulating the trace of the semidefinite Gram matrix. Analyze the Pareto frontier of solutions resulting from different distance approximation error tolerances. Discover an alternative Pareto frontier parametrization involving distance error minimization subject to trace constraints. Explore an inexact Newton method for determining optimal trace values. Conclude by examining techniques for refining initial positions through nonlinear least-squares problem solving.