# Hướng dẫn bình sai GPS Huace X20 - chương V

## Hướng dẫn bình sai GPS Huace X20 - chương V

5 Chapter V Static Baseline

The Baseline processing software has an important effect on the precision of the

relative static measurement, the reliability of the relative static measurement and

observation time. Therefore, desirable commercial baseline processing software must

be able to process baselines accurately and must be user-friendly and easy to handle.

Our software applies complicated baseline process theory to an easy-handling

interface. Normally, to have a precise result, you do not need to do any manual work on

a well-observed data. As for those unqualified data, you can make them meet your

requirement by doing a manual re-processing according to the processing information

the software gives.

5.1 Baseline Processing Flow

When you input all observation data in pointed format, the software will analyze the

relationship among collected points to form the static baselines. Then, the processing

will start.

[w8]:

The Following steps are involved:

5.1.1 Parameters Setting

The Control parameters of the baseline computation are for choosing the processing

method used for the baseline computation. It is a crucial step to set the parameters so

that can optimize the processing.

You can set these parameters in the ‘Computation Settings’. The Parameters available

here are data-sampling interval, elevation mask, reference satellite and its (ionosphere)

ionosphere, and computation model as well.

76

5.1.1.1 Fieldwork Data: Check and Modify

Before the baseline, computation is inputted, it is necessary, when fieldwork data is

entered, to check loaded observation data to avoid disoperation during fieldwork.

Checking the items include: observation station name and number, coordinates of

station and antenna height, etc.

5.1.1.2 Baseline Computation

The Typical process of computation is automatically carried out without any manual

work. The Following steps are involved:

5.1.1.2.1 Computation Self-check

the

Before doing the computation, the software will check

parameter settings,

observation data, ephemeris file and coordinate of starting pointed.

5.1.1.2.2 Read Ephemeris Data

The format of an ephemeris data could be either RINEX format, binary format *.HCN or

precise ephemeris data format SP3.

5.1.1.2.3 Read Ephemeris Data

The first step of baseline processing is reading the original observation data derived

from GPS receiver. The original data can be processed directly in the software that

comes with the GPS receiver from the same producer. However, software developed

by a third-party company may not be able to process data from different types of

receivers.

5.1.1.2.4 Triple Difference Computation

The triple difference is the difference between two double differences of different

77

epochs. The Triple Difference Computation is derived from the equation based on the

triple difference. Since in terms of short edges, the precision of results of such

computation is not high enough

, its typical function is getting approximate baseline

1

edges to process cycle slips reparation.

Single Baseline Computation Process:

Begin

Self-Check

Cycle Slip Repair

Read Ephemeris

Double-Diff. Computation

Read Obs. Data

Ambiguity

Triple-Diff. Computation

Double-Diff. Fixed

End

72. Process Chart

1. Normally, fixed double difference provides higher precision for short edges, while triple difference is

sometimes used for long edges.

78

5.1.1.2.5 Cycle Slip Reparation

It is the key point of the baseline computation to find a correct integer ambiguity.

Continuously tracking of the carrier phase by receivers provides the possibility of

having such integer ambiguity. However, blocks and disturbances may interrupt such

tracking and destroy its continuity. Therefore, So-called cycle slip, occurs in the carrier

phase observation results between different epochs. Then the main issue of the

baseline processing software becomes, how to detect and repair cycle slips.

5.1.1.2.6 Float Double Difference Computation

Suppose that signals from N satellites are observed, there would be N-1 extra

unknowns in the double-difference system-of-equations compares to that of

triple-difference. Therefore, double-difference computation derives advanced

coordinates of unknown points and float-marked integer ambiguity. Normally, this

method derives a float, which results from the absorbing of noises and others

un-modeled errors, instead of a theoretically literal ‘integer’ of the integer ambiguity.

Therefore, biases often exist, even up to several cycles, between such float and correct

integer.

5.1.1.2.7 Integer Ambiguity Solution

Under the circumstance of obtaining enough data in sufficient time, it is quite possible

to derive a correct integer ambiguity simply by rounding. Nevertheless, it is still true that

Fast Ambiguity Resolution Approach (FARA) and LAMBDA can largely accelerate

observation time and enhance working efficiency.

5.1.1.2.8 Fixed Double Difference Computation

The software will carry out Fixed Double Difference Computation when the integer

ambiguity is fixed correctly. The highest precision, will not come out without a correct

integer ambiguity, although it could be achieved by using this approach

79

5.1.1.3 Check Quality of Baseline

Only qualified baselines can be used later, thus quality checking will be required when

the baseline computation is done. The Checking process includes: RATIO, RDOP,

RMS, closed loop synchrony-sum/difference, closed loop asynchrony-sum/difference

and overlapped baseline correction

80

5.2 Settings of Baseline Processing

You need to set up baseline processing before you do the processing.

Select ‘

Baseline > Settings

’

from the Menu Bar. Below a dialog-box will appear, as

shown below:

73. Settings of Baseline Processing

There are four panels on this dialog-box

:

general settings, input rules,

troposphere/ionosphere settings and advanced settings.

For a Brief introduction of each panel see below:

81

5.2.1 General Settings

Epoch sampling interva

l

The Epoch sampling interval is the frequency, when doing baseline processing, of the

data sampled from the original observation data by the software. As the below shows:

74. Epoch Sampling Interval

Suppose that the sampling interval is 5 seconds for the static observation on two

devices. Such a high-density sampling interval often increases the time consumed

without deriving a significant improvement of the precision during the indoor work.

Therefore, a larger interval can be used to quicken the baseline processing.

What is a proper sampling interval? A common agreement is that relatively small

interval should be applied to short edges in short observation time, while larger interval,

to long edges. For example, you should set the sampling interval as 5 seconds for

static baselines shorter than 2 km within 20 minutes of observation time. However, you

should set a larger interval, which could reach 60 seconds or 120 seconds.

The reason you should set smaller interval during the fieldwork is that it is quite

possible to improve the processing result, which is derived from the limited-functional

software with imperfect and sometimes-random data, by reprocessing baseline with

modified epoch interval.

The Default epoch interval value is 60 seconds in our software.

82

5.2.1.1 Elevation Mask

The purpose of the elevation mask is to exclude the data collected from low elevation

satellites to the baseline computation.

Signals of low elevation satellite are complicatedly influenced by aerosphere and

difficult to correct with models. Additionally, many factors such as multi-path and

electromagnetic wave can put impact on them. Therefore, these signals are often poor

and should be deleted during the post processing.

Simply in terms of atmospheric refraction, the shorter observation range is the better

atmospheric refraction can be evened. Thus, a smaller elevation mask should be

applied to the short-range observation and larger value should be set on long range

ones. Off course, you should take into account the condition of the surroundings into

account.

To bring convenience into post processing, you should reduce elevation mask in

fieldworks to collect more data.

75. elevation mask

Default elevation mask is 20 degree.

5.2.1.2 Reference satellite

The Double difference is the difference of the two single differences between different

satellites. The software selects reference satellite to bring convenience to the

processing when the double difference is generated.

83

The Default setting AUTO makes the software pick a satellite obtaining most data with

the highest elevation angle as a reference. However, due to the influence of the

observation condition, it is not always the best choice. An improper reference satellite

will put a bad impact on the result of the processing, so you need to reset reference

satellite according to your data when the default option is not suitable.

You should select a new reference satellite according to the satellite alert, fieldwork

records, former baseline processing result comprehensively. It would not make any

sense to select an undetected satellite as your reference.

5.2.1.3 Gross Error Tolerance

It is often necessary to delete some unqualified data as gross error during data

processing. Data of which the observation offset exceeds (gross error tolerance × RMS)

will be taken as gross error.

5.2.1.4 Minimum Epoch Number

The Observed carrier phase must be continuous to ensure the good quality of the

baseline processing. Therefore, data with frequent cycle slips are poor-quality data and

in most cases, should be deleted. The software will consequently delete those data that

contains less continuous epochs than the minimum epoch number requires.

Minimum epoch number the software requires is two or bigger than two and the default

number is five.

5.2.1.5 Maximum Epoch Number

The Maximum epoch number is determined by the assigned memory in processing.

The Default value is 999.

84

5.2.2 Troposphere and Ionosphere Set Up

The Following figure shows the

Troposphere and Ionosphere Set Up

dialog-box. In

most cases, you do not have to make any change.

76. Troposphere and Ionosphere Set Up

85

5.2.3 Advance Settings

The figure below shows the

Advance

dialog-box. Normally, you do not have to make

any change for single frequency data. However, for dual-frequency data, you may often

change your options in the

Observation Method Combination

Combo Box.

77. Advance Settings

86

5.3 Baseline Processing

When the preparation mentioned above is done,

Select ‘

baseline

’>’

process all

’. The software will process all the baselines and an

information dialog-box will appear, shown in the figure below (figure 8-7).

The name of each baseline, computation progress and computation information of

each baseline are available in this dialog-box.

Multithreading is applied to the baseline computation.

You can click the ‘Stop’ button to stop the computation.

78. Advance Settings

87

When the computation is finished, the results of the computation are shown in the

figure below:

79. Compuatition Results

You can also view these results in the

Computation Result Details

or in the

Baseline

Properties

. For more Details, see relevant chapters

5.4 Recheck Process Results

5.4.1 Baseline Quality Control

There are several criterions of the quality of a baseline: RATIO, RDOP, RMS and Data

Deleting Rate.

A common agreement is that the larger RMS, the worse the quality of observation value

is, and the larger the RDOP, the worse observation conditions are. However, this is

comparatively true. That is to say, values of parameters above should not be taken as

absolute criterions of the baseline quality.

88

5.4.1.1 RMS

Root Mean Square (RMS) is:

V

PV

T

=

RMS

n

f

Where

V, residual error

P, observation value weight

N-f, overall observation value - an unknown

RMS shows the quality of the observation value. The smaller RMS, the better the

quality of observation value is, and the larger, the worse the quality of observation

value is.

According to the statistics theory, the probability is up to 95% of having an observation

error within the range from zero to 1.96 times of RMS

5.4.1.2 RATIO

RATIO is the ratio of the second minimum RMS and the minimum RMS when the

integer ambiguity solution came out, say:

RMS

=

RATIO

s ec

RMS

mi n

RATIO, of which the value depends on many factors, reflects the reliability of the

integer ambiguity. It relates to both the observation value quality and observation

conditions.

As a crucial criterion of the baseline quality, it is normally required to be larger than

three are.

89

5.4.1.3 Data Deleting Rate

An observation value, of which the adjusted value is larger than certain threshold value,

would be deleted as a gross error-contained value. Moreover, the ratio of deleted

values and overall values is called the data-deleting rate.

The Data deleting data, on certain aspect, reflects the quality of the original observation

data. The higher this value is, the worse the observation quality.

5.4.1.4 RDOP

Relative Dilution of Precision (RDOP) is the square root of the trace of the least

squares cofactor matrix (GPS World/UNSW), say:

=

RDOP

tr

(

Q

)

The RDOP value depends on the baseline position, satellites distribution and moving

tracks (observation condition). However, as long as baseline position is determined, it

becomes observation condition-related only. Since observation condition is expressed

as a function of time, the RDOP value is actually, for certain baseline vector,

observation session related.

The RDOP reflects the influence of the situation of the satellite on relative positioning; it

depends on the observation condition rather than on the observation value.

Apply

You should check the static baseline when process is done. As shown in the table

below , check whether all variance ratio values follow the ‘Fix’, and are larger than three.

In this table, some values are smaller than three are. That is to say, this baseline has

unqualified fixed double difference solutions.

To solve this problem, you can

•Change relevant values of the baseline processing setting

90

•Click this baseline to select it, then select ‘Process This Baseline’(or press F2)

•Repeat the two steps above until all baselines are qualified

In the result of each baseline, the line ‘Tri’ shows the fixed triple difference computation,

‘Flt’ shows the float double difference computation and ‘Fix’ shows the variance ratio of

the fixed double difference computation. Items shown along left to right are mean

square error of an edge, DX, DY DZ and the baseline length.

5.4.2 Closed Loop Checking

5.4.2.1 Conception of the Closing Error

Closed Loop Checking is a powerful tool to evaluate baseline quality.

Closed Loop includes synchrony-closed loop, asynchrony-closed loop and overlapped

baseline.

Closing Error of closed loop is theoretically zero, but it is allowed to have certain

deviation values. For more information about the allowed closing Error, see relevant

document or appendix.

There are several types of the closing Error:

1

.

Branch Closing Error:

=

X

X

=

Y

Y

=

Z

Z

2. Relative Closing Error:

+

+

2

2

2

=

X

Y

Z

S

91

Where

S

Stands for the loop length

5.4.2.2 Synchrony-closed loop, asynchrony asynchrony-closed

loop and overlapped baseline.

5.4.2.2.1 Synchrony-closed Loop

The Synchrony-closed loop closing Error is formed by simultaneous observation

baselines.

Because of certain inner relationship between simultaneous observation baselines,

synchrony-closed loop closing Error is theoretically zero. It is always true that if the

synchrony-closed loop closing Error exceeds allowed value, there is at least one

unqualified baseline among those formed by the loop. However, the opposite

hypothesis may not be true that the closing Error-within-allowed-value implies good

quality of every baseline. You can only confirm that most of baselines are qualified.

5.4.2.2.2 Asynchrony-closed Loop

If the Close loop is formed partly by the synchrony-observed baselines is called

asynchrony-closed Loop. The closing Error of the asynchrony-closed Loop is the

asynchrony-closed Loop closing Error.

If the asynchrony-closed Loop closing Error is smaller than allowed value, the

baselines forming the asynchrony-closed Loop are qualified. When the closing Error

exceeds the allowed value, there is at least one unqualified baseline. To pick

unqualified baseline vectors, you can check the adjacent

asynchrony-closed loops or

overlapped baselines.

92

5.4.2.2.3 Overlapped Baselines

The So-called overlapped baselines refer to the observation results in different session

between the same couple of the observation stations. The difference among these

results is the overlapped baselines correction.

5.4.2.3 How Do You Check?

The Following functions are available in Menu Bar ‘Check’, as shown in the figure

below:

80. Closing Error

The Closing Error values are stored in the closing Error file in the project folder, which

is a .txt file and named as ‘ProjectName.ISC’, ‘RINEX.ISC’, as example.

Note: It is for users’ continence of finding the unqualified baseline and comparing

baseline process results with each other to add the closing Error computation results of

the closing Error file ordered by the corresponding baseline computation results.

Therefore, if you do not need the previous results, you can delete your closing Error

records.

The figure below shows a closing Error file:

93

81. Check Report

For Details of the Closing Error files Formats see Chapter 13.

94

5.4.3 Check Free Network Adjustment

82. Network Adjustment Checking Result()

5.5 Distinguish Different Influence Factors

5.5.1 Influence Factors

The Following items are the influence factors of the baseline Computation:

1. Imprecise starting point coordinates: imprecise starting point coordinates leads to

bias on scale and direction.

2. Insufficient observation time leads to the failure of integer-unknown identification: if

the integer-unknowns of the satellites that involved in the baseline computation cannot

be identified precisely, the overall computation result would be spoiled.

3. Cycle slips reparation cannot be well achieved if there are too many cycle slips in

several sessions.

4. Serious multi-path effect exists within total observation session. It leads to big

residuals at large.

95

5. Troposphere or ionosphere reflections are too large.

6. Electromagnetic waves influence is too big.

7. Poor data quality due to some problems of the receiver itself, such as low-precision

phase determination and low-precision receiver clock, etc.

96

5.5.2 Influence Factors: Distinguish and Solve

5.5.2.1 Distinguish Different Factors

1. Summarize

Some of the factors are easy to distinguish, such as insufficient observation time,

redundant cycle slips, serious multi-path effect and large troposphere or ionosphere

reflections, etc., while others are not so obvious, imprecise starting point, for example.

2.Distinguish: Imprecise Starting Point

There is no easy way to distinguish the impact on the quality of baseline computation

made by Imprecise Starting Point at present. Thus, the only way to solve such

imprecision problem is making your start point as precise as possible.

3. Distinguish: Insufficient Observation Time

It is an easy-distinguished factor. You can simply check the observation data records to

find the number of observation data of each satellite. Satellites visibility figure is

available in our GPS software, which make these numbers more distinct.

4. Distinguish: redundant cycle slips

You can analyze this factor according to the obtained residual error of the observation

data. The Double difference, which is used by most typical baseline processing

applications, has an obvious integral-multiple-enlargement on its residual errors related

to an unprepared-cycle slip of a satellite observed by certain station.

5. Distinguish: Serious Multi-path Effect and Large troposphere / ionosphere reflections

Residual error of the observation data is also used to distinguish these factors. It has a

non-integral-multiple-enlargement of the residual error instead of the integral-multiple

one in the last case. Such enlargement is normally limited within 1 cycle, but it is still

bigger than that of common residual errors.

97

5.5.2.2 Solutions

1. Imprecise Start Point

To solve this problem, you should have a start point with higher precision. To do this,

you can do your single positioning in longer time or have the start point positioned

together with a more precise point in WGS-84 system, or you can derive, at the stage of

the overall baselines computation, all start points of all baselines from one single point

coordinate, to make all baseline results of the processing, having certain systematic

bias and then eliminate it, at the network adjustment stage, with imported parameters.

2. Insufficient Observation Time

To solve this problem, you can simply delete the data of the satellite with insufficient

observation time to exclude these data in the computation.

3. Redundant Cycle Slips

If the cycle slips occur in the same session of different satellites, you can delete such

session as an attempt of improving computation result. If it is a certain satellite that

often has cycle slips, you should delete the data of this satellite.

4. Serious Multi-path Effect

This factor often leads to a large residual error of the observation value. You can solve

it either by reducing the edit threshold to get rid of the values with large residual error,

or by deleting seriously affected sessions/satellites.

5. Large troposphere/ionosphere reflections

You can solve this by the following methods:

1) Increase the elevation mask to eliminate those data that tend to be effected by

troposphere/ionosphere. However, blindness exists in this method, since not all

low-elevation angle signals are seriously affected by troposphere/ionosphere.

2) Correct troposphere/ionosphere delay by models

3) For dual-frequency observed value, use ionosphere reflections-free observation

value to run baseline computation

98

5.5.2.3 A Useful Tool of Refined Process: Residual Error Chart

The Residual Error Chart is quite helpful to estimate influence factors and to

determine problematic sessions and satellites. A Residual Error Chart is a

chart drawn according to obtained residual errors, as shown in the figure

below.

Click

‘

up

’

/

’

down

’

to view each double difference residual error.

83. Residual Error Chart

Above figure is a typical chart of the double difference residual error chart, of which the

X-axis represents observation time and the Y-axis represents residual error.

SV14-SV07 on the top-right is the double difference residual error of satellite SV12 and

satellite SV15. Normally, the swing refers to 0 of residual error values, would not be

larger than 0.1 cycles.

99

The Figure below shows a cycle slip:

84. cycle slip

The data in the figure below shows the influence of unknown factors; they may be one

or more factors among multi-path effect, troposphere/ionosphere reflections, and

serious electromagnetic disturbance and so on.

85. Unknown Factor(s) Disturbance

100

5.6 Re-process a Baseline

When the influence factors are estimated. You can re-process a baseline by modifying

its processing settings or by editing the baseline sessions.

To select the data you want to delete, drag the pointer in the observation data chart. As

shown in the figure below, data framed with broken-line would be blocked and would be

involved in the computation.

86. Delete Observation Data

In some cases of unqualified baseline processing, you probably need to modify the

processing settings and edit sessions more than once. In some cases of

fail-to-have-qualified-solution may even happen on certain baselines. In this case, you

should delete them or block them from the network adjustment. If such baselines are

curial for your network, you have to measure them again!

101

The Baseline processing software has an important effect on the precision of the

relative static measurement, the reliability of the relative static measurement and

observation time. Therefore, desirable commercial baseline processing software must

be able to process baselines accurately and must be user-friendly and easy to handle.

Our software applies complicated baseline process theory to an easy-handling

interface. Normally, to have a precise result, you do not need to do any manual work on

a well-observed data. As for those unqualified data, you can make them meet your

requirement by doing a manual re-processing according to the processing information

the software gives.

5.1 Baseline Processing Flow

When you input all observation data in pointed format, the software will analyze the

relationship among collected points to form the static baselines. Then, the processing

will start.

[w8]:

The Following steps are involved:

5.1.1 Parameters Setting

The Control parameters of the baseline computation are for choosing the processing

method used for the baseline computation. It is a crucial step to set the parameters so

that can optimize the processing.

You can set these parameters in the ‘Computation Settings’. The Parameters available

here are data-sampling interval, elevation mask, reference satellite and its (ionosphere)

ionosphere, and computation model as well.

76

5.1.1.1 Fieldwork Data: Check and Modify

Before the baseline, computation is inputted, it is necessary, when fieldwork data is

entered, to check loaded observation data to avoid disoperation during fieldwork.

Checking the items include: observation station name and number, coordinates of

station and antenna height, etc.

5.1.1.2 Baseline Computation

The Typical process of computation is automatically carried out without any manual

work. The Following steps are involved:

5.1.1.2.1 Computation Self-check

the

Before doing the computation, the software will check

parameter settings,

observation data, ephemeris file and coordinate of starting pointed.

5.1.1.2.2 Read Ephemeris Data

The format of an ephemeris data could be either RINEX format, binary format *.HCN or

precise ephemeris data format SP3.

5.1.1.2.3 Read Ephemeris Data

The first step of baseline processing is reading the original observation data derived

from GPS receiver. The original data can be processed directly in the software that

comes with the GPS receiver from the same producer. However, software developed

by a third-party company may not be able to process data from different types of

receivers.

5.1.1.2.4 Triple Difference Computation

The triple difference is the difference between two double differences of different

77

epochs. The Triple Difference Computation is derived from the equation based on the

triple difference. Since in terms of short edges, the precision of results of such

computation is not high enough

, its typical function is getting approximate baseline

1

edges to process cycle slips reparation.

Single Baseline Computation Process:

Begin

Self-Check

Cycle Slip Repair

Read Ephemeris

Double-Diff. Computation

Read Obs. Data

Ambiguity

Triple-Diff. Computation

Double-Diff. Fixed

End

72. Process Chart

1. Normally, fixed double difference provides higher precision for short edges, while triple difference is

sometimes used for long edges.

78

5.1.1.2.5 Cycle Slip Reparation

It is the key point of the baseline computation to find a correct integer ambiguity.

Continuously tracking of the carrier phase by receivers provides the possibility of

having such integer ambiguity. However, blocks and disturbances may interrupt such

tracking and destroy its continuity. Therefore, So-called cycle slip, occurs in the carrier

phase observation results between different epochs. Then the main issue of the

baseline processing software becomes, how to detect and repair cycle slips.

5.1.1.2.6 Float Double Difference Computation

Suppose that signals from N satellites are observed, there would be N-1 extra

unknowns in the double-difference system-of-equations compares to that of

triple-difference. Therefore, double-difference computation derives advanced

coordinates of unknown points and float-marked integer ambiguity. Normally, this

method derives a float, which results from the absorbing of noises and others

un-modeled errors, instead of a theoretically literal ‘integer’ of the integer ambiguity.

Therefore, biases often exist, even up to several cycles, between such float and correct

integer.

5.1.1.2.7 Integer Ambiguity Solution

Under the circumstance of obtaining enough data in sufficient time, it is quite possible

to derive a correct integer ambiguity simply by rounding. Nevertheless, it is still true that

Fast Ambiguity Resolution Approach (FARA) and LAMBDA can largely accelerate

observation time and enhance working efficiency.

5.1.1.2.8 Fixed Double Difference Computation

The software will carry out Fixed Double Difference Computation when the integer

ambiguity is fixed correctly. The highest precision, will not come out without a correct

integer ambiguity, although it could be achieved by using this approach

79

5.1.1.3 Check Quality of Baseline

Only qualified baselines can be used later, thus quality checking will be required when

the baseline computation is done. The Checking process includes: RATIO, RDOP,

RMS, closed loop synchrony-sum/difference, closed loop asynchrony-sum/difference

and overlapped baseline correction

80

5.2 Settings of Baseline Processing

You need to set up baseline processing before you do the processing.

Select ‘

Baseline > Settings

’

from the Menu Bar. Below a dialog-box will appear, as

shown below:

73. Settings of Baseline Processing

There are four panels on this dialog-box

:

general settings, input rules,

troposphere/ionosphere settings and advanced settings.

For a Brief introduction of each panel see below:

81

5.2.1 General Settings

Epoch sampling interva

l

The Epoch sampling interval is the frequency, when doing baseline processing, of the

data sampled from the original observation data by the software. As the below shows:

74. Epoch Sampling Interval

Suppose that the sampling interval is 5 seconds for the static observation on two

devices. Such a high-density sampling interval often increases the time consumed

without deriving a significant improvement of the precision during the indoor work.

Therefore, a larger interval can be used to quicken the baseline processing.

What is a proper sampling interval? A common agreement is that relatively small

interval should be applied to short edges in short observation time, while larger interval,

to long edges. For example, you should set the sampling interval as 5 seconds for

static baselines shorter than 2 km within 20 minutes of observation time. However, you

should set a larger interval, which could reach 60 seconds or 120 seconds.

The reason you should set smaller interval during the fieldwork is that it is quite

possible to improve the processing result, which is derived from the limited-functional

software with imperfect and sometimes-random data, by reprocessing baseline with

modified epoch interval.

The Default epoch interval value is 60 seconds in our software.

82

5.2.1.1 Elevation Mask

The purpose of the elevation mask is to exclude the data collected from low elevation

satellites to the baseline computation.

Signals of low elevation satellite are complicatedly influenced by aerosphere and

difficult to correct with models. Additionally, many factors such as multi-path and

electromagnetic wave can put impact on them. Therefore, these signals are often poor

and should be deleted during the post processing.

Simply in terms of atmospheric refraction, the shorter observation range is the better

atmospheric refraction can be evened. Thus, a smaller elevation mask should be

applied to the short-range observation and larger value should be set on long range

ones. Off course, you should take into account the condition of the surroundings into

account.

To bring convenience into post processing, you should reduce elevation mask in

fieldworks to collect more data.

75. elevation mask

Default elevation mask is 20 degree.

5.2.1.2 Reference satellite

The Double difference is the difference of the two single differences between different

satellites. The software selects reference satellite to bring convenience to the

processing when the double difference is generated.

83

The Default setting AUTO makes the software pick a satellite obtaining most data with

the highest elevation angle as a reference. However, due to the influence of the

observation condition, it is not always the best choice. An improper reference satellite

will put a bad impact on the result of the processing, so you need to reset reference

satellite according to your data when the default option is not suitable.

You should select a new reference satellite according to the satellite alert, fieldwork

records, former baseline processing result comprehensively. It would not make any

sense to select an undetected satellite as your reference.

5.2.1.3 Gross Error Tolerance

It is often necessary to delete some unqualified data as gross error during data

processing. Data of which the observation offset exceeds (gross error tolerance × RMS)

will be taken as gross error.

5.2.1.4 Minimum Epoch Number

The Observed carrier phase must be continuous to ensure the good quality of the

baseline processing. Therefore, data with frequent cycle slips are poor-quality data and

in most cases, should be deleted. The software will consequently delete those data that

contains less continuous epochs than the minimum epoch number requires.

Minimum epoch number the software requires is two or bigger than two and the default

number is five.

5.2.1.5 Maximum Epoch Number

The Maximum epoch number is determined by the assigned memory in processing.

The Default value is 999.

84

5.2.2 Troposphere and Ionosphere Set Up

The Following figure shows the

Troposphere and Ionosphere Set Up

dialog-box. In

most cases, you do not have to make any change.

76. Troposphere and Ionosphere Set Up

85

5.2.3 Advance Settings

The figure below shows the

Advance

dialog-box. Normally, you do not have to make

any change for single frequency data. However, for dual-frequency data, you may often

change your options in the

Observation Method Combination

Combo Box.

77. Advance Settings

86

5.3 Baseline Processing

When the preparation mentioned above is done,

Select ‘

baseline

’>’

process all

’. The software will process all the baselines and an

information dialog-box will appear, shown in the figure below (figure 8-7).

The name of each baseline, computation progress and computation information of

each baseline are available in this dialog-box.

Multithreading is applied to the baseline computation.

You can click the ‘Stop’ button to stop the computation.

78. Advance Settings

87

When the computation is finished, the results of the computation are shown in the

figure below:

79. Compuatition Results

You can also view these results in the

Computation Result Details

or in the

Baseline

Properties

. For more Details, see relevant chapters

5.4 Recheck Process Results

5.4.1 Baseline Quality Control

There are several criterions of the quality of a baseline: RATIO, RDOP, RMS and Data

Deleting Rate.

A common agreement is that the larger RMS, the worse the quality of observation value

is, and the larger the RDOP, the worse observation conditions are. However, this is

comparatively true. That is to say, values of parameters above should not be taken as

absolute criterions of the baseline quality.

88

5.4.1.1 RMS

Root Mean Square (RMS) is:

V

PV

T

=

RMS

n

f

Where

V, residual error

P, observation value weight

N-f, overall observation value - an unknown

RMS shows the quality of the observation value. The smaller RMS, the better the

quality of observation value is, and the larger, the worse the quality of observation

value is.

According to the statistics theory, the probability is up to 95% of having an observation

error within the range from zero to 1.96 times of RMS

5.4.1.2 RATIO

RATIO is the ratio of the second minimum RMS and the minimum RMS when the

integer ambiguity solution came out, say:

RMS

=

RATIO

s ec

RMS

mi n

RATIO, of which the value depends on many factors, reflects the reliability of the

integer ambiguity. It relates to both the observation value quality and observation

conditions.

As a crucial criterion of the baseline quality, it is normally required to be larger than

three are.

89

5.4.1.3 Data Deleting Rate

An observation value, of which the adjusted value is larger than certain threshold value,

would be deleted as a gross error-contained value. Moreover, the ratio of deleted

values and overall values is called the data-deleting rate.

The Data deleting data, on certain aspect, reflects the quality of the original observation

data. The higher this value is, the worse the observation quality.

5.4.1.4 RDOP

Relative Dilution of Precision (RDOP) is the square root of the trace of the least

squares cofactor matrix (GPS World/UNSW), say:

=

RDOP

tr

(

Q

)

The RDOP value depends on the baseline position, satellites distribution and moving

tracks (observation condition). However, as long as baseline position is determined, it

becomes observation condition-related only. Since observation condition is expressed

as a function of time, the RDOP value is actually, for certain baseline vector,

observation session related.

The RDOP reflects the influence of the situation of the satellite on relative positioning; it

depends on the observation condition rather than on the observation value.

Apply

You should check the static baseline when process is done. As shown in the table

below , check whether all variance ratio values follow the ‘Fix’, and are larger than three.

In this table, some values are smaller than three are. That is to say, this baseline has

unqualified fixed double difference solutions.

To solve this problem, you can

•Change relevant values of the baseline processing setting

90

•Click this baseline to select it, then select ‘Process This Baseline’(or press F2)

•Repeat the two steps above until all baselines are qualified

In the result of each baseline, the line ‘Tri’ shows the fixed triple difference computation,

‘Flt’ shows the float double difference computation and ‘Fix’ shows the variance ratio of

the fixed double difference computation. Items shown along left to right are mean

square error of an edge, DX, DY DZ and the baseline length.

5.4.2 Closed Loop Checking

5.4.2.1 Conception of the Closing Error

Closed Loop Checking is a powerful tool to evaluate baseline quality.

Closed Loop includes synchrony-closed loop, asynchrony-closed loop and overlapped

baseline.

Closing Error of closed loop is theoretically zero, but it is allowed to have certain

deviation values. For more information about the allowed closing Error, see relevant

document or appendix.

There are several types of the closing Error:

1

.

Branch Closing Error:

=

X

X

=

Y

Y

=

Z

Z

2. Relative Closing Error:

+

+

2

2

2

=

X

Y

Z

S

91

Where

S

Stands for the loop length

5.4.2.2 Synchrony-closed loop, asynchrony asynchrony-closed

loop and overlapped baseline.

5.4.2.2.1 Synchrony-closed Loop

The Synchrony-closed loop closing Error is formed by simultaneous observation

baselines.

Because of certain inner relationship between simultaneous observation baselines,

synchrony-closed loop closing Error is theoretically zero. It is always true that if the

synchrony-closed loop closing Error exceeds allowed value, there is at least one

unqualified baseline among those formed by the loop. However, the opposite

hypothesis may not be true that the closing Error-within-allowed-value implies good

quality of every baseline. You can only confirm that most of baselines are qualified.

5.4.2.2.2 Asynchrony-closed Loop

If the Close loop is formed partly by the synchrony-observed baselines is called

asynchrony-closed Loop. The closing Error of the asynchrony-closed Loop is the

asynchrony-closed Loop closing Error.

If the asynchrony-closed Loop closing Error is smaller than allowed value, the

baselines forming the asynchrony-closed Loop are qualified. When the closing Error

exceeds the allowed value, there is at least one unqualified baseline. To pick

unqualified baseline vectors, you can check the adjacent

asynchrony-closed loops or

overlapped baselines.

92

5.4.2.2.3 Overlapped Baselines

The So-called overlapped baselines refer to the observation results in different session

between the same couple of the observation stations. The difference among these

results is the overlapped baselines correction.

5.4.2.3 How Do You Check?

The Following functions are available in Menu Bar ‘Check’, as shown in the figure

below:

80. Closing Error

The Closing Error values are stored in the closing Error file in the project folder, which

is a .txt file and named as ‘ProjectName.ISC’, ‘RINEX.ISC’, as example.

Note: It is for users’ continence of finding the unqualified baseline and comparing

baseline process results with each other to add the closing Error computation results of

the closing Error file ordered by the corresponding baseline computation results.

Therefore, if you do not need the previous results, you can delete your closing Error

records.

The figure below shows a closing Error file:

93

81. Check Report

For Details of the Closing Error files Formats see Chapter 13.

94

5.4.3 Check Free Network Adjustment

82. Network Adjustment Checking Result()

5.5 Distinguish Different Influence Factors

5.5.1 Influence Factors

The Following items are the influence factors of the baseline Computation:

1. Imprecise starting point coordinates: imprecise starting point coordinates leads to

bias on scale and direction.

2. Insufficient observation time leads to the failure of integer-unknown identification: if

the integer-unknowns of the satellites that involved in the baseline computation cannot

be identified precisely, the overall computation result would be spoiled.

3. Cycle slips reparation cannot be well achieved if there are too many cycle slips in

several sessions.

4. Serious multi-path effect exists within total observation session. It leads to big

residuals at large.

95

5. Troposphere or ionosphere reflections are too large.

6. Electromagnetic waves influence is too big.

7. Poor data quality due to some problems of the receiver itself, such as low-precision

phase determination and low-precision receiver clock, etc.

96

5.5.2 Influence Factors: Distinguish and Solve

5.5.2.1 Distinguish Different Factors

1. Summarize

Some of the factors are easy to distinguish, such as insufficient observation time,

redundant cycle slips, serious multi-path effect and large troposphere or ionosphere

reflections, etc., while others are not so obvious, imprecise starting point, for example.

2.Distinguish: Imprecise Starting Point

There is no easy way to distinguish the impact on the quality of baseline computation

made by Imprecise Starting Point at present. Thus, the only way to solve such

imprecision problem is making your start point as precise as possible.

3. Distinguish: Insufficient Observation Time

It is an easy-distinguished factor. You can simply check the observation data records to

find the number of observation data of each satellite. Satellites visibility figure is

available in our GPS software, which make these numbers more distinct.

4. Distinguish: redundant cycle slips

You can analyze this factor according to the obtained residual error of the observation

data. The Double difference, which is used by most typical baseline processing

applications, has an obvious integral-multiple-enlargement on its residual errors related

to an unprepared-cycle slip of a satellite observed by certain station.

5. Distinguish: Serious Multi-path Effect and Large troposphere / ionosphere reflections

Residual error of the observation data is also used to distinguish these factors. It has a

non-integral-multiple-enlargement of the residual error instead of the integral-multiple

one in the last case. Such enlargement is normally limited within 1 cycle, but it is still

bigger than that of common residual errors.

97

5.5.2.2 Solutions

1. Imprecise Start Point

To solve this problem, you should have a start point with higher precision. To do this,

you can do your single positioning in longer time or have the start point positioned

together with a more precise point in WGS-84 system, or you can derive, at the stage of

the overall baselines computation, all start points of all baselines from one single point

coordinate, to make all baseline results of the processing, having certain systematic

bias and then eliminate it, at the network adjustment stage, with imported parameters.

2. Insufficient Observation Time

To solve this problem, you can simply delete the data of the satellite with insufficient

observation time to exclude these data in the computation.

3. Redundant Cycle Slips

If the cycle slips occur in the same session of different satellites, you can delete such

session as an attempt of improving computation result. If it is a certain satellite that

often has cycle slips, you should delete the data of this satellite.

4. Serious Multi-path Effect

This factor often leads to a large residual error of the observation value. You can solve

it either by reducing the edit threshold to get rid of the values with large residual error,

or by deleting seriously affected sessions/satellites.

5. Large troposphere/ionosphere reflections

You can solve this by the following methods:

1) Increase the elevation mask to eliminate those data that tend to be effected by

troposphere/ionosphere. However, blindness exists in this method, since not all

low-elevation angle signals are seriously affected by troposphere/ionosphere.

2) Correct troposphere/ionosphere delay by models

3) For dual-frequency observed value, use ionosphere reflections-free observation

value to run baseline computation

98

5.5.2.3 A Useful Tool of Refined Process: Residual Error Chart

The Residual Error Chart is quite helpful to estimate influence factors and to

determine problematic sessions and satellites. A Residual Error Chart is a

chart drawn according to obtained residual errors, as shown in the figure

below.

Click

‘

up

’

/

’

down

’

to view each double difference residual error.

83. Residual Error Chart

Above figure is a typical chart of the double difference residual error chart, of which the

X-axis represents observation time and the Y-axis represents residual error.

SV14-SV07 on the top-right is the double difference residual error of satellite SV12 and

satellite SV15. Normally, the swing refers to 0 of residual error values, would not be

larger than 0.1 cycles.

99

The Figure below shows a cycle slip:

84. cycle slip

The data in the figure below shows the influence of unknown factors; they may be one

or more factors among multi-path effect, troposphere/ionosphere reflections, and

serious electromagnetic disturbance and so on.

85. Unknown Factor(s) Disturbance

100

5.6 Re-process a Baseline

When the influence factors are estimated. You can re-process a baseline by modifying

its processing settings or by editing the baseline sessions.

To select the data you want to delete, drag the pointer in the observation data chart. As

shown in the figure below, data framed with broken-line would be blocked and would be

involved in the computation.

86. Delete Observation Data

In some cases of unqualified baseline processing, you probably need to modify the

processing settings and edit sessions more than once. In some cases of

fail-to-have-qualified-solution may even happen on certain baselines. In this case, you

should delete them or block them from the network adjustment. If such baselines are

curial for your network, you have to measure them again!

101

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