A control system for a work vehicle includes a controller, a processor, and a memory that causes the processor to receive, via a sensor assembly, sensor signals and convert the sensor signals into a plurality of entries of a full measurement vector. The memory devices causes the processor to determine a first state vector using IMU Kalman filter, update a first subset of entries the full state vector, determine a second state vector using a spatial positioning Kalman filter, update a second subset of entries the full state vector based on the second state vector, determine a third state vector using a vehicle Kalman filter, update a third subset of entries of the plurality of entries of the full state vector based on the third state vector, and control movement of the work vehicle based on at least one of the first state vector, the second state vector, the third state vector, and the full state vector.

An apparatus includes a sensor module configured for receiving sensed information indicative of a sensed signal. The sensed signal includes a source signal component and a source noise component. The apparatus also includes a reference module configured for reference information indicative of a reference signal. The reference signal also includes a reference noise component. The apparatus also includes a filter module configured as a fixed lag Kalman smoother. The filter module is configured for adaptively filtering the reference signal to generate an estimate of the source noise component. The apparatus also includes a processing module configured for calculating an output signal based on the sensed signal and the estimate of the source noise component. The apparatus also includes an interface module configured for transmitting an indication of the output signal. The filter module is further configured for, based on the output signal, tuning the Kalman smoother.

An apparatus for controlling a system includes a memory to store a model of the system including a motion model of the system subject to process noise and a measurement model of the system subject to measurement noise, such that one or combination of the process noise and the measurement noise forms an uncertainty of the model of the system with unknown probabilistic parameters, wherein the uncertainty of the model of the system causes a state uncertainty of the system with unknown probabilistic parameters. The apparatus also includes a sensor to measure a signal to produce a sequence of measurements indicative of a state of the system, a processor to estimate a Gaussian distribution representing the state uncertainty, and a controller to determine a control input to the system using the model of the system with state uncertainty represented by the Gaussian distribution and control the system according to the control input. The processor is configured to estimate, using at least one or combination of the motion model, the measurement model, and the measurements of the state of the system, a first Student-t distribution representing the uncertainties of the model and a second Student-t distribution representing the state uncertainty of the system, the estimation is performed iteratively until a termination condition is met, and fit a Gaussian distribution representing the state uncertainty into the second Student-t distribution.

A storage system configured for use with an energy management system is provided herein and comprises a battery, a battery management unit coupled to the battery and a power converter comprising plurality of microinverters operably coupled to the battery and the battery management unit, each microinverter of the plurality of microinverters configured to calculate an estimate of state-of-charge of the battery and periodically communicate a calculated estimate of state-of-charge to the other microinverters, such that each microinverter of the plurality of microinverters calculates an average state-of-charge of the battery and communicates the calculated average state-of-charge to the battery management unit for controlling charging/discharging of the battery.

An object is to enable a state of a vehicle to be precisely estimated by a Kalman filter. A navigation system 1 includes an observation unit 21 that observes an observable concerning a variation of the vehicle, based on an output from a sensor, and an estimation unit 22 that estimates a state quantity indicating a state of the vehicle by a Kalman filter, and the estimation unit 22 calculates a prediction value of the state quantity of the vehicle, calculates an error covariance matrix of the prediction value, by the Kalman filter to which an error of the observable is inputted as an error of the state quantity which is in a relation of calculus with the observable, and calculates an estimation value of the state quantity of the vehicle and an error covariance matrix of the estimation value by the Kalman filter, based on the prediction value and the error covariance matrix of the prediction value which are calculated.

Disclosed is a hybrid data- and model-driven method for predicting remaining useful life of a mechanical component. The method of the present disclosure uses an extended Kalman filter to calibrate parameters of an exponential random model, automatically learns input embedded position information by means of an adaptive encoding layer of a hybrid driven prediction model, and then models a mapping relation between input data and the remaining useful life by means of a multi-head attention mechanism. The present disclosure retains both accuracy of a model-based method and a generalization capability of a data-driven method in combination with the calibrated exponential random model and a multi-head attention neural network structure, can improve accuracy of predicting the remaining useful life of the mechanical component, and has great significance for use of the hybrid data- and model-driven method in the field of intelligent manufacturing and health management of mechanical apparatuses.

An apparatus includes a sensor module configured for receiving sensed information indicative of a sensed signal. The sensed signal includes a source signal component and a source noise component. The apparatus also includes a reference module configured for reference information indicative of a reference signal. The reference signal also includes a reference noise component. The apparatus also includes a filter module configured as a fixed lag Kalman smoother. The filter module is configured for adaptively filtering the reference signal to generate an estimate of the source noise component. The apparatus also includes a processing module configured for calculating an output signal based on the sensed signal and the estimate of the source noise component. The apparatus also includes an interface module configured for transmitting an indication of the output signal. The filter module is further configured for, based on the output signal, tuning the Kalman smoother.

A dipole cancellation system and method may include a plurality of magnetometers for measuring a device magnetic field associated with a plurality of device coils generating a device magnetic field having a primary magnetic dipole moment. A compensating coil carrying a compensating current running a first direction that generates a compensating magnetic field having a compensating magnetic dipole moment. The compensating coil may be positioned and the first current may be selected so that the compensating magnetic dipole moment completely cancels the primary magnetic dipole moment. A method may use the system to stabilize a spacecraft by calculating an estimated torque of the spacecraft, receiving a value for an external magnetic field, receiving a value for a device magnetic field, and calculating and applying a compensating current may be then applied to the compensating coil to cancel the primary magnetic dipole moment, wherein the spacecraft is stabilized.

A method is provided. The method comprises: initializing at least one non-linear filter configured to provide at least one predictive measurement estimate probability density function; obtaining measurement data; determining if the measurement data is consistent; and if the measurement data is consistent, then estimating at least one state parameter with the at least one non-linear filter using the measurement data.

In one embodiment, a Kalman filter based capacity forecasting method includes acquiring a capacity time sequence of an object to be forecasted; establishing a dynamical model for the capacity time sequence, and extracting a state transition parameter and a process noise parameter of the dynamical model; performing Kalman filter estimation on the capacity time sequence by using the state transition parameter and the process noise parameter to generate at least one state characteristic signal; segmenting the capacity time sequence according to the at least one state characteristic signal, and determining at least one corresponding segmentation point; and forecasting the capacity at future time according to the at least one segmentation point determined in the capacity time sequence. By adopting the technical solutions of the invention, accurate forecasting of the capacity growth is achieved, to facilitate operation and maintenance personnel making a reasonable capacity expansion plan.