Bernhard Krach

Integration of foot-mounted inertial sensors into a Bayesian location estimation framework

An algorithm for integrating foot-mounted inertial sensors into a Bayesian location estimation framework is presented. The proposed integration scheme is based on a cascaded estimation architecture. A lower Kalman filter is used to estimate the step-wise change of position and direction of the foot. These estimates are used in turn as measurements in an upper particle filter, which is able to incorporate nonlinear map-matching techniques. Experimental data is used to verify the proposed algorithm.

Bayesian Time Delay Estimation of GNSS Signals in Dynamic Multipath Environments

A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops, it builds upon complexity reduction concepts that previously have been applied within maximum likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both BPSK-modulated and BOC-(1,1) modulated navigation signals.

An efficient two-fold marginalized Bayesian filter for multipath estimation in satellite navigation receivers

Multipath is today still one of the most critical problems in satellite navigation, in particular in urban environments, where the received navigation signals can be affected by blockage, shadowing, and multipath reception. Latest multipath mitigation algorithms are based on the concept of sequential Bayesian estimation and improve the receiver performance by exploiting the temporal constraints of the channel dynamics. In this paper, we specifically address the problem of estimating and adjusting the number of multipath replicas that is considered by the receiver algorithm. An efficient implementation via a two-fold marginalized Bayesian filter is presented, in which a particle filter, grid-based filters, and Kalman filters are suitably combined in order to mitigate the multipath channel by efficiently estimating its time-variant parameters in a track-before-detect fashion. Results based on an experimentally derived set of channel data corresponding to a typical urban propagation environment are used to confirm the benefit of our novel approach.

Joint Bayesian positioning and multipath mitigation in GNSS

A sequential Bayesian estimation algorithm for joint positioning and multipath mitigation within satellite navigation receivers is presented. The underlying process model is especially designed for dynamic user scenarios and dynamic channel conditions. To demonstrate its capabilities simulation results are presented.

Bayesian detection and tracking for joint positioning and multipath mitigation in GNSS

A sequential Bayesian estimation algorithm for joint positioning and multipath mitigation in global navigation satellite systems is presented, with an underlying process model that is especially designed for dynamic user scenarios and dynamic channel conditions. In order to facilitate efficient integration into receivers it builds upon complexity reduction concepts that previously have been applied within maximum likelihood estimators. To demonstrate its capabilities simulation results are presented.

Signal simulations in urban environments

This publication explores the influence of a realistic multipath channel model to satellite navigation applications in urban environments. In this context we have discovered that there is a tradeoff between accuracy and robustness concerning the bandwidth. While narrow band signals such as GPS C/A, BOC (1,1) proved to be robust especially under shadowing situations the wideband signals lost lock very likely in the shadowing situation while being more accurate in LOS situations. In severe multipath situations we have been able to prove the performance of the particle filter receiver wich is clearly showing a better performance than the conventional receivers and even than a maximum likelihood estimation DLL. Furthermore we have identified critical situations where receivers tend to loose lock.