okeefeEKF#

Executive Summary#

This module implements and tests a Extended Kalman Filter in order to estimate the sunline direction.

More information on can be found in the PDF Description

Message Connection Descriptions#

The following table lists all the module input and output messages. The module msg connection is set by the user from python. The msg type contains a link to the message structure definition, while the description provides information on what this message is used for.

Module I/O Messages#
Msg Variable Name	Msg Type	Description
navStateOutMsg	NavAttMsgPayload	name of the navigation output message containing the estimated states
filtDataOutMsg	SunlineFilterMsgPayload	name of the output filter data message
cssDataInMsg	CSSArraySensorMsgPayload	name of the CSS sensor input message
cssConfigInMsg	CSSConfigMsgPayload	name of the CSS configuration input message

Class OkeefeEKF#

class OkeefeEKF : public SysModel#

Data structure for CSS Extended kalman filter estimator without gyros measurements.

Public Functions

void reset(uint64_t callTime) override#

This method resets the sunline attitude filter to an initial state and initializes the internal estimation matrices.

Parameters:: callTime – The clock time at which the function was called (nanoseconds)
Returns:: void

void updateState(uint64_t callTime) override#

This method takes the parsed CSS sensor data and outputs an estimate of the sun vector in the ADCS body frame

Parameters:: callTime – The clock time at which the function was called (nanoseconds)
Returns:: void

void sunlineTimeUpdate(double updateTime)#

This method performs the time update for the sunline kalman filter. It calls for the updated Dynamics Matrix, as well as the new states and STM. It then updates the covariance, with process noise.

Parameters:: updateTime – The time that we need to fix the filter to (seconds)
Returns:: void

void sunlineMeasUpdate(double updateTime)#

This method performs the measurement update for the sunline kalman filter. It applies the observations in the obs vectors to the current state estimate and updates the state/covariance with that information.

Parameters:: updateTime – The time that we need to fix the filter to (seconds)
Returns:: void

Public Members

Message<NavAttMsgPayload> navStateOutMsg#: The name of the output message

Message<SunlineFilterMsgPayload> filtDataOutMsg#: The name of the output filter data message

ReadFunctor<CSSArraySensorMsgPayload> cssDataInMsg#: The name of the Input message

ReadFunctor<CSSConfigMsgPayload> cssConfigInMsg#: [-] The name of the CSS configuration message

double qObsVal#: [-] CSS instrument noise parameter

double qProcVal#: [-] Process noise parameter

double dt#: [s] seconds since last data epoch

double timeTag#: [s] Time tag for statecovar/etc

double state[SKF_N_STATES_HALF]#: [-] State estimate for time TimeTag

double prev_states[SKF_N_STATES_HALF]#: [-] State estimate for previous time TimeTag

double omega[SKF_N_STATES_HALF]#: [-] Rotation rate vector

double x[SKF_N_STATES_HALF]#: [-] State errors

double xBar[SKF_N_STATES_HALF]#: [-] Current time updated mean state estimate

double covarBar[SKF_N_STATES_HALF * SKF_N_STATES_HALF]#: [-] Time updated covariance

double covar[SKF_N_STATES_HALF * SKF_N_STATES_HALF]#: [-] covariance

double stateTransition[SKF_N_STATES_HALF * SKF_N_STATES_HALF]#: [-] covariance

double kalmanGain[SKF_N_STATES_HALF * MAX_N_CSS_MEAS]#: Kalman Gain

double dynMat[SKF_N_STATES_HALF * SKF_N_STATES_HALF]#: [-] Dynamics Matrix, A

double measMat[MAX_N_CSS_MEAS * SKF_N_STATES_HALF]#: [-] Measurement Matrix, H

double obs[MAX_N_CSS_MEAS]#: [-] Observation vector for frame

double yMeas[MAX_N_CSS_MEAS]#: [-] Linearized measurement model data

double procNoise[SKF_N_STATES_HALF * SKF_N_STATES_HALF]#: [-] process noise matrix

double measNoise[MAX_N_CSS_MEAS * MAX_N_CSS_MEAS]#: [-] Maximally sized obs noise matrix

double postFits[MAX_N_CSS_MEAS]#: [-] PostFit residuals

double cssNHat_B[MAX_NUM_CSS_SENSORS * 3]#: [-] CSS normal vectors converted over to body

double CBias[MAX_NUM_CSS_SENSORS]#: [-] CSS individual calibration coefficients

uint32_t numStates#: [-] Number of states for this filter

size_t numObs#: [-] Number of measurements this cycle

uint32_t numActiveCss#: — Number of currently active CSS sensors

uint32_t numCSSTotal#: [-] Count on the number of CSS we have on the spacecraft

double sensorUseThresh#: — Threshold below which we discount sensors

double eKFSwitch#: — Max covariance element after which the filter switches to an EKF

NavAttMsgPayload outputSunline#: — Output sunline estimate data

CSSArraySensorMsgPayload cssSensorInBuffer#: [-] CSS sensor data read in from message bus

BSKLogger bskLogger = {}#: BSK Logging.

okeefeEKF#

Executive Summary#

Message Connection Descriptions#

Class OkeefeEKF#

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