CN117908057A - Real-time cycle slip detection method suitable for GPS dynamic observation value - Google Patents

Abstract

The invention relates to a real-time cycle slip detection method suitable for GPS dynamic observation values, which comprises the steps of calculating MW combined observation values and LG combined observation values of a single satellite in a preset number of epochs respectively, then performing Kalman filtering treatment on the MW combined observation values and the LG combined observation values of each epoch in the preset number of epochs respectively in a traversing manner to obtain MW combined prediction values and LG combined prediction values corresponding to each epoch, and respectively calculating to obtain MW combined prediction residual errors and LG combined prediction residual errors of each epoch, and judging that the epochs corresponding to any prediction residual error have no cycle slip or wild value once judging that any prediction residual error is lower than a preset residual error threshold value after Kalman filtering is stable; otherwise, determining that the epoch corresponding to any prediction residual error has cycle slip or wild value, thereby realizing positioning and type judgment of the epoch with mutation in all obtained prediction residual errors and realizing real-time cycle slip detection of the GPS dynamic observation value.

Description

Real-time cycle slip detection method suitable for GPS dynamic observation value

Technical Field

The invention relates to the field of positioning processing, and is suitable for real-time cycle slip detection of a GPS dynamic observation value.

Background

In GPS precise positioning and navigation, outlier rejection, cycle slip detection and repair are important components of carrier phase data processing for GPS signals. At present, the Turbo-Edit method and the Kalman filtering method are widely applied because of high detection precision and easy realization of programs.

The current common cycle slip detection method has the following main defects: in the Turbo-Edit method, the MW combination (combining double frequency codes, melbourne-Tu bbena) uses pseudo-range observation values, which are limited by pseudo-range noise, so that the TurboEdit method is difficult to detect cycle slips of more than two weeks under a strong noise environment, and the MW combination fails when Zhou Tiaobian on different wave bands are identical; in the LG combination of the Turbo-Edit method, in order to eliminate noise influence, human errors are introduced, ionosphere interference is easy to occur, and cycle slip detection is not influenced sensitively to the 77/60 combination. In addition, the Kalman filtering method needs segmentation and can not detect frequent cycle slips and outliers in a short epoch.

Disclosure of Invention

The invention aims to provide a real-time cycle slip detection method suitable for a GPS dynamic observation value aiming at the prior art. The real-time cycle slip detection method suitable for the GPS dynamic observation value can well finish cycle slip detection and repair of the GPS dynamic observation value, and improves detection efficiency.

The technical scheme adopted for solving the technical problems is as follows: the real-time cycle slip detection method suitable for the GPS dynamic observation value is characterized by comprising the following steps of:

Step 1, calculating MW combined observed values and LG combined observed values of a single satellite in a preset number of epochs respectively;

Step 2, respectively carrying out Kalman filtering treatment on MW combined observed values and LG combined observed values of any epoch in a preset number of epochs to obtain MW combined predicted values and LG combined predicted values for the any epoch;

Step 3, processing the obtained MW combined observed value and MW combined predicted value to obtain MW combined prediction residual error for any epoch; processing the obtained LG combined observed value and the LG combined predicted value to obtain an LG combined prediction residual error aiming at any epoch;

step 4, traversing all epochs in a preset number of epochs to respectively obtain MW combined forecast residual errors and LG combined forecast residual errors of each epoch;

step 5, after the Kalman filtering is stable, judging the MW combined prediction residual errors and the LG combined prediction residual errors of all the obtained epochs:

When any predicted residual is lower than a preset residual threshold, judging that the epoch corresponding to any predicted residual has no cycle slip or wild value; otherwise, determining that the epoch corresponding to any prediction residual error has cycle slip or wild value.

In the method for detecting the real-time cycle slip suitable for the dynamic observation value of the GPS, in step 3, the MW combined prediction residual error and the LG combined prediction residual error are calculated as follows:

X_jk＝A^j-kX_kk,j>k；

Wherein X _i+1 is a state vector for the observed value at time t _i+1, A is a transition matrix from time t _i to time t _i+1, X _i is a state vector for the observed value at time t _i, B is a system driving matrix, W _i is a prediction error of Kalman filtering, The observation vector at time t _i is C is a state vector coefficient matrix, V _i is a measurement error, the variance of the measurement error is sigma ², and the observation vector/>For MW or LG combined observation vector, e _i+1 is the prediction residual matrix, X _jk is the filtered value of the predicted epoch j with the Kalman filtered value of epoch k X _kk.

Further, in the method for detecting a real-time cycle slip suitable for a GPS dynamic observation value, the state vector X _i+1 has a state vector initial value X ₀, and a filter error initial value corresponding to the state vector initial value X ₀ is P ₀; the state vector initial value X ₀ and the filter error initial value P ₀ are determined as follows:

a_(i)＝[1,(i-N)t,(i-N)²t²/2,(i-N)³t³/6]^T；

wherein N is the total number of epochs; when (when) When the cycle slip does not occur in the first N epochs; when/>And when the cycle slip occurs in the first N epochs.

Further improvement, in the method for detecting the real-time cycle slip suitable for the GPS dynamic observation value, the estimation value of the state vector X _i+1 The calculation method is as follows:

X_i+1|i＝AX_i；

K_i+1＝P_i+1|iC^T(CP_i+1|iC^T+σ²)^-1

P_i+1|i＝AP_iA^T+BQB^T；

P_i+1|i+1＝(I-K_i+1C)P_i+1|i。

in the method for detecting the real-time cycle slip applicable to the dynamic observation value of the GPS, in step 5, the method for determining whether the cycle slip or the wild value exists in the epoch corresponding to any prediction residual is as follows:

When the forecast residual matrix e _i > v, determining that cycle slip or wild value occurs in the i epoch; otherwise, judging that the cycle slip or the wild value does not appear in the i epoch.

Further, in the method for detecting the real-time cycle slip applicable to the dynamic observation value of the GPS, after determining that a cycle slip or a wild value occurs in an epoch corresponding to the residual error of any prediction, the method further includes a process of determining that the corresponding epoch is the cycle slip or the wild value based on a preset determination model; wherein, the preset determination model is as follows:

wherein L is an epoch length threshold value for limiting the continuous occurrence of the wild value, and M is an empirical value; wherein:

when the formula (1) and the formula (2) are simultaneously established, judging that continuous wild values appear from the i epoch to the i+L-1 epoch;

when the formula (1) is established and the formula (2) is not established, determining that the i epoch is a cycle slip;

When the formula (1) is not established and the formula (2) is established, the i epoch is judged to be the occurrence wild value.

In the invention, the real-time cycle slip detection method suitable for the GPS dynamic observation value further comprises the following steps: and performing elimination processing on the determined wild value.

Compared with the prior art, the invention has the advantages that: according to the real-time cycle slip detection method suitable for the GPS dynamic observation values, after MW combined observation values and LG combined observation values of a single satellite in a preset number of epochs are calculated, performing Kalman filtering processing on the MW combined observation values and the LG combined observation values of each epoch in the preset number of epochs respectively in a traversing mode to obtain MW combined prediction values and LG combined prediction values corresponding to each epoch, and respectively calculating to obtain MW combined prediction residual errors and LG combined prediction residual errors of each epoch, and judging that no cycle slip or wild value exists in the epoch corresponding to any prediction residual error once judging that any prediction residual error is lower than a preset residual error threshold value after Kalman filtering is stable; otherwise, determining that the epoch corresponding to any prediction residual error has cycle slip or wild value, thereby realizing positioning and type judgment of the epoch with mutation (i.e. abnormality) in all obtained prediction residual errors and realizing real-time cycle slip detection of the GPS dynamic observation value.

Drawings

Fig. 1 is a flow chart of a real-time cycle slip detection method suitable for a GPS dynamic observation value in an embodiment of the invention.

Detailed Description

The invention is described in further detail below with reference to the embodiments of the drawings.

The embodiment provides a real-time cycle slip detection method suitable for a GPS dynamic observation value. Specifically, referring to fig. 1, the real-time cycle slip detection method applicable to the GPS dynamic observation value of this embodiment includes the following steps:

Step 1, calculating MW combined observed values and LG combined observed values of a single satellite in a preset number of epochs respectively;

Step 2, respectively carrying out Kalman filtering treatment on MW combined observed values and LG combined observed values of any epoch in a preset number of epochs to obtain MW combined predicted values and LG combined predicted values for the any epoch;

Step 3, processing the obtained MW combined observed value and MW combined predicted value to obtain MW combined prediction residual error for any epoch; processing the obtained LG combined observed value and the LG combined predicted value to obtain an LG combined prediction residual error aiming at any epoch;

Specifically, in this embodiment, the MW combined prediction residual and the LG combined prediction residual are calculated as follows:

X_jk＝A^j-kX_kk,j>k；

Wherein X _i+1 is a state vector for the observed value at time t _i+1, A is a transition matrix from time t _i to time t _i+1, X _i is a state vector for the observed value at time t _i, B is a system driving matrix, W _i is a prediction error of Kalman filtering, The observation vector at time t _i is C is a state vector coefficient matrix, V _i is a measurement error, the variance of the measurement error is sigma ², and the observation vector/>For MW combined observation vector or LG combined observation vector, e _i+1 is a prediction residual matrix, X _jk is a filtered value of an epoch j predicted by a Kalman filtered value X _kk of epoch k;

For example, when calculating the MW combined prediction residual for that any epoch, the observed value for state vector X _i+1 is the MW combined observed value; of course, when calculating the LG combined prediction residual for any epoch, the observed value for the state vector X _i+1 is the LG combined observed value;

assuming a sampling interval of the carrier phase of T, the fourth order difference is generally close to zero, and can be represented by a third order polynomial model. Will be At/>The taylor expansion is as follows:

C＝[1 0 0 0]^T；

step 4, traversing all epochs in a preset number of epochs to respectively obtain MW combined forecast residual errors and LG combined forecast residual errors of each epoch;

step 5, after the Kalman filtering is stable, judging the MW combined prediction residual errors and the LG combined prediction residual errors of all the obtained epochs:

When any predicted residual is lower than a preset residual threshold, judging that the epoch corresponding to any predicted residual has no cycle slip or wild value; otherwise, determining that the epoch corresponding to any prediction residual error has cycle slip or wild value.

Specifically, in this embodiment, the state vector X _i+1 has a state vector initial value X ₀, and the filter error initial value corresponding to the state vector initial value X ₀ is P ₀; the state vector initial value X ₀ and the filter error initial value P ₀ are determined as follows:

a_(i)＝[1,(i-N)t,(i-N)²t²/2,(i-N)³t³/6]^T；

wherein N is the total number of epochs; when (when) When the cycle slip does not occur in the first N epochs; when/>And when the cycle slip occurs in the first N epochs.

It should be noted that, the estimation of the state vector X _i+1 The calculation method is as follows:

X_i+1i＝AX_i；

K_i+1＝P_i+1|iC^T(CP_i+1|iC^T+σ²)^-1

P_i+1|i＝AP_iA^T+BQB^T；

P_i+1|i+1＝(I-K_i+1C)P_i+1|i。

Wherein P _i is a filtering error matrix, and Q is a system noise matrix.

In step 5, the manner of determining whether or not a cycle slip or a wild value exists in an epoch corresponding to any prediction residual is as follows: when the forecast residual matrix e _i > v, determining that cycle slip or wild value occurs in the i epoch; otherwise, judging that the cycle slip or the wild value does not appear in the i epoch.

In order to determine whether the epoch appears in a cycle slip or a wild value more accurately, in this embodiment, after determining that the epoch corresponding to the any prediction residual error appears in the cycle slip or the wild value, a process of determining that the corresponding epoch is the cycle slip or the wild value based on a preset determination model is further included; wherein, the preset determination model is as follows:

wherein L is an epoch length threshold value for limiting the continuous occurrence of the wild value, and M is an empirical value; for example, here the meta-length threshold value l=12, the empirical value m=11. Wherein:

when the formula (1) and the formula (2) are simultaneously established, judging that continuous wild values appear from the i epoch to the i+L-1 epoch;

when the formula (1) is established and the formula (2) is not established, determining that the i epoch is a cycle slip;

When the formula (1) is not established and the formula (2) is established, the i epoch is judged to be the occurrence wild value.

Of course, after judging that the outlier appears in any epoch, the judged outlier is also subjected to rejection processing operation according to the requirement.

While the preferred embodiments of the present invention have been described in detail, it is to be clearly understood that the same may be varied in many ways by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. The real-time cycle slip detection method suitable for the GPS dynamic observation value is characterized by comprising the following steps of:

Step 1, calculating MW combined observed values and LG combined observed values of a single satellite in a preset number of epochs respectively;

Step 2, respectively carrying out Kalman filtering treatment on MW combined observed values and LG combined observed values of any epoch in a preset number of epochs to obtain MW combined predicted values and LG combined predicted values for the any epoch;

Step 3, processing the obtained MW combined observed value and MW combined predicted value to obtain MW combined prediction residual error for any epoch; processing the obtained LG combined observed value and the LG combined predicted value to obtain an LG combined prediction residual error aiming at any epoch;

step 4, traversing all epochs in a preset number of epochs to respectively obtain MW combined forecast residual errors and LG combined forecast residual errors of each epoch;

step 5, after the Kalman filtering is stable, judging the MW combined prediction residual errors and the LG combined prediction residual errors of all the obtained epochs:

when any predicted residual is lower than a preset residual threshold, judging that the epoch corresponding to any predicted residual does not have cycle slip or wild value; otherwise, determining that the epoch corresponding to any prediction residual error has cycle slip or wild value.

2. The method for detecting the real-time cycle slip suitable for the dynamic observation of the GPS according to claim 1, wherein in step 3, the MW combined prediction residual and the LG combined prediction residual are calculated as follows:

X_jk＝A^j-kX_kk,j>k；

Wherein X _i+1 is a state vector for the observed value at time t _i+1, A is a transition matrix from time t _i to time t _i+1, X _i is a state vector for the observed value at time t _i, B is a system driving matrix, W _i is a Kalman filtered prediction error matrix, The observation vector at time t _i is C is a state vector coefficient matrix, V _i is a measurement error matrix, the variance of the measurement error matrix is sigma ², and the observation vector/>For MW or LG combined observation vector, e _i+1 is the prediction residual matrix, X _jk is the filtered value of the predicted epoch j with the Kalman filtered value of epoch k X _kk.

3. The method for detecting the real-time cycle slip suitable for the dynamic observation of the GPS according to claim 2, wherein the state vector X _i+1 has a state vector initial value X ₀, and the initial value of the filtering error corresponding to the state vector initial value X ₀ is P ₀; the state vector initial value X ₀ and the filter error initial value P ₀ are determined as follows:

a_(i)＝[1,(i-N)t,(i-N)²t²/2,(i-N)³t³/6]^T；

Wherein N is the total number of epochs, a _(i) is the third-order polynomial model coefficient of the epochs, X is the state vector, and sigma is the medium error; when (when) When the cycle slip does not occur in the first N epochs; when/>And when the cycle slip occurs in the first N epochs.

4. A method of real time cycle slip detection for GPS dynamic observations as in claim 3 wherein the state vector X _i+1 is an estimateThe calculation method is as follows:

X_i+1|i＝AX_i；

K_i+1＝P_i+1|iC^T(CP_i+1|iC^T+σ²)^-1

P_i+1|i＝AP_iA^T+BQB^T；

P_i+1|i+1＝(I-K_i+1C)P_i+1|i；

Wherein P _i is a filtering error matrix, Q is a system noise matrix, and C is a state vector coefficient matrix.

5. The method for detecting the cycle slip in real time for the dynamic observation of the GPS according to claim 2, wherein in step 5, the method for determining whether the cycle slip or the wild value exists in the epoch corresponding to any prediction residual is as follows:

When the forecast residual matrix e _i > v, determining that cycle slip or wild value occurs in the i epoch; otherwise, judging that the cycle slip or the wild value does not appear in the i epoch.

6. The method for detecting the cycle slip in real time for the dynamic observation value of the GPS according to claim 5, wherein after the cycle slip or the wild value of the epoch corresponding to the prediction residual is judged, the method further comprises the process of judging that the corresponding epoch is the cycle slip or the wild value based on a preset determination model; wherein, the preset determination model is as follows:

wherein L is an epoch length threshold value for limiting the continuous occurrence of the wild value, and M is an empirical value; wherein:

when the formula (1) and the formula (2) are simultaneously established, judging that continuous wild values appear from the i epoch to the i+L-1 epoch;

when the formula (1) is established and the formula (2) is not established, determining that the i epoch is a cycle slip;

When the formula (1) is not established and the formula (2) is established, the i epoch is judged to be the occurrence wild value.

7. The method for real-time cycle slip detection for GPS dynamic observations according to any of claims 1-6 further comprising: and performing elimination processing on the determined wild value.