Robust estimator for indoor node localization - ZHAO Fang1; MA Yan1; 3; LUO Hai-yong 4 (Tongxin xuebao)
A novel indoor localization algorithm was presented, which employs robust estimator to identify and restrain ranging outliers or gross errors and uses DFP （davidon fletcher powell） method to majorize the global object function with a convergence within 2 steps. It first divides all the ranging measurements into three different domains （effective in- formation, usable information and bad information） according to the corresponding residual errors, and then adopts dif- ferent weighting scheme （maintaining, down-weighting, rejecting） through self-adaptation during iterative process. Ex- tensive simulation results confirm that this proposed localization scheme outperforms remarkably traditional least squares （LS）, which do not employ outlier identification and restraint.