A hybrid approach for using reference frames is presented in which a series of anchor frames is used, effectively resetting a global frame upon a trigger event. With each new anchor frame, parameter values for lane boundary estimates (known as lane boundary states) can be recalculated with respect to the new anchor frame. Triggering events may a based on a length of time, distance traveled, and/or an uncertainty value.

An Inertial Navigation System (INS) testing system includes a Unit Under Test (UUT) with an INS module, a DAT tool configured to provide command and control functions to a user, and a Dynamically Integrated Navigation Tester (DINT) in communication with the UUT and the DAT tool. The DINT includes a truth data conversion module configured to receive truth data and convert it to navigation data suitable for transmission to the UUT. The INS testing system advantageously allow a user to interface with a UUT while providing user-specified dynamic inputs from the DINT with the truth data selected by the user.

There is proposed a method of estimating a navigation state with several variables of a mobile carrier according to the extended Kalman filter method, comprising the steps of:—acquisition of measurements of at least one of the variables,—extended Kalman filtering (400) producing a current estimated state and a covariance matrix delimiting in the space of the navigation state a region of errors, with the help of a previous estimated state, of an observation matrix, of a transition matrix and of the measurements acquired, the method being characterized in that it comprises a step (310, 330) of adjustment of the transition matrix and of the observation matrix before their use in the extended Kalman filtering in such a way that the adjusted matrices satisfy an observability condition which depends on at least one of the variables of the state of the carrier, the observability condition being adjusted so as to prevent the Kalman filter from reducing the dimension of the region along at least one non-observable axis of the state space, in which the observability condition to be satisfied by the adjusted transition and observation matrices is the nullity of the kernel of an observability matrix associated therewith and in which the adjustment comprises the steps of:—calculation (301) of at least one primary basis of non-observable vectors with the help of the previous estimated state—for each matrix to be adjusted, calculation (306, 308) of at least one matrix deviation associated with the matrix with the help of the primary basis of vectors, shifting (330) of each matrix to be adjusted according to the matrix deviation associated therewith so as to satisfy the observability condition.

Described is a system for position estimation. A set of raw sensor outputs are acquired from a sensor of a platform. The set of raw sensor outputs are stored in non-transitory memory. A set of optimized sensor measurements is generated by deducing errors in the raw sensor outputs using an unconstrained optimization algorithm. The system determines a position of the platform based on the set of optimized sensor measurements.

An apparatus and method for providing an improved heading estimate of a mobile device in a vehicle is presented. First, the mobile device determines if it is mounted in a cradle attached to the vehicle; if so, inertia sensor data may be valid. While in a mounted stated, the mobile device determines whether it has been rotated in the cradle; if so, inertia sensor data may no longer be reliable and a recalibration to determine a new relative orientation between the vehicle and the mobile device is needed. If the mobile device is mounted and not recently rotated, heading data from multiple sensors (e.g., GPS, gyroscope, accelerometer) may be computed and combined to form the improved heading estimate. This improved heading estimate may be used to form an improved velocity estimate. The improved heading estimate may also be used to compute a bias to correct a gyroscope.

A method and a device resets an inertial unit of a transport on the basis of information delivered by a viewfinder of the transport. According to one embodiment: a horizontal velocity vector of the transport and coordinates of the transport are obtained from the inertial unit, a horizontal line of sight of the viewfinder is obtained on at least one landmark, coordinates of at least one landmark are obtained, an angle between the horizontal velocity vector and the horizontal line of sight is computed, the drift of the computed angle is computed, an error is computed on the basis of the obtained coordinates, the computed angle and its computed drift, and the computed error is transferred to a Kalman filter for filtering the error and resetting the inertial unit.

A system corrects errors of a measurement system. The system comprises base measurement system BS and a correction system. BS sensor data is acquired and provided to an algorithm. The data is stored associated with a time stamp indicating time of sensing. Output values are calculated and stored with time stamps indicating time of the sensor data upon which the output values are calculated. Data having corresponding time stamps are supplied to a filter where correction values and correction increments are calculated. The correction increments reflect the change of error in a base system output value over time due to integration or summing up BS sensor data errors in the processing algorithm. The correction values are applied to the BS data and corrected base navigation output values are calculated. These output values are corrected by the correction increments. Output values of the processing algorithm are further processed in succeeding applications.

The present disclosure relates to a method and apparatus for determining the misalignment between a device and a platform (such as for example a vessel or vehicle) using acceleration and/or deceleration of the platform, wherein the device can be strapped or non-strapped to the platform, wherein in case of non-strapped the mobility of the device may be constrained or unconstrained within the platform. In case of non-strapped, the device may be moved or tilted to any orientation within the platform and still provide a seamless navigation solution without degrading the performance of this navigation solution. When the device is in a holder in the platform, it is still considered non-strapped, as it may move with respect to the platform. The present method can utilize measurements (readings) from sensors (such as for example, accelerometers, odometer/wheel encoders, gyroscopes, etc.) whether in the presence or in the absence of navigational information updates (such as, for example, Global Navigation Satellite System (GNSS) or WiFi positioning).

A system (10) for pedestrian use includes an accelerometer (110) having multiple electronic sensors; an electronic circuit (100) operable to generate a signal stream representing magnitude of overall acceleration sensed by the accelerometer (110), and to electronically correlate a sliding window (520) of the signal stream with itself to produce peaks at least some of which represent walking steps, and further operable to electronically execute a periodicity check (540) to compare different step periods for similarity, and if sufficiently similar then to update (560) a portion of the circuit substantially representing a walking-step count; and an electronic display (190) responsive to the electronic circuit (100) to display information at least in part based on the step count. Other systems, electronic circuits and processes are disclosed.

The invention regards a navigation system and a method for error correction. The navigation system comprises a base navigation system and a correction system. Measurement uncertainties are assigned to each measurement and an error threshold is computed on the basis of these uncertainties. Redundant measurements are determined and residuals for at least a pair of redundant measurements as a discrepancy measure are calculated. In case that the residual exceeds a respective threshold an error count for each measurement involved in the determination of the residual is increased. All residuals for each measurement are summed up for a particular measurement and for carrying out the correction in a fusion filter measurements are selected on the basis of their respective error count and summed up residuals.