CN107561563B - Cycle slip detection method for singular point preserving filtering noise reduction - Google Patents

Cycle slip detection method for singular point preserving filtering noise reduction Download PDF

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CN107561563B
CN107561563B CN201710766614.4A CN201710766614A CN107561563B CN 107561563 B CN107561563 B CN 107561563B CN 201710766614 A CN201710766614 A CN 201710766614A CN 107561563 B CN107561563 B CN 107561563B
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cycle slip
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CN107561563A (en
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马岳鑫
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Hunan Aerospace Electronic Science And Technology Co Ltd
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Abstract

The invention discloses a cycle slip detection method for singular point preserving, filtering and denoising, which comprises the following steps: step1, establishing a MW combined observation sequence; step2, initializing; step3, removing wild values in the MW combined observation sequence; step 4: performing first order difference on the sequence; step 5: carrying out extremum median filtering on the difference sequence; step 6: moving average noise reduction to obtain epochiAn estimate of (d) and an estimate of its variance; step 7: judging whether the cycle slip is caused according to a cycle slip judgment criterion, and recording a cycle slip epoch and a slip amount; step 8: the average window is slid until the last epoch is reached. The invention can realize the noise reduction of the MW combination and simultaneously keep the cycle slip signal not filtered, thereby achieving the effect of improving the detection precision and solving the problem that the small cycle slip of one to two weeks can not be accurately detected due to the overlarge noise of the MW combination.

Description

Cycle slip detection method for singular point preserving filtering noise reduction
Technical Field
The invention belongs to the technical field of GNSS observation data processing, relates to a GNSS precision single-point positioning preprocessing technology, and particularly relates to a cycle slip detection method with singular point preserving, filtering and noise reducing functions.
Background
Currently, with the application and development of GNSS (Global Navigation Satellite System) in various fields, different users need positioning services with different accuracies. The service precision required in the fields of mapping, atmosphere, earthquake monitoring and the like is higher and higher. Compared with pseudo-range observed quantity, the carrier phase observed quantity has the advantages of high precision, low noise and the like, and is widely applied to high-precision positioning and orbit determination services. However, because the carrier phase itself has the problems of cycle slip, non-fixed integer ambiguity and the like, in the high-precision positioning and orbit determination service, accurate detection of cycle slip and resolution of ambiguity must be realized at first.
Cycle slip detection is the premise of ambiguity resolution, and only accurate cycle slip detection can correctly parameterize ambiguity, so that a correct normal equation is constructed to solve ambiguity, and the precision and reliability of GNSS precision positioning are improved. Cycle slip occurs when the GNSS receiver phase-locked loop is out of lock, and may occur at low signal-to-noise ratios and at satellite elevation angles that are too low. Particularly, for the Beidou satellite navigation system in China, the cycle slip is more due to the fact that Geosynchronous orbit (GEO) satellites are high in satellite orbit, poor in satellite geometry, and required to be adjusted flexibly frequently. Particularly for some GEO satellites with low elevation angles (15-20 degrees), frequent small cycle slips of one week exist, and the classical TurboEdit method cannot accurately and reliably detect the cycle slips.
The MW combination is used as a classical combination quantity for cycle slip detection, has the advantage of no influence of geometric quantity and ionospheric residual error, but is unfavorable for detection of small cycle slip due to large noise.
The TurboEdit method uses MW combination as main probing measure for a certain epoch
Figure 915880DEST_PATH_IMAGE001
The following formula is used for estimation and variance update:
Figure 223365DEST_PATH_IMAGE002
wherein
Figure 113960DEST_PATH_IMAGE003
Is an epochThe MW of (a) is combined with an estimate,an estimator is made for the epoch variance. When in use
Figure 847671DEST_PATH_IMAGE005
Actual amount of epoch minus
Figure 327193DEST_PATH_IMAGE006
The absolute value of the estimate of the epoch is greater than
Figure 763991DEST_PATH_IMAGE007
When it is, first, it is judgedJudging whether the current is a wild value or not, if not, judging the current to be a cycle slip and recording
Figure 578232DEST_PATH_IMAGE005
Epoch, then updated from new initialization
Figure 227519DEST_PATH_IMAGE003
And
Figure 92707DEST_PATH_IMAGE004
. In fact, the updating process is a moving average process, and once a small cycle slip of a certain epoch is not detected, the variance updating is diverged, and finally the small cycle slip after the epoch cannot be detected at all; in addition, if cycle slip occurs in the initialization process, the precision of subsequent cycle slip detection is seriously influenced. Therefore, this method is only applicable to cases where cycle slip does not occur frequently. Meanwhile, due to the influence of pseudo-range noise, the method can hardly detect the small cycle slip of one to two weeks.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a cycle slip detection method with singular point preserving, filtering and noise reducing functions, which can realize noise reduction of MW combination and simultaneously keep cycle slip signals from being filtered, thereby achieving the effect of improving detection precision and solving the problem that one to two weeks of small cycle slips of MW combination cannot be accurately detected due to overlarge noise.
The purpose of the invention is realized by the following technical scheme:
a cycle slip detection method for singular point preserving filtering noise reduction is provided, which comprises the following steps:
step1, establishing MW combined observation sequence
Figure 919980DEST_PATH_IMAGE008
，
Figure 972250DEST_PATH_IMAGE009
Combining the observed values for the ith epoch MW;
step2, initializing, and selecting a proper sliding average window length
Figure 221965DEST_PATH_IMAGE010
Median filter window length of sum extreme
Figure 676080DEST_PATH_IMAGE011
And satisfy the relationship
Figure 172790DEST_PATH_IMAGE012
；
Step3, eliminating outliers in the MW combined observation sequence through a median filter;
step 4: for the sequence:
Figure 446776DEST_PATH_IMAGE013
performing a first order difference to obtain:
Figure 543225DEST_PATH_IMAGE015
wherein
Figure 240529DEST_PATH_IMAGE016
，
Figure 1812DEST_PATH_IMAGE017
，
Figure 327751DEST_PATH_IMAGE018
Is an integer;
step 5: the two first order difference sequences obtained in Step4 are respectively subjected to window length division
Figure 490879DEST_PATH_IMAGE019
Filtering the extreme value median value to obtain a sequence:
Figure 860550DEST_PATH_IMAGE020
wherein
Figure 843549DEST_PATH_IMAGE021
Filtered for median of extrema
Figure 707600DEST_PATH_IMAGE022
A sequence;
step 6: calculating window length
Figure 475967DEST_PATH_IMAGE023
Each epoch in
Figure 32850DEST_PATH_IMAGE024
The value of (c):
Figure 768725DEST_PATH_IMAGE025
，
wherein the content of the first and second substances,
Figure 170887DEST_PATH_IMAGE026
is an integer;
obtain the epoch
Figure 557875DEST_PATH_IMAGE001
Estimate of the amount of treatment MW combination
Figure 816818DEST_PATH_IMAGE027
And an estimate of its variance
Figure 508831DEST_PATH_IMAGE004
：
Figure 245843DEST_PATH_IMAGE028
；
Step 7: if it is
Figure 238069DEST_PATH_IMAGE029
Then calculate
Figure 618979DEST_PATH_IMAGE030
And：
Figure DEST_PATH_IMAGE002
，
whereinComprises the following steps:
，
and, if
Figure DEST_PATH_IMAGE004
Wherein const1 is a constant, determining an epoch
Figure 515577DEST_PATH_IMAGE001
The cycle slip is present and the cycle slip epoch is recorded
Figure 747975DEST_PATH_IMAGE001
And amount of jump
Figure 722884DEST_PATH_IMAGE036
；
Step 8: if it is
Figure 158676DEST_PATH_IMAGE006
After existence
Figure 510023DEST_PATH_IMAGE037
An observed value, then order
Figure 331348DEST_PATH_IMAGE038
And returns to Step4 until sliding to the last epoch, otherwise the loop ends.
As a further improvement, in Step1, pseudo-range and carrier phase data are read from the RINEX observation file and are linearly combined to obtain a MW combined observation sequence
Figure 8317DEST_PATH_IMAGE008
。
As a further improvement, in Step3, outliers in the MW combined observation sequence are removed by a median filter with a window length of 3.
As a further improvement, in Step5, the extremum median filtering process is as follows:
if it is
Figure 915093DEST_PATH_IMAGE039
Is a sequence to be filtered of length T,
Figure 319399DEST_PATH_IMAGE019
for the extreme value, the median filter window length
Figure 729651DEST_PATH_IMAGE040
And then:
step5.1: search out collections
Figure 577522DEST_PATH_IMAGE041
：
Figure 971594DEST_PATH_IMAGE042
Maximum value ofMinimum value of
Figure 220183DEST_PATH_IMAGE044
And median value
Figure 973375DEST_PATH_IMAGE045
And make an order
Figure 100002_DEST_PATH_IMAGE006
，
Wherein
Figure 100002_DEST_PATH_IMAGE008
Is a given threshold;
step5.2: according to the formula:
Figure 516855DEST_PATH_IMAGE048
updating
Figure 175369DEST_PATH_IMAGE049
；
Step5.3: if it isLet us order
Figure 844565DEST_PATH_IMAGE051
Return to Step5.1.
As a further improvement, since the first order difference sequence of MW combinations is almost zero-mean, in Step5.2, the update is done
Figure 100228DEST_PATH_IMAGE052
When, if
Figure 195223DEST_PATH_IMAGE053
Then get。
By the method, the noise reduction sequence with cycle slip reserved can be obtained
Figure 155406DEST_PATH_IMAGE055
And its variance estimation sequenceIf the cycle slip detection is carried out by using the sequence and the criterion given by Step7, the detection precision can be greatly improved, and the purpose of detecting the frequent small cycle slip by using the MW combination is realized. The invention can also sequentially reduce noise and complete cycle slip detection, and delay
Figure 295586DEST_PATH_IMAGE037
And giving a cycle slip detection result after each epoch. The invention can also solve TThe urboEdit method can not detect the problem of frequent small cycle slip, improves the capability of MW combined cycle slip detection, can detect cycle slip sequentially, can be applied to the pretreatment work of real-time precise single-point positioning data, provides correct cycle slip epoch, facilitates fuzzy estimation and improves positioning precision.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a flow chart of the cycle slip detection method for singular point preserving filtering noise reduction according to the present invention.
Fig. 2 is an effect diagram of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings and specific embodiments, and it is to be noted that the embodiments and features of the embodiments of the present application can be combined with each other without conflict.
As shown in fig. 1, a cycle slip detection method for singular point preserving filtering noise reduction according to an embodiment of the present invention includes the following steps:
step1, reading a Receiver Independent Exchange Format (Receiver Independent Exchange Format) observation file to obtain pseudo range and carrier phase data, and performing linear combination to obtain a MW combined observation sequence
Figure 107684DEST_PATH_IMAGE008
，
Figure 15597DEST_PATH_IMAGE009
Combine observations for the ith epoch MW:
step2, selecting a proper sliding average window length
Figure 760699DEST_PATH_IMAGE057
Sum-extremum median filteringWindow length
Figure 197497DEST_PATH_IMAGE011
Satisfy the relationship
Figure 510273DEST_PATH_IMAGE012
。
And Step3, initializing, and removing outliers in the MW combined observation sequence from the data in the sliding window L through a median filter with the window length of 3. The calculation of the second Step was carried out according to the following steps 4 to 7
Figure 690719DEST_PATH_IMAGE017
Of epoch (sliding mean window L central epoch point) points
Figure 290327DEST_PATH_IMAGE027
And
Figure 898026DEST_PATH_IMAGE004
and make an order
Figure 933984DEST_PATH_IMAGE051
。
Step4, sequence Pair
Performing a first order difference to obtain:
，
wherein
Figure 955981DEST_PATH_IMAGE059
。
Step5, performing the two first order difference sequences, respectively, and performing the window length of
Figure 228962DEST_PATH_IMAGE019
Filtering the extreme value median value to obtain a sequence:
，
wherein
Figure 6425DEST_PATH_IMAGE021
Filtered for median of extrema
Figure 955926DEST_PATH_IMAGE022
And (4) sequencing.
Step6, calculating window
Figure 232056DEST_PATH_IMAGE023
Each epoch in
Figure 26836DEST_PATH_IMAGE061
The value of (c):
obtain the epoch
Figure 721123DEST_PATH_IMAGE001
Estimate of the amount of treatment MW combination
Figure 841526DEST_PATH_IMAGE027
And an estimate of its variance
Figure 837907DEST_PATH_IMAGE004
：
。
Step7, if
Figure DEST_PATH_IMAGE064
Then calculate
Figure 985172DEST_PATH_IMAGE030
And：
Figure DEST_PATH_IMAGE010
，
whereinComprises the following steps:
Figure 67080DEST_PATH_IMAGE034
，
and if
Figure DEST_PATH_IMAGE012
(const 1 is a constant), then the epoch is determined
Figure 529286DEST_PATH_IMAGE001
The cycle slip is present and the cycle slip epoch is recorded
Figure 503189DEST_PATH_IMAGE001
And amount of jump
Figure 974622DEST_PATH_IMAGE067
。
Step8, if
Figure 966848DEST_PATH_IMAGE006
After existence
Figure 599955DEST_PATH_IMAGE037
An observed value, then order
Figure 559690DEST_PATH_IMAGE038
And returns to Step 4; the sliding window L is moved one epoch backwards until it slides to the last epoch.
Through the steps, the noise reduction sequence with cycle slip reserved can be obtained
Figure 303655DEST_PATH_IMAGE055
And its variance estimation sequence
Figure DEST_PATH_IMAGE068
If the cycle slip detection is carried out by using the sequence and the criterion given by Step7, the detection precision can be greatly improved, and the purpose of detecting the frequent small cycle slip by using the MW combination is realized. The invention can also sequentially reduce noise and complete cycle slip detection, and delay
Figure 415967DEST_PATH_IMAGE037
And giving a cycle slip detection result after each epoch. The invention can also solve the problem that the TurboEdit method can not detect frequent small cycle slips, improve the capability of MW combined cycle slip detection, can detect cycle slips sequentially, can be applied to the pretreatment work of real-time precise single-point positioning data, provides correct cycle slip epoch, is convenient for fuzzy estimation and improves positioning precision.
As a further preferred implementation, in Step5, the extremum median filtering process is as follows:
if it is
Figure 485554DEST_PATH_IMAGE039
Is a sequence to be filtered of length T,for the extreme value, the median filter window length
Figure 978776DEST_PATH_IMAGE040
And then:
step5.1: search out collections：
Figure 920504DEST_PATH_IMAGE042
Maximum value ofMinimum value of
Figure 675020DEST_PATH_IMAGE044
And median value
Figure 27504DEST_PATH_IMAGE045
And make an order
Figure DEST_PATH_IMAGE014
，
Wherein
Figure DEST_PATH_IMAGE016
Is a given threshold;
step5.2: according to the formula:
updating
Figure 192851DEST_PATH_IMAGE049
；
Step5.3: if it is
Figure 775142DEST_PATH_IMAGE071
Let us order
Figure 169214DEST_PATH_IMAGE051
Return to Step5.1.
As a further preferred embodiment, since the first order difference sequence of MW combinations is almost zero mean, in step5.2, the update is doneWhen, if
Figure 641970DEST_PATH_IMAGE053
Then get
Figure 660741DEST_PATH_IMAGE054
。
As shown in FIG. 2, the upper graph in FIG. 2 is a simulation effect graph of the Jfng station C01 satellite MW combined cycle slip detection, and the yellow point graph in the graph is MWCombining original data points, wherein a blue solid line is a MW combined smooth curve after filtering and noise reduction of the new algorithm, and a red dotted line is
Figure 807689DEST_PATH_IMAGE072
And the threshold curve and the black triangular points are the mutation points of the cycle slip obtained by the detection of the new algorithm. It can be seen that all 15 cycle-slip points are detected, especially for the few relatively frequent week-slips that are added within 4 to 5 hours. The lower graph in fig. 2 is a combined cycle slip detection actual map of a Jfng station C05 satellite MW that is low in elevation and frequently occurs with a small cycle slip. It can be seen that most of the small cycle slips are detected, the number of false detections is 4, the number of missed detections is 8, and the effect is much higher than that of the detection effect of the TurboEdit method.
In the description above, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore should not be construed as limiting the scope of the present invention.
In conclusion, although the present invention has been described with reference to the preferred embodiments, it should be noted that, although various changes and modifications may be made by those skilled in the art, they should be included in the scope of the present invention unless they depart from the scope of the present invention.

Claims (5)

1. A cycle slip detection method for singular point preserving filtering noise reduction is characterized by comprising the following steps:
step 1: establishing MW combined observation sequence，
Figure 652542DEST_PATH_IMAGE002
Is as followsiCombining observed values of epochs MW;
step 2: initializing, selecting proper sliding average window lengthLMedian filter window length of sum extremelAnd satisfy the relationship
Figure 554639DEST_PATH_IMAGE003
；
Step3, eliminating outliers in the MW combined observation sequence through a median filter;
step 4: for the sequence:
Figure 628774DEST_PATH_IMAGE004
performing a first order difference to obtain:
wherein，
Figure 747329DEST_PATH_IMAGE009
K is an integer;
step 5: the two first order difference sequences obtained in Step4 are respectively subjected to window length divisionlFiltering the extreme value median value to obtain a sequence:
Figure 261487DEST_PATH_IMAGE010
wherein
Figure 250172DEST_PATH_IMAGE011
Filtered for median of extrema
Figure 798965DEST_PATH_IMAGE012
A sequence;
step 6: calculating each epoch within the window lengthThe value of (c):
Figure 216357DEST_PATH_IMAGE014
，
wherein the content of the first and second substances,
Figure 352940DEST_PATH_IMAGE015
is an integer;
obtain the epochiEstimate of the amount of treatment MW combination
Figure 880873DEST_PATH_IMAGE016
And an estimate of its variance
Figure 139816DEST_PATH_IMAGE017
：
Figure 97408DEST_PATH_IMAGE018
Figure 194939DEST_PATH_IMAGE019
；
Step 7: if it is
Figure 187166DEST_PATH_IMAGE020
Then calculate
Figure 944907DEST_PATH_IMAGE021
And：
Figure 789552DEST_PATH_IMAGE023
Figure 174856DEST_PATH_IMAGE002
，
wherein
Figure 830506DEST_PATH_IMAGE025
Comprises the following steps:
Figure 762690DEST_PATH_IMAGE026
，
and, if
Figure 694699DEST_PATH_IMAGE004
Wherein const1 is a constant, determining an epochiThe cycle slip is present and the cycle slip epoch is recordediAnd amount of jump
Figure 697071DEST_PATH_IMAGE028
；
Step 8: if it isiAfter existence
Figure 671981DEST_PATH_IMAGE029
An observed value, then order
Figure 481674DEST_PATH_IMAGE030
And returns to Step4 until sliding to the last epoch, otherwise the loop ends.
2. The cycle slip detection method for singular point preserving filtering noise reduction according to claim 1, characterized in that: in Step1, pseudo-range and carrier phase data are obtained by reading RINEX observation files, and a MW combined observation sequence is obtained by linear combination
Figure 833021DEST_PATH_IMAGE031
。
3. The cycle slip detection method for singular point preserving filtering noise reduction according to claim 2, characterized in that: in Step3, outliers in the MW combined observation sequence are removed by a median filter with a window length of 3.
4. The cycle slip detection method for singularity preserving filtering noise reduction according to claim 1, 2 or 3, wherein: in Step5, the extremum median filtering process is as follows:
if it is
Figure 778980DEST_PATH_IMAGE032
Is a sequence to be filtered of length T,
Figure 659211DEST_PATH_IMAGE033
for the extreme value, the median filter window length
Figure 97146DEST_PATH_IMAGE034
And then:
step5.1: search out collections
Figure 642397DEST_PATH_IMAGE035
：
Figure 318229DEST_PATH_IMAGE036
Maximum value of
Figure 526618DEST_PATH_IMAGE037
Minimum value of
Figure 920691DEST_PATH_IMAGE038
And median valueAnd make an order
Figure DEST_PATH_IMAGE006
，
Wherein
Figure DEST_PATH_IMAGE008
Is a given threshold;
step5.2: according to the formula:
Figure 762428DEST_PATH_IMAGE042
updating
Figure 649481DEST_PATH_IMAGE043
；
Step5.3: if it is
Figure 299905DEST_PATH_IMAGE044
Let us order
Figure 316009DEST_PATH_IMAGE045
Return to Step5.1.
5. The cycle slip detection method for singular point preserving filtering noise reduction according to claim 4, characterized in that: in Step5.2, updateWhen, if
Figure 375418DEST_PATH_IMAGE047
Then get
Figure DEST_PATH_IMAGE048
。
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