Abstract: In a method of maintenance of a location fingerprint database for an area, signal measurement data is collected together with motion sensor data from each of a plurality of portable devices transiting a portion of the area of the location fingerprint database. The motion sensor data is collected by a sensor assembly in each of the plurality of portable devices. Offline non-causal processing is used to process the collected motion sensor data into reference positions associated with the portion of the area. The collected signal measurement data is employed together with the reference positions to update the location fingerprint database.

Abstract: In a method of maintenance of a location fingerprint database for an area, signal measurement data is collected together with motion sensor data from each of a plurality of portable devices transiting a portion of the area of the location fingerprint database. The motion sensor data is collected by a sensor assembly in each of the plurality of portable devices. Offline non-causal processing is used to process the collected motion sensor data into reference positions associated with the portion of the area. The collected signal measurement data is employed together with the reference positions to update the location fingerprint database.

Abstract: A survey for building fingerprint maps for an area may be achieved using map information to generate a grid of nodes and links, then transforming the grid to a directed graph, generating an improved tour that entirely traverses the directed graph and providing at least one survey route based at least in part on the tour to a surveyor that will perform the survey route. The provided survey route may be traversed with at least one device having an integrated sensor assembly that outputs data representing motion of the device and recording signal measurements with each device at a plurality of positions to generate a fingerprint map. The traversal of the provided survey route may be assessed through in-route assessment and/or post-route assessment. Depending on the assessment, the provided survey route may be re-traversed or a next survey route may be scheduled.

Abstract: A navigation module and method for providing an INS/GNSS navigation solution for a device that can either be tethered or move freely within a moving platform is provided, comprising a receiver for receiving absolute navigational information from an external source (e.g., such as a satellite), an assembly of self-contained sensors capable of obtaining readings (e.g. such as relative or non-reference based navigational information) about the device, and further comprising at least one processor, coupled to receive the output information from the receiver and sensor assembly, and operative to integrate the output information to produce an enhanced navigation solution. The at least one processor may operate to provide a navigation solution by benefiting from nonlinear models and filters that do not suffer from approximation or linearization and which enhance the navigation solution of the device.

Abstract: A portable navigation module and its method of operation are disclosed for seamlessly providing navigation and positioning information to a user. The module comprises: means, such as a GPS receiver, for receiving a first set of navigational information from an external source, such as satellites; and an inertial sensor unit for generating a second set of navigational information at the module. The navigational information is used by a processor programmed with a core algorithm, to identify the mode of conveyance algorithm and an orientation or misalignment algorithm. The algorithm utilizes the navigational information, aided by the mode of conveyance information and the orientation information, to produce a filtered navigation solution (which comprises position, velocity and attitude). The solution is suitably displayed, preferably on a detachable display unit.

Abstract: Disclosed is a reinforcement learning technique for online tuning of integration filters of navigation systems needing a priori tuning parameters, such as Kalman Filters and the like. The method includes receiving GNSS measurements from the GNSS unit of the navigation system; and IMU measurements from IMU of the navigation system. The method further includes providing a priori tuning parameters to tune the integration filter of the navigation system. The method further includes processing the GNSS and IMU measurements using the tuned integration filter to compute a position estimate and updating the a priori tuning parameters based on the computer position estimate.

Abstract: A moving platform INS range corrector (“MPIRC”) module and its method of operation for providing navigation and positioning information. The module comprises: means, such as a receiver, for receiving a first set of absolute navigational information from an external source (such as satellites in case of GNSS); an inertial sensor unit for generating a second set of navigational information at the module; and a transceiver, for receiving and/or transmitting signals and estimating distance measurement from a known position and receiving position coordinates. The navigational information is used by a processor programmed with a core algorithm, to produce a navigation solution (which comprises position, velocity and attitude). The system has the following attributes: the solution is produced seamlessly, even if one source of navigational information is temporarily out of service; the accuracy of the solution is assisted by use of distance and position coordinate measurement from a known position.

Abstract: A navigation module and method for providing an INS/GNSS navigation solution for a device that can either be tethered or move freely within a moving platform is provided, comprising a receiver for receiving absolute navigational information from an external source (e.g., such as a satellite), an assembly of self-contained sensors capable of obtaining readings (e.g. such as relative or non-reference based navigational information) about the device, and further comprising at least one processor, coupled to receive the output information from the receiver and sensor assembly, and operative to integrate the output information to produce an enhanced navigation solution. The at least one processor may operate to provide a navigation solution by benefiting from nonlinear models and filters that do not suffer from approximation or linearization and which enhance the navigation solution of the device.

Abstract: A moving platform INS range corrector (“MPIRC”) module and its method of operation are disclosed for providing navigation and positioning information. The module comprises: means, such as a receiver, for receiving a first set of absolute navigational information from an external source (such as satellites in case of GNSS); an inertial sensor unit for generating a second set of navigational information at the module; and a transceiver, for receiving and/or transmitting signals and estimating distance measurement from a known position and receiving position coordinates. The navigational information is used by a processor programmed with a core algorithm, to produce a navigation solution (which comprises position, velocity and attitude). The system has the following attributes: the solution is produced seamlessly, even if one source of navigational information is temporarily out of service; the accuracy of the solution is assisted by use of distance and position coordinate measurement from a known position.

Abstract: A portable navigation module and its method of operation are disclosed for seamlessly providing navigation and positioning information to a user. The module comprises: means, such as a GPS receiver, for receiving a first set of navigational information from an external source, such as satellites; and an inertial sensor unit for generating a second set of navigational information at the module. The navigational information is used by a processor programmed with a core algorithm, to identify the mode of conveyance algorithm and an orientation or misalignment algorithm. The algorithm utilizes the navigational information, aided by the mode of conveyance information and the orientation information, to produce a filtered navigation solution (which comprises position, velocity and attitude). The solution is suitably displayed, preferably on a detachable display unit.

Abstract: Disclosed is a reinforcement learning technique for online tuning of integration filters of navigation systems needing a priori tuning parameters, such as Kalman Filters and the like. The method includes receiving GNSS measurements from the GNSS unit of the navigation system; and IMU measurements from IMU of the navigation system. The method further includes providing a priori tuning parameters to tune the integration filter of the navigation system. The method further includes processing the GNSS and IMU measurements using the tuned integration filter to compute a position estimate and updating the a priori turning parameters based on the computer position estimate.