Module: sunlineUKF¶

Executive Summary¶

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

More information 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

Functions

void SelfInit_sunlineUKF(SunlineUKFConfig *configData, int64_t moduleID)¶

This method initializes the configData for theCSS WLS estimator. It checks to ensure that the inputs are sane and then creates the output message

Parameters

configData – The configuration data associated with the CSS WLS estimator
moduleID – The module identifier

Returns

void

void Update_sunlineUKF(SunlineUKFConfig *configData, uint64_t callTime, int64_t moduleID)¶

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

Parameters

configData – The configuration data associated with the CSS estimator
callTime – The clock time at which the function was called (nanoseconds)
moduleID – The module identifier

Returns

void

void Reset_sunlineUKF(SunlineUKFConfig *configData, uint64_t callTime, int64_t moduleID)¶

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

Parameters

configData – The configuration data associated with the CSS estimator
callTime – The clock time at which the function was called (nanoseconds)
moduleID – The module identifier

Returns

void

void sunlineUKFTimeUpdate(SunlineUKFConfig *configData, double updateTime)¶

This method performs the time update for the sunline kalman filter. It propagates the sigma points forward in time and then gets the current covariance and state estimates.

Parameters

configData – The configuration data associated with the CSS estimator
updateTime – The time that we need to fix the filter to (seconds)

Returns

void

void sunlineUKFMeasUpdate(SunlineUKFConfig *configData, 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

configData – The configuration data associated with the CSS estimator
updateTime – The time that we need to fix the filter to (seconds)

Returns

void

void sunlineStateProp(double *stateInOut, double dt)¶

This method propagates a sunline state vector forward in time. Note that the calling parameter is updated in place to save on data copies.

Parameters

stateInOut – The state that is propagated
dt – Time step (s)

Returns

void

void sunlineUKFMeasModel(SunlineUKFConfig *configData)¶

This method computes what the expected measurement vector is for each CSS that is present on the spacecraft. All data is transacted from the main data structure for the model because there are many variables that would have to be updated otherwise.

Parameters: configData – The configuration data associated with the CSS estimator
Returns: void

struct SunlineUKFConfig¶

#include <sunlineUKF.h>

Top level structure for the CSS-based unscented Kalman Filter. Used to estimate the sun state in the vehicle body frame.

Public Members

NavAttMsg_C navStateOutMsg¶: The name of the output message

SunlineFilterMsg_C filtDataOutMsg¶: The name of the output filter data message

CSSArraySensorMsg_C cssDataInMsg¶: The name of the Input message

CSSConfigMsg_C cssConfigInMsg¶: [-] The name of the CSS configuration message

int numStates¶: [-] Number of states for this filter

int countHalfSPs¶: [-] Number of sigma points over 2

int numObs¶: [-] Number of measurements this cycle

double beta¶: [-] Beta parameter for filter

double alpha¶: [-] Alpha parameter for filter

double kappa¶: [-] Kappa parameter for filter

double lambdaVal¶: [-] Lambda parameter for filter

double gamma¶: [-] Gamma parameter for filter

double qObsVal¶: [-] CSS instrument noise parameter

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

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

double wM[2 * SKF_N_STATES + 1]¶: [-] Weighting vector for sigma points

double wC[2 * SKF_N_STATES + 1]¶: [-] Weighting vector for sigma points

double state[SKF_N_STATES]¶: [-] State estimate for time TimeTag

double sBar[SKF_N_STATES * SKF_N_STATES]¶: [-] Time updated covariance

double covar[SKF_N_STATES * SKF_N_STATES]¶: [-] covariance

double xBar[SKF_N_STATES]¶: [-] Current mean state estimate

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

double yMeas[MAX_N_CSS_MEAS * (2 * SKF_N_STATES + 1)]¶: [-] Measurement model data

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

double SP[(2 * SKF_N_STATES + 1) * SKF_N_STATES]¶: [-] sigma point matrix

double qNoise[SKF_N_STATES * SKF_N_STATES]¶: [-] process noise matrix

double sQnoise[SKF_N_STATES * SKF_N_STATES]¶: [-] cholesky of Qnoise

double qObs[MAX_N_CSS_MEAS * MAX_N_CSS_MEAS]¶: [-] Maximally sized obs noise matrix

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 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

NavAttMsgPayload outputSunline¶: — Output sunline estimate data

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

BSKLogger *bskLogger¶: BSK Logging.