A BeiDou satellite signal vector tracking method and system based on Kalman filtering and phase compensation

Abstract

The present invention discloses a Beidou satellite signal vector tracking method and system based on Kalman filtering and phase compensation, belonging to the field of navigation satellite positioning technology. The method and system include: step 1, receiving Beidou satellite signals through an incoherent vector tracking loop to obtain a phase detector output result; step 2, using a Kalman filter for pre-processing filtering to obtain pseudorange and pseudorange rate observations; step 3, inputting the pseudorange and pseudorange rate observations into a navigation filter for state estimation and correction to obtain a state quantity update value; step 4, performing phase compensation using a carrier phase detector value to obtain a phase compensation value, inputting the phase compensation value into an NCO, and repeating steps 1 and 4; step 5, controlling the loop NCO in a Beidou satellite receiver and simultaneously clearing and resetting the loop filter to complete Beidou satellite signal vector tracking. The present invention achieves high-sensitivity tracking of Beidou satellite signals and eliminates frequency deviation caused by external disturbances such as thermal noise.

Description

Technical Field

[0001] The present invention belongs to the technical field of navigation satellite positioning, and in particular relates to a Beidou satellite signal vector tracking method and system based on Kalman filtering and phase compensation. Background Art

[0002] With the advancement of satellite navigation technology, GNSS is increasingly being used in positioning, navigation, and timing. Traditional navigation satellite receivers face significant challenges in the increasingly demanding environments of cities, canyons, forests, and inside large buildings. In these scenarios, satellite signal strength is significantly attenuated. Therefore, the research and design of highly sensitive satellite receivers for weak signals is crucial. Compared to traditional receivers, receivers for smartphones and in-car navigation systems place even higher demands on receiver sensitivity due to the complex operating environments. The sensitivity of satellite navigation receivers is primarily determined by the RF channel performance and baseband algorithm performance. RF channel performance primarily refers to factors such as antenna gain, channel gain, channel noise figure, and A / D quantization loss. Baseband algorithms primarily encompass signal processing processes such as acquisition, synchronization, and tracking. While professional receiver manufacturers have achieved advanced levels of RF hardware design, further improving receiver sensitivity requires optimizing and improving baseband algorithms. Signal tracking is a crucial component of baseband signal processing.

[0003] In weak signal environments, high-sensitivity satellite navigation receivers need to continuously track satellite signals to generate measurements such as pseudorange and Doppler frequency for positioning and velocity measurement. There are three basic approaches to improving receiver tracking performance for weak signals: increasing the integration time, optimizing the tracking loop structure, and leveraging external sensor information for assistance. First, similar to the weak signal acquisition process, increasing the integration time through coherent or incoherent integration can improve the signal-to-noise ratio (SNR) in the tracking loop. This also requires consideration of issues such as data bit symbol transitions and square loss. During signal tracking, if bit synchronization is achieved, the coherent integration time can be increased based on the position of the acquired data bit edges. Secondly, in terms of optimizing the tracking loop structure, Cornell University has proposed using a Kalman filter to replace the loop filters in the traditional carrier and code loops. Compared to traditional loop filters that use the same weights, Kalman filters can adjust the weights of relevant parameters based on changes in measurement noise and system noise. Stanford University has proposed the concept of a vector tracking loop, which combines code and carrier tracking for different satellite channels in a satellite navigation receiver into a single extended Kalman filter. In the vector tracking loop structure, the single loop filter is replaced by a navigation filter. Once the receiver's position and velocity are determined, the pseudorange and pseudorange rate information for each satellite is calculated in conjunction with the satellite ephemeris. This estimated pseudorange and pseudorange rate information is then used as feedback to assist in controlling the generation of the local carrier and code signals. Because noise is reduced in all satellite tracking channels and strong signal channels can assist weak signal channels, this results in improved weak-signal and interference-resistant tracking capabilities.

[0004] Vector tracking loops are categorized into vector code phase-locked loops (VDLLs), vector carrier phase-locked loops (VPLLs), and vector carrier frequency-locked loops (VFLLs), depending on the phase detector and the tracking target. A VDLL uses the code phase detector value as a navigation filter observation and predicts the code phase based on the navigation filter result to control local code generation. A VPLL tracks and locks to the carrier phase, controlling the local carrier NCO to generate the same phase as the received signal's carrier. A VFLL tracks and locks to the carrier frequency, controlling the local carrier NCO to generate the same frequency as the received signal's carrier.

[0005] Therefore, it is necessary to design a Beidou satellite signal vector tracking method and system based on Kalman filtering and phase compensation to address the above problems. Summary of the Invention

[0006] The purpose of the present invention is to address the problems existing in the prior art and provide a Beidou satellite signal vector tracking method and system based on Kalman filtering and phase compensation. In order to solve the problem that traditional navigation satellite receivers have difficulty in accurately tracking satellite signals in weak signal environments, the present invention adopts an incoherent method combined with Kalman filtering, uses the Kalman filter as a navigation filter, optimizes the signal tracking accuracy by dynamically adjusting the measurement noise and system noise, and uses phase compensation technology to correct the carrier phase prediction error and eliminate the frequency deviation caused by external disturbances such as thermal noise, thereby ensuring that the receiver can still stably track satellite signals in harsh environments. This not only improves the tracking accuracy and reliability in weak signal environments, but also effectively copes with challenges such as the multipath effect and thermal noise interference of satellite signals.

[0007] According to one aspect of this specification, a BeiDou satellite signal vector tracking method based on Kalman filtering and phase compensation is provided, comprising:

[0008] Step 1: Receive a BeiDou satellite signal through an incoherent vector tracking loop to obtain a phase detector output result of the BeiDou satellite signal;

[0009] Step 2: Based on the output of the phase detector, a Kalman filter is used for pre-processing filtering to obtain the pseudorange and pseudorange rate observations;

[0010] Step 3: Input the obtained pseudorange and pseudorange rate observations into the Kalman filter to obtain a state quantity estimation value of the Beidou satellite receiver, perform state estimation and correction on the initial state quantity value, and obtain a state quantity update value;

[0011] Step 4: Based on the loop filter, use the carrier phase value to perform phase compensation to obtain the phase compensation value, which is then input into the NCO. Repeat steps 1 to 4 until an accurate state update value is obtained.

[0012] Step 5: Based on the precise state update value, the NCO (Numerically Controlled Oscillator) in the Beidou satellite receiver is controlled, and the phase compensation value is reset to zero to complete the Beidou satellite signal vector tracking.

[0013] Furthermore, in step 2, a Kalman filter is used for pre-processing filtering to obtain pseudorange and pseudorange rate observations, including:

[0014] According to the relationship between the various parameters in the loop, we can obtain:

[0015]

[0016] in, represents the code phase difference at time k, represents the carrier phase difference at time k, represents the carrier frequency difference at time k-1, represents the carrier frequency difference change rate at time k-1, Represents the working period of the preprocessing filter;

[0017] Select the code phase difference Δτ and carrier phase difference between the local signal and the input signal , carrier frequency difference and the carrier frequency difference change rate As the state quantity of preprocessing filtering, the state equation is expressed as:

[0018]

[0019] in, is the working period of the preprocessing filter, is the system noise;

[0020] The code phase detection value and the carrier phase detection value are selected as the observation quantities of the preprocessing filter, and the observation equation is expressed as:

[0021]

[0022] in, is the observation noise;

[0023] After preprocessing and filtering, the pseudorange is obtained and pseudorange rate observations :

[0024]

[0025] in, represents the frequency difference observation, , represents the frequency error estimate, represents the phase compensation amount obtained by the loop filter, Indicates the C / A code chip length, Indicates the carrier wavelength.

[0026] Furthermore, the state estimation and correction in step 3 include:

[0027] Establish the discrete state equation and measurement equation of the navigation filter,

[0028] Based on discrete state equations and measurement equations, Kalman filtering is used for state estimation and correction.

[0029] Furthermore, the phase compensation using the carrier phase detection value in step 4 includes:

[0030]

[0031] in, represents the predicted value of carrier frequency, is the phase compensation amount obtained by the loop filter, is the damping coefficient, is the characteristic frequency, is the relevant cumulative time.

[0032] Furthermore, the navigation filter in step 3 adopts a position state Kalman filter, and the state quantity update value includes position error, velocity error, clock error and clock drift error.

[0033] According to one aspect of this specification, a BeiDou satellite signal vector tracking system based on Kalman filtering and phase compensation is provided, comprising:

[0034] A phase detector output module is used to receive Beidou satellite signals through an incoherent vector tracking loop and obtain a phase detector output result of the Beidou satellite signal;

[0035] A preprocessing filter module is used to perform preprocessing filtering based on the output of the phase detector using a Kalman filter to obtain pseudorange and pseudorange rate observations;

[0036] A state estimation and correction module is used to input the obtained pseudorange and pseudorange rate observations into a Kalman filter to obtain an estimated state value of the Beidou satellite receiver, perform state estimation and correction on the initial state value, and obtain an updated state value;

[0037] The phase compensation and update module is used to perform phase compensation based on the loop filter and the carrier phase detection value to obtain the phase compensation value, which is then input into the NCO and steps 1 to 4 are repeated until an accurate state update value is obtained;

[0038] The vector tracking module is used to control the NCO in the Beidou satellite receiver based on the precise state update value, and reset the phase compensation value to complete the Beidou satellite signal vector tracking.

[0039] According to one aspect of this specification, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the Beidou satellite signal vector tracking method based on Kalman filtering and phase compensation when executing the computer program.

[0040] According to one aspect of this specification, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the Beidou satellite signal vector tracking method based on Kalman filtering and phase compensation are implemented.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention addresses the problem that traditional navigation satellite receivers have difficulty accurately tracking satellite signals in weak signal environments, especially in highly obstructed environments such as cities, canyons, forests, and large buildings. By combining an incoherent method with a Kalman filter, the present invention achieves highly sensitive tracking of Beidou satellite signals. The Kalman filter is used as a navigation filter to optimize signal tracking accuracy through dynamic adjustment of measurement noise and system noise.

[0043] 2. The present invention uses phase compensation technology to correct carrier phase prediction errors and eliminate frequency deviations caused by external disturbances such as thermal noise, ensuring that the receiver can stably track satellite signals in harsh environments. This not only improves tracking accuracy and reliability in weak signal environments, but also effectively addresses challenges such as the multipath effect and thermal noise interference of satellite signals. It has strong anti-interference capabilities and broad application prospects, and is particularly suitable for application scenarios such as smartphones and in-vehicle navigation that require high receiver sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A schematic diagram of a BeiDou satellite signal vector tracking method based on Kalman filtering and phase compensation according to an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of the structure of a vector tracking receiver according to an embodiment of the present invention;

[0047] Figure 3 Schematic diagram of the internal structure of the incoherent vector loop according to an embodiment of the present invention;

[0048] Figure 4 This is a principle block diagram of carrier loop replica signal generation based on phase compensation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] This embodiment of the present invention uses the Beidou satellite signal B1I as an example. Its carrier wavelength is approximately 19.2 cm, making it difficult to accurately estimate the carrier phase based on navigation information. Furthermore, the receiver is subject to unavoidable influences such as thermal noise, which causes carrier phase jitter, making it difficult to predict using navigation or inertial information. Therefore, the vector carrier phase-locked loop (VPLL) is only a theoretical structure and is difficult to implement physically.

[0051] Specifically, in order to compensate for the tracking of the carrier phase and improve the performance of the vector tracking loop, the embodiment of the present invention proposes a Beidou satellite signal vector tracking method based on Kalman filtering and phase compensation, such as Figure 1 As shown, specifically:

[0052] Step 1: Receive a BeiDou satellite signal through an incoherent vector tracking loop to obtain a phase detector output result of the BeiDou satellite signal;

[0053] Step 2: Based on the output of the phase detector, a Kalman filter is used for pre-processing filtering to obtain the pseudorange and pseudorange rate observations;

[0054] Step 3: Input the obtained pseudorange and pseudorange rate observations into the Kalman filter to obtain a state quantity estimation value of the Beidou satellite receiver, perform state estimation and correction on the initial state quantity value, and obtain a state quantity update value;

[0055] Step 4: Based on the loop filter, use the carrier phase value to perform phase compensation to obtain the phase compensation value, which is then input into the NCO. Repeat steps 1 to 4 until an accurate state update value is obtained.

[0056] Step 5: Based on the accurate state update value, the NCO in the Beidou satellite receiver is controlled, and the phase compensation value is reset to zero to complete the Beidou satellite signal vector tracking.

[0057] Specifically, the vector tracking loop navigation filter accepts the correlator or discriminator output as its input, such as Figure 2 As shown in FIG, according to the different input data forms of the navigation filter, it can be divided into two different loop structures: coherent and incoherent. The present invention selects the incoherent structure as the implementation form of the vector tracking loop. In the incoherent structure, the navigation filter takes the output result of the channel discriminator as input, such as Figure 3shown.

[0058] Specifically, the Kalman filter serves as the navigation filter of the vector tracking loop. It combines information from all satellites to obtain the navigation state estimate of the receiver. The position state is selected as the state quantity of the navigation filter, including the position, velocity, and clock state of the receiver. In the position state filter, the correlation between each channel can be directly reflected in the measurement equation, so the position state filter can more intuitively reflect the essence of the vector tracking loop. For the position state VDFLL algorithm, the actual navigation filter state vector selected is ,in Indicates that the user position error is , , The three axial components and clock error are in units of , Represents the three components of user speed error and clock drift error, in units of The specific steps include:

[0059] 1. Establish the state equation, such as formula (1):

[0060] (1)

[0061] in, is the state transition matrix, is the system noise driving matrix, see formula (2):

[0062] (2)

[0063] in, Indicates that the receiver is , , Dynamic noise in the three axes, as well as clock error and clock drift noise, are assumed to be independent Gaussian white noises that satisfy equation (3):

[0064] (3)

[0065] in, , , The value of is selected reasonably according to the user's dynamic level. , Usually, the empirical value is taken based on the type of crystal oscillator used in the receiver.

[0066] 2. Assume that the Kalman filter update period is And it is small enough, the state equation is discretized and high-order terms are ignored to obtain the discrete state equation, as shown in formula (4):

[0067] (4)

[0068] in, , , They are Moment and The state vector at time t, for The discrete state transfer matrix at time , see formula (5) for details:

[0069] (5)

[0070] for The discrete state noise matrix at the moment satisfies the following formula:

[0071] (6)

[0072] (7)

[0073] (8)

[0074] in, , , , , Indicates that the receiver is , , The dynamic noise in the three axes and the variance of the clock error and clock drift noise, T is the Kalman filter update period, It is a matrix composed of the above quantities.

[0075] Specifically, if the receiver's actual position and predicted position do not coincide, the difference in their positions will create a shadow on the line of sight (LOS) between the receiver and the satellite, causing a pseudorange error. Furthermore, the user receiver's clock error can also cause a pseudorange error. The pseudorange error caused by the two can be expressed as:

[0076] (9)

[0077] in, is the pseudorange error measurement noise, Indicates the receiver's predicted position and the satellite The unit sight vector between Expressed as formula (10):

[0078] (10)

[0079] in, express Time Satellite The position is calculated based on the satellite ephemeris. express The predicted value of the receiver position at time .

[0080] Specifically, the receiver speed and clock drift error will also affect the change of pseudorange rate. Similarly, we can get:

[0081] (11)

[0082] in, is the speed error state quantity, is the pseudorange rate error measurement noise.

[0083] Specifically, combining the signals of n visible satellites, the measurement equation of the navigation filter can be expressed as:

[0084] (12)

[0085] in, for The measurement vector of the moment, is the measurement matrix, which is specifically expanded as shown in formula (13):

[0086] (13)

[0087] in, is the measurement noise vector, , assuming that the measurement noise of the system is independent Gaussian white noise, satisfying:

[0088] (14)

[0089] (15)

[0090] in, represents the measurement noise vector is Gaussian white noise, and its expectation and variance meet the characteristics of Gaussian white noise. and Respectively The measurement noise variance of the pseudorange error and pseudorange rate error of the satellites.

[0091] Specifically, the discrete state equation and measurement equation of the navigation filter are known, and the Kalman filter equation can be used to obtain The estimated state error at time , participate in the subsequent navigation state estimation and tracking channel control quantity update. The filtering process is implemented as follows:

[0092] (16)

[0093] (17)

[0094] (18)

[0095] (19)

[0096] (20)

[0097] in, Represents the prior state estimate at time k+1, that is, the predicted value of the current state based on the state estimate value at the previous moment and the system input; represents the estimated value of the state error at time k; is the discrete state transfer matrix at time k. Represents the prior estimate covariance matrix at time k+1, describing the prior state estimate uncertainty. Represents the posterior estimated covariance matrix at time k, which is the uncertainty of the state estimate after the measurement update at the previous moment. is the process noise covariance matrix, which is used to describe the statistical characteristics of the system process noise and reflects the uncertainty of the system model itself. is the Kalman gain, is the observation matrix, is the measurement noise covariance matrix, which describes the statistical characteristics of the measurement noise and reflects the uncertainty in the measurement process; is the measurement value vector at time k.

[0098] Specifically, due to the non-coherent method used, the carrier phase cannot be tracked. In order to better track the satellite signal, the carrier phase value can be used for phase compensation, such as Figure 4 shown.

[0099] Specifically, the carrier NCO control amount can be expressed as:

[0100] (twenty one)

[0101] in, represents the predicted value of carrier frequency, is the phase compensation amount obtained by the loop filter, is the damping coefficient, is the characteristic frequency, is the relevant cumulative time.

[0102] Specifically, the loop filter is reset at each observation update time. In each observation update cycle, it is equivalent to using a small bandwidth phase-locked loop to perform phase tracking. As the tracking loop stabilizes, the frequency and phase of the local replica carrier will eventually become consistent with the input signal carrier frequency and phase. It should contain two parts, one of which is due to the carrier frequency prediction value The compensation amount generated by the inaccuracy is the frequency amount generated to make the local carrier phase track the input carrier phase. In addition, when in a weak signal environment, In addition to the two components mentioned above, frequencies generated by thermal noise and other factors are also included. As the signal decays, the frequencies generated by thermal noise and other factors will exceed the other two components, causing the loop phase to lose lock. Therefore, clearing the loop filter promptly at each observation update can prevent the accumulation of frequencies generated by thermal noise and other factors and quickly restore the loop phase lock.

[0103] Specifically, based on the phase compensation, the observed quantity needs to be adjusted accordingly. , obtain the frequency error estimate through preprocessing filtering , represents the local copy signal frequency The difference between the frequency of the input signal and the carrier frequency prediction value we need to extract is The deviation between the actual input signal frequency and the The frequency amount of phase compensation is deducted, and the frequency difference observation amount It can be expressed as:

[0104] (twenty two)

[0105] Specifically, in order to match the processed signal, a Kalman filter is used for information preprocessing and filtering. According to the relationship between the various parameters in the loop, we can obtain:

[0106] (twenty three)

[0107] (twenty four)

[0108] in, is the carrier frequency difference at time k-1, represents the code phase difference at time k, represents the carrier phase difference at time k, Indicates the carrier frequency difference change rate at time k-1.

[0109] Specifically, the code phase difference Δτ and the carrier phase difference between the local signal and the input signal are selected. , carrier frequency difference and the carrier frequency difference change rate As the state quantity of preprocessing filtering, the state equation can be expressed as:

[0110] (25)

[0111] in, is the working period of the preprocessing filter, is the system noise.

[0112] Specifically, the code phase discrimination value and the carrier phase discrimination value are selected as the observation quantities for preprocessing filtering, and the observation equation can be expressed as:

[0113] (26)

[0114] in, is the observation noise.

[0115] Specifically, after preprocessing and filtering, the pseudorange and pseudorange rate observations can be obtained:

[0116] (27)

[0117] (28)

[0118] in, represents the frequency difference observation, , represents the frequency error estimate, represents the phase compensation amount obtained by the loop filter, Indicates the C / A code chip length, Indicates the carrier wavelength.

[0119] Pseudorange and pseudorange rate observations are used for navigation filtering, filtering, estimating, and correcting inertial and receiver errors to obtain a navigation solution. In the next loop cycle, the corrected inertial information and satellite ephemeris are used to control the receiver loop NCO, forming a closed loop.

[0120] Specifically, the embodiment of the present invention also provides a specific implementation process of this algorithm, which includes the following steps:

[0121] 1. Use according to the equation of motion The receiver state at time The predicted value of the state at time:

[0122] (29)

[0123] in, represents the position at time k, represents the speed at time k, Indicates the clock difference, Indicates clock drift.

[0124] 2. Based on the predicted value, the LOS unit vector between the satellite and the receiver is obtained:

[0125] (30)

[0126] The predicted values ​​of carrier frequency, carrier phase, code frequency and code phase are calculated and input into the carrier NCO, C / A code NCO and C / A code generator. The code generator and carrier generator generate local replica C / A code and replica carrier according to these predicted values, and perform mixing, correlation integration and other processing with the input intermediate frequency sampling signal respectively. The obtained correlation integration results are input into the phase detector for operation, and the results are multiplied by a proportional coefficient to obtain the pseudorange error and pseudorange rate error. Afterwards, after obtaining sufficient effective channel quantity measurement information (no less than 4), the navigation filter obtains the estimate of the state quantity through the Kalman filter algorithm, and applies this state quantity to the update of the predicted value to obtain the Kalman filter correction based on the Kalman filter. The navigation state estimation at the moment, and the next cycle can be carried out based on these state estimation values.

[0127] (31)

[0128] in Represents the speed of light, and the meanings of other parameters are the same as before.

[0129] Specifically, an embodiment of the present invention also provides a process for setting initial filter values. The initialization parameters required by the vector tracking loop include navigation state estimates such as the receiver's position, velocity, clock error, and clock drift, as well as the satellite's position and velocity information. The navigation state estimates can be calculated using pseudorange, Doppler, and other measurement information obtained by a traditional tracking loop, while the satellite's position and velocity information can be calculated using satellite ephemeris. At the same time, if the scalar tracking channel can provide accurate carrier frequency, code frequency, and code phase information to the corresponding vector tracking channel tracking the same satellite signal, the vector tracking loop can achieve lock faster with the aid of this information.

[0130] Specifically, the embodiment of the present invention also provides a process for adjusting the filter parameters. and the measurement noise variance matrix There are two important parameters that determine the performance of the Kalman filter. The array is related to the dynamic characteristics of the receiver. The array is correlated with the output noise of the discriminator, Zhenhe The matrix is ​​directly related to the system model, and the commonly established model is often difficult to accurately describe the actual situation of the actual system. At the same time, the system will change the model parameters due to the instability of internal components and external uncertain interference. An inaccurate system model may deteriorate the performance of the filter and even cause filter divergence. For satellite receivers, since satellite signals are easily affected by weather and the working scenarios of the receivers are complex and changeable, this will have a certain impact on the noise model of the system. An adaptive filtering algorithm can be used to estimate the system noise and observation noise of the tracking loop in real time to obtain a more realistic and accurate system model. The basic idea is to use the measurement residual to continuously estimate the system noise variance matrix. and the measurement noise variance matrix Real-time estimation and correction are performed to minimize the system model error as much as possible, thereby making the system model closer to the actual situation, improving the filter performance, and obtaining more accurate state estimation values.

[0131] The implementation basis of each embodiment of the present invention is achieved through programmed processing by a device with processor functionality. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this reality, and in addition to the above-mentioned embodiments, an embodiment of the present invention provides a Beidou satellite signal vector tracking system based on Kalman filtering and phase compensation. This system is used to implement the Beidou satellite signal vector tracking method based on Kalman filtering and phase compensation described in the above-mentioned method embodiments.

[0132] The system comprises: a phase detector output module, which is used to receive Beidou satellite signals through an incoherent vector tracking loop and obtain a phase detector output result of the Beidou satellite signal; a preprocessing filtering module, which is used to perform preprocessing filtering using a Kalman filter based on the phase detector output result to obtain pseudorange and pseudorange rate observation quantities; a state estimation and correction module, which is used to input the obtained pseudorange and pseudorange rate observation quantities into the Kalman filter to obtain a state quantity estimation value of the Beidou satellite receiver, perform state estimation and correction on the initial state quantity value, and obtain a state quantity update value; a phase compensation and update module, which is used to perform phase compensation using a carrier phase detector value based on the loop filter to obtain a phase compensation quantity, and then input the phase compensation quantity into an NCO, and repeatedly perform steps 1 to 4 until an accurate state quantity update value is obtained; and a vector tracking module, which is used to control the NCO in the Beidou satellite receiver based on the accurate state quantity update value, and simultaneously reset the phase compensation quantity to complete Beidou satellite signal vector tracking.

[0133] The Beidou satellite signal vector tracking system based on Kalman filtering and phase compensation provided by an embodiment of the present invention addresses the problem that traditional navigation satellite receivers have difficulty accurately tracking satellite signals in weak signal environments. The system adopts several modules, optimizes the baseband algorithm, and combines an incoherent method with the Kalman filter to achieve high-sensitivity tracking of Beidou satellite signals. The Kalman filter is used as a navigation filter to optimize signal tracking accuracy through dynamic adjustment of measurement noise and system noise. Phase compensation technology is used to correct carrier phase prediction errors and eliminate frequency deviations caused by external disturbances such as thermal noise, ensuring that the receiver can still stably track satellite signals in harsh environments.

[0134] Based on the same inventive concept as the above-mentioned embodiment, an embodiment of the present invention also provides an electronic device, including a memory and a processor, the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a Beidou satellite signal vector tracking method based on Kalman filtering and phase compensation as proposed in the above-mentioned embodiment.

[0135] An embodiment of the present invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, this program overcomes the difficulty faced by traditional navigation satellite receivers in accurately tracking satellite signals in weak signal environments. It achieves highly sensitive tracking of Beidou satellite signals, eliminates frequency deviation caused by external disturbances such as thermal noise, and ensures that the receiver can stably track satellite signals even in harsh environments. This not only improves tracking accuracy and reliability in weak signal environments, but also effectively addresses challenges such as multipath effects and thermal noise interference with satellite signals. The storage medium can be any non-volatile storage device, such as a hard disk, solid-state drive, flash drive, or optical disk, and is used to store computer program code and necessary data files. The stored computer program includes a phase detector output module, a preprocessing filter module, a state estimation and correction module, a phase compensation and update module, and a vector tracking module.

[0136] Finally, it should be noted that the above specific embodiments are merely representative examples of the present invention. Obviously, the present invention is not limited to the above specific embodiments and is susceptible to numerous variations. Any simple modifications, equivalent variations, and modifications to the above specific embodiments based on the technical essence of the present invention shall be deemed to fall within the scope of protection of the present invention.

Claims

1. A BeiDou satellite signal vector tracking method based on Kalman filtering and phase compensation, characterized in that: include: Step 1: Receive a BeiDou satellite signal through an incoherent vector tracking loop to obtain a phase detector output result of the BeiDou satellite signal; Step 2: Based on the output of the phase detector, a Kalman filter is used for pre-processing filtering to obtain the pseudorange and pseudorange rate observations; Step 3: Input the obtained pseudorange and pseudorange rate observations into the Kalman filter to obtain the estimated value of the state quantity of the Beidou satellite receiver, perform state estimation and correction on the initial value of the state quantity, and obtain the updated value of the state quantity; Step 4: Based on the loop filter, use the carrier phase value to perform phase compensation to obtain the phase compensation value, which is then input into the NCO. Repeat steps 1 to 4 until an accurate state update value is obtained. Step 5: Based on the accurate state update value, the NCO in the Beidou satellite receiver is controlled, and the phase compensation value is reset to zero to complete the Beidou satellite signal vector tracking.

2. A BeiDou satellite signal vector tracking method based on Kalman filtering and phase compensation according to claim 1, characterized in that, In step 2, a Kalman filter is used for pre-processing filtering to obtain pseudorange and pseudorange rate observations, including: According to the relationship between the various parameters in the loop, we can obtain: ， in, represents the code phase difference at time k, represents the carrier phase difference at time k, represents the carrier frequency difference at time k-1, represents the carrier frequency difference change rate at time k-1, Represents the working period of the pre-processing filter; Select the code phase difference Δτ and carrier phase difference between the local signal and the input signal , carrier frequency difference and the carrier frequency difference change rate As the state quantity of preprocessing filtering, the state equation is expressed as: ， in, is the working period of the preprocessing filter, is the system noise; The code phase discrimination value and the carrier phase discrimination value are selected as the observation quantities of the preprocessing filter, and the observation equation is expressed as: ， in, is the observation noise; After preprocessing and filtering, the pseudorange is obtained and pseudorange rate observations : ， in, represents the frequency difference observation, , represents the frequency error estimate, represents the phase compensation amount obtained by the loop filter, is the C / A code chip length, is the carrier wavelength.

3. A BeiDou satellite signal vector tracking method based on Kalman filtering and phase compensation according to claim 1, characterized in that: The state estimation and correction in step 3 include: Establish the discrete state equation and measurement equation of the navigation filter, Based on discrete state equations and measurement equations, Kalman filtering is used for state estimation and correction.

4. A BeiDou satellite signal vector tracking method based on Kalman filtering and phase compensation according to claim 2, characterized in that, The phase compensation using the carrier phase detection value in step 4 includes: ， in represents the predicted value of carrier frequency, is the phase compensation amount obtained by the loop filter, is the damping coefficient, is the characteristic frequency, is the relevant cumulative time.

5. A BeiDou satellite signal vector tracking method based on Kalman filtering and phase compensation according to claim 1, characterized in that: The updated state value in step 3 includes position error, velocity error, clock error and clock drift error.

6. A BeiDou satellite signal vector tracking system based on Kalman filtering and phase compensation, characterized in that: include: A phase detector output module is used to receive Beidou satellite signals through an incoherent vector tracking loop and obtain a phase detector output result of the Beidou satellite signal; A preprocessing filter module is used to perform preprocessing filtering based on the output of the phase detector using a Kalman filter to obtain pseudorange and pseudorange rate observations; The state estimation and correction module is used to input the obtained pseudorange and pseudorange rate observations into the Kalman filter to obtain the estimated value of the state quantity of the Beidou satellite receiver, perform state estimation and correction on the initial value of the state quantity, and obtain the updated value of the state quantity; The phase compensation and update module is used to perform phase compensation based on the loop filter and the carrier phase detection value to obtain the phase compensation value, which is then input into the NCO and steps 1 to 4 are repeated until an accurate state update value is obtained; The vector tracking module is used to control the NCO in the Beidou satellite receiver based on the precise state update value, and reset the phase compensation value to complete the Beidou satellite signal vector tracking.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the Beidou satellite signal vector tracking method based on Kalman filtering and phase compensation are implemented as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the Beidou satellite signal vector tracking method based on Kalman filtering and phase compensation according to any one of claims 1 to 5 are implemented.

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