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

Wenxin Xiong, Christian Schindelhauer, Hing Cheung So, Joan Bordoy, Andrea Gabbrielli, Junli Liang
2020 arXiv   pre-print
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 of convex relaxation and, thus, are computationally inefficient. Besides, previous studies show that manipulating directly on the TDOA metric usually gives rise to intricate estimators. Aiming at
more » ... ing these challenges, we turn to retrieve the underlying time-of-arrival framework by treating the unknown source onset time as an optimization variable and imposing certain inequality constraints on it, mitigate the NLOS errors through the ℓ_1-norm robustification, and finally apply a hardware realizable neurodynamic model based on the redefined augmented Lagrangian and projection theorem to solve the resultant nonconvex optimization problem with inequality constraints. It is validated through extensive simulations that the proposed scheme can strike a nice balance between localization accuracy, computational complexity, and prior knowledge requirement.
arXiv:2004.10492v2 fatcat:g3rawybzgnb4zojqx4sgj7z2yi