Residual doppler predicted low earth orbit satellite signal tracking method and storage medium

Abstract

The embodiment of the application provides a kind of residual Doppler prediction low-orbit satellite signal tracking method and storage medium.The method comprises: obtaining the measured frequency and measured Doppler rate in the current tracking process of receiver, calculate the geometric Doppler frequency and geometric Doppler rate corresponding to current tracking period;According to the measured result and the geometric Doppler result, calculate frequency residual and rate residual, construct residual Doppler feature in combination with receiver state parameter;The residual Doppler feature of continuous multiple tracking periods is input into residual rate prediction model, and the rate residual prediction sequence of future multiple tracking periods is obtained;According to current frequency residual and rate residual prediction sequence, generate the residual frequency prediction result corresponding to target future tracking period, generate the frequency control amount of number control oscillator in combination with the geometric Doppler frequency corresponding to target future tracking period, to adjust the local carrier frequency of receiver to improve the accuracy and stability of low-orbit satellite signal carrier tracking.

Description

Technical Field

[0001] This application relates to the field of satellite communication technology, specifically to a method and storage medium for tracking low-Earth orbit satellite signals based on residual Doppler prediction. Background Technology

[0002] Low-Earth orbit (LEO) satellites move at high speeds relative to the receiver, resulting in signals with significant Doppler frequency offset and high Doppler rate of change. In scenarios involving satellite overhead, high-speed terminal movement, low carrier-to-noise ratio, delayed ephemeris updates, or receiver local oscillator drift, carrier frequency and phase changes become more pronounced. The receiver needs to complete frequency compensation and phase tracking within a short time; otherwise, it is prone to increased carrier tracking errors, phase jitter, or even loss of lock.

[0003] Existing carrier tracking methods for low-Earth orbit (LEO) satellite signals typically combine geometrical Doppler (GDO) pre-compensation, synchronization signal frequency offset estimation, and Kalman filtering. This method first calculates the GDO frequency based on satellite ephemeris, time-to-earth error (TLE), orbital parameters, beam center position, or receiver position, and then uses this GDO frequency to pre-compensate the received signal, reducing the frequency search range during carrier tracking. Subsequently, the receiver estimates the current frequency offset using the synchronization signal, pilot signal, or training sequence, and uses Kalman filtering to smooth the carrier phase, frequency, and rate of change, thereby improving the stability of the carrier tracking process. However, this method relies heavily on GDO priors and current or historical tracking residuals for compensation and filtering. Under conditions of ephemeris errors, local oscillator drift, or high-dynamic nonlinear variations, tracking lag can occur. Simply increasing the feedback loop bandwidth introduces more noise, making it difficult to balance dynamic response and tracking stability. Summary of the Invention

[0004] The purpose of this application is to provide a method and storage medium for tracking low-Earth orbit satellite signals based on residual Doppler prediction.

[0005] To achieve the above objectives, the first aspect of this application provides a method for tracking low-Earth orbit satellite signals based on residual Doppler prediction, the method comprising: Obtain the measured frequency and measured Doppler rate of change during the current tracking process of the receiver; Acquire satellite orbital operation information and receiver motion status information, and calculate the geometric Doppler frequency and geometric Doppler rate of change corresponding to the current tracking period using the orbital geometric model; The frequency residual is calculated based on the measured frequency and the geometric Doppler frequency, and the rate of change residual is calculated based on the measured Doppler rate of change and the geometric Doppler rate of change. Residual Doppler features are constructed based on frequency residuals, rate of change residuals, and receiver state parameters; The residual Doppler features of multiple consecutive tracking cycles are input into the residual rate of change prediction model to obtain the rate of change residual prediction sequence for multiple future tracking cycles; Based on the frequency residual corresponding to the current tracking period and the prediction sequence of the rate of change residuals for multiple future tracking periods, the residual frequency prediction result corresponding to the target future tracking period is generated, wherein the target future tracking period is at least one of the multiple future tracking periods. Based on satellite orbital operation information and receiver motion status information, the geometric Doppler frequency corresponding to the future tracking period of the target is calculated using an orbital geometric model; Based on the predicted results of the geometric Doppler frequency and the residual frequency corresponding to the future tracking period of the target, the frequency control quantity of the numerically controlled oscillator is generated. The receiver's local carrier frequency is adjusted according to the frequency control value of the numerically controlled oscillator in order to perform carrier tracking of low-Earth orbit satellite signals.

[0006] In this embodiment of the application, residual Doppler features from multiple consecutive tracking cycles are input into a residual rate of change prediction model to obtain a prediction sequence of rate of change residuals for future tracking cycles, including: The residual Doppler features of multiple consecutive tracking cycles are input into the residual rate of change prediction model to obtain the rate of change residual prediction sequence for multiple future tracking cycles and the prediction variance corresponding to the rate of change residual prediction sequence. Generate prediction confidence weighting coefficients based on prediction variance and receiver state parameters; Based on the predicted geometric Doppler frequency and residual frequency corresponding to the future tracking period of the target, the frequency control quantities of the numerically controlled oscillator are generated, including: Based on the prediction confidence weight coefficient, the weight of the predicted residual frequency corresponding to the future tracking period of the target in the frequency control quantity of the numerically controlled oscillator is determined. Based on the geometric Doppler frequency corresponding to the future tracking period of the target, the predicted residual frequency corresponding to the future tracking period of the target, and the prediction confidence weight coefficient, the frequency control quantity of the numerically controlled oscillator is generated.

[0007] In this embodiment of the application, before obtaining the measured frequency and measured Doppler change rate during the current tracking process of the receiver, the method further includes: The receiver performs low-noise amplification, down-conversion, analog-to-digital conversion, and digital down-conversion on the low-Earth orbit satellite radio frequency signal to obtain a complex intermediate frequency sampling signal. Coarse acquisition is performed on the complex intermediate frequency sampled signal to obtain coarse acquisition results, which include initial frequency offset, initial code phase, initial carrier-to-noise ratio and initial control input of the numerically controlled oscillator; Based on the coarse acquisition results, the complex intermediate frequency sampled signal is processed by current carrier tracking, and the measured frequency and measured Doppler rate of change are obtained in the current carrier tracking process.

[0008] In this embodiment of the application, obtaining the measured frequency and measured Doppler rate of change in the current carrier tracking process includes: The complex intermediate frequency sampled signal corresponding to the current tracking period in the current carrier tracking process is coherently integrated to obtain the coherent integration result corresponding to the current tracking period. The coherent integration result includes in-phase integration components and quadrature integration components. The phase difference between adjacent tracking cycles is determined based on the coherent integral result corresponding to the current tracking cycle and the coherent integral result corresponding to the previous tracking cycle. The measured frequency corresponding to the current tracking cycle is determined based on the phase difference, the time interval between adjacent tracking cycles, and the frequency control quantity of the numerically controlled oscillator corresponding to the previous tracking cycle. The measured Doppler change rate corresponding to the current tracking period is obtained by median difference estimation based on the measured frequencies corresponding to multiple consecutive tracking periods.

[0009] In this embodiment of the application, residual Doppler features are constructed based on frequency residuals, rate of change residuals, and receiver state parameters, including: Normalize the frequency residual corresponding to the current tracking period; Normalize the rate of change residual corresponding to the current tracking period; Normalize the difference between the rate of change residual corresponding to the current tracking period and the rate of change residual corresponding to the previous tracking period. The residual Doppler features are constructed based on the normalized frequency residual, the normalized rate of change residual, the normalized difference, and the receiver state parameters. The receiver status parameters include at least one of the following: satellite elevation angle, carrier-to-noise ratio, phase difference, and normalized automatic gain control.

[0010] In this embodiment of the application, the receiver state parameters include carrier-to-noise ratio and phase difference. The phase difference is the difference between the carrier phase determined by the coherent integration result corresponding to the current tracking period and the prediction result of the carrier phase corresponding to the current tracking period in the current carrier tracking process. The prediction confidence weighting coefficients are generated based on the prediction variance and receiver state parameters, including: The original confidence level is calculated based on the phase difference and the prediction variance. The original confidence level is corrected based on the carrier-to-noise ratio and phase difference to obtain the prediction confidence weight coefficient; Specifically, when the phase difference is less than or equal to a preset phase difference threshold and the carrier-to-noise ratio is greater than or equal to a preset carrier-to-noise ratio threshold, the original confidence level is used as the prediction confidence level weight coefficient; when the phase difference is greater than a preset phase difference threshold or the carrier-to-noise ratio is less than a preset carrier-to-noise ratio threshold, the prediction confidence level weight coefficient is set to zero.

[0011] In this embodiment of the application, the weight of the predicted residual frequency corresponding to the future tracking period of the target in the frequency control quantity of the numerically controlled oscillator is determined according to the prediction confidence weight coefficient. Then, based on the geometric Doppler frequency corresponding to the future tracking period of the target, the predicted residual frequency corresponding to the future tracking period of the target, and the prediction confidence weight coefficient, the frequency control quantity of the numerically controlled oscillator is generated, including: The frequency residual is subjected to residual Kalman filtering to obtain the frequency residual estimate; Based on the prediction confidence weighting coefficient, determine the weights of the residual frequency prediction result and frequency residual estimate corresponding to the future tracking period of the target in the frequency control quantity of the numerically controlled oscillator. Based on the geometric Doppler frequency corresponding to the future tracking period of the target, the predicted residual frequency corresponding to the future tracking period of the target, the frequency residual estimate, and the prediction confidence weighting coefficient, the frequency control quantity of the numerically controlled oscillator is generated.

[0012] In this embodiment of the application, the method further includes: The bandwidth of the carrier tracking feedback loop is determined based on the prediction confidence weighting coefficient and the prediction variance. When the prediction confidence weighting coefficient increases, the bandwidth of the carrier tracking feedback loop is reduced, and the Doppler dynamics of the low-orbit satellite signal is compensated by the frequency control of the numerically controlled oscillator. When the prediction confidence weight coefficient decreases, the bandwidth of the carrier tracking feedback loop is increased, and the weight of the frequency residual estimate in the frequency control quantity of the numerically controlled oscillator is increased.

[0013] In this embodiment of the application, the method further includes: In the event of errors in satellite orbital information, a second-order polynomial extrapolation is performed based on the geometric Doppler frequencies corresponding to the most recent tracking cycles to obtain the extrapolated geometric Doppler frequencies, and the upper limit of the prediction confidence weight coefficient is limited. If the loss time of the pilot signal or synchronization signal does not exceed the preset time period, the geometric Doppler frequency is maintained, and prediction is made through the residual rate of change prediction model. If the pilot signal or synchronization signal is lost for a period of time exceeding a preset time period, the receiver is controlled to enter a local reacquisition state. If the phase difference exceeds the preset phase difference threshold for multiple consecutive tracking cycles, the prediction confidence weight coefficient is set to zero, the bandwidth of the carrier tracking feedback loop is increased, and a local frequency search is initiated.

[0014] A second aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform a low-Earth orbit satellite signal tracking method based on residual Doppler prediction.

[0015] Compared with existing technologies, this application proposes a low-Earth orbit (LEO) satellite signal tracking method based on residual Doppler prediction. It determines the frequency residual and rate of change residual based on the measured frequency and rate of change during the current tracking process, as well as the geometrical Doppler frequency and rate of change calculated through the orbital geometry model. Residual Doppler features are then constructed based on the frequency residual, rate of change residual, and receiver state parameters. Finally, a residual rate of change prediction model is used to predict the rate of change residual prediction sequence for multiple future tracking cycles, generating the residual frequency prediction result corresponding to the target's future tracking cycle. This allows the frequency control quantity of the numerically controlled oscillator to simultaneously combine the geometrical Doppler frequency and residual frequency prediction result corresponding to the target's future tracking cycle. This solves the problem that, under conditions of high Doppler frequency offset and high Doppler rate of change, relying solely on geometrical Doppler compensation or carrier tracking feedback adjustment is insufficient to balance dynamic response and tracking stability for LEO satellite signals.

[0016] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The schematic diagram illustrates a flowchart of a low-Earth orbit satellite signal tracking method based on residual Doppler prediction according to an embodiment of this application; Figure 2 A schematic diagram illustrates a system block diagram of a low-Earth orbit satellite signal tracking method based on residual Doppler prediction according to an embodiment of this application; Figure 3 The schematic diagram illustrates the structure of an LSTM residual Doppler rate of change predictor for a low-Earth orbit satellite signal tracking method based on residual Doppler prediction according to an embodiment of this application. Figure 4 The diagram illustrates the confidence weight control and NCO control word synthesis block diagram of the low-Earth orbit satellite signal tracking method based on residual Doppler prediction according to an embodiment of this application. Figure 5The diagram illustrates the adaptive bandwidth control and anomaly protection state of the low-Earth orbit satellite signal tracking method based on residual Doppler prediction according to an embodiment of this application. Figure 6 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Figure 1 A schematic flowchart illustrating a low-Earth orbit satellite signal tracking method based on residual Doppler prediction according to an embodiment of this application is shown. Figure 1 As shown in one embodiment of this application, a method for tracking low-Earth orbit satellite signals based on residual Doppler prediction is provided, comprising the following steps: Step 102: Obtain the measured frequency and measured Doppler change rate during the current tracking process of the receiver; Step 104: Obtain satellite orbital operation information and receiver motion status information, and calculate the geometric Doppler frequency and geometric Doppler rate of change corresponding to the current tracking period using the orbital geometric model; Step 106: Calculate the frequency residual based on the measured frequency and geometric Doppler frequency, and calculate the rate of change residual based on the measured Doppler rate of change and geometric Doppler rate of change; Step 108: Construct residual Doppler features based on frequency residual, rate of change residual, and receiver state parameters; Step 110: Input the residual Doppler features of multiple consecutive tracking cycles into the residual rate of change prediction model to obtain the rate of change residual prediction sequence for multiple future tracking cycles; Step 112: Based on the frequency residual corresponding to the current tracking period and the prediction sequence of the rate of change residuals for multiple future tracking periods, generate the residual frequency prediction result corresponding to the target future tracking period, wherein the target future tracking period is at least one of the multiple future tracking periods. Step 114: Based on the satellite orbital operation information and the receiver motion state information, calculate the geometric Doppler frequency corresponding to the future tracking period of the target using the orbital geometric model; Step 116: Generate the frequency control quantity of the numerically controlled oscillator based on the predicted results of the geometric Doppler frequency and the residual frequency corresponding to the future tracking period of the target. Step 118: Adjust the local carrier frequency of the receiver according to the frequency control value of the numerically controlled oscillator in order to perform carrier tracking of the low-orbit satellite signal.

[0020] Specifically, during the current tracking process, the receiver acquires the measured frequency and measured Doppler rate of change. The measured frequency characterizes the carrier frequency state currently tracked by the receiver, and the measured Doppler rate of change characterizes the trend of carrier frequency change over time. Simultaneously, it acquires satellite orbital information and receiver motion state information, and calculates the geometrical Doppler frequency and geometrical Doppler rate of change corresponding to the current tracking period using the orbital geometry model. The satellite orbital information can include the position, velocity, orbital parameters, or ephemeris information of the low-Earth orbit satellite, while the receiver motion state information can include the position, velocity, or direction of motion of the receiver. The geometrical Doppler frequency and geometrical Doppler rate of change characterize the theoretical Doppler state generated by the relative motion between the satellite and the receiver. Then, the measured frequency is compared with the geometrical Doppler frequency to obtain the frequency residual, and the measured Doppler rate of change is compared with the geometrical Doppler rate of change to obtain the rate of change residual. This allows the residual deviation of the receiver relative to the orbital geometry model in the actual tracking state to be extracted, avoiding the problem that carrier tracking based solely on theoretical geometrical Doppler may fail to reflect actual tracking errors.

[0021] After obtaining the frequency residual and the rate of change residual, residual Doppler features are constructed based on the frequency residual, the rate of change residual, and receiver state parameters. These receiver state parameters include, but are not limited to, at least one of the following: carrier-to-noise ratio, tracking loop state, locked state, loop bandwidth, received signal strength, or receiver motion state. The frequency residual characterizes the current residual frequency deviation, the rate of change residual characterizes the trend of the residual frequency deviation, and the receiver state parameters characterize the reliability of the current measurement result and the receiver's operating state. By inputting the residual Doppler features from multiple consecutive tracking cycles into the residual rate of change prediction model, a prediction sequence of the rate of change residual for multiple future tracking cycles can be obtained. Since the residual Doppler features from multiple consecutive tracking cycles can reflect the process of residual Doppler changing over time, the residual rate of change prediction model can predict the future residual rate of change based on historical trends. Subsequently, based on the frequency residual corresponding to the current tracking cycle and the prediction sequence of the rate of change residual for multiple future tracking cycles, a residual frequency prediction result corresponding to the target future tracking cycle is generated. The target future tracking cycle is at least one of the multiple future tracking cycles. This method allows us to derive the residual frequency in subsequent tracking periods by starting with the current frequency residual and combining it with the prediction sequence of the future rate of change residual, thereby obtaining the residual frequency prediction result for advance compensation.

[0022] After obtaining the predicted residual frequency for the target's future tracking period, the geometric Doppler frequency for the target's future tracking period is calculated using an orbital geometry model based on satellite orbital information and receiver motion status information. This geometric Doppler frequency characterizes the theoretical Doppler frequency generated by the relative motion between the satellite and the receiver during the target's future tracking period. Subsequently, based on the predicted geometric Doppler frequency and residual frequency for the target's future tracking period, a frequency control quantity for the numerically controlled oscillator is generated. This frequency control quantity is then used to adjust the receiver's local carrier frequency for carrier tracking of the low-Earth orbit satellite signal. Therefore, the frequency control quantity of the numerically controlled oscillator includes not only the main Doppler compensation quantity calculated from the orbital geometry model but also the residual frequency compensation quantity derived from the actual tracking state. This allows the local carrier frequency to adapt in advance to the rapid Doppler changes of the low-Earth orbit satellite signal, improving the accuracy, continuity, and stability of carrier tracking.

[0023] In one embodiment, inputting the residual Doppler features of multiple consecutive tracking cycles into a residual rate of change prediction model to obtain a rate of change residual prediction sequence for multiple future tracking cycles includes: inputting the residual Doppler features of multiple consecutive tracking cycles into a residual rate of change prediction model to obtain a rate of change residual prediction sequence for multiple future tracking cycles and a prediction variance corresponding to the rate of change residual prediction sequence; generating prediction confidence weighting coefficients based on the prediction variance and receiver state parameters; and generating a frequency control quantity for a numerically controlled oscillator based on the geometric Doppler frequency corresponding to the target's future tracking cycle and the predicted residual frequency corresponding to the target's future tracking cycle, including: determining the weight of the predicted residual frequency corresponding to the target's future tracking cycle in the frequency control quantity of the numerically controlled oscillator based on the prediction confidence weighting coefficients, and generating the frequency control quantity of the numerically controlled oscillator based on the geometric Doppler frequency corresponding to the target's future tracking cycle, the predicted residual frequency corresponding to the target's future tracking cycle, and the prediction confidence weighting coefficients.

[0024] Specifically, prediction variance can be used to characterize the uncertainty of the residual rate of change prediction model for the predicted results of the residual rate of change over multiple future tracking cycles. A larger prediction variance indicates lower stability of the corresponding residual rate of change prediction sequence and relatively lower reliability of the residual frequency prediction results; conversely, a smaller prediction variance indicates higher stability of the corresponding residual rate of change prediction sequence and relatively higher reliability of the residual frequency prediction results. Receiver state parameters can reflect the current received signal quality and tracking status. For example, when the carrier-to-noise ratio is low, the tracking error is large, or the signal lock-in state is unstable, the weight of the residual frequency prediction results in the CNC oscillator frequency control quantities can be reduced; when the carrier-to-noise ratio is high, the tracking error is small, and the signal lock-in state is stable, the weight of the residual frequency prediction results in the CNC oscillator frequency control quantities can be increased. By introducing a prediction reliability weight coefficient, over-reliance on the residual frequency prediction results can be avoided when the model prediction is unstable or the receiving status is poor, while fully utilizing the residual frequency prediction results when the prediction is relatively reliable.

[0025] In one embodiment, before obtaining the measured frequency and measured Doppler rate of change during the current tracking process of the receiver, the method further includes: the receiver performing low-noise amplification, down-conversion, analog-to-digital conversion, and digital down-conversion on the low-Earth orbit satellite radio frequency signal to obtain a complex intermediate frequency (IF) sampled signal; performing coarse acquisition on the complex IF sampled signal to obtain a coarse acquisition result, which includes the initial frequency offset, initial code phase, initial carrier-to-noise ratio, and initial control quantity of the numerically controlled oscillator; and performing current carrier tracking processing on the complex IF sampled signal based on the coarse acquisition result, and obtaining the measured frequency and measured Doppler rate of change during the current carrier tracking processing. Specifically, after receiving the low-Earth orbit satellite radio frequency signal, the receiver can first increase the signal amplitude through low-noise amplification and reduce the impact of front-end noise on subsequent processing; then convert the radio frequency signal to an IF signal through down-conversion; then convert the analog IF signal to a digital signal through analog-to-digital conversion; and finally obtain a complex IF sampled signal suitable for digital baseband processing through digital down-conversion. When coarsely acquiring complex intermediate frequency (IF) samples, the approximate frequency offset and code phase of the low-Earth orbit (LEO) satellite signal can be determined within the frequency and code phase search range, and the initial carrier-to-noise ratio (CNR) and initial control parameters of the numerically controlled oscillator (CNC) can be obtained. The receiver can then proceed to the current carrier tracking process based on the coarse acquisition results, starting carrier tracking from an initial position that is closer to the actual signal state, and extracting the measured frequency and measured Doppler rate of change during this process.

[0026] In one embodiment, obtaining the measured frequency and measured Doppler rate of change in the current carrier tracking process includes: coherently integrating the complex intermediate frequency sampled signal corresponding to the current tracking period in the current carrier tracking process to obtain the coherent integration result corresponding to the current tracking period, wherein the coherent integration result includes an in-phase integral component and a quadrature integral component; determining the phase difference between adjacent tracking periods based on the coherent integration result corresponding to the current tracking period and the coherent integration result corresponding to the previous tracking period; determining the measured frequency corresponding to the current tracking period based on the phase difference, the time interval between adjacent tracking periods, and the frequency control quantity of the numerically controlled oscillator corresponding to the previous tracking period; and performing median difference estimation based on the measured frequencies corresponding to multiple consecutive tracking periods to obtain the measured Doppler rate of change corresponding to the current tracking period. Specifically, in the current carrier tracking process, the receiver can coherently integrate the complex intermediate frequency sampled signal within the current tracking period to accumulate signal energy and suppress random noise. The in-phase integral component and the quadrature integral component in the coherent integration result can jointly characterize the carrier phase state within the current tracking period. By comparing the coherent integral result of the current tracking period with that of the previous tracking period, the phase change, or phase difference, between adjacent tracking periods can be obtained. This phase difference reflects the residual frequency deviation between the received signal carrier frequency and the local carrier frequency within adjacent tracking periods. Then, by combining the time interval between adjacent tracking periods and the frequency control quantity of the numerically controlled oscillator corresponding to the previous tracking period, the measured frequency corresponding to the current tracking period can be determined, ensuring that the measured frequency simultaneously reflects both the local carrier control state and the actual phase change. Furthermore, median difference estimation based on the measured frequencies corresponding to multiple consecutive tracking periods can reduce the impact of individual abnormal frequency estimates on the Doppler rate calculation results, thereby obtaining a more stable measured Doppler rate. In this way, the measured frequency and measured Doppler rate can be extracted from the carrier tracking process itself, providing reliable measured input for residual Doppler prediction.

[0027] In one embodiment, constructing residual Doppler features based on frequency residuals, rate of change residuals, and receiver state parameters includes: normalizing the frequency residual corresponding to the current tracking period; normalizing the rate of change residual corresponding to the current tracking period; normalizing the difference between the rate of change residual corresponding to the current tracking period and the rate of change residual corresponding to the previous tracking period; and constructing residual Doppler features based on the normalized frequency residual, the normalized rate of change residual, the normalized difference, and the receiver state parameters. The receiver state parameters include at least one of satellite elevation angle, carrier-to-noise ratio, phase difference, and normalized automatic gain control. Specifically, since the frequency residual, rate of change residual, and the difference between rate of change residuals differ in numerical magnitude and physical meaning, directly inputting them into the residual rate of change prediction model may cause a certain type of feature to have an excessive impact on the model's prediction results. Therefore, the frequency residual, rate of change residual, and the difference between the rate of change residual of the current tracking period and the rate of change residual of the previous tracking period are normalized to ensure that different types of input features are within a relatively consistent numerical range. The frequency residual characterizes the difference between the current measured frequency and the geometric Doppler frequency; the rate of change residual characterizes the difference between the current measured Doppler trend and the geometric Doppler trend; and the difference between the rate of change residuals characterizes whether the residual Doppler trend itself has abruptly changed. The satellite elevation angle reflects the geometric observation conditions between the satellite and the receiver; the carrier-to-noise ratio reflects the current signal quality; the phase difference reflects the current carrier tracking error; and the normalized automatic gain control value reflects the strength of the received signal and the receiver gain adjustment status. By constructing residual Doppler features together with the receiver state parameters, the residual rate of change prediction model can simultaneously obtain residual Doppler change information and receiver current operating status information, thereby improving the accuracy of subsequent rate of change residual prediction.

[0028] In one embodiment, the receiver state parameters include carrier-to-noise ratio (CNR) and phase difference. The phase difference is the difference between the carrier phase determined by the coherent integration result corresponding to the current tracking period and the predicted carrier phase corresponding to the current tracking period in the current carrier tracking process. Generating a prediction confidence weight coefficient based on the prediction variance and receiver state parameters includes: calculating the original confidence based on the phase difference and prediction variance; correcting the original confidence based on the CNR and phase difference to obtain the prediction confidence weight coefficient; wherein, if the phase difference is less than or equal to a preset phase difference threshold and the CNR is greater than or equal to a preset CNR threshold, the original confidence is used as the prediction confidence weight coefficient; if the phase difference is greater than the preset phase difference threshold or the CNR is less than the preset CNR threshold, the prediction confidence weight coefficient is set to zero. Specifically, the phase difference can be used to characterize the deviation between the actually measured carrier phase and the predicted carrier phase during the current carrier tracking process. A smaller phase difference indicates a more stable current carrier tracking state, and the prediction results obtained by the residual rate of change prediction model based on the current tracking state are generally more reliable. A larger phase difference indicates a larger current carrier tracking error, and the prediction results may be affected by abnormal tracking states. Prediction variance can be used to characterize the uncertainty of the residual rate of change prediction model's output; a larger prediction variance indicates poorer stability of the model's prediction results. Therefore, the initial confidence level can be calculated based on the phase difference and prediction variance, allowing the initial confidence level to simultaneously reflect both carrier phase tracking error and model prediction uncertainty. Subsequently, the initial confidence level is corrected based on the carrier-to-noise ratio (CNR) and phase difference. When the phase difference is less than or equal to a preset phase difference threshold and the CNR is greater than or equal to a preset CNR threshold, it indicates a smaller current phase tracking error and better signal quality. The initial confidence level can then be used as a weighting coefficient for the prediction confidence level, allowing the residual frequency prediction results to participate in the frequency control generation of the numerically controlled oscillator. When the phase difference is greater than the preset phase difference threshold or the carrier-to-noise ratio is less than the preset carrier-to-noise ratio threshold, it indicates that the current tracking state or signal quality does not meet the conditions for using the prediction results. The prediction confidence weight coefficient can be set to zero to reduce the impact of abnormal prediction results on the frequency control of the numerically controlled oscillator.

[0029] In one embodiment, the weight of the predicted residual frequency for the future tracking period in the frequency control quantity of the numerically controlled oscillator is determined based on the prediction confidence weight coefficient. The frequency control quantity of the numerically controlled oscillator is then generated based on the geometric Doppler frequency for the future tracking period, the predicted residual frequency for the future tracking period, and the prediction confidence weight coefficient. This includes: performing residual Kalman filtering on the frequency residual to obtain a frequency residual estimate; determining the weights of the predicted residual frequency and the frequency residual estimate for the future tracking period in the frequency control quantity of the numerically controlled oscillator based on the prediction confidence weight coefficient; and generating the frequency control quantity of the numerically controlled oscillator based on the geometric Doppler frequency for the future tracking period, the predicted residual frequency for the future tracking period, the frequency residual estimate, and the prediction confidence weight coefficient. Specifically, the frequency residual estimate can be obtained by residual Kalman filtering to estimate the state of the frequency residual over multiple tracking periods. Residual Kalman filtering can suppress random noise in the current frequency residual based on historical frequency residual changes, thereby obtaining a smoother frequency residual estimate. The residual frequency prediction result mainly reflects the trend of residual frequency changes within the target's future tracking period, while the frequency residual estimate mainly reflects the relatively stable residual frequency state during the current tracking process. A larger prediction confidence weighting coefficient indicates a more reliable residual frequency prediction result. In this case, the weight of the residual frequency prediction result in the CNC oscillator's frequency control quantity can be increased, allowing the CNC oscillator to utilize more prediction information to compensate for future frequency changes. Conversely, a smaller prediction confidence weighting coefficient indicates lower reliability of the residual frequency prediction result. In this case, the weight of the frequency residual estimate in the CNC oscillator's frequency control quantity can be increased, allowing the CNC oscillator to adjust more based on the filtered current residual state. By using the geometric Doppler frequency, residual frequency prediction result, and frequency residual estimate together to generate the CNC oscillator's frequency control quantity, it is possible to consider track geometry compensation, future residual prediction, and current residual filtering estimation, thereby improving the stability and accuracy of the frequency control quantity.

[0030] In one embodiment, the method further includes: determining the bandwidth of the carrier tracking feedback loop based on the prediction confidence weighting coefficient and the prediction variance; decreasing the bandwidth of the carrier tracking feedback loop when the prediction confidence weighting coefficient increases, and compensating for the Doppler dynamics of the low-Earth orbit satellite signal through the frequency control quantity of the numerically controlled oscillator; increasing the bandwidth of the carrier tracking feedback loop when the prediction confidence weighting coefficient decreases, and increasing the weight of the frequency residual estimate in the frequency control quantity of the numerically controlled oscillator. Specifically, the bandwidth of the carrier tracking feedback loop can be used to affect the response capability of the carrier tracking feedback loop to frequency dynamic changes and noise disturbances. When the bandwidth is larger, the carrier tracking feedback loop can respond to frequency changes faster, but is also more susceptible to noise; when the bandwidth is smaller, the carrier tracking feedback loop can suppress noise better, but its ability to follow rapid frequency changes is relatively reduced. The prediction confidence weighting coefficient and the prediction variance can jointly reflect the reliability of the residual frequency prediction result. When the prediction confidence weighting coefficient increases and the prediction variance is small, it indicates that the future residual change trend output by the residual rate of change prediction model is relatively reliable. In this case, the bandwidth of the carrier tracking feedback loop can be appropriately reduced, and more reliance can be placed on the frequency control of the numerically controlled oscillator (CNC) for feedforward compensation of the Doppler dynamics of the low-Earth orbit (LEO) satellite signal, thereby reducing the sensitivity of the feedback loop to noise. When the prediction confidence weighting coefficient decreases or the prediction variance is large, it indicates that the reliability of the prediction result decreases. In this case, the bandwidth of the carrier tracking feedback loop can be increased to enable the feedback loop to respond more quickly to the actual tracking error. At the same time, the weight of the frequency residual estimate in the CNC oscillator's frequency control can be increased to reduce the impact of unreliable predictions on frequency control. By dynamically adjusting the bandwidth of the carrier tracking feedback loop according to the prediction confidence, the impact of noise can be reduced when the prediction is reliable, and the feedback correction capability can be enhanced when the prediction is unreliable, thereby improving the adaptability of LEO satellite signal carrier tracking.

[0031] In one embodiment, the method further includes: when the satellite orbital information is incorrect, performing second-order polynomial extrapolation based on the geometrical Doppler frequencies corresponding to the most recent tracking cycles to obtain the extrapolated geometrical Doppler frequency, and limiting the upper limit of the prediction confidence weight coefficient; maintaining the geometrical Doppler frequency and predicting it using a residual rate of change prediction model when the loss duration of the pilot signal or synchronization signal does not exceed a preset time period; controlling the receiver to enter a local reacquisition state when the loss duration of the pilot signal or synchronization signal exceeds a preset time period; and setting the prediction confidence weight coefficient to zero, increasing the bandwidth of the carrier tracking feedback loop, and initiating a local frequency search when the phase difference in multiple consecutive tracking cycles is greater than a preset phase difference threshold. Specifically, incorrect satellite orbital information may include abnormal ephemeris information, failed orbital parameter updates, or mismatch between orbital information and current observation results. In this case, directly using the incorrect satellite orbital information to calculate the geometrical Doppler frequency may lead to a large deviation in the frequency control of the numerically controlled oscillator. Therefore, second-order polynomial extrapolation can be performed based on the geometrical Doppler frequencies already calculated in the most recent tracking cycles to obtain a substitute geometrical Doppler frequency in a short time. Meanwhile, since the extrapolation results still have uncertainties, the upper limit of the prediction confidence weight coefficient can be limited to avoid over-reliance on the residual frequency prediction results and the extrapolated geometric Doppler frequency. When the pilot signal or synchronization signal is lost for a short time, the current geometric Doppler frequency can be maintained, and the residual Doppler change can be predicted through the residual change rate prediction model, thereby maintaining carrier tracking continuity for a short period of time. When the loss time of the pilot signal or synchronization signal exceeds a preset time period, it indicates that the current tracking state is difficult to maintain reliably, and the receiver can be controlled to enter a local reacquisition state to reacquire the signal frequency and code phase. When the phase difference of multiple consecutive tracking cycles is greater than a preset phase difference threshold, it indicates that the current carrier tracking error is continuously large. At this time, the prediction confidence weight coefficient can be set to zero to avoid continuing to use potentially unreliable prediction results; at the same time, the bandwidth of the carrier tracking feedback loop is increased and a local frequency search is initiated, enabling the receiver to readjust the carrier frequency within a local frequency range. By employing the above-mentioned anomaly handling methods, a certain tracking capability can be maintained when orbital information is abnormal, pilot signals or synchronization signals are lost for a short time, or phase tracking is abnormal. Furthermore, when the anomaly persists, the system can be switched to reacquisition or local search mode in a timely manner, thereby improving the reliability of the low-Earth orbit satellite signal tracking process.

[0032] In one embodiment, the receiver performs low-noise amplification, down-conversion, analog-to-digital conversion, and digital down-conversion on the low-Earth orbit satellite radio frequency signal to obtain a complex intermediate frequency (IF) sampled signal. The complex IF sampled signal can be expressed as:

[0033] in, The complex intermediate frequency sampled signal at the nth sampling point For the received signal amplitude, The delayed baseband signal For the nth sampling time, For propagation delay or code phase delay, This is the intermediate frequency. Let be the Doppler frequency at the nth sampling time. The initial carrier phase, For noise, The imaginary unit. When the numerically controlled oscillator operates in zero-IF baseband control mode. Set to 0; when the numerically controlled oscillator is operating in intermediate frequency carrier stripping mode, This is the intermediate frequency (IF) reference frequency. The receiver performs coarse acquisition on the complex IF sampled signal to obtain the coarse acquisition result, which includes the initial frequency offset, initial code phase, initial carrier-to-noise ratio (CNR), and initial control parameters of the numerically controlled oscillator (CNC). The coarse acquisition method can be FFT parallel search, sliding correlation search, or synchronous sequence correlation search.

[0034] In the k-th tracking control cycle, the receiver calculates the geometrical Doppler frequency and geometrical Doppler rate of change corresponding to the current tracking cycle based on the satellite orbital information and the receiver motion status information. The satellite orbital information may include at least one of ephemeris, TLE, orbital propagation results, or orbital parameters. The receiver motion status information may include at least one of receiver position, receiver velocity, or beam center position. The receiver can calculate the satellite position, satellite velocity, receiver position, and receiver velocity based on the above information. The line-of-sight unit vector can be represented as:

[0035] The geometric Doppler frequency can be expressed as:

[0036] The geometrical Doppler rate of change can be expressed as:

[0037] in, For satellite position vectors, For the receiver position vector, For the satellite velocity vector, For the receiver velocity vector, Represents the magnitude of a vector. The unit vector of the line of sight. For carrier frequency, At the speed of light, The geometric Doppler frequency corresponding to the k-th tracking control cycle. The geometrical Doppler rate of change corresponds to the k-th tracking control cycle. To track the update cycle, the sign direction of the aforementioned geometric Doppler frequency is consistent with the compensation direction of the frequency control quantity of the numerically controlled oscillator.

[0038] In the context of inter-satellite links, the geometric Doppler rate of change can also be calculated directly from the relative acceleration.

[0039] in, For carrier frequency, At the speed of light, This is the inter-satellite relative acceleration vector.

[0040] In the current carrier tracking process, the receiver performs coherent integration on the complex intermediate frequency sampled signal corresponding to the k-th tracking control period to obtain the coherent integration result:

[0041] in, This is the coherent integral result corresponding to the k-th tracking control cycle. For in-phase integral components, These are orthogonal integral components.

[0042] The receiver can determine the phase difference between adjacent tracking control cycles based on the coherent integration result of the current tracking control cycle and the coherent integration result of the previous tracking control cycle, and use the phase difference between adjacent tracking control cycles to estimate the measured frequency.

[0043] in, This represents the measured frequency corresponding to the k-th tracking control cycle. It is the conjugate of the coherent integral result corresponding to the previous tracking control cycle. For phase winding function, This represents the frequency control value of the numerically controlled oscillator (CNC) corresponding to the previous tracking control cycle. If the frequency discrimination output in the current carrier tracking process is defined as the residual frequency after the CNC oscillator is stripped, then the measured frequency corresponding to the k-th tracking control cycle can be synthesized from this residual frequency and the frequency control value of the CNC oscillator corresponding to the previous tracking control cycle. If the receiver operates entirely in the residual domain, the measured frequency can be replaced with the estimated residual frequency, and subsequent calculations can be performed in the residual domain.

[0044] The measured Doppler rate of change can be obtained using median difference estimation:

[0045] in, The measured Doppler change rate corresponds to the k-th tracking control cycle. For median operations, The median difference window length. To track and control the update cycle, median differencing estimation can reduce the impact of individual outlier frequency estimates on the measured Doppler rate of change.

[0046] After obtaining the measured frequency, measured Doppler rate of change, geometric Doppler frequency, and geometric Doppler rate of change, the receiver calculates the frequency residual and the rate of change residual. The frequency residual can be expressed as:

[0047] The rate of change residual can be expressed as:

[0048] in, The frequency residual corresponding to the k-th tracking control cycle, The residual of the rate of change corresponding to the k-th tracking control cycle. The geometric Doppler frequency corresponding to the k-th tracking control cycle. The geometrical Doppler rate of change corresponds to the k-th tracking control cycle. Let be the geometric Doppler frequency of the k-th tracking period. Let be the geometric Doppler rate of change during the k-th tracking period. The receiver can construct the residual Doppler feature based on the frequency residual, the rate of change residual, the difference in rate of change residuals between adjacent tracking control periods, and the receiver state parameters.

[0049] in, The residual Doppler feature corresponds to the k-th tracking control cycle. The frequency residual corresponding to the k-th tracking control cycle, The residual of the rate of change corresponding to the k-th tracking control cycle. These are the frequency residual normalization coefficients. The normalized coefficient of the rate of change residual. The normalization coefficient for the rate of change residual difference is... The satellite elevation angle. Carrier-to-noise ratio, This is the phase difference quantity. This is the normalized automatic gain control input. By constructing the residual Doppler feature together with the frequency residual, the rate of change residual, and the receiver state parameters, the residual rate of change prediction model can simultaneously obtain the residual Doppler change trend and the receiver's current operating state.

[0050] In one embodiment, the residual rate of change prediction model can be a long short-term memory network predictor. The receiver takes the residual Doppler features from the most recent L tracking control cycles to construct a feature sequence:

[0051] The feature sequence is then input into the residual rate of change prediction model. The residual rate of change prediction model outputs the predicted residual value and prediction variance for the h-th tracking control cycle.

[0052] Where h = 1, 2, ..., H, H is the maximum number of prediction steps, and h is the prediction step number. The hidden state of the residual rate of change prediction model. This is the predicted value of the rate of change residual corresponding to the h-th tracking control cycle. The corresponding prediction variance, This is the output layer weight matrix. This is used for output layer bias. The rate of change residual prediction sequence is composed of the predicted rate of change residual values ​​corresponding to multiple future tracking control cycles.

[0053] When training a residual rate of change prediction model, the following training loss function can be used:

[0054] The physical consistency constraint can be expressed as:

[0055] in, This represents the true rate of change residual corresponding to the h-th tracking control cycle. To prevent constants with a denominator of zero, To predict variance constraint weights, For physical consistency constraint weights, This is used to ensure that the residual frequency change obtained by accumulating the rate of change residual prediction sequence over time is consistent with the frequency residual change between the corresponding tracking control cycles. This reduces the risk of residual frequency prediction results drifting during multi-step prediction.

[0056] After obtaining the prediction sequence of the rate of change residual and the prediction variance, the receiver generates prediction confidence weighting coefficients based on the prediction variance and receiver state parameters. (Phase difference) It can be represented as:

[0057] in:

[0058] The carrier phase is determined based on the coherent integral result corresponding to the k-th tracking control cycle. This represents the predicted carrier phase for the k-th tracking control cycle in the current carrier tracking process. The receiver first calculates the initial confidence level based on the phase difference and the prediction variance:

[0059] The original confidence level is then corrected based on the carrier-to-noise ratio and phase difference to obtain the prediction confidence weighting coefficients:

[0060] in, To predict the credibility weighting coefficient, The variance of the phase error. To predict the normalized parameter of standard deviation, To preset the phase difference threshold, The preset carrier-to-noise ratio threshold is used. The larger the prediction confidence weight coefficient, the more reliable the predicted rate of change residual sequence; the smaller the prediction confidence weight coefficient, the higher the weight of the frequency residual estimate in the frequency control quantity of the numerically controlled oscillator.

[0061] The receiver can generate the residual frequency prediction result for the next tracking control cycle based on the frequency residual corresponding to the current tracking control cycle and the predicted value of the rate of change residual corresponding to the next tracking control cycle.

[0062] in, This is the predicted residual frequency for the next tracking control cycle. The receiver can also perform residual Kalman filtering on the frequency residual to obtain an estimated frequency residual. The state of the residual Kalman filter can be expressed as:

[0063] in, For residual phase, Let be the frequency residual. The state prediction can be expressed as:

[0064] The process noise covariance of residual Kalman filtering can be expressed as:

[0065] in, The basic process noise parameters corresponding to the residual phase. These are the basic process noise parameters corresponding to the frequency residual. To predict the variance adjustment factor for process noise, This is the adjustment factor for the process noise caused by the predicted confidence weighting coefficient. In this way, the predicted variance can participate in the adjustment of the noise in the residual Kalman filtering process.

[0066] The receiver generates the frequency control quantity for the numerically controlled oscillator based on the geometric Doppler frequency, residual frequency prediction result, frequency residual estimate, and prediction confidence weighting coefficient corresponding to the target's future tracking period. Taking the next tracking control period as an example, the frequency control quantity of the numerically controlled oscillator can be expressed as:

[0067] in, This is the frequency control value of the numerically controlled oscillator corresponding to the next tracking control cycle. The geometric Doppler frequency corresponding to the next tracking control cycle. This is the frequency residual estimate from the residual Kalman filter output. When the numerically controlled oscillator operates in zero-IF baseband control mode... Set to 0; when the numerically controlled oscillator is operating in intermediate frequency carrier stripping mode, This is the intermediate frequency reference frequency. Therefore, the frequency control quantities of the numerically controlled oscillator simultaneously include the intermediate frequency reference quantity, the geometric Doppler compensation quantity, the residual frequency prediction compensation quantity, and the frequency residual estimation compensation quantity.

[0068] In one embodiment, the receiver can also determine the bandwidth of the carrier tracking feedback loop based on the prediction confidence weighting coefficient and the prediction variance. The bandwidth of the carrier tracking feedback loop can be expressed as:

[0069] in, Let be the bandwidth of the carrier tracking feedback loop corresponding to the k-th tracking control cycle. To minimize the feedback loop bandwidth, For the maximum feedback loop bandwidth, This is the mapping coefficient from the prediction standard deviation to the bandwidth. When the prediction confidence weight coefficient is high, the bandwidth of the carrier tracking feedback loop is close to the minimum feedback loop bandwidth, and the frequency control of the numerically controlled oscillator (CNC) provides feedforward compensation for the main Doppler dynamics of the low-Earth orbit satellite signal. When the prediction confidence weight coefficient is low, the bandwidth of the carrier tracking feedback loop increases, the receiver increases the weight of the frequency residual estimate in the frequency control of the CNC, and carrier tracking is maintained by residual Kalman filtering and local reacquisition protection.

[0070] At the end of each tracking control cycle, the receiver updates the status information for the current tracking process. This status information includes the frequency control parameters and phase state of the numerically controlled oscillator (CNC), the state of the residual Kalman filter, the hidden state of the residual rate of change prediction model, the residual Doppler feature queues for the most recent L tracking control cycles, the loss-of-lock counter, and the local reacquisition state. By updating these states at the end of each tracking control cycle, residual Doppler prediction, CNC oscillator control, and carrier tracking feedback processing can be iteratively executed across multiple consecutive tracking control cycles, thereby improving the continuity and stability of low-Earth orbit (LEO) satellite signal carrier tracking.

[0071] In one embodiment, the receiver performs anomaly protection processing during low-Earth orbit satellite signal tracking. Specifically, if the ephemeris or TLE is temporarily unavailable, or if the satellite orbital information does not match the current observation results, the receiver performs a second-order polynomial extrapolation based on the geometrical Doppler frequencies corresponding to the most recent N tracking periods to obtain the extrapolated geometrical Doppler frequencies corresponding to the target in future tracking periods. The second-order polynomial extrapolation can be expressed as:

[0072] in, For the first The extrapolated geometric Doppler frequencies corresponding to i tracking cycles , and The polynomial coefficients are obtained by fitting the geometric Doppler frequencies corresponding to the most recent N tracking cycles. This will be the tracking period number for the future. To track and control the update cycle, since the extrapolated geometric Doppler frequency has uncertainty, the upper limit of the prediction confidence weight coefficient can be limited, for example, by making the prediction confidence weight coefficient Gk ≤ 0.6, to avoid over-reliance on the prediction results output by the residual rate of change prediction model when satellite orbital information is unreliable.

[0073] In the event of a brief loss of the pilot or synchronization signal, if the loss duration does not exceed a preset time period (e.g., 100ms), the receiver maintains the current geometrical Doppler frequency and performs a one-step prediction using the residual rate of change prediction model to maintain the continuity of LEO satellite signal carrier tracking. If the loss duration exceeds the preset time period, it indicates that the current tracking state cannot be reliably maintained. The receiver clears the hidden state of the residual rate of change prediction model and enters a local reacquisition state to reacquire the frequency and code phase of the LEO satellite signal.

[0074] In cases of abnormal phase difference, if continuous All tracking periods meet the following requirements:

[0075] Then the prediction confidence weight coefficient is set to zero, that is:

[0076] in, This is the phase difference quantity. To preset the phase difference threshold, To preset the number of consecutive abnormal cycles, the receiver increases the bandwidth of the carrier tracking feedback loop, for example, by increasing the bandwidth of the carrier tracking feedback loop to the maximum feedback loop bandwidth. This initiates a local frequency search window. In this way, unreliable residual frequency predictions can be discontinued when the phase difference is continuously abnormal, and the ability to re-stabilize tracking is improved through increased feedback loop bandwidth and local frequency search.

[0077] In another embodiment, the carrier frequency of the low-Earth orbit (LEO) satellite signal is 2.2 GHz, the orbital altitude of the LEO satellite is 550 km to 1200 km, the maximum frequency offset configuration range is ±60 kHz, the maximum Doppler change rate configuration range is 1000 Hz / s, the modulation method is QPSK, the sampling rate is 7.68 Msps, the coherent integration time is 1 ms, the tracking control update period is 10 ms, and the receiver local oscillator stability is 10. -8 Up to 10 -9 In this embodiment, the receiver performs low-noise amplification, down-conversion, analog-to-digital conversion, and digital down-conversion on the low-Earth orbit (LEO) satellite RF signal to obtain a complex intermediate frequency (IF) sampling signal, and then performs coarse acquisition on the complex IF sampling signal. Coarse acquisition can employ a 4096-point FFT parallel search, with a search range of -70kHz to 70kHz and a search step of 250Hz, to obtain the initial frequency offset, initial code phase, initial carrier-to-noise ratio (CNR), and initial control inputs for the numerically controlled oscillator (NCO). When calculating the geometrical Doppler frequency and geometrical Doppler rate of change, the receiver reads the ephemeris or TLE propagation result every 100ms and interpolates the LEO satellite's position, velocity, and elevation angle every 1ms. If the receiver's position is unknown, the cell center position can be used as the receiver's position, and the position uncertainty is set to 5km. Based on the LEO satellite's position, velocity, and the receiver's position, the receiver calculates the geometrical Doppler frequency and geometrical Doppler rate of change corresponding to the current tracking period. In the current carrier tracking process, the receiver outputs a coherent integration result every 1ms, expressed as: ;in, This is the coherent integral result corresponding to the k-th tracking period. For in-phase integral components, These are orthogonal integral components. The receiver estimates the measured frequency using the phase difference between adjacent tracking cycles, and performs median difference estimation based on the measured frequencies corresponding to multiple consecutive tracking cycles to obtain the measured Doppler rate of change. The median difference window... Take 5, and track the update cycle. Take 10ms.

[0078] When constructing residual Doppler features, the frequency residual normalization coefficients Take 5000Hz, and the normalization coefficient of the rate of change residual. Take 1000 Hz / s, and the normalization coefficient of the residual difference in the rate of change. The frequency is set to 4000 Hz / s². The receiver constructs residual Doppler features based on the normalized frequency residual, the normalized rate of change residual, the difference in normalized rate of change residuals, and the receiver state parameters. In this embodiment, the residual rate of change prediction model can be a Long Short-Term Memory (LSTM) network predictor. The input length L of the residual rate of change prediction model is 40, the prediction step size H is 5, and two parallel LSM network layers are used, each containing 48 hidden units. The receiver inputs the residual Doppler features of the most recent 40 tracking control cycles into the residual rate of change prediction model to obtain the predicted sequence of rate of change residuals for the next 5 tracking control cycles and the corresponding prediction variance.

[0079] When generating prediction confidence weight coefficients, a phase difference threshold is preset. Take π / 3, and set the preset carrier-to-noise ratio threshold. Using 26 dB-Hz, the normalized parameter for the prediction standard deviation. The frequency is set to 1000 Hz / s. The receiver generates prediction confidence weighting coefficients based on the prediction variance, phase difference, and carrier-to-noise ratio. And based on the prediction confidence weight coefficient Determine the weights of the residual frequency prediction results and the frequency residual estimate in the frequency control quantities of the numerically controlled oscillator.

[0080] When adjusting the bandwidth of the carrier tracking feedback loop, the minimum feedback loop bandwidth is... Take 8Hz as the maximum feedback loop bandwidth. Take 80Hz. When the prediction confidence weighting coefficient... A value greater than 0.75 indicates that the residual rate of change prediction sequence output by the residual rate of change prediction model is relatively reliable. The receiver limits the bandwidth of the carrier tracking feedback loop to 8Hz to 15Hz and compensates for the Doppler dynamics of the low-Earth orbit satellite signal through the frequency control of the numerically controlled oscillator. When the prediction reliability weighting coefficient... When the value is less than 0.25, it indicates that the reliability of the residual frequency prediction result is low. The receiver increases the bandwidth of the carrier tracking feedback loop to 40Hz to 80Hz and triggers a local protection search to avoid unreliable prediction results affecting carrier tracking stability. Under simulation conditions of a carrier-to-noise ratio of 30dB-Hz, a maximum frequency offset of ±60kHz, and a maximum Doppler change rate of 1000Hz / s, this embodiment can control the residual frequency RMS within 10Hz to 20Hz and the phase RMS within 0.04rad to 0.08rad during the stable tracking phase.

[0081] In another embodiment, the low-Earth orbit (LEO) satellite signal tracking method is applied to a LEO inter-satellite link. The LEO satellite signal has a carrier frequency of 20 GHz, a maximum frequency offset configuration range of ±350 kHz, a maximum Doppler change rate configuration range of 3000 Hz / s, a QPSK modulation scheme, a sampling rate of 61.44 Msps, a coherent integration time of 0.25 ms, a tracking control update period of 2 ms, and a receiver local oscillator stability of 10. -9 Up to 10 - ¹ 0 In this embodiment, the receiver performs low-noise amplification, down-conversion, analog-to-digital conversion, and digital down-conversion on the low-Earth orbit (LEO) satellite radio frequency (RF) signal to obtain a complex intermediate frequency (IF) sampled signal, and then performs coarse acquisition on the complex IF sampled signal. The coarse acquisition can employ a two-stage FFT search. The first-stage search step is 2 kHz, with a search range of ±400 kHz. The second-stage search step is 125 Hz, with a search range of ±8 kHz near the peak value of the first-stage search. Through the two-stage FFT search, the approximate frequency offset position of the LEO inter-satellite link signal can be determined first within a relatively large frequency offset range, and then a fine search can be performed near the peak value, thereby obtaining the initial frequency offset, initial code phase, initial carrier-to-noise ratio (CNR), and initial control parameters of the numerically controlled oscillator (CNC).

[0082] When calculating the geometrical Doppler frequency and geometrical Doppler rate of change, the receiver calculates the geometrical Doppler frequency and geometrical Doppler rate of change corresponding to the current tracking period based on the inter-satellite relative orbit parameters, and also calculates the geometrical Doppler frequency corresponding to the target's future tracking period. The update period for the geometrical Doppler frequency and geometrical Doppler rate of change is 2ms. By performing geometrical Doppler prior calculations based on the inter-satellite relative orbit parameters, the frequency control quantity of the numerically controlled oscillator can be pre-included with the main Doppler compensation quantity caused by the relative high-speed motion in the low-Earth orbit inter-satellite link.

[0083] When constructing residual Doppler features, the frequency residual normalization coefficients Take 20000Hz, normalization coefficient of the rate of change residual Take 4000 Hz / s, and the normalization coefficient of the residual difference in the rate of change. The frequency is set to 15000 Hz / s². The receiver constructs residual Doppler features based on the normalized frequency residual, the normalized rate of change residual, the difference in the normalized rate of change residual, and the receiver state parameters, for prediction by the residual rate of change prediction model.

[0084] In this embodiment, the residual rate of change prediction model can be a Long Short-Term Memory (LSTM) network predictor. The input length L of the residual rate of change prediction model is 80, the prediction step size H is 8, and two parallel LSM network layers are used, each containing 96 hidden units. The receiver inputs the residual Doppler features of the most recent 80 tracking control cycles into the residual rate of change prediction model to obtain the predicted sequence of residual rate of change for the next 8 tracking control cycles and the corresponding prediction variance. Since the maximum frequency offset and maximum Doppler rate of change of the low-Earth orbit inter-satellite link are relatively large, increasing the input length and the number of hidden units can enhance the residual rate of change prediction model's ability to express continuous dynamic changes.

[0085] When generating prediction confidence weight coefficients, a phase difference threshold is preset. Set the value to 0.45π and set the preset carrier-to-noise ratio threshold. Take 28 dB-Hz as the normalized parameter for the prediction standard deviation. The frequency is set to 3000 Hz / s. The receiver generates prediction confidence weighting coefficients based on the prediction variance, phase difference, and carrier-to-noise ratio. And based on the prediction confidence weight coefficient Determine the weights of the residual frequency prediction results and the frequency residual estimate in the frequency control quantities of the numerically controlled oscillator.

[0086] In one embodiment, the numerically controlled oscillator can also perform phase interpolation based on the frequency control value of the numerically controlled oscillator. Phase interpolation can be expressed as:

[0087] in, Let t be the phase of the numerically controlled oscillator. The phase of the numerically controlled oscillator at the start of the k-th tracking cycle. This represents the frequency control value of the numerically controlled oscillator corresponding to the k-th tracking cycle. Let k be the start time of the kth tracking period. This is the Doppler rate of change control quantity used for phase interpolation. The Doppler rate of change control quantity can be determined based on the predicted values ​​of the rate of change residuals output from the geometric Doppler rate of change and residual rate of change prediction models, for example:

[0088] in, The geometric Doppler rate of change corresponds to the k-th tracking period. This is the predicted residual value of the rate of change for the next tracking cycle. By adding a Doppler rate of change control to the phase interpolation, the phase of the numerically controlled oscillator can be continuously changed within the tracking control cycle, reducing phase abrupt changes in high-dynamic low-Earth orbit inter-satellite links.

[0089] During short-term interruption protection, if the interruption duration of the pilot signal or synchronization signal does not exceed 100ms, the receiver maintains the current geometrical Doppler frequency and performs a one-step prediction using the residual rate of change prediction model to maintain carrier tracking continuity of the LEO inter-satellite link signal. If the interruption duration of the pilot signal or synchronization signal exceeds 100ms, the receiver clears the hidden state of the residual rate of change prediction model and controls the receiver to enter a local reacquisition state to reacquire the frequency and code phase of the LEO inter-satellite link signal.

[0090] Under the conditions of a maximum frequency offset of ±350kHz, a maximum Doppler change rate of 3000Hz / s, and a carrier-to-noise ratio of 32dB-Hz, this embodiment can maintain the bandwidth of the carrier tracking feedback loop at 12Hz to 25Hz during the prediction confidence stage, and control the residual frequency root mean square error at 35Hz to 60Hz.

[0091] In one embodiment, the residual rate of change prediction model can be trained based on training samples. The training samples include at least one of orbit simulation data, radio frequency link simulation data, and hardware-injected data. The orbit simulation data can cover orbital scenarios with altitudes from 500 km to 1500 km, inclinations from 0° to 98°, and minimum elevation angles from 5° to 30°, used to characterize the geometric Doppler variation characteristics under different low-Earth orbit satellite operating conditions. The radio frequency link simulation data can cover link scenarios with carrier frequencies from 2 GHz to 30 GHz and carrier-to-noise ratios from 20 dB-Hz to 45 dB-Hz, used to characterize the receiver tracking status under different carrier frequency bands and signal quality conditions. The hardware-injected data can inject frequency offset, rate of change, obstruction, and phase jumps through a software-defined radio platform or vector signal source to simulate the high dynamic changes and abnormal tracking states that may occur during actual receiver operation.

[0092] When constructing training samples, the true rate of change residual corresponding to the (k+h)th future tracking period can be used as the label, which can be represented as:

[0093] in, This represents the true rate of change residual corresponding to the (k+h)th tracking period in the future. This represents the measured Doppler change rate corresponding to the (k+h)th tracking period in the future. Let be the geometric Doppler rate of change corresponding to the (k+h)th tracking period. By using the true rate of change residual as a label, the residual rate of change prediction model can learn the residual relationship between the geometric Doppler rate of change and the measured Doppler rate of change, without needing to directly learn the absolute Doppler frequency change.

[0094] In one embodiment, the training set, validation set, and test set can be divided according to orbital scenarios, rather than randomly shuffling all training samples. The training set can include samples from orbital altitudes of 500km to 1200km and typical elevation angle scenarios; the validation set can include samples from scenarios with different initial phases and different crystal oscillator drifts; and the test set can include samples from orbital altitudes not used in training, different carrier frequency bands, and short-term obstruction scenarios. This data partitioning method allows for verification of the residual rate of change prediction model's generalization ability for unknown orbital scenarios, unknown link states, and short-term anomaly scenarios, thereby improving the reliability of subsequent rate of change residual prediction sequences.

[0095] This application also proposes a low-Earth orbit satellite signal tracking device based on residual Doppler prediction, comprising: The memory is configured to store instructions; The processor is configured to retrieve instructions from memory and, when executing instructions, implement a low-Earth orbit satellite signal tracking method based on residual Doppler prediction.

[0096] like Figure 2 As shown, in one embodiment, this application also proposes a low-Earth orbit satellite signal tracking system based on residual Doppler prediction, comprising: The RF front end is used to receive low-Earth orbit satellite radio frequency signals and perform low-noise amplification and down-conversion processing on the received low-Earth orbit satellite radio frequency signals to obtain analog intermediate frequency signals. The ADC / DDC module is used to perform analog-to-digital conversion and digital down-conversion processing on the analog intermediate frequency signal output from the RF front end to obtain a complex intermediate frequency sampling signal, and then output the complex intermediate frequency sampling signal to the coarse acquisition module. The coarse acquisition module is used to perform coarse acquisition processing on the complex intermediate frequency sampled signal output by the ADC / DDC module to obtain the coarse acquisition result. The coarse acquisition result includes the initial frequency offset, initial code phase, initial carrier-to-noise ratio and initial control quantity of the numerically controlled oscillator, and outputs the coarse acquisition result to the coherent integration module. The coherent integration module is used to perform current carrier tracking processing on the complex intermediate frequency sampled signal based on the coarse acquisition result, and to perform coherent integration on the signal after current carrier tracking processing to obtain the coherent integration result, and output the coherent integration result to the phase and frequency discrimination module. The phase and frequency discrimination module is used to determine the phase difference between adjacent tracking cycles based on the coherent integration result corresponding to the current tracking cycle and the coherent integration result corresponding to the previous tracking cycle, and to determine the measured frequency corresponding to the current tracking cycle based on the phase difference, the time interval between adjacent tracking cycles, and the frequency control quantity of the numerically controlled oscillator corresponding to the previous tracking cycle; it is also used to perform median difference estimation based on the measured frequencies corresponding to multiple consecutive tracking cycles to obtain the measured Doppler rate of change corresponding to the current tracking cycle; and it is also used to determine the phase difference by comparing the carrier phase determined by the coherent integration result corresponding to the current tracking cycle with the predicted carrier phase corresponding to the current tracking cycle in the current carrier tracking processing. The measured frequency, measured Doppler rate of change, phase difference, carrier-to-noise ratio, and automatic gain control quantity are then output to the residual feature construction module. The orbit prior module is used to acquire satellite orbital operation information and receiver motion status information, and output the satellite orbital operation information and receiver motion status information to the geometric Doppler calculation module. The geometric Doppler calculation module is used to calculate the geometric Doppler frequency, geometric Doppler rate of change, and satellite elevation angle corresponding to the current tracking period, as well as the geometric Doppler frequency corresponding to the target's future tracking period, based on the satellite orbit operation information and receiver motion state information output by the orbit prior module and the orbit geometric model. The calculation results are then output to the residual feature construction module. The residual feature construction module is used to determine the frequency residual and the rate of change residual based on the measured frequency and measured Doppler change rate output by the phase and frequency discrimination module, and the geometric Doppler frequency and geometric Doppler change rate output by the geometric Doppler calculation module, respectively. It is also used to combine the frequency residual, the rate of change residual, the satellite elevation angle, the carrier-to-noise ratio, the phase difference, and the automatic gain control quantity to obtain the residual Doppler feature corresponding to the current tracking period, and output the residual Doppler feature corresponding to multiple consecutive tracking periods to the LSTM residual Doppler change rate prediction module. The LSTM residual Doppler rate of change prediction module is used to input the residual Doppler features corresponding to multiple consecutive tracking cycles into the residual rate of change prediction model to obtain the rate of change residual prediction sequence and prediction variance for multiple future tracking cycles; it is also used to determine the residual frequency prediction result corresponding to the target in future tracking cycles based on the rate of change residual prediction sequence, output the residual frequency prediction result to the NCO control word synthesis module, and output the prediction variance to the confidence weight control module, the residual Kalman filter module, and the adaptive bandwidth and protection module.

[0097] The confidence weight control module is used to determine the prediction confidence weight coefficient based on the prediction variance and the receiver state parameters, and output the prediction confidence weight coefficient to the residual Kalman filter module, the NCO control word synthesis module and the adaptive bandwidth and protection module.

[0098] The residual Kalman filter module is used to perform residual Kalman filtering on the frequency residual to obtain the frequency residual estimate; it is also used to adjust the residual Kalman filter parameters according to the prediction variance and prediction confidence weight coefficient, and output the frequency residual estimate to the NCO control word synthesis module. The NCO control word synthesis module is used to determine the weights of the residual frequency prediction result and frequency residual estimate corresponding to the future tracking period of the target in the frequency control quantity of the numerically controlled oscillator based on the prediction confidence weight coefficient, and to generate the frequency control quantity of the numerically controlled oscillator based on the geometric Doppler frequency, residual frequency prediction result, frequency residual estimate and prediction confidence weight coefficient corresponding to the future tracking period of the target. The frequency control quantity of the numerically controlled oscillator is then output to the adaptive bandwidth and protection module and the carrier tracking loop feedback module.

[0099] The adaptive bandwidth and protection module is used to adjust the bandwidth of the carrier tracking feedback loop according to the prediction confidence weight coefficient and prediction variance, and output the frequency control quantity of the numerically controlled oscillator and the adjusted bandwidth of the carrier tracking feedback loop to the carrier tracking loop feedback module; it is also used to enable the control system to enter a local reacquisition state and perform a local frequency search when satellite orbit operation information is temporarily unavailable or the prediction result is abnormal. The carrier tracking loop feedback module is used to perform carrier tracking on low-Earth orbit satellite signals based on the frequency control input of the numerically controlled oscillator and the bandwidth of the carrier tracking feedback loop, to obtain the carrier frequency and carrier phase after tracking, and to feed the carrier tracking results back to the RF front end.

[0100] like Figure 3 As shown, Figure 3 This is a structural diagram of an LSTM residual Doppler rate of change predictor. In one embodiment, residual Doppler features corresponding to multiple consecutive tracking cycles are cached as historical sequences, and a historical input sequence is formed according to the chronological order of the tracking cycles. The length of the historical sequence represents the number of consecutive tracking cycles involved in the current prediction. The historical input sequence is input into a first LSTM layer and a second LSTM layer configured in parallel, respectively, to extract the temporal variation features from the historical input sequence. The first hidden state output by the first LSTM layer and the second hidden state output by the second LSTM layer are concatenated to obtain a joint hidden state, which is then input to the output mapping layer.

[0101] The output mapping layer maps the hidden state to obtain the rate of change residual prediction sequence for multiple future tracking cycles, as well as the prediction variance corresponding to the rate of change residual prediction sequence.

[0102] During the training phase of the residual rate of change prediction model, the training loss is determined based on the prediction error term, variance constraint term, and physical consistency constraint term. The physical consistency constraint term is used to compare the cumulative results of the predicted rate of change residuals for multiple future tracking periods with the actual changes in the frequency residuals during the corresponding periods, and adjusts the model parameters of the residual rate of change prediction model according to the difference between the two, so that the cumulative results of the rate of change residual prediction sequence are consistent with the changing pattern of the frequency residuals.

[0103] like Figure 4 As shown, Figure 4 This is a block diagram showing the synthesis of the confidence weight control and NCO control word. In one embodiment, the LSTM residual Doppler rate of change prediction module outputs a rate of change residual prediction sequence corresponding to multiple future tracking periods, and a prediction variance corresponding to the rate of change residual prediction sequence. The rate of change residual prediction sequence is input to the confidence weight control module and the residual frequency prediction module, respectively, and the prediction variance is input to the confidence weight control module.

[0104] The phase and frequency discrimination module determines the phase difference and carrier-to-noise ratio corresponding to the current tracking period based on the feedback results from the carrier tracking loop feedback module.

[0105] The confidence weight control module determines the prediction confidence weight coefficient based on the rate of change residual prediction sequence, prediction variance, phase difference, and carrier-to-noise ratio (CNR). Specifically, a smaller prediction variance, a smaller phase difference, and a higher CNR indicate a higher confidence level in the rate of change residual prediction sequence, and the prediction confidence weight coefficient is increased accordingly. Conversely, a larger prediction variance, a larger phase difference, or a lower CNR indicates a lower confidence level in the rate of change residual prediction sequence, and the prediction confidence weight coefficient is decreased accordingly. When the rate of change residual prediction sequence is abnormal or the CNR is lower than a preset CNR threshold, the prediction confidence weight coefficient can be set to zero to stop using the residual frequency prediction results determined based on the rate of change residual prediction sequence to generate the frequency control quantity of the numerically controlled oscillator. The confidence weight control module outputs the prediction confidence weight coefficient to the NCO control word synthesis module.

[0106] The residual frequency prediction module determines the residual frequency prediction result for the target's future tracking period based on the rate of change residual prediction sequence and the frequency residual corresponding to the current tracking period. Specifically, it accumulates the predicted rate of change residual values ​​between the current tracking period and the target's future tracking period to obtain the corresponding frequency residual change, and determines the residual frequency prediction result for the target's future tracking period based on the frequency residual corresponding to the current tracking period and the frequency residual change. The residual frequency prediction module outputs the residual frequency prediction result to the NCO control word synthesis module.

[0107] The geometric Doppler calculation module is used to calculate the geometric Doppler frequency corresponding to the future tracking period of the target based on the satellite orbit operation information and the motion state information of the receiver, and directly outputs the geometric Doppler frequency corresponding to the future tracking period of the target to the NCO control word synthesis module.

[0108] The residual Kalman filter module is used to determine the frequency residual corresponding to the current tracking period based on the phase and frequency discrimination results fed back by the carrier tracking loop feedback module, and to perform residual Kalman filtering on the frequency residual to obtain the frequency residual estimate corresponding to the future tracking period of the target, and output the frequency residual estimate to the NCO control word synthesis module.

[0109] The NCO control word synthesis module determines the weights of the residual frequency prediction and frequency residual estimate in the frequency control quantities of the numerically controlled oscillator (CNC) for the future tracking period of the target, based on the prediction confidence weight coefficient. Specifically, a larger prediction confidence weight coefficient results in a greater weight for the residual frequency prediction and a smaller weight for the frequency residual estimate. Conversely, a smaller prediction confidence weight coefficient results in a smaller weight for the residual frequency prediction and a greater weight for the frequency residual estimate. When the prediction confidence weight coefficient is zero, the NCO control word synthesis module uses the frequency residual estimate for frequency compensation.

[0110] The NCO control word synthesis module is also used to generate the frequency control quantity of the numerically controlled oscillator based on the geometric Doppler frequency, residual frequency prediction result, frequency residual estimate and prediction confidence weight coefficient corresponding to the future tracking period of the target, and output the frequency control quantity of the numerically controlled oscillator to the carrier tracking loop feedback module.

[0111] The carrier tracking loop feedback module performs carrier stripping and coherent integration on the received low-Earth orbit satellite signal based on the frequency control input of the numerically controlled oscillator (CNC oscillator). It then performs phase and frequency discrimination processing on the coherent integration result to obtain the phase and frequency discrimination result for the next tracking cycle. This result is used to determine the measured frequency, measured Doppler rate of change, phase difference, and carrier-to-noise ratio (CNR) for the next tracking cycle. The phase difference and CNR are fed back to the reliability weight control module. The frequency residual, determined based on the measured frequency and the geometrical Doppler frequency for the next tracking cycle, is fed back to the residual Kalman filter module. This allows for the re-performing of residual Kalman filtering, residual frequency prediction, prediction reliability weight control, and CNC oscillator frequency control input generation in the next tracking cycle.

[0112] like Figure 5 As shown, Figure 5 This is a state diagram for adaptive bandwidth control and anomaly protection. In one embodiment, the system switches between initialization and acquisition states, reliable prediction tracking states, hybrid tracking states, unreliable tracking states, and protection or local reacquisition states based on prediction confidence weighting coefficients, prediction variance, phase difference, and carrier-to-noise ratio, and adjusts the bandwidth of the carrier tracking feedback loop according to the current state.

[0113] After entering the initialization and acquisition state S0, the system acquires the low-Earth orbit satellite signal, obtaining the initial frequency offset, initial code phase, and carrier-to-noise ratio. After initialization and acquisition, the subsequent tracking state is determined based on the prediction confidence weight coefficient. When the prediction confidence weight coefficient is greater than the first weight threshold, the system transitions from the initialization and acquisition state S0 to the reliable prediction tracking state S1; when the prediction confidence weight coefficient is greater than the second weight threshold but not greater than the first weight threshold, the system transitions from the initialization and acquisition state S0 to the hybrid tracking state S2; when the prediction confidence weight coefficient is not greater than the second weight threshold, the system transitions from the initialization and acquisition state S0 to the unreliable tracking state S3.

[0114] In the reliable prediction tracking state S1, the prediction reliability weight coefficient is greater than the first weight threshold. The first weight threshold can be set to 0.75. At this time, the weight of the residual frequency prediction result in the frequency control quantity of the numerically controlled oscillator is increased, so that the frequency control quantity of the numerically controlled oscillator is mainly generated based on the residual frequency prediction result, and the bandwidth of the carrier tracking feedback loop is reduced, making the bandwidth of the carrier tracking feedback loop close to the preset minimum bandwidth. Because the reliability of the residual frequency prediction result is high, using a narrower bandwidth of the carrier tracking feedback loop can reduce the impact of tracking noise on the carrier frequency and carrier phase. When the prediction reliability weight coefficient drops to no greater than the first weight threshold, the system switches from reliable prediction tracking state S1 to hybrid tracking state S2.

[0115] In hybrid tracking state S2, the prediction confidence weight coefficient is greater than the second weight threshold but not greater than the first weight threshold. The second weight threshold can be set to 0.25. At this time, based on the prediction confidence weight coefficient, the weights of the residual frequency prediction result and the frequency residual estimate in the frequency control quantities of the numerically controlled oscillator are determined, and the bandwidth of the carrier tracking feedback loop is adjusted to between the preset minimum bandwidth and the preset maximum bandwidth. When the prediction confidence weight coefficient increases, the weight of the residual frequency prediction result is increased; when the prediction confidence weight coefficient decreases, the weight of the frequency residual estimate is increased.

[0116] In the hybrid tracking state S2, when the prediction confidence weight coefficient recovers to a level greater than the first weight threshold, the system switches from hybrid tracking state S2 to reliable prediction tracking state S1. When the prediction confidence weight coefficient continues to decrease to a level not greater than the second weight threshold, the system switches from hybrid tracking state S2 to unreliable tracking state S3. When the phase difference continuously exceeds a preset difference threshold, or the carrier-to-noise ratio is lower than a preset carrier-to-noise ratio threshold, the system switches from hybrid tracking state S2 to protection or local reacquisition state S4.

[0117] In the untrusted tracking state S3, the prediction confidence weight coefficient is no greater than the second weight threshold. At this time, the weight of the residual frequency prediction result in the frequency control quantity of the numerically controlled oscillator (CNC) is reduced, while the weight of the frequency residual estimate is increased, so that the CNC's frequency control quantity is mainly generated based on the frequency residual estimate. Simultaneously, the bandwidth of the carrier tracking feedback loop is increased to improve its ability to follow carrier frequency and phase changes. When the prediction confidence weight coefficient again exceeds the second weight threshold, the system switches from untrusted tracking state S3 to hybrid tracking state S2. When the risk of carrier tracking loss increases, the system switches from untrusted tracking state S3 to protection or partial reacquisition state S4.

[0118] In the protection or partial reacquisition state S4, abnormal protection or partial reacquisition is performed during the carrier tracking process of the low-Earth orbit satellite signal. Specifically, when the phase difference continuously exceeds a preset difference threshold, or the duration of pilot signal loss exceeds a preset loss time threshold, the frequency control quantity of the numerically controlled oscillator generated using the residual frequency prediction results is reduced or stopped, and a local frequency search is performed to reacquire the low-Earth orbit satellite signal. When the protection condition is released, the system switches from the protection or partial reacquisition state S4 to the hybrid tracking state S2. When partial reacquisition is successful and the prediction confidence weight coefficient recovers to a value greater than the first weight threshold, the system switches from the protection or partial reacquisition state S4 to the reliable prediction tracking state S1.

[0119] The bandwidth of the carrier tracking feedback loop is determined based on the prediction confidence weighting coefficient and the prediction variance. A larger prediction confidence weighting coefficient and a smaller prediction variance result in a carrier tracking feedback loop bandwidth closer to the preset minimum bandwidth; conversely, a smaller prediction confidence weighting coefficient or a larger prediction variance results in a carrier tracking feedback loop bandwidth closer to the preset maximum bandwidth. Therefore, a narrower carrier tracking feedback loop bandwidth is used when the reliability of the residual frequency prediction result is high; a wider bandwidth is used when the reliability of the residual frequency prediction result is low; and when the phase difference exceeds the limit continuously, pilot signals are lost, or the risk of loss of lock-up increases, the loop enters a protection or local reacquisition state, thereby reducing the impact of abnormal predictions on the tracking stability of low-Earth orbit satellite signals.

[0120] This application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform a low-Earth orbit satellite signal tracking method based on residual Doppler prediction.

[0121] Compared with existing technologies, this application proposes a low-Earth orbit (LEO) satellite signal tracking method based on residual Doppler prediction. It determines the frequency residual and rate of change residual based on the measured frequency and rate of change during the current tracking process, as well as the geometrical Doppler frequency and rate of change calculated through the orbital geometry model. Residual Doppler features are then constructed based on the frequency residual, rate of change residual, and receiver state parameters. Finally, a residual rate of change prediction model is used to predict the rate of change residual prediction sequence for multiple future tracking cycles, generating the residual frequency prediction result corresponding to the target's future tracking cycle. This allows the frequency control quantity of the numerically controlled oscillator to simultaneously combine the geometrical Doppler frequency and residual frequency prediction result corresponding to the target's future tracking cycle. This solves the problem that, under conditions of high Doppler frequency offset and high Doppler rate of change, relying solely on geometrical Doppler compensation or carrier tracking feedback adjustment is insufficient to balance dynamic response and tracking stability for LEO satellite signals.

[0122] Figure 1 This is a flowchart illustrating a low-Earth orbit satellite signal tracking method based on residual Doppler prediction in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0123] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor A01, a network interface A02, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The network interface A02 is used for communication with external terminals via a network connection. When executed by the processor A01, the computer program B02 implements a residual Doppler prediction method for tracking low-Earth orbit satellite signals.

[0124] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0129] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0130] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0131] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0132] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for tracking low-Earth orbit satellite signals based on residual Doppler prediction, characterized in that, The method includes: Obtain the measured frequency and measured Doppler rate of change during the current tracking process of the receiver; Acquire satellite orbital operation information and receiver motion status information, and calculate the geometric Doppler frequency and geometric Doppler rate of change corresponding to the current tracking period using the orbital geometric model; The frequency residual is calculated based on the measured frequency and the geometric Doppler frequency, and the rate of change residual is calculated based on the measured Doppler rate of change and the geometric Doppler rate of change. Residual Doppler features are constructed based on the frequency residual, the rate of change residual, and the receiver state parameters; The residual Doppler features from multiple consecutive tracking cycles are input into the residual rate of change prediction model to obtain a prediction sequence of the rate of change residuals for multiple future tracking cycles. Based on the frequency residual corresponding to the current tracking period and the prediction sequence of the rate of change residuals for the future multiple tracking periods, a residual frequency prediction result corresponding to the target future tracking period is generated, wherein the target future tracking period is at least one of the future multiple tracking periods; Based on the satellite orbital operation information and the receiver motion state information, the geometric Doppler frequency corresponding to the future tracking period of the target is calculated using the orbital geometric model; Based on the geometric Doppler frequency corresponding to the future tracking period of the target and the prediction result of the residual frequency corresponding to the future tracking period of the target, the frequency control quantity of the numerically controlled oscillator is generated. The receiver's local carrier frequency is adjusted according to the frequency control value of the numerically controlled oscillator in order to perform carrier tracking of low-Earth orbit satellite signals.

2. The method for tracking low-Earth orbit satellite signals based on residual Doppler prediction according to claim 1, characterized in that, The step of inputting the residual Doppler features from multiple consecutive tracking periods into the residual rate of change prediction model to obtain a prediction sequence of the rate of change residuals for future tracking periods includes: The residual Doppler features from multiple consecutive tracking cycles are input into the residual rate of change prediction model to obtain the rate of change residual prediction sequence for the next multiple tracking cycles and the prediction variance corresponding to the rate of change residual prediction sequence. Generate prediction confidence weight coefficients based on the prediction variance and the receiver state parameters; The step of generating the frequency control quantity of the numerically controlled oscillator based on the geometric Doppler frequency corresponding to the future tracking period of the target and the predicted residual frequency corresponding to the future tracking period of the target includes: Based on the prediction confidence weight coefficient, the weight of the residual frequency prediction result corresponding to the target future tracking period in the frequency control quantity of the numerically controlled oscillator is determined, and the frequency control quantity of the numerically controlled oscillator is generated based on the geometric Doppler frequency corresponding to the target future tracking period, the residual frequency prediction result corresponding to the target future tracking period, and the prediction confidence weight coefficient.

3. The method for tracking low-Earth orbit satellite signals based on residual Doppler prediction according to claim 1, characterized in that, Before acquiring the measured frequency and measured Doppler rate of change during the current tracking process of the receiver, the method further includes: The receiver performs low-noise amplification, down-conversion, analog-to-digital conversion, and digital down-conversion on the low-Earth orbit satellite radio frequency signal to obtain a complex intermediate frequency sampling signal. The complex intermediate frequency sampling signal is coarsely acquired to obtain coarse acquisition results, which include initial frequency offset, initial code phase, initial carrier-to-noise ratio, and initial control input of the numerically controlled oscillator. Based on the coarse acquisition result, the complex intermediate frequency sampled signal is subjected to current carrier tracking processing, and the measured frequency and the measured Doppler rate of change are obtained in the current carrier tracking processing.

4. The method for tracking low-Earth orbit satellite signals based on residual Doppler prediction according to claim 3, characterized in that, The step of obtaining the measured frequency and the measured Doppler rate of change in the current carrier tracking process includes: The complex intermediate frequency sampling signal corresponding to the current tracking period in the current carrier tracking process is coherently integrated to obtain the coherent integration result corresponding to the current tracking period, wherein the coherent integration result includes an in-phase integration component and a quadrature integration component. The phase difference between adjacent tracking cycles is determined based on the coherent integration result corresponding to the current tracking cycle and the coherent integration result corresponding to the previous tracking cycle. The measured frequency corresponding to the current tracking cycle is determined based on the phase difference, the time interval between adjacent tracking cycles, and the frequency control amount of the numerically controlled oscillator corresponding to the previous tracking cycle. The measured Doppler change rate corresponding to the current tracking period is obtained by median difference estimation based on the measured frequencies corresponding to multiple consecutive tracking periods.

5. The method for tracking low-Earth orbit satellite signals based on residual Doppler prediction according to claim 1, characterized in that, The construction of residual Doppler features based on the frequency residual, the rate of change residual, and the receiver state parameters includes: The frequency residual corresponding to the current tracking period is normalized. The rate of change residual corresponding to the current tracking period is normalized. The difference between the rate of change residual corresponding to the current tracking period and the rate of change residual corresponding to the previous tracking period is normalized. The residual Doppler feature is constructed based on the normalized frequency residual, the normalized rate of change residual, the normalized difference, and the receiver state parameters. The receiver status parameters include at least one of satellite elevation angle, carrier-to-noise ratio, phase difference, and normalized automatic gain control.

6. The method for tracking low-Earth orbit satellite signals based on residual Doppler prediction according to claim 2, characterized in that, The receiver state parameters include carrier-to-noise ratio and phase difference, wherein the phase difference is the difference between the carrier phase determined by the coherent integration result corresponding to the current tracking period and the prediction result of the carrier phase corresponding to the current tracking period in the current carrier tracking process. The step of generating prediction confidence weight coefficients based on the prediction variance and the receiver state parameters includes: The original confidence level is calculated based on the phase difference and the prediction variance. The original confidence level is corrected based on the carrier-to-noise ratio and the phase difference to obtain the predicted confidence level weighting coefficient. Wherein, if the phase difference is less than or equal to a preset phase difference threshold and the carrier-to-noise ratio is greater than or equal to a preset carrier-to-noise ratio threshold, the original confidence level is used as the prediction confidence weight coefficient; if the phase difference is greater than the preset phase difference threshold or the carrier-to-noise ratio is less than the preset carrier-to-noise ratio threshold, the prediction confidence weight coefficient is determined to be zero.

7. The method for tracking low-Earth orbit satellite signals based on residual Doppler prediction according to claim 2, characterized in that, The step of determining the weight of the residual frequency prediction result corresponding to the future tracking period of the target in the frequency control quantity of the numerically controlled oscillator according to the prediction confidence weight coefficient, and generating the frequency control quantity of the numerically controlled oscillator according to the geometric Doppler frequency corresponding to the future tracking period of the target, the residual frequency prediction result corresponding to the future tracking period of the target, and the prediction confidence weight coefficient, includes: The frequency residual is subjected to residual Kalman filtering to obtain the frequency residual estimate; Based on the prediction confidence weight coefficient, determine the weights of the residual frequency prediction result and the frequency residual estimate corresponding to the future tracking period of the target in the frequency control quantity of the numerically controlled oscillator; The frequency control quantity of the numerically controlled oscillator is generated based on the geometric Doppler frequency corresponding to the future tracking period of the target, the predicted residual frequency corresponding to the future tracking period of the target, the estimated frequency residual value, and the prediction confidence weighting coefficient.

8. The method for tracking low-Earth orbit satellite signals based on residual Doppler prediction according to claim 7, characterized in that, The method further includes: The bandwidth of the carrier tracking feedback loop is determined based on the prediction confidence weight coefficient and the prediction variance. When the prediction confidence weight coefficient increases relative to the previous tracking period, the bandwidth of the carrier tracking feedback loop is reduced, and the Doppler dynamics of the low-orbit satellite signal are compensated by the frequency control of the numerically controlled oscillator. When the prediction confidence weight coefficient decreases relative to the previous tracking period, the bandwidth of the carrier tracking feedback loop is increased, and the weight of the frequency residual estimate in the frequency control quantity of the numerically controlled oscillator is increased.

9. The method for tracking low-Earth orbit satellite signals based on residual Doppler prediction according to claim 8, characterized in that, The method further includes: In the event of an error in the satellite orbital information, a second-order polynomial extrapolation is performed based on the geometric Doppler frequencies corresponding to the most recent tracking cycles to obtain the extrapolated geometric Doppler frequencies, and the upper limit of the prediction confidence weight coefficient is limited. If the loss time of the pilot signal or synchronization signal does not exceed a preset time period, the geometric Doppler frequency is maintained, and prediction is made using the residual rate of change prediction model. If the duration of loss of the pilot signal or the synchronization signal exceeds a preset time period, the receiver is controlled to enter a local reacquisition state. If the phase difference exceeds a preset phase difference threshold for multiple consecutive tracking cycles, the prediction confidence weight coefficient is set to zero, the bandwidth of the carrier tracking feedback loop is increased, and a local frequency search is initiated.

10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform a low-Earth orbit satellite signal tracking method based on residual Doppler predictions according to any one of claims 1 to 9.

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