Deutsches Zentrum Fur Luft-und Raumfahrt E.v.

Deutsches Zentrum Fuer Luft-und Raumfahrt E.v

Bernhard Krach

Patrick Robertson

Michael Lentmaier

Michael Angermann

Thanawat Thiasiriphet

The invention relates to a method for creating a map relating to location-related data on the likelihood of the future movement of a person in a spatial environment, such as a building, forest, tunnel system or public place, and in particular a shopping center, airport or train station. In the method according to the invention, at least one person is supplied with one or more sensors (e.g. inertial sensors, rotation rate sensors, optical sensors) for odometrical measurement (odometry) (e.g. attached to a shoe), wherein the odometry has errors due to inherent measurement inaccuracies of the sensor(s) (e.g. angle deviations, length deviations). The at least one person moves by foot through the spatial environment. Information regarding the pedestrian step lengths and/or pedestrian step direction and/or orientation of the sensor or the person (called odometry information Z) is determined from the measurement signals of the sensor(s). Based on said odometry information, a map is created using a Bayesian estimator (e.g. particle filter, Rao-Blackwellized particle filter, Kalman filter, Mone-Cario hypothesis filter or combined forms of such filters), the state-variable space thereof (e.g. hypothetical space of the particle of a particle filter) comprising both the current pedestrian step (pedestrian step length and optionally movement direction change as well) of the person U, odometry errors E (e.g. drift parameters with respect to the determined pedestrian step lengths and/or pedestrian step direction and/or orientation of the sensor or the person) and also the location-related data on the probabilities of the future movement of the person.

Method for creating a map relating to location-related data on the probability of future movement of a person

A method for estimating hidden channel parameters of a GNSS navigation signal received in a multipath environment includes a sequential Bayesian estimation. A movement model forms the basis of the estimation and is designed especially for dynamic channel scenarios and takes into account the speeds of change and the variable life cycles of the path. The estimator is implemented as a recursive Bayesian filter for a combined determination of positions and suppression of multipaths. The binding of the delay of the direct path is taken into consideration via position and clock parameters. The method is carried out with GNSS satellite navigation receivers, e.g. GPS and Galileo. With the method, multipath error of received GNSS navigation signals is reduced. The multipath channel includes a direct path and Nm-I echo signals and each of these paths may be considered on or off for the estimation.

Method for estimating hidden channel parameters of a GNSS navigation signal received in a multipath environment

For the reduction of the multipath error of received GNSS navigation signals, a sequential Bayesian estimation is used, with a movement model underlying this estimation, which model is particularly designed for dynamic channel situations. Sequential Monte Carlo methods are used to calculate the posterior probability density functions of the signal parameters. To facilitate an efficient integration in received signal tracking loops, the invention builds on complexity reduction concepts that have previously been used in maximum likelihood (ML) estimators. Applicable with GNSS satellite navigation receivers, e.g. GPS and Galileo.

Method for estimating hidden channel parameters of a received GNNS navigation signal

The method for estimating parameters of a navigation signal received by a receiver which receives navigation signals through a plurality of paths wherein the parameters include data modulated on the navigation signal and complex amplitudes i.e. amplitude and phase shift, and time delays of the individual paths, comprises receiving a navigation signal and sampling the received navigation signal. Moreover, the parameters are sequentially estimated in terms of a posterior probability density function. For facilitating the sequential estimation, the received vector is transformed into a compressed vector without loss of information by using a correlator bank having a plurality of correlator reference signals. The sets of samples representing the posterior probability function of the parameters are sequentially calculated by nonlinearly, recursively filtering the compressed vector and optionally predicting the complex amplitude and time delays by performing equivalent time delay shifts and phase shifts of the correlator signals of the correlator bank.

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Hilpoltstein, Germany

Bernhard Krach