Abstract: Systems and methods are disclosed for image capture. For example, methods may include accessing a sequence of images from an image sensor; determining a sequence of parameters for respective images in the sequence of images based on the respective images; storing the sequence of images in a buffer; determining a temporally smoothed parameter for a current image in the sequence of images based on the sequence of parameters, wherein the sequence of parameters includes parameters for images in the sequence of images that were captured after the current image; applying image processing to the current image based on the temporally smoothed parameter to obtain a processed image; and storing, displaying, or transmitting an output image based on the processed image.

Abstract: Systems and methods are disclosed for image capture. For example, methods may include accessing a sequence of images from an image sensor; determining a sequence of parameters for respective images in the sequence of images based on the respective images; storing the sequence of images in a buffer; determining a temporally smoothed parameter for a current image in the sequence of images based on the sequence of parameters, wherein the sequence of parameters includes parameters for images in the sequence of images that were captured after the current image; applying image processing to the current image based on the temporally smoothed parameter to obtain a processed image; and storing, displaying, or transmitting an output image based on the processed image.

Abstract: Methods, systems, and computer readable storage media are presented for directional scaling of inertial path data to satisfy ranging constraints. The presented techniques take into account scaling confidence information. In addition to bounding potential scale corrections based on the reliability of the inertial path and the magnetic heading confidence, the techniques bound potential scale parameters based on constraints and solve for directional scale parameters.

Abstract: An attitude estimator provides non-causal attitude estimates for real-time motion compensation of sensed images on a moving platform. A non-causal filter processes uncompensated attitude samples received with a latency from an IMU at a high rate clock to provide an attitude estimate that is strictly non-causal but satisfies a just-in-time (JIT) criteria for real-time motion compensation of images captured at a low rate clock. On-average the error of the non-causal attitude estimate is less than the error of a causal attitude estimate. If the lag added by the non-causal filter is greater than the latency, the effective transfer function of the non-causal filter has a negative gain slope that attenuates high frequency noise of the uncompensated attitude samples. The attitude estimator may also include a causal filter to generate a causal attitude estimate for real-time active stabilization of the image sensor at the high rate clock.

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: Methods and systems for producing data describing states of a plurality of targets using a processor in a system having at least one onboard sensor. The method includes obtaining data from at least one onboard sensor and performing a first data fusion process on the obtained onboard sensor data to produce onboard sensor fused data. Data is also obtained from at least one off-board sensor, and a second, different data fusion process is performed on the obtained off-board sensor data and the onboard sensor fused data to produce target state data.

Abstract: A navigation system includes at least one inertial sensor configured to detect motion of the system and generate inertial data; at least one aiding device configured to generate aiding device measurement data; at least one processing unit configured to generate an un-smoothed navigation solution inclusive of navigation state variable error resets based on the inertial data and the aiding device measurement data; wherein the at least one processing unit is further configured to sum the state variable error resets into a cumulative sum of the state variable error resets; wherein the at least one processing unit is further configured to high pass filter the cumulative sum of the state variable error resets; and wherein the at least one processing unit is further configured to subtract the high pass filtered cumulative sum of the state variable error resets from the un-smoothed navigation solution to generate a smoothed navigation solution.

Abstract: The invention relates to a guidance system comprising estimation means able to estimate, in the course of flight, the attitude and the aerodynamic speed of a projectile, as well as the variations in the speed of the wind, on the basis of guidance orders formulated by guidance means of the guidance system, of a reference trajectory and of measurements obtained by measurement means of the system, using a model of the dynamic behavior of the projectile and a model of the dynamics of the wind.

Abstract: An attitude estimator provides non-causal attitude estimates for real-time motion compensation of sensed images on a moving platform. A non-causal filter processes uncompensated attitude samples received with a latency from an IMU at a high rate clock to provide an attitude estimate that is strictly non-causal but satisfies a just-in-time (JIT) criteria for real-time motion compensation of images captured at a low rate clock. On-average the error of the non-causal attitude estimate is less than the error of a causal attitude estimate. If the lag added by the non-causal filter is greater than the latency, the effective transfer function of the non-causal filter has a negative gain slope that attenuates high frequency noise of the uncompensated attitude samples. The attitude estimator may also include a causal filter to generate a causal attitude estimate for real-time active stabilization of the image sensor at the high rate clock.

Abstract: A system comprises a gyroscope configured to produce a gyroscope signal, an accelerometer configured to produce an accelerometer signal, and a filter unit coupled to the gyroscope and having a configurable bandwidth. The filter unit configured to filter the gyroscope signal. The system also comprises control logic that is configured to alter the bandwidth of the filter unit based on the accelerometer signal.

Abstract: Embodiments include systems and methods of navigation. In on embodiment, a plurality of position and motion states of a vehicle are estimated. The states may be estimated based on information received from a satellite receiver and an inertial measurement sensor. Estimating the states comprises performing one or more of a plurality of update steps at the rate that information is received from the satellite receiver. The states are estimated at a rate greater than the rate at which the update steps are performed. In one embodiment, the states are estimated using a stepped extended Kalman filter.

Abstract: Embodiments of the invention provide methods of calibrating 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). In one embodiment a coordinate transformation matrix using a latest position fix is created. The state variables (for user velocity) are transformed to a local navigation coordinate. The state variables of said integration filter is estimated. A blended calibrated position fix is the output of the method.

Abstract: A GPS measuring unit receives a signal from a GPS satellite and outputs GPS measurement data including at least orientation of an object. An angular velocity sensor outputs angular velocity of the object. An offset value computing unit estimates a running condition of the object on the basis of the measurement data and the angular velocity. The offset value computing unit sequentially derives a temporary offset value of the angular velocity sensor in accordance with the estimated running condition of the object. The offset value computing unit derives an offset value of the angular velocity sensor by executing statistical process on the temporary offset value of the angular velocity sensor. A forgetting factor in the statistical process is changed according to the running condition of the object.

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: 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 system for navigation and tracking may include an inertial navigation system adapted to generate a replica GNSS signal and a global navigation satellite system. The global navigation satellite system may include a module to digitize a GNSS signal received from a constellation of global navigation satellites. A correlator receives the digitized GNSS signal and the replica GNSS signal. The correlator correlates the digitized GNSS signal to the replica GNSS signal to generate a correlated GNSS signal. A coherent integration module coherently integrates the correlated GNSS signal to generate an integrated signal having a predetermined rate. A filter receives the integrated signal and generates a data signal for navigation and tracking. An output device may present the navigation and tracking information based on the data signal, or the navigation and tracking information may be used to provide guidance for a vehicle or may be used to track a target.

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: A method for processing data in an inertial navigation system having a Kalman filter and computer-readable storage medium containing instructions to configure a processor to perform the same. The method produces more accurate estimates of the position, velocity and attitude of the inertial measurement unit. The method is fully automatic and enables zero-velocity updates and fixed-azimuth updates to be performed simultaneously. The method may also include a multi-stage filtering process to filter digital compass data when used in an environment with extraterrestrial magnetic field sources. The method may also include a fixed-lag smoother to improve estimates of the position, velocity and attitude of the inertial measurement unit. The method also may include processes to constrain altitude errors.

Abstract: A system comprises a gyroscope configured to produce a gyroscope signal, an accelerometer configured to produce an accelerometer signal, and a filter unit coupled to the gyroscope and having a configurable bandwidth. The filter unit configured to filter the gyroscope signal. The system also comprises control logic that is configured to alter the bandwidth of the filter unit based on the accelerometer signal.

Abstract: Systems and methods are provided for tracking a moving person. The system comprises a controller configured to receive acceleration data that characterizes an acceleration of the moving person in three dimensions. The controller comprises a step rate component that determines a step rate for the person based on a vertical component of the acceleration data. The controller also comprises a body offset component that determines a body offset angle based on a spectral analysis of the acceleration data and the step rate. The controller further comprises a velocity component that determines a reference velocity vector based on the body offset angle and the step rate.