Abstract: A method and apparatus configured to create a zone of privacy within which tracking signals, transmitted by a tracking device located within the privacy zone, cannot be used to identify a location of the tracking device.

Abstract: A method and system for combining data obtained by sensors, having particular application in the field of navigation systems, are disclosed. The techniques provide significant improvement over state-of-the-art Markovian methods that use statistical noise filters such as Kalman filters to filter data by comparing instantaneous data with the corresponding instantaneous estimates from a model. In contrast, the techniques disclosed herein use multiple time periods of various lengths to process multiple sensor data streams, in order to combine sensor measurements with motion models at a given time epoch with greater confidence and accuracy than is possible with traditional “single epoch” methods. The techniques provide particular benefit when the first and/or second sensors are low-cost sensors (for example as seen in smart phones) which are typically of low quality and have large inherent biases.

Abstract: A method for performing in a positioning, navigation, tracking, frequency-measuring, or timing system is provided.

Abstract: A system is disclosed for determining a physical metric such as position. The system comprises a local signal generator (8) configured to provide a local signal and a receiver (4) configured to receive a signal having properties corresponding to those in a signal transmitted by a trusted remote source. An inertial measurement unit (12) is configured to provide a measured or assumed movement of the receiver. A correlator (6) is configured to provide a correlation signal by correlating the local signal with the received signal. A motion compensation unit (14) is configured to provide motion compensation of at least one of the local signal, the received signal, and the correlation signal based on the measured or assumed movement.

Abstract: A method for performing in a positioning, navigation, tracking, frequency-measuring, or timing system is provided.

Abstract: A method, apparatus, computer program, data structure, signal relating to: causing correlation of a digital signal provided by a receiver with a motion-compensated correlation code, wherein the motion-compensated correlation code is a correlation code that has been compensated before correlation using one or more phasors dependent upon an assumed or measured movement of the receiver.

Abstract: There is disclosed a computer-implemented method performed in a tracking system for tracking the motion of a body, as a function of time, the method comprising: (a) during a first time period, obtaining first data related to the motion of a body from at least one primary positioning unit, wherein said at least one primary positioning unit is mounted on a first platform carried on the body, or wherein said at least one primary positioning unit is separate to the body, said primary positioning unit being operational during the first time period; (b) during the first time period, obtaining second data from one or more secondary sensors configured to make measurements from which position or movement may be determined, said one or more secondary sensors being mounted on one or more second platforms carried on the body; (c) generating first training data comprising the first data and second data; (d) during a second time period, obtaining third data from the one or more secondary sensors, and; (e) analysing the thi

Abstract: A method and system for combining data obtained by sensors, having particular application in the field of navigation systems, are disclosed. The techniques provide significant improvement over state-of-the-art Markovian methods that use statistical noise filters such as Kalman filters to filter data by comparing instantaneous data with the corresponding instantaneous estimates from a model. In contrast, the techniques disclosed herein use multiple time periods of various lengths to process multiple sensor data streams, in order to combine sensor measurements with motion models at a given time epoch with greater confidence and accuracy than is possible with traditional “single epoch” methods. The techniques provide particular benefit when the first and/or second sensors are low-cost sensors (for example as seen in smart phones) which are typically of low quality and have large inherent biases.