An algorithm for mobile terminal (MT) tracking based on time-of-arrival measurements in non-line-of-sight (NLOS) environments where NLOS measurements are modeled as positive outliers is proposed. Standard filters such as the extended Kalman filter (EKF) fail because they are sensitive to outliers. In contrast, a robust EKF (REKF) always trades off efficiency in line-of-sight (LOS) versus robustness in NLOS environments and it is not possible to achieve both with the same filter. Instead, we propose to use two filters in parallel in a multiple model framework. An EKF yields high precision in LOS environments whereas an REKF provides robust state estimates when NLOS propagation comes into play. The state estimates of either filters are combined automatically based on the confidence we have for the underlying situation. It is shown via numerical studies that the proposed algorithm yields positioning accuracy similar to the EKF in LOS environments and even significantly outperforms the REKF in NLOS environments.
We address the problem of parameter estimation of signals in noise of unknown distribution and propose a semiparametric estimator. Classical parametric estimators, such as the least-squares or Huber's minimax methods, are limited in terms of robustness and generally suboptimal in practice. Alternative methods which are based on nonparametric probability density function (pdf) estimation have been proposed recently. They automatically adapt to the measurements and thus outperform classical techniques. The semiparametric technique we suggest, which also automatically adapts to the data and relies on transformation pdf estimation, provides a further improvement and overcomes the computational weaknesses of the previous methods. The power of the technique is highlighted in an example of amplitude estimation of sinusoidal signals in impulsive noise.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.