2316 results about "Kaiman filter" patented technology

A gesture recognition system using a skin-color based method combined with motion information to achieve real-time segmentation. A Kalman filter is used to track the centroid of the hand. The palm center, palm bottom, as well as the largest distance from the palm center to the contour from extracted hand mask are computed. The computed distance to a threshold is then compared to decide if the current posture is “open” or “closed.” In a preferred embodiment, the transition between the “open” and “closed” posture to decide if the current gesture is in “select” or “grab” state.

A method of estimating the navigational state of a system entails acquiring observation data produced by noisy measurement sensors and providing a probabilistic inference system to combine the observation data with prediction values of the system state space model to estimate the navigational state of the system. The probabilistic inference system is implemented to include a realization of a Gaussian approximate random variable propagation technique performing deterministic sampling without analytic derivative calculations. This technique achieves for the navigational state of the system an estimation accuracy that is greater than that achievable with an extended Kalman filter-based probabilistic inference system.

A six state Kalman filter is adapted based on a current acceleration mode of an INS device. Gyro measurements are used to determine the acceleration mode and the Kalman filter estimates bias and small angle error of the measurements based on the acceleration mode. The bias error corrects the gyro measurement and the small angle error is used along with the corrected gyro measurement to update an attitude sensed by the gyro.

A closed-loop LRF pointing technology to measure the range of a target satellite from a chaser satellite for rendezvous is provided that includes several component technologies: LOS angle measurements of the target satellite on a visible sensor focal plane and the angles' relationships with the relative position of the target in inertial or LVLH frame, a relative navigation Kalman filter, attitude determination of the visible sensor with gyros, star trackers and a Kalman filter, pointing and rate commands for tracking the target, and an attitude controller. An analytical, steady-state, three-axis, six-state Kalman filter is provided for attitude determination. The system and its component technologies provide improved functionality and precision for relative navigation, attitude determination, pointing, and tracking for rendezvous. Kalman filters are designed specifically for the architecture of the closed-loop system to allow for pointing the laser rangefinder to a target even if a visible sensor, a laser rangefinder, gyros and a star tracker are misaligned and the LOS angle measurements from the visible sensor are interrupted.

An inertial (“INS”) / GPS receiver includes an INS sub-system which incorporates, into a modified Kalman filter, GPS observables and / or other observables that span previous and current times. The INS filter utilizes the observables to update position information relating to both the current and the previous times, and to propagate the current position, velocity and attitude related information. The GPS observable may be delta phase measurements, and the other observables may be, for example, wheel pick-offs (or counts of wheel revolutions) that are used to calculate along track differences, and so forth. The inclusion of the measurements in the filter together with the current and the previous position related information essentially eliminates the effect of system dynamics from the system model. A position difference can thus be formed that is directly observable by the phase difference or along track difference measured between the previous and current time epochs. Further, the delta phase measurements can be incorporated in the INS filter without having to maintain GPS carrier ambiguity states. The INS sub-system and the GPS sub-system share GPS and INS position and covariance information. The receiver time tags the INS and any other non-GPS measurement data with GPS time, and then uses the INS and GPS filters to produce INS and GPS position information that is synchronized in time. The GPS / INS receiver utilizes GPS position and associated covariance information and the GPS and / or other observables in the updating of the INS filter. The INS filter, in turn, provides updated system error information that is used to propagate inertial current position, velocity and attitude information. Further, the receiver utilizes the inertial position, velocity and covariance information in the GPS filters to speed up GPS satellite signal re-acquisition and associated ambiguity resolution operations

The invention discloses a vision-inertia integrated SLAM (Simultaneous Localization and Mapping) method based on a genetic algorithm. The method comprises the following steps: integrating a vision navigation coordinate system with an inertia navigation coordinate system, and calibrating parameters of a binocular camera so as to solve a three-dimensional space coordinate according to an image pixel coordinate; independently calculating by inertia navigation; calculating by vision navigation; integrating vision navigation information with inertia navigation information by using an extended Kalman filter, and building a system filter model; and observing global feature point road signs by using the binocular camera by taking localization locality into account, carrying out data association on map features based on the genetic algorithm, and feeding extended state vectors back to a filter. The method is capable of carrying out long-time and high-accuracy localization; the genetic algorithm is added for improving the data association of the map, so that the simultaneous mapping accuracy is greatly improved.

A method, system and apparatus for real-time reservoir model updating using ensemble Kalman filters is described. The method includes a conforming step for bring bringing static and dynamic state variables into conformance with one another during a time step of the updating. Also, an iterative damping method is used in conjunction with the conformance step to account for nonGaussian and nonlinear features in a system. Also, a re-sampling method is described which reduces the ensemble size of reservoir models which are to be updated.

The invention discloses an estimation method of state of charge (SOC) of a power battery. The method comprises the following steps: estimating the SOC estimated value SOC2 of the power battery by an electricity accumulative method; taking a Sigma-point Kalman filter (SPKF) as a basic estimation tool, taking a dual-RC circuit battery model as a time and measurement update engine of the SPKF, and estimating the SOC estimated value SOC1 of the power battery by a Kalman filter method; and obtaining the final SOC estimated value SOC based on the SOC estimated value SOC2 by the electricity accumulative method and the SOC estimated value SOC1 by the Kalman filter method by utilizing a weighted average method. Correspondingly, the invention further discloses an estimation system of the SOC of thepower battery. The invention has the advantages of high SOC estimation accuracy, stable operation and the like, and is convenient in real-time estimation, thus being applicable to pure electric vehicles and hybrid electric vehicles in need of the power battery.

The present application relates to tracking a position of a device, e.g. for detecting slow and rapid earth deformation, by making use of a recursive filter having the filter characteristic adapted to a detected type of motion. If the motion of the position tracking device is rapid, the filter characteristic is set such that the rapid motion can be tracked with the necessary speed. On the other hand, if the motion is slow, e.g. during times of a normal tectonic drift, the filter characteristic is set such that the motion is slowly tracked with the advantage of efficient noise reduction, i.e. noise in the input signal is effectively barred and does not pass through the filter to the output signal. Thus, in times of rapid motion the convergence speed of the filter output signal to the input signal is set high for fast convergence and in times of slow motion the convergence speed of the filter output signal to the input signal is set low for a slow convergence. The filter may be a Kalman filter.