Abstract: Embodiments of the invention provide a blending filter based on extended Kalman filter (EKF), which optimally integrates the IMU navigation data with all other satellite measurements tightly-coupled integration filter. This blending filter can be easily implemented with minor modification to the position engine of stand-alone GNSS receiver. Provided is a low-complexity tightly-coupled integration filter for sensor-assisted global navigation satellite system (GNSS) receiver. The inertial measurement unit (IMU) contains inertial sensors such as accelerometer, magnetometer, and/or gyroscopes Embodiments also include method for pedestrian dead reckoning (PDR) data conversion for ease of GNSS/PDR integration. The PDR position data is converted to user velocity measured at the time instances where GNSS position/velocity estimates are available.

Abstract: Methods and apparatus for detecting, measuring, and/or mitigating effects of moving an inertial navigation device's cradle are provided. In an example, provided are methods and apparatus to mitigate cradle rotation-induced inertial navigation errors. In an example, a method for mitigating an inertial navigation error includes receiving inertial sensor data and processing the inertial sensor data with a first navigation algorithm having a non-holonomic constraint (NHC). A second navigation algorithm, lacking a NHC, also processes the inertial sensor data simultaneously with the first algorithm. A cradle rotation is detected by the second navigation algorithm. A first navigation algorithm result, produced from the inertial sensor data generated during the cradle rotation, is discarded. The first algorithm can be computationally realigned, based on a second navigation algorithm result produced from the inertial sensor data generated during the cradle rotation.

Abstract: Systems and methods for determining a position of a vehicle are described. The system includes at least one GNSS sensor mounted to the vehicle for receiving GNSS signals of a global positioning system and at least one physical sensor mounted to the vehicle for generating physical data indicative of a physical parameter of at least a part of the vehicle. The system also includes a recursive statistical estimator, such as a Kalman Filter, in communication with the GNSS sensor(s) for seeding the recursive statistical estimator with an output of the GNSS sensor(s) to determine an estimated position of the vehicle. A data fusion module combines the estimated position and velocity of the vehicle with the physical data thus generating combined data, which is used to seed the recursive statistical estimator to determine an updated estimated position of the vehicle.

Abstract: A hybrid navigation device includes at least one auxiliary sensor adapted to deliver at least one auxiliary signal and a plurality of hybrid navigation systems, each including at least one inertial navigation system and one calculator configured to form an hybrid signal at the output of each hybrid navigation system. The hybrid navigation device includes a module for the detection of good operating condition and the weighting of the hybrid navigation systems, the module being configured to receive the at least one auxiliary signal, and the hybrid signals of each hybrid navigation system, respectively, to deduce therefrom an indicator of good operating condition and a weighting coefficient for each hybrid navigation system, and to calculate a weighted hybrid signal as a function of the hybrid signals and of the weighting coefficients of each hybrid navigation system, respectively.

Abstract: A tracking device may be used to detect a position or movement of an object. The tracking device may include a housing that supports an emitter. The emitter may transmit a first signal. A measurement device within the housing may measure rotational movement of an object. Circuitry may receive a second signal in response to the first signal. The circuitry may reset an angle value to a predetermined value based on the second signal. The angle value may be determined by a measurement of the rotational movement of the object.

Abstract: A landmark-based method of estimating the position of an AGV (autonomous ground vehicle) or conventional vehicle, which has an onboard database of landmarks and their locations coordinates. During travel, the AGV looks for and identifies landmarks. It navigates according to position estimations that are based on measured yaw rate and speed. When a landmark s encountered and recognized, its true location coordinates are looked up from the database. These true coordinates are then used to enhance position estimation accuracy between landmarks.

Abstract: A system vitally determines a position of a train. The system includes a plurality of diverse sensors, such as tachometers and accelerometers, structured to repetitively sense at least change in position and acceleration of the train, a global positioning system sensor, which is diverse from each of the diverse sensors, structured to repetitively sense position of the train, and a track map including a plurality of track segments which may be occupied by the train. A processor cooperates with the diverse sensors, the global positioning system sensor and the track map. The processor includes a routine structured to provide measurement uncertainty for each of the diverse sensors and the global positioning system sensor. The routine cross-checks measurements for the diverse sensors, and cross-checks the global positioning system sensor against the track map. The routine provides the vitally determined position of the train and the uncertainty of the vitally determined position.

Abstract: Embodiments of the present invention provide improved systems and methods for estimating N-dimensional parameters while sensing fewer than N dimensions. In one embodiment a navigational system comprises a processor and an inertial measurement unit (IMU) that provides an output to the processor, the processor providing a navigation solution based on the output of the IMU, wherein the navigation solution includes a calculation of an n-dimensional parameter. Further, the navigational system includes at most two sensors that provide an output to the processor, wherein the processor computes an estimate of an n-dimensional parameter from the output of the at most two sensors for bounding errors in the n-dimensional parameter as calculated by the processor when the trajectory measured by the IMU satisfies movement requirements, wherein “n” is greater than the number of the at most two sensors.

Abstract: A method of defining a navigation system comprising at least one inertial platform and involving terrain correlation, the estimations of the state vector of a platform being made by a navigation filter which furthermore accesses the data of an onboard map, allows the definition of the parameters relating to, respectively, the inertial platform and at least one terrain sensor allowing Terrain-Aided-Navigation, the definition of the parameters being carried out on the basis of computations carried out with the help of statistical syntheses not involving a modeling of the navigation filter.

Abstract: An inertial system is provided. The system includes at least one inertial sensor, a processing unit and a plurality of Kalman filters implemented in the processing unit. The Kalman filters receive information from the at least one inertial sensor. At most one of the plurality of Kalman filters has processed zero velocity updates on the last cycle.

Abstract: According to a vehicular behavior determination device (100) of the present invention, an angular velocity vector ?(k) which represents an angular velocity around each of 3 axes of a vehicle (1) can be determined in high accuracy on the basis of a temporal variation mode of a posture vector psti(k) (i=x, y, z) which represents a posture of the vehicle (1) in a global coordinate system in an attempt to curtail a manufacture cost and size of the vehicle (1) by avoiding the usage of a 3-axis gyro sensor which is relatively expensive in price and large in size.