Abstract
For the robust localization in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) indoor environments, we proposed a max-min optimization estimator from a measurement model and introduced an adaptive loss function to optimize the estimation. However, this estimator is highly nonconvex leading to difficulties in solving it directly. We employed the neurodynamic to solve it. In addition, we checked the local equilibrium stability of the corresponding projective neural network model. The proposed algorithm does not require any prerequisites compared to existing algorithms, which either require knowledge of the magnitude of the NLOS bias or a priori distinction between LOS and NLOS. We proposed an adaptive distance error upper bound method to improve the accuracy of localization model. Tested in representative numerical simulation and real environments, our proposed robust adaptive positioning algorithm outperforms existing methods in terms of localization accuracy and robustness, especially in severe NLOS environments.