An OLOS error robust TOA-based WSN localization algorithm in indoor environment

With the consideration of the character of WSN and the indoor environment, weighing the amount of communication, computation and localization accuracy, a Low-Computational Rwgh(LCRwgh) algorithm which is robust to Obstructed Line of Sight (OLOS) is presented. By picking out the subsets of range measurements with minimum mean Residual Square and calculating the weighted mean of their position estimations, this algorithm can optimize the combinations of range measurements without knowing the channel characters, it also escaped iterative communication. The algorithm's computational complexity is lower than the cogeneric algorithms, and its performance is tested by simulations which show the improvement of location accuracy and the ability of OLOS error mitigation.