ppppp.cpp File Reference

Data Structures
struct	Duo

Functions
Architecture	Main_Filter__ ()
	Primary estimation and filtering.
Architecture	Combinators__ ()
void	explainMeasurements (Trace &trace, KFMeas &kfMeas, KFState &kfState)
void	alternatePostfits (Trace &trace, KFMeas &kfMeas, KFState &kfState)
void	makeIFLCs (Trace &trace, KFState &kfState, KFMeasEntryList &kfMeasEntryList)
KFMeas	makeGFLCs (KFMeas &kfMeas, KFState &kfState)
	Replace individual measurements with linear combinations.
KFMeas	makeRTKLCs1 (KFMeas &kfMeas, KFState &kfState)
	Replace individual measurements with linear combinations.
KFMeas	makeRTKLCs2 (KFMeas &kfMeas, KFState &kfState)
	Replace individual measurements with linear combinations.
void	mergeCorrelated (Trace &trace, KFState &kfState, KFMeasEntryList &kfMeasEntryList)
void	updateRecClocks (Trace &trace, ReceiverMap &receiverMap, KFState &kfState)
	Prepare receiver clocks using SPP values to minimise pre-fit residuals.
void	updateAvgIonosphere (Trace &trace, GTime time, KFState &kfState)
	Prepare stec values clocks to minimise residuals to klobuchar model.
void	updateAvgOrbits (Trace &trace, GTime time, KFState &kfState)
	Prepare Satellite clocks to minimise residuals to broadcast orbits Provide a weak tiedown using mu values, which the state will attempt to exponentially decay toward using the configured tau value in stateTransition.
void	updateAvgClocks (Trace &trace, GTime time, KFState &kfState)
	Prepare satellite clocks to minimise residuals to broadcast clocks.
KFState	propagateUncertainty (Trace &trace, KFState &kfState)
void	chunkFilter (Trace &trace, KFState &kfState, KFMeas &kfMeas, ReceiverMap &receiverMap, map< string, FilterChunk > &filterChunkMap, map< string, std::ofstream > &traceList)
bool	isIntervalReset (double epoch, double prev_epoch, double reset_interval)
	Checks if a reset interval has been reached between two epochs.
bool	isSpecificTimeReset (double epoch, double prev_epoch, const std::vector< double > &resetTimes)
	Checks if a specific time reset has occurred between two epochs.
void	updatePseudoPulses (Trace &trace, KFState &kfState)
void	resetFilterbyConfig (Trace &trace, KFState &kfState)
void	removeBadAmbiguities (Trace &trace, KFState &kfState, ReceiverMap &receiverMap)
	Remove ambiguity states from filter when they deemed old or bad This effectively reinitialises them on the following epoch as a new state, and can be used for simple resolution of cycle-slips.
void	removeBadSatellites (Trace &trace, KFState &kfState)
void	removeBadReceivers (Trace &trace, KFState &kfState, ReceiverMap &receiverMap)
void	removeBadIonospheres (Trace &trace, KFState &kfState)
	Remove ambiguity states from filter when they deemed old or bad This effectively reinitialises them on the following epoch as a new state, and can be used for simple resolution of cycle-slips.
void	checkOrbits (Trace &trace, KFState &kfState)
void	updateFilter (Trace &trace, ReceiverMap &receiverMap, KFState &kfState)
void	perRecMeasurements (Trace &trace, Receiver &rec, ReceiverMap &receiverMap, KFMeasEntryList &kfMeasEntryList, KFState &kfState, KFState &remoteState)
void	pppLinearCombinations (KFMeas &kfMeas, KFState &kfState)
void	pppPseudoObs (Trace &trace, ReceiverMap &receiverMap, KFState &kfState, KFMeasEntryList &kfMeasEntryList)
void	ppp (Trace &trace, ReceiverMap &receiverMap, KFState &kfState, KFState &remoteState)

Function Documentation

alternatePostfits()

void alternatePostfits	(	Trace &	trace,
		KFMeas &	kfMeas,
		KFState &	kfState )

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checkOrbits()

void checkOrbits	(	Trace &	trace,
		KFState &	kfState )

chunkFilter()

void chunkFilter	(	Trace &	trace,
		KFState &	kfState,
		KFMeas &	kfMeas,
		ReceiverMap &	receiverMap,
		map< string, FilterChunk > &	filterChunkMap,
		map< string, std::ofstream > &	traceList )

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Combinators__()

Architecture Combinators__

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explainMeasurements()

void explainMeasurements	(	Trace &	trace,
		KFMeas &	kfMeas,
		KFState &	kfState )

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isIntervalReset()

bool isIntervalReset	(	double	epoch,
		double	prev_epoch,
		double	reset_interval )

Checks if a reset interval has been reached between two epochs.

Determines whether the current epoch (epoch) or the transition from the previous epoch (prev_epoch) to the current epoch crosses a specified reset interval (reset_interval).

Parameters

epoch	The current epoch time.
prev_epoch	The previous epoch time.
reset_interval	The interval at which resets occur.

Returns

True if a reset interval is reached or crossed, false otherwise.

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isSpecificTimeReset()

bool isSpecificTimeReset	(	double	epoch,
		double	prev_epoch,
		const std::vector< double > &	resetTimes )

Checks if a specific time reset has occurred between two epochs.

Determines whether a given epoch or the transition between two epochs corresponds to any of the specified reset times within a day. A reset time can either match the exact time of the epoch or occur between the two epochs.

Parameters

epoch	The current epoch time in seconds since the start of the day.
prev_epoch	The previous epoch time in seconds since the start of the day.
resetTimes	A vector of reset times in seconds within a day (0 to 86400).

Returns

True If a reset time matches the current epoch or occurs between the previous and current epochs, False otherwise.

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Main_Filter__()

Architecture Main_Filter__

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Primary estimation and filtering.

While there are other auxiliary filters and states used within the Pea, all PPP processing flows through a common filtering stage.

The residuals for all observations are first computed in an undifferenced-uncombined state, which leads to the greatest generality and extensibility. As each receiver's observations are then independent of each other, these computations are computed in parallel, using openMP directives, increasing thoughput.

Bookkeeping around the initialisation of state elements and their state transitions is taken care of automatically at the point they are first referenced in the observation equations, using values as configured in the yaml file.

The distinction between "Network" and "User" positioning modes that may be used in other software packages is not required in Ginan. All receivers are always stored in a large single filter. Depending on the configuration, the state and it's covariance matrix may turn out to be block-diagonal (user-mode), which will automatically be treated optimally upon the estimation stage by applying 'chunking'.

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makeGFLCs()

KFMeas makeGFLCs	(	KFMeas &	kfMeas,
		KFState &	kfState )

Replace individual measurements with linear combinations.

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makeIFLCs()

void makeIFLCs	(	Trace &	trace,
		KFState &	kfState,
		KFMeasEntryList &	kfMeasEntryList )

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makeRTKLCs1()

KFMeas makeRTKLCs1	(	KFMeas &	kfMeas,
		KFState &	kfState )

Replace individual measurements with linear combinations.

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makeRTKLCs2()

KFMeas makeRTKLCs2	(	KFMeas &	kfMeas,
		KFState &	kfState )

Replace individual measurements with linear combinations.

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mergeCorrelated()

void mergeCorrelated	(	Trace &	trace,
		KFState &	kfState,
		KFMeasEntryList &	kfMeasEntryList )

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perRecMeasurements()

void perRecMeasurements	(	Trace &	trace,
		Receiver &	rec,
		ReceiverMap &	receiverMap,
		KFMeasEntryList &	kfMeasEntryList,
		KFState &	kfState,
		KFState &	remoteState )

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ppp()

void ppp	(	Trace &	trace,
		ReceiverMap &	receiverMap,
		KFState &	kfState,
		KFState &	remoteState )

Parameters

trace	Trace to output to
receiverMap	List of receivers containing observations for this epoch
kfState	Kalman filter object containing the network state parameters
remoteState	Optional pointer to remote kalman filter

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pppLinearCombinations()

void pppLinearCombinations	(	KFMeas &	kfMeas,
		KFState &	kfState )

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pppPseudoObs()

void pppPseudoObs	(	Trace &	trace,
		ReceiverMap &	receiverMap,
		KFState &	kfState,
		KFMeasEntryList &	kfMeasEntryList )

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propagateUncertainty()

KFState propagateUncertainty	(	Trace &	trace,
		KFState &	kfState )

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removeBadAmbiguities()

void removeBadAmbiguities	(	Trace &	trace,
		KFState &	kfState,
		ReceiverMap &	receiverMap )

Remove ambiguity states from filter when they deemed old or bad This effectively reinitialises them on the following epoch as a new state, and can be used for simple resolution of cycle-slips.

Parameters

trace	Trace to output to
kfState	Filter to remove states from
receiverMap	List of receivers containing observations for this epoch

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removeBadIonospheres()

void removeBadIonospheres	(	Trace &	trace,
		KFState &	kfState )

Remove ambiguity states from filter when they deemed old or bad This effectively reinitialises them on the following epoch as a new state, and can be used for simple resolution of cycle-slips.

Parameters

trace	Trace to output to
kfState	Filter to remove states from

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removeBadReceivers()

void removeBadReceivers	(	Trace &	trace,
		KFState &	kfState,
		ReceiverMap &	receiverMap )

Parameters

trace	Trace to output to
kfState	Filter to remove states from
receiverMap	List of receivers containing observations for this epoch

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removeBadSatellites()

void removeBadSatellites	(	Trace &	trace,
		KFState &	kfState )

Parameters

trace	Trace to output to
kfState	Filter to remove states from

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resetFilterbyConfig()

void resetFilterbyConfig	(	Trace &	trace,
		KFState &	kfState )

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updateAvgClocks()

void updateAvgClocks	(	Trace &	trace,
		GTime	time,
		KFState &	kfState )

Prepare satellite clocks to minimise residuals to broadcast clocks.

Provide a weak tiedown using mu values, which the state will attempt to exponentially decay toward using the configured tau value in stateTransition

Parameters

trace	Trace to output to
time	Time
kfState	Kalman filter object containing the network state parameters

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updateAvgIonosphere()

void updateAvgIonosphere	(	Trace &	trace,
		GTime	time,
		KFState &	kfState )

Prepare stec values clocks to minimise residuals to klobuchar model.

Parameters

trace	Trace to output to
time	Time
kfState	Kalman filter object containing the network state parameters

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updateAvgOrbits()

void updateAvgOrbits	(	Trace &	trace,
		GTime	time,
		KFState &	kfState )

Prepare Satellite clocks to minimise residuals to broadcast orbits Provide a weak tiedown using mu values, which the state will attempt to exponentially decay toward using the configured tau value in stateTransition.

Parameters

trace	Trace to output to
time	Time
kfState	Kalman filter object containing the network state parameters

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updateFilter()

void updateFilter	(	Trace &	trace,
		ReceiverMap &	receiverMap,
		KFState &	kfState )

Parameters

trace	Trace to output to
receiverMap	List of receivers containing observations for this epoch
kfState	Kalman filter object containing the network state parameters

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updatePseudoPulses()

void updatePseudoPulses	(	Trace &	trace,
		KFState &	kfState )

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updateRecClocks()

void updateRecClocks	(	Trace &	trace,
		ReceiverMap &	receiverMap,
		KFState &	kfState )

Prepare receiver clocks using SPP values to minimise pre-fit residuals.

Parameters

trace	Trace to output to
receiverMap	List of receivers containing observations for this epoch
kfState	Kalman filter object containing the network state parameters

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