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 process to preserve the kinematic state of a target includes determining an initial kinematic state estimate of the target using an observer with an active sensor. The initial kinematic state estimate includes a range from the observer to the target, a velocity of the target, and an acceleration of the target. The process further includes constructing an Extended Kalman Filter using the initial kinematic state estimate. The Extended Kalman filter is constructed by updating the initial kinematic state estimate as a function of time, receiving an angles-only measurement into the observer, calculating a new kinematic state as a function of the angles-only measurement, comparing the initial kinematic state estimate with the new kinematic state, constraining a rate of the target, and correcting the initial kinematic state estimate as a function of the comparison of the initial kinematic state estimate with the new kinematic state. The target is permitted to maneuver, and the observer is not required to maneuver.