Abstract: Systems and methods for detecting and locating a signal source. The system contains an antenna array with antennas, a control unit that is connected to the antenna array, and an evaluation unit to receive data from the control unit. The antennas are designed to acquire a signal emitted by the signal source. The control unit is designed to ascertain a cross-correlation or covariance matrix of a signal received from the antenna array, and to transmit the cross-correlation or covariance matrix to the evaluation unit. The evaluation unit is designed to ascertain a position of the signal source on the basis of the cross-correlation or covariance matrix received from the control unit.

Abstract: A method for determining positions of n target objects, each emitting electromagnetic signals, by m spatially distributed observers, by: carrying out direction-finding measurements by each of the m observers with respect to at least a part of the n targets, collecting the direction-finding measurements by one of the m observers or an external evaluation unit; ascertaining a geometric probability distribution from each of the direction-finding measurements in a partial map; combining the partial maps in order to generate a plurality of possible hypotheses; adding up the possible hypotheses with a respective weighting to form an overall map in order to obtain a marginalized probability distribution that takes all hypotheses into account; and eliminating, step-by-step, hypotheses that are incompatible with the marginalized probability distribution. Also a system for determining positions of emitters from target objects.

Abstract: Systems and methods for detecting and locating a signal source. The system contains an antenna array with antennas, a control unit that is connected to the antenna array, and an evaluation unit to receive data from the control unit. The antennas are designed to acquire a signal emitted by the signal source. The control unit is designed to ascertain a cross-correlation or covariance matrix of a signal received from the antenna array, and to transmit the cross-correlation or covariance matrix to the evaluation unit. The evaluation unit is designed to ascertain a position of the signal source on the basis of the cross-correlation or covariance matrix received from the control unit.

Abstract: The present disclosure is concerned with a method for searching a search area, a missile, and a missile formation. According to the method, in each case at least one radar is arranged in at least two missiles, wherein the method involves: determining a total search time for searching the search area, splitting the search area into at least two search subareas, and searching the at least two search subareas by means of the respective radar of the at least two missiles. The at least two missiles carry out the searching cooperatively, wherein the search subareas are chosen such that a detection probability is maximized.

Abstract: The present disclosure is concerned with a method for searching a search area, a missile, and a missile formation. According to the method, in each case at least one radar is arranged in at least two missiles, wherein the method involves: determining a total search time for searching the search area, splitting the search area into at least two search subareas, and searching the at least two search subareas by means of the respective radar of the at least two missiles. The at least two missiles carry out the searching cooperatively, wherein the search subareas are chosen such that a detection probability is maximized.

Abstract: A method for searching a search area, with a missile and with a missile formation is described Based on the method, in each case at least one radar is arranged in at least two missiles, wherein the method includes: splitting the search area into at least two search subareas, searching the at least two search subareas by the respective radar of the at least two missiles, wherein the at least two missiles carry out the searching cooperatively, wherein the search subareas are chosen such that a total search time is minimal.

Abstract: 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 Zu) is determined from the measurement signals of the sensor(s).

Abstract: 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.

Abstract: 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 Zu) is determined from the measurement signals of the sensor(s).

Abstract: 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.

Abstract: 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.

Abstract: For the reduction of the multipath error of received GNSS navigation signals, a sequential Bayesian estimation is used, with a movement model forming the basis of the estimation, which model 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. Application with GNSS satellite navigation receivers, e.g. GPS and Galileo.

Abstract: 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.

Abstract: 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.