Abstract: An example method for determining a confidence value for an estimated parameter in a wireless positioning system includes receiving a signal comprising a number of signal samples, pre-processing each sample of the number of signal samples and therefrom providing a number of pre-processed samples, and calculating a first confidence value, The method further includes performing a pre-estimation of a parameter and providing a pre-estimated parameter, calculating a second confidence value for the pre-estimated parameter, performing a final estimation of the parameter and providing the estimated parameter, and calculating a third confidence value for the estimated parameter. The method includes combining at least two of the first, second and third confidence value and providing the confidence value for the estimated parameter. The confidence value is configured to be used in determining a position of at least one mobile device based on the estimated parameter.

Abstract: A method for position estimation in a positioning system includes receiving, by a locator devices, positioning signals from a mobile device, generating positioning information with associated synchronization identifiers based on the respective received positioning signals, and providing the positioning information with the associated synchronization identifiers to a positioning engine. The positioning engine compiles a set of related positioning information selected from the provided positioning information based on a similarity measure of the associated synchronization identifiers, and determines an estimated position of the mobile device based on the set of related positioning information.

Abstract: An example method for providing at least one estimated parameter of a wireless communication channel includes receiving at least one sample of raw data via the wireless communication channel, processing the at least one sample using a machine learning inference algorithm and therefrom providing a first estimated value, processing the at least one sample using a signal processing algorithm and therefrom providing a second estimated value, acquiring a first confidence value for the first estimated value, acquiring a second confidence value for the second estimated value, evaluating the first confidence value and the second confidence value, and providing either the first estimated value or the second estimated value or a combination of the first and the second estimated value as the at least one estimated parameter based on the evaluation.

Abstract: Calibrating an anchor point in a positioning system includes, in an example implementation, transmitting a localization signal between a mobile device and the anchor point, determining a first direction between the mobile device and the anchor point in a local coordinate system based on the transmitted localization signal, and determining a second direction between the mobile device and the anchor point in a global coordinate system based on the determined first direction and a stored anchor orientation of the anchor point, the anchor orientation defining the local coordinate system of the anchor point with respect to the global coordinate system.

Abstract: Calibrating an anchor point in a positioning system includes, in an example implementation, transmitting a localization signal between a mobile device and the anchor point, determining a first direction between the mobile device and the anchor point in a local coordinate system based on the transmitted localization signal, and determining a second direction between the mobile device and the anchor point in a global coordinate system based on the determined first direction and a stored anchor orientation of the anchor point, the anchor orientation defining the local coordinate system of the anchor point with respect to the global coordinate system.

Abstract: A method for detecting anomalies in a positioning system comprises receiving, for a plurality of locator devices (ANC1, ANC2, ANC3, ANC4), respective location indicators associated with at least one mobile device (TG1, TG2, TG3), determining a position of the at least one mobile device (TG1, TG2, TG3) based on the received location indicators, acquiring, for at least one of the location indicators received, a confidence indicator associated with the at least one of the location indicators, determining, for each acquired confidence indicator, whether a confidence criterion is met, and storing an anomaly indicator in an anomaly cache, if the confidence criterion is not met, wherein each anomaly indicator is associated with the locator device, for which the location indicator is received; and generating an anomaly alert message for at least one of the locator devices based on an evaluation of the anomaly indicators that are stored for the at least one of the locator devices.

Abstract: An example method of determining a position of a mobile device includes, for each of a plurality of anchor points, receiving respective relative position information between the respective anchor point and the mobile device such that the received relative position information for the plurality of anchor points forms a set of position information, each relative position information including at least one of: a direction between the respective anchor point and the mobile device, a distance between the respective anchor point and the mobile device, or a direction and a distance between the respective anchor point and the mobile device.

Abstract: The present disclosure relates to a method (100) for determining an angle of arrival, AoA, of received radio frequency, RF, measurement signals. The method (100) comprises obtaining (101) measurement data based on the received RF measurement signals from an antenna array, wherein the RF measurement signals are representative of multiple frequency channels. The method (100) further comprises determining (102) power spectra, comprising determining at least one power spectrum for each of the multiple frequency channels by using the measurement data. The method (100) further comprises providing (105) a machine learning algorithm, which is pre-trained to determine an AoA based on power spectra of multiple frequency channels. The method (100) further comprises determining (106) the AoA of the received RF measurement signals by using the machine learning algorithm and the determined power spectra.