Ulrich Hammes

Robust Tracking and Geolocation for Wireless Networks in NLOS Environments

We address the problem of robust tracking and geolocation using time of arrival estimates in wireless networks. Especially in urban or indoor environments and hilly terrains, these estimates are often contaminated by interference due to non-line-of-sight (NLOS) propagation. Standard techniques such as least-squares are inadequate as they lead to erroneous position estimates. We propose robust methods for tracking and geolocation based on a semi-parametric approach that does not require specification of the noise density. Unlike conventional, minimax based, robust techniques, we show that our proposed techniques are more robust as they adapt automatically to the interfering environment. Specifically, we propose a robust extended Kalman filter for tracking a mobile terminal based on robust semi-parametric estimators. Numerical studies for different network scenarios illustrate a substantial gain in performance compared to standard robust competitors.

Robust mobile terminal tracking in NLOS environments using interacting multiple model algorithm

An extended Kalman filter-based interacting multiple model algorithm (IMM-EKF) is proposed for mobile terminal tracking in cellular networks based on time of arrival estimates. The proposed IMM-EKF is able to cope with line-of-sight (LOS) and non-line-of-sight (NLOS) conditions modeled by a Markov chain, where the LOS and NLOS errors are described by different noise models. Road-constraints are included into the IMM-EKF to improve performance. Simulation results show that the IMM-EKF outperforms conventional methods. A comparison to the posterior Cramér-Rao lower bound is given to demonstrate the effectiveness of the IMM-EKF.

Robust Mobile Terminal Tracking in NLOS Environments Based on Data Association

An algorithm for mobile terminal (MT) tracking based on time-of-arrival measurements in non-line-of-sight (NLOS) environments is proposed. It is based on NLOS detection together with a modified probabilistic data association approach where different subgroups of range measurements are constructed. Each of the subgroups provide a position estimate of the MT with its corresponding covariance matrix that are both used in a hypothesis test for NLOS detection. The accepted position estimates are weighted with different probabilities in a Kalman filter framework. Simulation results show a significant increase in positioning accuracy in NLOS environments with respect to both, the extended Kalman filter (EKF) and a NLOS mitigation algorithm from the literature. In LOS environments similar performance to the EKF is achieved. The proposed method does not assume any statistical knowledge of the NLOS errors and only assumes the sensor noise variance to be known.

Transformation-Based Robust Semiparametric Estimation

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 Hubers 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.

A semi-parametric approach for robust multiuser detection

Robust parameter estimation in impulsive noise has risen a lot of attention in wireless communications. Previously, we proposed a non-parametric estimator for multiuser detection based on non-parametric density estimation. Here, we present a semi-parametric estimator that outperforms its non-parametric counterpart by combating multiple access interference and impulsive noise altogether. The approach is termed semi-parametric since a nonlinear parametric function is used to transform the noise data while non-parametric estimation of the score function is performed using the transformed sample. This estimate is then used to determine the parameters of interest, i.e., the transmitted symbols. We also propose a parametric function and an estimator for its parameter.

Model selection for adaptive robust parameter estimation and its impact on multiuser detection

Robust parameter estimation in impulsive noise environments has become an important issue in wireless communications. In previous work, an adaptive robust estimator was developed which modelled the noise score function as a weighted sum of basis functions where the weights best fitted the empirical distribution. Here, this adaptive robust estimator is extended by using model selection to find a parsimonious set of basis functions to model the unknown noise distribution thereby improving small sample performance. It was found that the best model for small sample sizes is a single basis. Finally, we apply this procedure to robust multiuser detection in impulsive noise channels.

Robust semiparametric amplitude estimation of sinusoidal signals: The multi-sensor case

The problem of robust estimation of the complex amplitudes of sinusoidal signals using multiple sensors, in an unknown heavy-tailed, spatially and temporally i.i.d. noise environement is considered. A semiparametric approach for this case is presented, where non-parametric estimation of the noise density is succeeded by maximum likelihood estimation incorporating the estimated density. The suggested approach adapts to the sensor measurements using a compact, and conceptually simple non-parametric transformation density estimation. Simulation results are provided, which illustrate the improvement of the presented approach over classical robust or non-robust estimation procedures, e.g. Hubers minimax estimator or the least-squares estimator.