A Carrier Phase Tracking Method and System Based on a Fusion Filter

Abstract

The present invention provides a carrier phase tracking method and device based on a fusion filter. The method includes: in the initial tracking stage, the data acquisition steps for constructing a carrier tracking extended model of a Kalman-like unbiased FIR filter are as follows: obtaining the state parameters of the carrier device of the receiver; and retrieving corresponding data from a pre-configured database according to the state parameters. The carrier phase tracking method based on a fusion filter of the present invention uses a pre-configured database to retrieve data according to the state parameters of the current carrier device, thereby obtaining the construction of the carrier tracking extended model of the Kalman-like unbiased FIR filter in the initial stage, and further ensuring the tracking of the carrier phase in the initial stage.

Description

Technical Field

[0001] The present invention relates to the technical field of carrier synchronization, and particularly relates to a carrier phase tracking method and system based on a fusion filter. Background Art

[0002] Carrier phase tracking is to perform real-time compensation and accurate tracking on the frequency error and phase error between the received signal and the local signal, which is related to the accuracy of subsequent data demodulation and other processes. In order to overcome the problems that the Kalman filter in the prior art introduces relatively large estimation errors in a high-dynamic environment and the unbiased FIR filter similar to the Kalman filter cannot ensure the minimum estimation error, the existing patent CN202410074207.7, a carrier phase tracking method and device based on a fusion filter, discloses a carrier phase tracking method based on a fusion filter, including: establishing a Kalman filter carrier tracking model according to the relationship between the carrier phase difference, Doppler frequency and Doppler frequency change rate of the received signal and the reproduced signal; establishing an extended carrier tracking model of an unbiased FIR filter similar to the Kalman filter according to N historical data; running the Kalman filter carrier tracking model and the extended carrier tracking model of the unbiased FIR filter similar to the Kalman filter in parallel to output an estimated value of the carrier phase deviation; calculating an influence matrix according to the estimated values of the carrier phase deviation output by the Kalman filter carrier tracking model and the extended carrier tracking model of the unbiased FIR filter similar to the Kalman filter; determining a normalized influence value according to the influence matrix; calculating the estimated values of the carrier phase, Doppler frequency and Doppler frequency change rate errors after fusion according to the fusion method and the normalized influence value; calculating the local NCO frequency control amount based on the estimated values of the Doppler frequency and Doppler frequency change rate errors output after fusion, and updating the local NCO frequency. Since this method fuses the unbiased FIR filter similar to the Kalman filter and the Kalman filter into a new carrier tracking loop tracking method without calculating the error covariance, and inherits the advantages of the Kalman and the unbiased FIR filter similar to the Kalman filter, and can automatically prioritize its performance according to the optimality or robustness according to the result of the influence function to adapt to its operating environment and cope with sudden interference situations. In addition, since the fusion step no longer requires noise statistics, this method is insensitive to the statistical error of noise and has a significant improvement over the existing fusion methods in different scenarios.

[0003] Since the historical data is used to construct the extended carrier tracking model of the unbiased FIR filter similar to the Kalman filter in the above method, there is not enough historical data support at the beginning of the reception period, and the desired effect cannot be achieved. Summary of the Invention

[0004] One of the objectives of the present invention is to provide a carrier phase tracking method and system based on a fusion filter, which uses a pre-configured database to retrieve data according to the state parameters of the current carrying device, so as to obtain the construction of an extended carrier tracking model of a Kalman-like unbiased FIR filter in the initial stage, thereby ensuring the tracking of the carrier phase in the initial stage.

[0005] A carrier phase tracking method based on a fusion filter provided by an embodiment of the present invention includes:

[0006] Establish a carrier tracking model of a Kalman filter and an extended carrier tracking model of a Kalman-like unbiased FIR filter;

[0007] Run the model to output an estimated value of the carrier phase deviation, calculate the influence matrix, and obtain a normalized influence value based on the influence matrix;

[0008] Fusion calculation to obtain estimated values of carrier phase, Doppler frequency, and Doppler frequency change rate errors;

[0009] Calculate the NCO frequency control amount and update the local NCO frequency;

[0010] In the initial tracking link, the data acquisition steps for constructing an extended carrier tracking model of a Kalman-like unbiased FIR filter are as follows:

[0011] Obtain the state parameters of the carrying device of the receiver;

[0012] According to the state parameters, retrieve the corresponding data from the pre-configured database.

[0013] Preferably, the state parameters include one or a combination of moving speed, moving acceleration, and moving direction.

[0014] Preferably, obtaining the state data of the carrying device of the receiver includes:

[0015] According to the time difference between the historical data when entering the formal tracking link, sample the state data at the current moment and before from the state data repository to obtain a preset number of state data.

[0016] Preferably, the data retrieval steps include:

[0017] Arrange each parameter in the state data in sequence to form a state parameter set;

[0018] Retrieve the data associated with the state parameter set from the database.

[0019] Preferably, in the reconnection link after silence, the data acquisition steps for constructing an extended carrier tracking model of a Kalman-like unbiased FIR filter are as follows:

[0020] Obtain the state parameters of the carrier devices of a preset number of receivers in sequence from the reconnection time point forward and construct a first identification parameter set;

[0021] Obtain the state parameters of the carrier devices of a preset number of receivers in sequence from the silent time point forward and construct a second identification parameter set;

[0022] Calculate the similarity between the first identification parameter set and the second identification parameter set;

[0023] When the similarity is greater than a preset threshold, obtain a preset number of data before the silent time point as the data for constructing the carrier tracking extended model of the class Kalman unbiased FIR filter.

[0024] The present invention also provides a carrier phase tracking system based on a fusion filter, including: an establishment module, an operation module, a fusion calculation module, and an update module; wherein, the establishment module establishes a carrier tracking model of a Kalman filter and a carrier tracking extended model of a class Kalman unbiased FIR filter; the operation module operates the model to output an estimated value of the carrier phase deviation, calculates an influence matrix, and obtains a normalized influence value based on the influence matrix; the fusion calculation module performs fusion calculation to obtain estimated values of the carrier phase, Doppler frequency, and Doppler frequency change rate error; the update module calculates the NCO frequency control amount and updates the local NCO frequency;

[0025] It further includes a data acquisition module. In the initial tracking link, the steps for the data acquisition module to obtain the data for constructing the carrier tracking extended model of the class Kalman unbiased FIR filter are as follows:

[0026] Obtain the state parameters of the carrier device of the receiver;

[0027] According to the state parameters, retrieve the corresponding data from a pre-configured database.

[0028] Preferably, the state parameters include one or a combination of more of: moving speed, moving acceleration, and moving direction.

[0029] Preferably, obtaining the state data of the carrier device of the receiver includes:

[0030] According to the time difference between the historical data when entering the formal tracking link, sample the state data at the current moment and before from the state data repository to obtain a preset number of state data.

[0031] Preferably, the data retrieval steps include:

[0032] Arrange each parameter in the state data in sequence to form a state parameter set;

[0033] Retrieve the data associated with the state parameter set from the database.

[0034] Preferably, in the reconnection process after silence, the data acquisition steps for the data for constructing the extended carrier tracking model of the Kalman unbiased FIR filter are as follows:

[0035] Sequentially obtain the state parameters of the carrier devices of a preset number of receivers from the reconnection time point forward and construct a first identification parameter set;

[0036] Sequentially obtain the state parameters of the carrier devices of a preset number of receivers from the silence time point forward and construct a second identification parameter set;

[0037] Calculate the similarity between the first identification parameter set and the second identification parameter set;

[0038] When the similarity is greater than a preset threshold, obtain a preset number of data before the silence time point as the data for constructing the extended carrier tracking model of the Kalman unbiased FIR filter.

[0039] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will become apparent from the specification or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings.

[0040] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0041] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0042] Figure 1 is a schematic diagram of a carrier phase tracking method based on a fusion filter in an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of a carrier phase tracking system based on a fusion filter in an embodiment of the present invention. Detailed Embodiments

[0044] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0045] The embodiment of the present invention provides a carrier phase tracking method based on a fusion filter, as Figure 1As shown in the figure, it includes: establishing a carrier tracking model of a Kalman filter and an extended model of a carrier tracking of a Kalman-like unbiased FIR filter; running the model to output an estimated value of the carrier phase deviation, calculating an influence matrix, and obtaining a normalized influence value based on the influence matrix; performing a fusion calculation to obtain estimated values of the carrier phase, Doppler frequency, and Doppler frequency change rate errors; calculating the NCO frequency control amount, and updating the local NCO frequency.

[0046] This embodiment is an extension based on the prior art. The Kalman filter carrier tracking model is constructed according to the relationship between the carrier phase difference, Doppler frequency, and Doppler frequency change rate of the received signal and the reproduced signal; the extended model of the carrier tracking of the Kalman-like unbiased FIR filter is constructed according to the preset historical data; subsequent steps such as allowing calculation and fusion are all prior art solutions and will not be elaborated here; however, when starting up, etc., there will be a period of time without historical data. This period of time is regarded as the initial tracking link, and the link after generating a preset number of historical data is regarded as the formal tracking link; in the initial tracking link, the data acquisition steps for constructing the extended model of the carrier tracking of the Kalman-like unbiased FIR filter are as follows:

[0047] Step 1: Obtain the state parameters of the carrier device of the receiver.

[0048] The state parameters include one or a combination of the following: moving speed, moving acceleration, moving direction, and configured position; obtaining the state data of the carrier device of the receiver includes: according to the time difference between the historical data when entering the formal tracking link (i.e., the time difference corresponding to the last data and the first data among the preset number of historical data), sampling the state data at the current moment and before from the state data repository to obtain a preset number of state data. The state data repository stores the historical state data of the carrier device; for example: when the sampling frequency of the state data is the same as the generation frequency of the historical data for constructing the model, the state data will correspond one by one with the historical data; therefore, the preset number of historical data requires an equal amount of state data.

[0049] Step 2: According to the state parameters, retrieve the corresponding data from the pre-configured database.

[0050] The data retrieval steps include: arranging each parameter in the state data in sequence to form a state parameter set; retrieving the data associated with the state parameter set from the database. The database is pre-configured, and the state parameter set in the database is associated one by one with the data (equivalent to historical data); the database is constructed based on the corresponding analysis of a large number of state parameters and historical data. In the initial tracking stage, each time a historical data is generated, the corresponding part of the retrieved data needs to be discarded synchronously until entering the formal tracking stage.

[0051] The carrier phase tracking method based on a fusion filter of the present invention uses a pre-configured database to retrieve data based on the status parameters of the current carrying device, thereby obtaining the construction of an extended model of a Kalman-like unbiased FIR filter carrier tracking in the initial stage, and then ensuring the tracking of the carrier phase in the initial stage.

[0052] To cope with an emergency state, the carrying device needs to enter a silent state. To enable the carrying device to continue tracking after exiting the silent state, in one embodiment, in the reconnection link after silence, the steps for obtaining data for constructing an extended model of a Kalman-like unbiased FIR filter carrier tracking are as follows:

[0053] Sequentially obtain the status parameters of the carrying device of a preset number of receivers from the reconnection time point backwards and construct a first identification parameter set; the status parameters are arranged in order to form the first identification parameter set;

[0054] Sequentially obtain the status parameters of the carrying device of a preset number of receivers from the silence time point backwards and construct a second identification parameter set; the status parameters are arranged in order to form the second identification parameter set;

[0055] Calculate the similarity between the first identification parameter set and the second identification parameter set; the similarity calculation can adopt the cosine similarity calculation method;

[0056] When the similarity is greater than a preset threshold (any value between 0.85 and 1), obtain a preset number of data before the silence time point as the data for constructing an extended model of a Kalman-like unbiased FIR filter carrier tracking. Synchronously delete the first data in the order of generating each historical data; until the generated historical data reaches the preset number.

[0057] In addition, when the similarity is less than or equal to the preset threshold, retrieve the corresponding data from the pre-configured database according to the status parameters.

[0058] To further improve the accuracy after silence, it is necessary to correct the data according to the motion conditions before and after silence. The specific correction steps are as follows: construct a trajectory tracking space during the silence time, construct a trajectory in the trajectory tracking space according to the status parameters during the silence time, and determine the position change situation before and after the silence time (represented by the direction vector from the first point to the last point of the trajectory); then, according to the position change situation, retrieve a correction data set from the pre-configured correction library, and correct the preset number of data before the silence time point in sequence according to the data in the correction data set. Among them, the correction library is pre-analyzed and constructed, and the correction data set in the library is in one-to-one correspondence with the direction vector.

[0059] The present invention also provides a carrier phase tracking system based on a fusion filter, as Figure 2As shown in the figure, it includes: an establishment module 1, an operation module 2, a fusion calculation module 3, an update module 4, and a data acquisition module 5; among them, the establishment module 1 establishes a Kalman filter carrier tracking model and a Kalman-like unbiased FIR filter carrier tracking extended model; the operation module 2 runs the model to output an estimated value of the carrier phase deviation, calculates an influence matrix, and obtains a normalized influence value based on the influence matrix; the fusion calculation module 3 performs fusion calculation to obtain estimated values of carrier phase, Doppler frequency, and Doppler frequency change rate errors; the update module 4 calculates the NCO frequency control amount and updates the local NCO frequency.

[0060] In the initial tracking stage, the data acquisition steps for the data acquisition module 5 to obtain the data for constructing the Kalman-like unbiased FIR filter carrier tracking extended model are as follows:

[0061] Obtain the state parameters of the carrier equipment of the receiver.

[0062] According to the state parameters, retrieve the corresponding data from the pre-configured database.

[0063] Among them, the state parameters include one or a combination of more of: moving speed, moving acceleration, and moving direction.

[0064] Among them, obtaining the state data of the carrier equipment of the receiver includes:

[0065] According to the time difference between the historical data when entering the formal tracking stage, sample the state data at the current moment and before from the state data repository to obtain a preset number of state data.

[0066] Among them, the data retrieval steps include:

[0067] Arrange each parameter in the state data in sequence to form a state parameter set;

[0068] Retrieve the data associated with the state parameter set from the database.

[0069] In one embodiment, in the reconnection stage after silence, the data acquisition steps for the data acquisition module to obtain the data for constructing the Kalman-like unbiased FIR filter carrier tracking extended model are as follows:

[0070] Sequentially obtain the state parameters of the carrier equipment of the receiver for a preset number from the reconnection time point forward and construct a first identification parameter set;

[0071] Sequentially obtain the state parameters of the carrier equipment of the receiver for a preset number from the silence time point forward and construct a second identification parameter set;

[0072] Calculate the similarity between the first identification parameter set and the second identification parameter set;

[0073] When the similarity is greater than a preset threshold, obtain a preset number of data before the silent time point as the data for constructing the carrier tracking extended model of the class Kalman unbiased FIR filter.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A carrier phase tracking method based on a fusion filter, comprising: Establish Kalman filter carrier tracking model and Kalman-like unbiased FIR filter carrier tracking extended model; Running the model to output a carrier phase deviation estimate, calculating an influence matrix and obtaining a normalized influence value based on the influence matrix; The carrier phase, Doppler frequency, and Doppler frequency change rate error estimation values ​​are obtained by fusion calculation; Calculate the NCO frequency control amount and update the local NCO frequency; It is characterized in that, in the initial tracking link, the data acquisition steps of constructing the Kalman-like unbiased FIR filter carrier tracking extension model are as follows: Step 1: Obtain the status parameters of the device carried by the receiver; Step 2: According to the status parameters, corresponding data is retrieved from a pre-configured database; The state parameters include: one or more combinations of moving speed, moving acceleration, and moving direction; The data retrieval steps include: Arrange the parameters in the state data in order to form a state parameter set; Retrieving data associated with the state parameter set from a database; The database is pre-configured, and the data is historical data. The database is constructed based on the corresponding analysis of a large number of state parameters and historical data. In the initial tracking stage, each time a historical data is generated, the corresponding part of the retrieved data must be synchronously abandoned until entering the formal tracking stage.

2. The carrier phase tracking method based on the fusion filter as claimed in claim 1, characterized in that: Get the status data of the receiver's onboard device, including: According to the time difference between the historical data entering the formal tracking link, the current and previous state data are sampled from the state data repository to obtain a preset number of state data.

3. The carrier phase tracking method based on fusion filter as claimed in claim 1, characterized in that: In the post-silence reconnection phase, the data acquisition steps for constructing the Kalman-like unbiased FIR filter carrier tracking extended model are as follows: From the reconnection time point forward, sequentially obtain the state parameters of the devices on board a preset number of receivers and construct a first identification parameter set; Sequentially acquiring state parameters of a preset number of devices mounted on the receivers from the silent time point forward and constructing a second identification parameter set; Calculating the similarity between the first identification parameter set and the second identification parameter set; When the similarity is greater than a preset threshold, a preset amount of data before the silent time point is obtained as data for constructing a Kalman-like unbiased FIR filter carrier tracking extension model.

4. A carrier phase tracking system based on a fusion filter, comprising: Establishment module, operation module, fusion calculation module and update module; wherein, the establishment module establishes a Kalman filter carrier tracking model and a Kalman-like unbiased FIR filter carrier tracking extension model; the operation module runs the model to output a carrier phase deviation estimate, calculates an influence matrix and obtains a normalized influence value based on the influence matrix; the fusion calculation module obtains carrier phase, Doppler frequency, and Doppler frequency change rate error estimates through fusion calculation; the update module calculates the NCO frequency control amount and updates the local NCO frequency; The method is characterized in that it also includes a data acquisition module. In the initial tracking stage, the data acquisition module acquires data for constructing a Kalman-like unbiased FIR filter carrier tracking extension model in the following steps: Get the status parameters of the receiver's onboard device; According to the status parameters, the corresponding data is retrieved from the pre-configured database; The state parameters include: one or more combinations of moving speed, moving acceleration, and moving direction; The data retrieval steps include: Arrange the parameters in the state data in order to form a state parameter set; Retrieving data associated with the state parameter set from a database; The database is pre-configured, and the data is historical data. The database is constructed based on the corresponding analysis of a large number of state parameters and historical data. In the initial tracking stage, each time a historical data is generated, the corresponding part of the retrieved data must be synchronously abandoned until entering the formal tracking stage.

5. The carrier phase tracking system based on fusion filter as claimed in claim 4, characterized in that: Get the status data of the receiver's onboard device, including: According to the time difference between the historical data entering the formal tracking link, the current and previous state data are sampled from the state data repository to obtain a preset number of state data.

6. The carrier phase tracking system based on fusion filter as claimed in claim 4, characterized in that: In the reconnection phase after silence, the data acquisition module acquires data for constructing a Kalman-like unbiased FIR filter carrier tracking extended model in the following steps: From the reconnection time point forward, sequentially obtain the state parameters of the devices on board a preset number of receivers and construct a first identification parameter set; Sequentially acquiring state parameters of a preset number of devices mounted on the receivers from the silent time point forward and constructing a second identification parameter set; Calculating the similarity between the first identification parameter set and the second identification parameter set; When the similarity is greater than a preset threshold, a preset amount of data before the silent time point is obtained as data for constructing a Kalman-like unbiased FIR filter carrier tracking extension model.

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