CN117607921A - Carrier phase tracking method and device based on fusion filter - Google Patents
Carrier phase tracking method and device based on fusion filter Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
- G01S19/29—Acquisition or tracking or demodulation of signals transmitted by the system carrier including Doppler, related
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
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Abstract
The utility model discloses a carrier phase tracking method and device based on a fusion filter, which comprises the steps of running a carrier tracking model of the Kalman filter and a carrier tracking expansion model of the Kalman unbiased FIR filter in parallel to output a carrier phase deviation estimated value, calculating an influence matrix, determining a normalized influence value according to the influence matrix, calculating the error estimated value of the change rate of the carrier phase, the Doppler frequency and the Doppler frequency after fusion, calculating the control quantity of the local NCO frequency, and updating the local NCO frequency.
Description
Technical Field
The application relates to the technical field of carrier synchronization, in particular to a carrier phase tracking method and device based on a fusion filter.
Background
The carrier synchronization of the receiver is a process of compensating and accurately tracking the frequency error and the phase error of the received signal and the local signal in real time by utilizing a tracking loop, and whether the error estimation is accurate or not directly influences the accuracy of subsequent processing steps such as data demodulation and the like.
The high dynamic motion characteristics result in a signal with a larger Doppler frequency shift and Doppler first and multi-order frequency offsets, and the capturing and tracking processes generally adapt to high dynamic stress through a larger receiving bandwidth, so that the probability of signal unlocking is reduced. The increase of the bandwidth of the receiver introduces more loop noise, which results in the decrease of signal processing gain and tracking precision, thus possibly causing signal lock loss, and finally, the subsequent navigation message cannot be successfully demodulated.
With the rapid development of military and civil aircrafts and missile systems, the radial instantaneous speed, acceleration and jerk between an airborne/missile-borne target and a satellite are larger and larger, and the high-speed carrier has the characteristics of strong nonlinearity, rapid time variation, strong coupling and the like in a dynamic environment, so that the difficulty of precise carrier synchronization of a high-dynamic receiver is sharply increased.
The classical Kalman filter and the derivative filter thereof are widely applied to a carrier tracking loop of a dynamic scene, and are simple and globally optimal state machines of a linear Gaussian process. The Kalman and a series of Kalman-derived algorithms have strict requirements on the matching degree of a high-dynamic signal processing model, have poor adaptability to different models and have low robustness; at the same time the process noise matrix Q and the measured noise variance R need to be known in advance. In the practical application process, random external interference hardly obeys Gaussian statistics, and the Kalman filter and the derivative algorithm thereof also introduce larger estimation errors once the motion state of the receiver changes or other interferences exist.
The Kalman-like unbiased FIR filter does not need to consider the statistical characteristics of noise sources and initial distribution, and uses N historical data to obtain a carrier tracking expansion model, so as to provide unbiased estimation for the system. In a poor measured environment, the kalman-like unbiased FIR filter is more robust to interference uncertainty and insensitive to noise environment variations, but it cannot guarantee minimum estimation errors.
Disclosure of Invention
The carrier phase tracking method and device based on the fusion filter aim to solve the problems that in the prior art, the estimation error of a Kalman filter is large under a high dynamic environment, and the minimum estimation error cannot be guaranteed by an unbiased FIR filter similar to the Kalman filter.
In a first aspect, a carrier phase tracking method based on a fusion filter is provided, including:
establishing a Kalman filter carrier tracking model according to the relation among the carrier phase difference of the received signal and the reproduction signal, the Doppler frequency and the Doppler frequency change rate;
establishing a Kalman-like unbiased FIR filter carrier tracking expansion model according to N historical data;
running a Kalman filter carrier tracking model and a Kalman-like unbiased FIR filter carrier tracking expansion model in parallel to output a carrier phase deviation estimated value;
calculating an influence matrix according to the carrier phase deviation estimated value output by the carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter;
determining a normalized influence value according to the influence matrix;
calculating the error estimation values of the carrier phase, the Doppler frequency and the Doppler frequency change rate after fusion according to the fusion method and the normalization influence value;
and calculating the local NCO frequency control quantity based on the Doppler frequency and Doppler frequency change rate error estimated value output after fusion, and updating the local NCO frequency.
Further, the carrier tracking model of the kalman filter is:
（1）
（2）
wherein,representing a state transition matrix>Representing a state transition coefficient matrix,/->Representing a random deviation coefficient matrix, ">Representing a state noise matrix of the device,representing a random bias matrix, ">Representing a matrix of observation coefficients>Representing a random deviation coefficient matrix, ">Representing carrier phase deviation, +.>Indicating Doppler frequency deviation, < >>Indicating the Doppler frequency difference rate of change, < >>、/>Respectively isn-1 time-of-day filter predicted carrier phase offset and local NCO replication yieldRaw Doppler frequency, < >>Representing carrier loop update time, process noise +.>Caused by phase deviation of the reference clock of the receiver, frequency deviation and acceleration between the receiver and the moving carrier in the direction of the line of sight>Is the phase difference between the received signal output by the loop phase discriminator and the local reproduction carrier wave, < >>Representing the measured gaussian white noise, the variance isR。
Further, establishing a carrier tracking expansion model of the Kalman-like unbiased FIR filter according to the N pieces of historical data, wherein the carrier tracking expansion model comprises the following steps:
taking N pieces of history data from m=n-n+1 to N as inputs of a Kalman-like unbiased FIR filter, estimating the state of an nth point, and performing N-point expansion based on a formula (1) and a formula (2) without considering system state noise and measurement noise so as to obtain a Kalman-like unbiased FIR filter carrier tracking expansion model:
（3）
（4）
wherein,representing extensionsNA state matrix after the point is selected,representation ofNExpansion matrix of point observations, +.>Representing the expanded state transition matrix, +.>Representing the matrix of observation coefficients after expansion,representing the expanded random bias matrix;
（5）
（6）
wherein,、/>all represent the extended random error coefficient matrix.
Further, running a kalman filter carrier tracking model includes:
iteratively operating a Kalman filter carrier tracking model according to formulas (7) to (11), and outputting carrier phase deviation, doppler frequency and Doppler frequency change rate estimation values in real time:
（7）
（8）
（9）
（10）
（11）
wherein,is the state prediction vector at time n, +.>Is the state estimation vector at time n-1, < >>Is the n moment error covariance matrix,>is an updated matrix of the n moment error covariance matrix,/>Is the kalman gain matrix at time n.
Further, the carrier tracking extension model of the Kalman-like unbiased FIR filter is operated, and the carrier tracking extension model comprises the following steps:
iterative operation is carried out on the carrier tracking expansion model of the Kalman-like unbiased FIR filter according to the formulas (12) to (15), and the carrier phase deviation estimated value is output in real time:
（12）
（13）
（14）
（15）
wherein,for the n moment state prediction matrix,/o>Kalman-like unbiased FIR filter state estimation matrix for time n-1 +.>For the n instant auxiliary gain matrix +.>An artificial filter gain matrix that is a kalman-like unbiased FIR filter,a matrix is estimated for the n time states.
Further, the calculated influence matrix is calculated by the formulas (16) to (21):
（16）
wherein,indicating that the observed quantity is about to occur>Is a modified influence matrix of->Representing a new function->For gain->Is of the type of (A) and (B)>Representing an empirical risk value, ++>Representation->At->Secondary bias in the direction;
（17）
wherein, in the process, the liquid crystal display device,indicating when the observed value is +.>An innovation function of time Kalman;
（18）
wherein,brepresenting the estimated error of the estimate usingb-a number of interfered observations;
（19）
（20）
（21）
wherein,representing a new function->For gain->Is a secondary bias of (a).
Further, determining a normalized impact value from the impact matrix includes:
the normalized impact value caused by the interference is determined using a history of b impact matrices, the accumulation of which is represented by equation (22):
（22）
wherein,representing each element in the influence matrix to take absolute value and keeping the original position unchanged;
（23）
wherein,representing normalized influence values>Representing two matrices as Hadamard inner product, < ->An identity matrix representing a main diagonal of 1, < ->、/>The carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter are respectively shown in the specificationbThe cumulative sum of the impact matrices under the action of the anomaly observations.
Further, the fusion method comprises the following steps: the greater the normalization influence value, the greater the degree of noise interference, and the fused state estimation matrix tends to be an estimation value with a smaller normalization influence value:
（24）
（25）
wherein,in the form of a matrix of historical information,ais amnesia factor, is->、/>、/>The method comprises the steps of respectively representing an estimation matrix output by an n-moment Kalman unbiased FIR filter carrier tracking expansion model, an estimation matrix output by an n-moment Kalman filter carrier tracking model, and an estimation matrix output by a Kalman filter carrier tracking model and an Kalman unbiased FIR filter carrier tracking expansion model which are fused by using an influence function.
Further, the calculation formula of the local NCO frequency control amount is as follows:
（26）
wherein,for the local NCO frequency control quantity, +.>、/>Respectively fusion estimation matrix->Is a second value, a third value,/->The update time of the loop is tracked for the carrier.
In a second aspect, a carrier phase tracking device based on a fusion filter is provided, including:
the establishing module is used for establishing a Kalman filter carrier tracking model according to the relation among the carrier phase difference of the received signal and the reproduction signal, the Doppler frequency and the Doppler frequency change rate;
the expansion module is used for establishing a carrier tracking expansion model of the Kalman-like unbiased FIR filter according to the N pieces of historical data;
the operation module is used for operating the carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter in parallel to output a carrier phase deviation estimated value;
the first calculation module is used for calculating an influence matrix according to the carrier phase deviation estimated value output by the carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter;
the determining module is used for determining a normalized influence value according to the influence matrix;
the fusion module is used for calculating the fused carrier phase, doppler frequency and Doppler frequency change rate error estimation value according to the fusion method and the normalized influence value;
the second calculation module is used for calculating the local NCO frequency control quantity based on the Doppler frequency and the Doppler frequency change rate error estimated value output after fusion and updating the local NCO frequency.
The application has the following beneficial effects: according to the method, under the condition that error covariance is not calculated, a Kalman-like unbiased FIR filter and a Kalman filter are combined into a novel carrier tracking loop tracking method, the advantages of the Kalman-like unbiased FIR filter and the Kalman-like unbiased FIR filter are inherited, the performance of the Kalman-like unbiased FIR filter can be automatically prioritized according to the optimality or robustness according to the influence function result, so that the method is suitable for an operation environment of the Kalman-like unbiased FIR filter, and is suitable for an emergency interference situation.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application.
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a carrier phase tracking method based on a fusion filter according to embodiment 1 of the present application;
fig. 2 is a schematic block diagram of a high dynamic carrier tracking loop in the carrier phase tracking method based on the fusion filter in embodiment 1 of the present application;
fig. 3 is a block diagram of a carrier phase tracking device based on a fusion filter according to embodiment 2 of the present application.
Reference numerals:
100. establishing a module; 200. an expansion module; 300. an operation module; 400. a first computing module; 500. a determining module; 600. a fusion module; 700. and a second calculation module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The carrier phase tracking method based on the fusion filter according to embodiment 1 of the present application includes: establishing a Kalman filter carrier tracking model according to the relation among the carrier phase difference of the received signal and the reproduction signal, the Doppler frequency and the Doppler frequency change rate; establishing a Kalman-like unbiased FIR filter carrier tracking expansion model according to N historical data; running a Kalman filter carrier tracking model and a Kalman-like unbiased FIR filter carrier tracking expansion model in parallel to output a carrier phase deviation estimated value; calculating an influence matrix according to the carrier phase deviation estimated value output by the carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter; determining a normalized influence value according to the influence matrix; calculating the error estimation values of the carrier phase, the Doppler frequency and the Doppler frequency change rate after fusion according to the fusion method and the normalization influence value; the method combines a Kalman unbiased FIR filter and a Kalman filter into a new carrier tracking loop tracking method under the condition of not calculating error covariance, inherits the advantages of the Kalman unbiased FIR filter and the Kalman unbiased FIR filter, can automatically prioritize the performance of the Kalman unbiased FIR filter according to the influence function result according to the optimality or robustness so as to adapt to the running environment of the Kalman unbiased FIR filter and cope with the bursty interference situation, and in addition, the method is insensitive to the statistical error of noise and has obvious improvement compared with the existing fusion method under different scenes because the fusion step does not need noise statistics.
Specifically, fig. 1 shows a flowchart of a carrier phase tracking method based on a fusion filter in application embodiment 1, including:
s100, establishing a Kalman filter carrier tracking model according to the relation between the carrier phase difference of the received signal and the reproduction signal, the Doppler frequency and the Doppler frequency change rate;
specifically, the carrier tracking model of the kalman filter is as follows:
（1）
（2）
wherein,representing a state transition matrix>A matrix of state transition coefficients is represented,representing a random deviation coefficient matrix, ">Representing a state noise matrix>Representing a random bias matrix, ">Representing a matrix of observation coefficients>Representing a matrix of random deviation coefficients,representing carrier phase deviation, +.>Indicating Doppler frequency deviation, < >>Indicating the rate of change of the doppler frequency difference,、/>respectively isn-1 carrier phase offset predicted by the time-of-day filter and doppler frequency generated by local NCO replication,/->Representing carrier loop update time, process noise +.>Caused by phase deviation of the reference clock of the receiver, frequency deviation and acceleration between the receiver and the moving carrier in the direction of the line of sight>Is the phase difference between the received signal output by the loop phase discriminator and the local reproduction carrier wave, < >>Representing the measured gaussian white noise, the variance isR。
S200, establishing a Kalman-like unbiased FIR filter carrier tracking expansion model according to N pieces of historical data;
specifically, establishing a carrier tracking expansion model of the Kalman-like unbiased FIR filter according to N pieces of historical data, wherein the carrier tracking expansion model comprises the following steps:
taking N pieces of history data from m=n-n+1 to N as inputs of a Kalman-like unbiased FIR filter, estimating the state of an nth point, and performing N-point expansion based on a formula (1) and a formula (2) without considering system state noise and measurement noise so as to obtain a Kalman-like unbiased FIR filter carrier tracking expansion model:
（3）
（4）
wherein,representing extensionsNA state matrix after the point is selected,representation ofNExpansion matrix of point observations, +.>Representing the expanded state transition matrix, +.>Representing the matrix of observation coefficients after expansion,representing the expanded random bias matrix;
（5）
（6）
wherein,、/>all represent the extended random error coefficient matrix.
S300, parallel running a Kalman filter carrier tracking model and a Kalman-like unbiased FIR filter carrier tracking expansion model to output a carrier phase deviation estimated value;
specifically, running a kalman filter carrier tracking model includes:
iteratively operating a Kalman filter carrier tracking model according to formulas (7) to (11), and outputting carrier phase deviation, doppler frequency and Doppler frequency change rate estimation values in real time:
（7）
（8）
（9）
（10）
（11）
wherein,is the state prediction vector at time n, +.>Is the state estimation vector at time n-1, < >>Is the n moment error covariance matrix,>is an updated matrix of the n moment error covariance matrix,/>Is the kalman gain matrix at time n.
The carrier tracking extension model of the Kalman-like unbiased FIR filter is operated, which comprises the following steps:
iterative operation is carried out on the carrier tracking expansion model of the Kalman-like unbiased FIR filter according to the formulas (12) to (15), and the carrier phase deviation estimated value is output in real time:
（12）
（13）
（14）
（15）
wherein,for the n moment state prediction matrix,/o>Kalman-like unbiased FIR filter state estimation matrix for time n-1 +.>For the n instant auxiliary gain matrix +.>An artificial filter gain matrix that is a kalman-like unbiased FIR filter,a matrix is estimated for the n time states.
S400, calculating an influence matrix according to carrier phase deviation estimated values output by a Kalman filter carrier tracking model and a Kalman-like unbiased FIR filter carrier tracking expansion model;
illustratively, taking a Kalman filter as an example, in the state estimation process, when external interference exists, the observed quantity is caused to occurA variation of (2) which causes the estimated matrix of the Kalman filter to superimpose the influencing matrixThe calculation influence matrix is calculated by the formulas (16) to (21):
（16）
wherein,indicating that the observed quantity is about to occur>Is a modified influence matrix of->Representing a new function->For gain->Is of the type of (A) and (B)>Representing an empirical risk value, ++>Representation->At->Secondary bias in the direction;
（17）
wherein, in the process, the liquid crystal display device,indicating when the observed value is +.>An innovation function of time kalman, wherein the innovation function can be used to evaluate an estimation error caused when an observed value changes;
（18）
wherein,brepresenting the estimated error of the estimate usingb-a number of interfered observations;
（19）
（20）
（21）
wherein,representing a new function->For gain->Is a secondary bias of (a).
S500, determining a normalized influence value according to the influence matrix;
specifically, determining a normalized impact value according to the impact matrix includes:
the normalized impact value caused by the interference is determined using a history of b impact matrices, the accumulation of which is represented by equation (22):
（22）
wherein,representing each element in the influence matrix to take absolute value and keeping the original position unchanged;
（23）
wherein,representing normalized influence values>Representing two matrices as Hadamard inner product, < ->An identity matrix representing a main diagonal of 1, < ->、/>The carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter are respectively shown in the specificationbThe cumulative sum of the impact matrices under the action of the anomaly observations.
S600, calculating error estimation values of the carrier phase, the Doppler frequency and the Doppler frequency change rate after fusion according to a fusion method and a normalization influence value;
the fusion method comprises the following steps: the greater the normalization influence value, the greater the degree of noise interference, and the fused state estimation matrix tends to be an estimation value with a smaller normalization influence value:
（24）
（25）
wherein,in the form of a matrix of historical information,ais amnesia factor, is->、/>、/>The method comprises the steps of respectively representing an estimation matrix output by an n-moment Kalman unbiased FIR filter carrier tracking expansion model, an estimation matrix output by an n-moment Kalman filter carrier tracking model, and an estimation matrix output by a Kalman filter carrier tracking model and an Kalman unbiased FIR filter carrier tracking expansion model which are fused by using an influence function.
And S700, calculating a local NCO frequency control quantity based on the Doppler frequency and Doppler frequency change rate error estimated value output after fusion, and updating the local NCO frequency.
The calculation formula of the local NCO frequency control quantity is as follows:
（26）
wherein,for the local NCO frequency control quantity, +.>、/>Respectively fusion estimation matrix->Is a second value, a third value,/->For the update time of the carrier tracking loop, the present embodiment uses an influence function fusion filter to improve the principle of the high dynamic carrier tracking loop as shown in fig. 2.
Example 2
As shown in fig. 3, a carrier phase tracking device based on a fusion filter according to embodiment 2 of the present application includes:
the establishing module 100 is configured to establish a kalman filter carrier tracking model according to a relationship between a carrier phase difference between a received signal and a reproduced signal, a doppler frequency and a doppler frequency change rate;
the expansion module 200 is used for establishing a carrier tracking expansion model of the Kalman-like unbiased FIR filter according to the N pieces of historical data;
the operation module 300 is used for operating the carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter in parallel to output a carrier phase deviation estimated value;
the first calculation module 400 is configured to calculate an impact matrix according to the carrier phase deviation estimated value output by the carrier tracking model of the kalman filter and the carrier tracking expansion model of the kalman-like unbiased FIR filter;
a determining module 500, configured to determine a normalized impact value according to the impact matrix;
the fusion module 600 is configured to calculate, according to the fusion method and the normalized impact value, a fused carrier phase, doppler frequency, and a doppler frequency change rate error estimation value;
the second calculation module 700 is configured to calculate a local NCO frequency control amount based on the doppler frequency and the doppler frequency change rate error estimation value outputted after the fusion, and update the local NCO frequency.
It should be noted that, for other specific embodiments of the carrier phase tracking device based on the fusion filter in the embodiment of the present invention, reference may be made to the specific embodiments of the carrier phase tracking method based on the fusion filter, and for avoiding redundancy, details are not repeated here.
The above is only a preferred embodiment of the present application; the scope of protection of the present application is not limited in this respect. Any person skilled in the art, within the technical scope of the present disclosure, shall cover the protection scope of the present application by making equivalent substitutions or alterations to the technical solution and the improved concepts thereof.
Claims (10)
1. A carrier phase tracking method based on a fusion filter, comprising:
establishing a Kalman filter carrier tracking model according to the relation among the carrier phase difference of the received signal and the reproduction signal, the Doppler frequency and the Doppler frequency change rate;
establishing a Kalman-like unbiased FIR filter carrier tracking expansion model according to N historical data;
running a Kalman filter carrier tracking model and a Kalman-like unbiased FIR filter carrier tracking expansion model in parallel to output a carrier phase deviation estimated value;
calculating an influence matrix according to the carrier phase deviation estimated value output by the carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter;
determining a normalized influence value according to the influence matrix;
calculating the error estimation values of the carrier phase, the Doppler frequency and the Doppler frequency change rate after fusion according to the fusion method and the normalization influence value;
and calculating the local NCO frequency control quantity based on the Doppler frequency and Doppler frequency change rate error estimated value output after fusion, and updating the local NCO frequency.
2. The carrier phase tracking method based on a fusion filter according to claim 1, wherein the kalman filter carrier tracking model is:
（1）
（2）
wherein,representing a state transition matrix>A matrix of state transition coefficients is represented,representing a random deviation coefficient matrix, ">Representing a state noise matrix>Representing a random bias matrix, ">Representing a matrix of observation coefficients>Representing a random deviation coefficient matrix, ">Representing carrier phase deviation, +.>Indicating Doppler frequency deviation, < >>Indicating the Doppler frequency difference rate of change, < >>、/>Respectively isn-1 time-of-day filter predicted carrier phase offsetDifference and Doppler frequency generated by local NCO replication, < >>Representing carrier loop update time, process noise +.>Caused by phase deviation of the reference clock of the receiver, frequency deviation and acceleration between the receiver and the moving carrier in the direction of the line of sight>Is the phase difference between the received signal output by the loop phase discriminator and the local reproduction carrier wave, < >>Representing the measured gaussian white noise, the variance isR。
3. The carrier phase tracking method based on a fusion filter according to claim 1, wherein establishing a kalman-like unbiased FIR filter carrier tracking extension model according to N pieces of history data includes:
taking N pieces of history data from m=n-n+1 to N as inputs of a Kalman-like unbiased FIR filter, estimating the state of an nth point, and performing N-point expansion based on a formula (1) and a formula (2) without considering system state noise and measurement noise so as to obtain a Kalman-like unbiased FIR filter carrier tracking expansion model:
（3）
（4）
wherein,representing extensionsNState matrix after point, +.>Representation ofNExpansion matrix of point observations, +.>Representing the state transition matrix after expansion,representing the matrix of observation coefficients after expansion,representing the expanded random bias matrix;
（5）
（6）
wherein,、/>all represent the extended random error coefficient matrix.
4. The fusion filter-based carrier phase tracking method of claim 1, wherein running a kalman filter carrier tracking model comprises:
iteratively operating a Kalman filter carrier tracking model according to formulas (7) to (11), and outputting carrier phase deviation, doppler frequency and Doppler frequency change rate estimation values in real time:
（7）
（8）
（9）
（10）
（11）
wherein,is the state prediction vector at time n, +.>Is the state estimation vector at time n-1, < >>Is the n moment error covariance matrix,>is an updated matrix of the n moment error covariance matrix,/>Is the kalman gain matrix at time n.
5. The carrier phase tracking method based on the fusion filter according to claim 1, wherein running a kalman-like unbiased FIR filter carrier tracking extension model includes:
iterative operation is carried out on the carrier tracking expansion model of the Kalman-like unbiased FIR filter according to the formulas (12) to (15), and the carrier phase deviation estimated value is output in real time:
（12）
（13）
（14）
（15）
wherein,for the n moment state prediction matrix,/o>Kalman-like unbiased FIR filter state estimation matrix for time n-1 +.>For the n instant auxiliary gain matrix +.>Artificial filter gain matrix being Kalman-like unbiased FIR filter ++>A matrix is estimated for the n time states.
6. The fusion filter-based carrier phase tracking method of claim 1, wherein the calculated influence matrix is calculated by formulas (16) to (21):
（16）
wherein,indicating that the observed quantity is about to occur>Is a modified influence matrix of->Representing an innovation functionFor gain->Is of the type of (A) and (B)>Representing an empirical risk value, ++>Representation->At->Secondary bias in the direction;
（17）
wherein,indicating when the observed value is +.>An innovation function of time Kalman;
（18）
wherein,brepresenting the estimated error of the estimate usingb-a number of interfered observations;
（19）
（20）
（21）
wherein,representing a new function->For gain->Is a secondary bias of (a).
7. The fusion filter-based carrier phase tracking method of claim 1, wherein determining a normalized impact value from the impact matrix comprises:
the normalized impact value caused by the interference is determined using a history of b impact matrices, the accumulation of which is represented by equation (22):
（22）
wherein,representing each element in the influence matrix to take absolute value and keeping the original position unchanged;
（23）
wherein,representing normalized influence values>Representing two matrices as Hadamard inner product, < ->An identity matrix representing a main diagonal of 1, < ->、/>The carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter are respectively shown in the specificationbThe cumulative sum of the impact matrices under the action of the anomaly observations.
8. The carrier phase tracking method based on a fusion filter according to claim 1, wherein the fusion method is: the greater the normalization influence value, the greater the degree of noise interference, and the fused state estimation matrix tends to be an estimation value with a smaller normalization influence value:
（24）
（25）
wherein,in the form of a matrix of historical information,ais amnesia factor, is->、/>、/>The method comprises the steps of respectively representing an estimation matrix output by an n-moment Kalman unbiased FIR filter carrier tracking expansion model, an estimation matrix output by an n-moment Kalman filter carrier tracking model, and an estimation matrix output by a Kalman filter carrier tracking model and an Kalman unbiased FIR filter carrier tracking expansion model which are fused by using an influence function.
9. The carrier phase tracking method based on a fusion filter according to claim 1, wherein the calculation formula of the local NCO frequency control amount is:
（26）
wherein,for the local NCO frequency control quantity, +.>、/>Respectively fusion estimation matrix->Is a second value, a third value,/->The update time of the loop is tracked for the carrier.
10. A carrier phase tracking device based on a fusion filter, comprising:
the establishing module is used for establishing a Kalman filter carrier tracking model according to the relation among the carrier phase difference of the received signal and the reproduction signal, the Doppler frequency and the Doppler frequency change rate;
the expansion module is used for establishing a carrier tracking expansion model of the Kalman-like unbiased FIR filter according to the N pieces of historical data;
the operation module is used for operating the carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter in parallel to output a carrier phase deviation estimated value;
the first calculation module is used for calculating an influence matrix according to the carrier phase deviation estimated value output by the carrier tracking model of the Kalman filter and the carrier tracking expansion model of the Kalman-like unbiased FIR filter;
the determining module is used for determining a normalized influence value according to the influence matrix;
the fusion module is used for calculating the fused carrier phase, doppler frequency and Doppler frequency change rate error estimation value according to the fusion method and the normalized influence value;
the second calculation module is used for calculating the local NCO frequency control quantity based on the Doppler frequency and the Doppler frequency change rate error estimated value output after fusion and updating the local NCO frequency.
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