Non-line-of-sight mitigation via lagrange programming neural networks in TOA-based localization

Abstract

A common measurement model for locating a mobile source is time-of-arrival (TOA). However, when non-line-of-sight (NLOS) bias error exists, the error can seriously degrade the estimation accuracy. This paper formulates the problem of estimating a mobile source position under the NLOS situation as a nonlinear constrained optimization problem. Afterwards, we apply the concept of Lagrange programming neural networks (LPNNs) to solve the problem. In order to improve the stability at the equilibrium point, we add an augmented term into the LPNN objective function. Simulation results show that the proposed method provides much robust estimation performance.