In a general aspect, a method is presented for increasing the measurement precision of an optical instrument. The method includes determining, based on optical data and environmental data, a measured value of an optical property measured by the optical instrument. The optical instrument includes an optical path and a sensor configured to measure an environmental parameter. The method also includes determining a predicted value of the optical property based on a model representing time evolution of the optical instrument. The method additionally includes calculating an effective value of the optical property based on the measured value, the predicted value, and a Kalman gain. The Kalman gain is based on respective uncertainties in the measured and predicted values and defines a relative weighting of the measured and predicted values in the effective value.

Device (1) and method for detecting battery cell states, BZZ, and/or battery cell parameters, BZP, of at least one battery cell (BZ), comprising a dual Kalman filter (2) which includes a state estimator (2A) for estimating battery cell states, BZZ, and a parameter estimator (2B) for estimating battery cell parameters, BZP, and comprising a determination unit (3) which is suitable for determining noise components (n, v) of the state estimator (2A) and of the parameter estimator (2B) on the basis of a stored characteristic parameter behaviour of the battery cell (BZ), wherein the battery cell states, BZZ, and the battery cell parameters, BZP, can be adapted automatically to a specified battery model (BM) of the battery cell (BZ) by means of the dual Kalman filter (2) on the basis of the noise components (n, v) determined by the determination unit.

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.

Provided is a control module of a converter, in particular a power converter of a wind power installation, which is configured to control the converter in such a way that the converter emulates a behavior of a synchronous machine, comprising an, in particular internal, control loop which has an, in particular adjustable, virtual admittance by means of which the converter is controlled in order to emulate the behavior of the synchronous machine.

An ensemble of motion sensors is tested under known conditions to automatically ascertain instrument biases, which are modeled as autoregressive-moving-average (ARMA) processes in order to construct a Kalman filter. The calibration includes motion profiles, temperature profiles and vibration profiles that are operationally significant, i.e., designed by means of covariance analysis or other means to maximize, or at least improve, the observability of the calibration model's structure and coefficients relevant to the prospective application of each sensor.

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 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.

In order to at least approximately determine statistical properties of raw measured values, e.g., measurement noise and/or average errors, without knowledge of a filter applied to raw measured values and only with the aid of a useful signal obtained from the filtering, i.e., in order to make statements regarding measurement conditions by assuming a few frequently encountered boundary conditions, statistical properties of raw measured values are determined from the useful signal, which is composed of a temporal sequence of filter output values. A filter characteristic of the filter is ascertained from the temporal sequence of output values obtained under stable measurement conditions, the ascertained filter characteristic is inverted, raw measured values are reconstructed from the inverse of the filter characteristic and from the useful signal, and the statistical properties are ascertained from the reconstructed raw measured values and/or from the inverse of the filter characteristic and from the useful signal.

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.

Device (1) and method for detecting battery cell states, BZZ, and/or battery cell parameters, BZP, of at least one battery cell (BZ), comprising a dual Kalman filter (2) which includes a state estimator (2A) for estimating battery cell states, BZZ, and a parameter estimator (2B) for estimating battery cell parameters, BZP, and comprising a determination unit (3) which is suitable for determining noise components (n, v) of the state estimator (2A) and of the parameter estimator (2B) on the basis of a stored characteristic parameter behaviour of the battery cell (BZ), wherein the battery cell states, BZZ, and the battery cell parameters, BZP, can be adapted automatically to a specified battery model (BM) of the battery cell (BZ) by means of the dual Kalman filter (2) on the basis of the noise components (n, v) determined by the determination unit.