TDOA-based localization with NLOS mitigation via robust model transformation and neurodynamic optimization

This paper revisits the problem of locating a signal-emitting source from time-difference-of-arrival (TDOA) measurements under non-line-of-sight (NLOS) propagation. Many currently fashionable methods for NLOS mitigation in TDOA-based localization tend to solve their optimization problems by means convex relaxation and, thus, are computationally inefficient. Besides, previous studies show that manipulating directly on TDOA metric usually gives rise intricate estimators. Aiming at bypassing these challenges, we turn retrieve underlying time-of-arrival framework treating unknown onset time as an variable and imposing certain inequality constraints it, mitigate errors through ℓ1-norm robustification, finally apply hardware realizable neurodynamic model based redefined augmented Lagrangian projection theorem resultant nonconvex with constraints. It is validated extensive simulations proposed scheme can strike nice balance between accuracy, computational complexity, prior knowledge requirement.