Method and apparatus for dynamic step-by-step decomposition of carrier adaptive tracking

Abstract

The application provides a dynamic step-by-step decomposition carrier adaptive tracking method and device, and belongs to the technical field of satellite-borne TT&C transponder signal processing. The method comprises the following steps: obtaining loop state parameters and integrated signal-to-noise ratios of each loop through a multistage loop based on target baseband signals of multiple tracking periods; determining bandwidth scaling factors of each loop based on the loop state parameters and the integrated signal-to-noise ratios, multiplying the bandwidth before updating of each loop with the bandwidth scaling factor corresponding to the loop to obtain the bandwidth after updating of the loop; correcting the bandwidth after updating of the loop based on the maximum loop bandwidth and the minimum loop bandwidth corresponding to the loop to obtain a target loop bandwidth of the loop; and configuring loop parameters of each loop based on the target loop bandwidths of the multistage loop, and performing carrier tracking in the next tracking period according to the configured loop parameters of each loop. The application can realize multistage loop parameter collaborative adaptive adjustment, and takes into account tracking stability and precision.

Description

Technical Field

[0001] This application belongs to the field of signal processing technology for spaceborne telemetry and control transponders, and more specifically, it relates to a carrier adaptive tracking method and apparatus with dynamic step-by-step decomposition. Background Technology

[0002] In satellite telemetry, tracking, and command (TT&C) systems, carrier tracking is the core technology for enabling reliable information exchange between the onboard TT&C transponder and the ground station. Its core objective is to accurately extract carrier phase and frequency information from highly dynamic TT&C signals, offset Doppler frequency offset, frequency change rate, and phase deviation caused by the high-speed motion of the satellite, and ensure the accuracy of signal demodulation and decoding.

[0003] Currently, for carrier tracking requirements of high-dynamic measurement and control signals, the industry widely adopts a multi-ring cascaded tracking architecture. This architecture is usually composed of a frequency rate loop (FRL), a frequency locked loop (FLL), and a phase locked loop (PLL) cascaded in series. The FRL tracks the Doppler frequency change rate of the signal (corresponding to the radial acceleration term) to remove the carrier frequency change rate component. The FLL takes over the residual signal to eliminate the frequency offset caused by velocity. Finally, the PLL achieves high-precision carrier phase locking.

[0004] However, existing multi-ring cascaded tracking methods still have significant technical defects. As the core parameter that determines tracking performance, the loop bandwidth of existing schemes generally adopts a fixed bandwidth design, which makes it difficult to balance tracking stability and accuracy. Summary of the Invention

[0005] The purpose of this application is to provide a dynamic, step-by-step carrier adaptive tracking method and apparatus to solve the problem that existing solutions cannot simultaneously achieve tracking stability and accuracy. To achieve the above objective, the technical solution provided by this application is as follows: Firstly, a carrier adaptive tracking method with dynamic step-by-step decomposition is provided, including: After down-conversion and despreading of the received signal, the target baseband signal is obtained. Based on the target baseband signal, the state parameters of each loop and the signal-to-noise ratio after integration are obtained through a multi-level loop. The multi-level loop includes a frequency change rate loop, a frequency locking loop and a phase-locked loop. The state parameters of each loop include the residual of the frequency change rate loop, the residual of the frequency locking loop and the phase error variance of the phase-locked loop. Based on the state parameters of each loop level and the signal-to-noise ratio after integration, the bandwidth scaling factor of each loop level is determined, and the bandwidth before update of each loop level corresponding to multiple tracking cycles is obtained. For each loop level, the bandwidth before update of the loop level is multiplied by the bandwidth scaling factor corresponding to the loop level to obtain the updated bandwidth of the loop level. Based on the maximum and minimum loop bandwidths corresponding to the loop level, the updated bandwidth of the loop level is corrected to obtain the target loop bandwidth of the loop level. Based on the target loop bandwidth of the multi-level loop, the loop parameters of each level of the loop are configured, and carrier tracking is performed in the next tracking cycle according to the configured loop parameters of each level of the loop.

[0006] Secondly, a carrier adaptive tracking device with dynamic step-by-step decomposition is provided, including: The multi-level loop state parameter extraction module is used to down-convert and despread the received signal to obtain the target baseband signal. Based on the target baseband signal, the state parameters of each loop level and the integrated signal-to-noise ratio are obtained through the multi-level loop. The multi-level loop includes a frequency change rate loop, a frequency locking loop, and a phase-locked loop. The state parameters of each loop level include the residual of the frequency change rate loop, the residual of the frequency locking loop, and the phase error variance of the phase-locked loop. The loop bandwidth adjustment module is used to determine the bandwidth scaling factor of each loop level based on the loop state parameters and the integrated signal-to-noise ratio, and to obtain the pre-update bandwidth of each loop level for multiple tracking cycles. For each loop level, the pre-update bandwidth of the loop level is multiplied by the bandwidth scaling factor corresponding to the loop level to obtain the updated bandwidth of the loop level. The updated bandwidth of the loop level is corrected based on the maximum and minimum loop bandwidths corresponding to the loop level to obtain the target loop bandwidth of the loop level. The carrier tracking parameter update module is used to configure the loop parameters of each level of the loop based on the target loop bandwidth of the multi-level loop, and to perform carrier tracking for the next tracking cycle according to the configured loop parameters of each level of the loop.

[0007] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement a dynamically decomposed carrier adaptive tracking method provided by any possible implementation of the first aspect.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements a dynamically decomposed carrier adaptive tracking method provided by any possible implementation of the first aspect.

[0009] The beneficial effects of the technical solution provided in this application are as follows: The carrier adaptive tracking method and apparatus with dynamic step-by-step decomposition provided in this application, compared with related technologies, are as follows: To address the shortcomings of existing fixed-bandwidth schemes in balancing dynamic adaptability and noise suppression, this application embodiment demodulates the expanded baseband signal in real time, acquiring loop state parameters such as the frequency change rate loop residual, frequency-locked loop residual, phase-locked loop phase error variance, and integrated signal-to-noise ratio. This allows for dynamic calculation of the bandwidth scaling factor, enabling adaptive adjustment of the multi-level loop bandwidth instead of using fixed bandwidth parameters. Simultaneously, this application embodiment corrects the updated bandwidth of each loop with maximum and minimum bandwidth constraints, preventing tracking loss or noise degradation caused by exceeding loop bandwidth limits, ensuring continuous and reliable tracking. Compared to a fixed-bandwidth architecture, the adaptive adjustment mechanism can rapidly broaden the bandwidth to ensure tracking robustness when satellite high maneuvers cause significant Doppler frequency shifts and frequency change rates; and narrow the bandwidth to improve carrier phase extraction accuracy when the signal is stable. Through multi-level loop collaborative adaptive configuration, this application embodiment significantly improves the carrier tracking capability of the onboard telemetry and control transponder under high dynamic and strong interference conditions, reducing the probability of signal loss and demodulation error rate, providing solid support for stable and reliable telemetry and control between the satellite and ground station. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0011] Figure 1 A flowchart illustrating a dynamic step-by-step decomposition carrier adaptive tracking method provided in an embodiment of this application; Figure 2 A schematic diagram illustrating the principle of a dynamic step-by-step decomposition carrier adaptive tracking method provided in an embodiment of this application; Figure 3 A structural block diagram of a carrier adaptive tracking device with dynamic step-by-step decomposition provided in an embodiment of this application; Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0013] Those skilled in the art will understand that, unless otherwise stated, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.” When describing multiple (two or more) items, if the relationship between the multiple items is not explicitly defined, the multiple items can refer to one, several or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" can be implemented as parameter A includes A1 or A2 or A3, or it can be implemented as parameter A includes at least two of the three items A1, A2 and A3.

[0014] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0016] This application provides a dynamically decomposed carrier adaptive tracking method, which can be executed by an electronic device, such as... Figure 1 As shown, the method may include: S101: After down-converting and despreading the received signal, the target baseband signal is obtained. Based on the target baseband signal, the state parameters of each loop and the signal-to-noise ratio after integration are obtained through a multi-level loop. The multi-level loop includes a frequency change rate loop, a frequency locking loop, and a phase-locked loop. The state parameters of each loop include the residual of the frequency change rate loop, the residual of the frequency locking loop, and the variance of the phase error of the phase-locked loop.

[0017] In this embodiment, based on the target baseband signal, the state parameters of each loop level and the integrated signal-to-noise ratio are obtained through a multi-level loop, including: The target baseband signal is subjected to frequency rate change loop quadrature demodulation and integration cleanup by frequency rate change loop, and the frequency rate change loop integration cleanup result is obtained. The target baseband signal after orthogonal demodulation by the frequency rate change loop is subjected to frequency-locked loop orthogonal demodulation and integral clearing to obtain the frequency-locked loop integral clearing result. The target baseband signal, which has undergone quadrature demodulation by a rate-of-change loop and quadrature demodulation by a frequency-locked loop, is subjected to phase-locked loop quadrature demodulation and integration and clearing by a phase-locked loop to obtain the phase-locked loop integration and clearing result. The signal-to-noise ratio after integration is obtained based on the phase-locked loop integration and clearing result. Using the frequency change rate loop integral clearing result, frequency lock loop integral clearing result, and phase lock loop integral clearing result as input data, the output data of each loop discriminator is obtained through each loop discriminator; The state parameters of each loop level are obtained based on the output data of the loop discriminators at each level.

[0018] In this embodiment, the loop discriminators at each level include a rate of change loop discriminator, a phase-locked loop phase discriminator, and a frequency-locked loop frequency discriminator; Using the frequency change rate loop integral clearing result, the frequency-locked loop integral clearing result, and the phase-locked loop integral clearing result as input data, the output data of each loop discriminator is obtained through each loop discriminator, including: Using the frequency change rate loop integral clearing result as input data, the frequency change rate loop discriminator output data is obtained through the frequency change rate loop discriminator. Using the phase-locked loop integral clearing result as input data, the phase-locked loop phase discriminator output data is obtained through the phase-locked loop phase discriminator. Using the frequency-locked loop integral clearing result as input data, the frequency-locked loop discriminator output data is obtained through the frequency-locked loop discriminator. The output data of the frequency change rate loop discriminator, the output data of the phase-locked loop phase discriminator, and the output data of the frequency-locked loop discriminator are used as the output data of each loop discriminator.

[0019] In this embodiment, the loop state parameters at each level are obtained based on the output data of the loop discriminators at each level, including: The output data of the frequency change rate loop discriminator is used as the frequency change rate loop residual. The output data of the frequency-locked loop discriminator is used as the frequency-locked loop residual. The phase error variance of the phase-locked loop is obtained based on the output data of the phase-locked loop phase discriminator. The residual of the frequency change rate loop, the residual of the frequency-locked loop, and the variance of the phase-locked loop phase error are used as the state parameters of each loop stage.

[0020] In this embodiment, the received signal refers to the radio frequency input signal acquired by the receiver of the spread spectrum communication system, such as the spread spectrum modulated radio frequency signal received by a satellite navigation receiver. Down-conversion refers to the process of mixing and reducing the radio frequency signal to an intermediate frequency (IF) signal, for example, converting a high-frequency received signal into an IF signal suitable for baseband processing using a mixer. Despreading refers to the process of stripping the spread spectrum modulation of the received signal using a local spreading code, for example, recovering the baseband information of the received signal through pseudo-random code correlation operations. The target baseband signal refers to the baseband processed signal input to a multi-stage loop after preprocessing, such as the in-phase and quadrature baseband signals obtained after down-conversion and despreading. The frequency change rate loop refers to the synchronization loop used to estimate and compensate for second-order carrier frequency changes; the frequency locking loop refers to the synchronization loop used to estimate and compensate for first-order carrier frequency deviations; and the phase-locked loop refers to the synchronization loop used to achieve precise carrier phase locking. Loop state parameters refer to quantitative parameters characterizing the operating state of each stage of the synchronization loop, used to reflect the dynamic tracking and locking status of the loop. Each level of the loop discriminator refers to the set of functional modules in each level of the loop used to extract synchronization errors, and the discriminator output data refers to the quantification results of the synchronization errors output by each level of the discriminator.

[0021] The frequency change rate loop residual refers to the frequency change rate estimation deviation value output by the frequency change rate loop discriminator; the frequency locking loop residual refers to the frequency estimation deviation value output by the frequency locking loop discriminator; and the phase-locked loop phase error variance refers to the phase error fluctuation statistics obtained based on the output statistics of the phase-locked loop phase discriminator. The signal-to-noise ratio after integration refers to the baseband signal-to-noise ratio calculated based on the phase-locked loop integration and clearing results. Quadrature demodulation refers to the process of converting the intermediate frequency signal into two in-phase orthogonal baseband signals; integration clearing refers to the process of segmented integration of the baseband signal and periodic zeroing; and the integration clearing result refers to the coherent integral output data obtained after integration clearing. The quadrature demodulation process of each loop stage is executed by the numerically controlled oscillator (NCO) corresponding to that loop stage, and the local carrier output by the NCO of different loops is different.

[0022] For example, this embodiment can be executed in the signal processing device of the spaceborne telemetry and control transponder. During multiple tracking cycles, after the radio frequency receiving device of the transponder completes the down-conversion of the signal, it transmits the down-converted digital baseband signal of multiple tracking cycles to the baseband processing module. In response to the signal, the baseband processing module calls the pre-stored spreading pseudocode synchronized with the transmitter, performs relevant calculation operations on the down-converted digital baseband signal, completes the despreading process through pseudocode matching, filters out the interference components in the signal, and restores the narrowband target baseband signal.

[0023] This embodiment can orthogonally demodulate the target baseband signal input to the frequency change rate loop, then input the orthogonally demodulated signal to the frequency-locked loop for further orthogonal demodulation, and finally input the orthogonally demodulated signal output from the frequency-locked loop to the phase-locked loop (PLL), which performs the final orthogonal demodulation on the input signal. Specifically, the numerically controlled oscillators corresponding to each loop stage generate multiple local carrier reference signals. These local carrier reference signals are used to mix and separate the input signal, completing full-dimensional orthogonal demodulation processing and separating it into two mutually orthogonal baseband components.

[0024] The integrated signal-to-noise ratio (SNR) is used to characterize the physical channel quality of the current signal. In this embodiment, integration and clearing processing can be performed on the two orthogonal baseband components after final phase-locked loop (PLL) quadrature demodulation. The energy of the target baseband signal is accumulated and integrated according to a preset tracking period. After the integration period ends, a clearing operation is performed to obtain the PLL integration and clearing results for the current tracking period and several previous tracking periods. The classical blind signal-to-noise ratio (SNR) estimation algorithm calculates the integrated SNR using the (second-order moment - fourth-order moment) classical method. Specifically, let the tracking period be... The phase-locked loop integral clearing result is Its modulus squared is defined as Based on the squared modulus data of L consecutive tracking cycles within a sliding window, the second moment of the target baseband signal is calculated. and fourth moment : ; The signal-to-noise ratio after integration is calculated based on the second and fourth moments. : ; in, Let L be the signal-to-noise ratio after integration, and L be the number of tracking cycles involved in the signal-to-noise ratio estimation. Indicates the tracking period The squared modulus of the phase-locked loop integral clearing result.

[0025] In this embodiment, the two quadrature baseband components after quadrature demodulation output from the frequency rate of change loop can be input to the frequency rate of change loop discriminator, the two quadrature baseband components after quadrature demodulation output from the frequency-locked loop can be input to the frequency-locked loop discriminator, and the two quadrature baseband components after quadrature demodulation output from the phase-locked loop can be input to the phase-locked loop phase discriminator. The frequency change rate discriminator compares the frequency change rate of the two input quadrature baseband components with the reference signal output from the local digitally controlled oscillator, and calculates the difference in their frequency change rates, which is the output data of the frequency change rate discriminator for the current tracking period. The frequency-locked loop discriminator compares the frequency of the two input quadrature baseband components with the reference signal output from the local digitally controlled oscillator, and obtains the carrier frequency offset difference between them, which is the output data of the frequency-locked loop discriminator for the current tracking period. The phase-locked loop phase discriminator compares the phase of the two input quadrature baseband components with the reference signal output from the local digitally controlled oscillator, and obtains the phase difference between them, which is the output data of the phase-locked loop phase discriminator for the current tracking period. In this embodiment, the three types of output data can be integrated into the output data of each level of the loop discriminator.

[0026] For example, the frequency rate of change loop is used to track the Doppler rate of change of the signal (i.e., the first time derivative of the carrier frequency, in Hz / s). In this embodiment, the frequency rate of change loop residual for the k-th tracking period can be directly output through a frequency rate of change loop discriminator. Alternatively, in this embodiment, the frequency-locked loop residual for the k-th tracking period can be directly output through a frequency-locked loop discriminator.

[0027] For example, this embodiment can estimate the variance of the phase-locked loop phase error over the tracking period k. To quantify the locking quality of the PLL, in the k-th tracking cycle, a sliding window of length M is selected, and the output data of the phase-locked loop phase discriminator within the sliding window is collected. Statistical calculations are then performed to evaluate the locking performance of the PLL. ; in, This represents the variance of the phase-locked loop phase error during the tracking period k. To track the phase discriminator output data for period ki, The phase discriminator outputs data for the tracking period kim. The typical value for the sliding window M is 10. Too large a value for M increases loop delay and reduces real-time tracking performance, while too small a value fails to reflect the statistical characteristics of the error variance, leading to increased loop jitter. The phase error variance of the phase-locked loop comprehensively reflects the jitter caused by thermal noise and the phase shift caused by dynamic residuals. The smaller the phase error variance of the phase-locked loop, the higher the tracking accuracy of the PLL.

[0028] This embodiment utilizes the phase-locked loop (PLL) integration and clearing results to accurately obtain the integrated signal-to-noise ratio (SNR). Combined with the output data from the two-stage discriminator, comprehensive loop state parameters are derived, achieving a holistic characterization of signal channel quality, dynamic stress, and loop locking quality. The parameter extraction steps are tailored to the processing characteristics of high-dynamic telemetry and control signals, and the operational methods are adapted to the working requirements of the spaceborne equipment. The extracted parameters are accurate and comprehensive, providing solid data support for the adaptive adjustment of loop parameters in subsequent carrier tracking, ensuring the stability and accuracy of subsequent carrier tracking.

[0029] S102: Determine the bandwidth scaling factor of each loop level based on the loop state parameters and the integrated signal-to-noise ratio, and obtain the pre-update bandwidth of each loop level for multiple tracking cycles; for each loop level, multiply the pre-update bandwidth of the loop level by the bandwidth scaling factor corresponding to the loop level to obtain the updated bandwidth of the loop level; correct the updated bandwidth of the loop level based on the maximum and minimum loop bandwidth corresponding to the loop level to obtain the target loop bandwidth of the loop level.

[0030] In this embodiment, the bandwidth scaling factor is a coefficient that adapts to the bandwidth adjustment of each loop level. It is used to calculate the updated bandwidth of the loop, and different loops correspond to different bandwidth scaling factors. The target loop bandwidth of the multi-level loop is the final bandwidth obtained after scaling and correction of each loop level, serving as the basis for bandwidth execution in carrier tracking. The bandwidth before update is the original bandwidth of the multi-level loop in multiple tracking cycles before adjustment, which can be obtained from the loop parameter storage unit. The updated bandwidth is the intermediate result of multiplying the bandwidth before update by the corresponding bandwidth scaling factor, serving as the basis for calculating the target loop bandwidth. The maximum loop bandwidth and minimum loop bandwidth are bandwidth constraint thresholds for each loop level, preset based on engineering experience, and used to limit and correct the updated bandwidth.

[0031] The consideration in this embodiment is that the bandwidth scaling factor is the core link between the loop operating state and bandwidth adjustment. This embodiment determines the bandwidth scaling factor based on the loop state parameters and the integrated signal-to-noise ratio, allowing bandwidth adjustment to accurately match the actual dynamics of the signal and the channel quality. This embodiment obtains the bandwidth before the update for multiple tracking cycles, which can provide a unified benchmark for dynamic bandwidth adjustment. The bandwidth can be directly widened or narrowed through multiplication operations. This embodiment sets the maximum and minimum loop bandwidth and corrects the updated bandwidth to avoid excessive noise introduced by excessive bandwidth and loop lockout caused by excessive bandwidth, thus preventing bandwidth divergence and ensuring that bandwidth adjustment both fits the signal state and conforms to the loop operating constraints of engineering practice.

[0032] For example, this embodiment can first rely on the extracted loop state parameters and integrated signal-to-noise ratio at each level, and then, through fuzzy characterization, fuzzy inference, and defuzzification processing, obtain the bandwidth scaling factor corresponding one-to-one with the frequency change rate loop, frequency-locked loop, and phase-locked loop, thus completing the determination of the bandwidth scaling factor. This embodiment can retrieve the pre-update bandwidth corresponding to the frequency change rate loop, frequency-locked loop, and phase-locked loop for multiple tracking cycles. This pre-update bandwidth is the loop bandwidth finally used in the previous tracking cycle or a preset initial reference bandwidth.

[0033] This embodiment can sequentially calculate the bandwidth of each loop level. The bandwidth before the update of the frequency change rate loop is multiplied by the corresponding bandwidth scaling factor to obtain the updated bandwidth of the frequency change rate loop. Similarly, the updated bandwidths of the frequency-locked loop and phase-locked loop are calculated. This embodiment can retrieve the pre-configured maximum and minimum loop bandwidths for each loop level. If the updated bandwidth of a certain loop level is greater than the maximum loop bandwidth of that level, the maximum loop bandwidth of that level is used as the correction value; if it is less than the minimum loop bandwidth, the minimum loop bandwidth is used as the correction value; if it is within the threshold range, the updated bandwidth remains unchanged. The corrected value is the target loop bandwidth of that loop level. After sequentially correcting all loops, the target loop bandwidth of the multi-level loops is obtained.

[0034] For example, in this embodiment, the bandwidth before each update in the previous tracking period is multiplied by the corresponding bandwidth scaling factor to calculate the loop bandwidth value for each level in the current environment. Furthermore, to prevent loop bandwidth divergence, it is limited. The bandwidth before the update of the frequency-locked loop in tracking period k is used as the reference. This example illustrates the bandwidth adjustment rules. This embodiment allows setting the maximum loop bandwidth. and minimum loop bandwidth The updated loop bandwidth is obtained according to the bandwidth update formula. The bandwidth update formula is as follows:

[0035] in, This is the bandwidth scaling factor for the frequency-locked loop.

[0036] This embodiment can Set the carrier loop bandwidth for the next tracking cycle and start tracking for a new cycle.

[0037] S103: Configure the loop parameters of each level of the loop based on the target loop bandwidth of the multi-level loop, and perform carrier tracking for the next tracking cycle according to the configured loop parameters of each level of the loop.

[0038] In this embodiment, the loop parameters are the core operating parameters of the carrier tracking loop, including filter coefficients, etc. Configuration involves converting the target loop bandwidth into usable parameters for each loop stage and loading them. Carrier tracking in the next tracking cycle applies the updated loop parameters to the next tracking cycle to achieve carrier tracking of the measurement and control signals.

[0039] In this embodiment, considering that the target loop bandwidth is only a numerical indicator, it needs to be converted into specific loop parameters before it can actually be applied to the carrier tracking loop. This embodiment applies the converted loop parameters to the next tracking cycle, realizing adaptive parameter updates within the tracking cycle. This allows the operating parameters of the carrier tracking loop to be dynamically adjusted according to the signal state, solving the problem that fixed parameters cannot balance stability and accuracy. At the same time, it ensures the collaborative work of the multi-loop cascaded architecture, allowing each loop level to cooperate in completing dynamic carrier stripping.

[0040] For example, in this embodiment, based on the target loop bandwidth of each of the frequency rate of change loop, frequency-locked loop, and phase-locked loop, the target loop bandwidth of each loop stage is converted into corresponding filter coefficients according to the generally accepted digital filter coefficient calculation rules in the art. These filter coefficients, as core loop parameters, are the key basis for the operation of the carrier tracking loop. In this embodiment, loop parameter configuration operations can be performed sequentially for each loop stage. The converted frequency rate of change loop filter coefficients are loaded into the corresponding loop filter module to complete the writing and activation of the parameters. Then, the filter coefficients of the frequency-locked loop and phase-locked loop are loaded into their respective loop filter modules in the same way to ensure that the filter parameters of each loop stage are accurately matched with its own target loop bandwidth.

[0041] This embodiment can synchronize the configured loop parameters at each level to the corresponding digitally controlled oscillator (DCO). The DCO then generates a local reference signal adapted to the current signal state based on the updated loop parameters, providing a benchmark for carrier tracking. Upon receiving the down-converted digital baseband signal in the next tracking cycle, this embodiment can initiate a dynamically decomposed, step-by-step carrier adaptive tracking process. The frequency change rate loop first strips the frequency change rate from the signal based on the configured loop parameters. The frequency-locked loop receives the processed residual signal, eliminating frequency offsets. Finally, the phase-locked loop performs phase deviation stripping on the signal. Throughout the process, all tracking actions are strictly performed according to the configured loop parameters, completing the high-dynamic measurement and control signal carrier tracking for the next tracking cycle.

[0042] In one embodiment of this application, the state parameters of each loop stage include the frequency change rate loop residual, the frequency-locked loop residual, and the phase-locked loop phase error variance; the bandwidth scaling factor of each loop stage is determined based on the state parameters of each loop stage and the signal-to-noise ratio after integration, including: The state parameters of each loop level and the integrated signal-to-noise ratio are used as multiple input variables; Determine multiple fuzzy subsets corresponding to each of the multiple input variables; Among them, the multiple fuzzy subsets corresponding to the frequency-locked loop residuals include the residual large negative subset, the residual zero subset, and the residual large positive subset; the multiple fuzzy subsets corresponding to the frequency change rate loop residuals include the change rate large negative subset, the change rate zero subset, and the change rate large positive subset; the multiple fuzzy subsets corresponding to the phase-locked loop phase error variance include the variance small subset, the variance medium subset, and the variance large subset; the multiple fuzzy subsets corresponding to the integrated signal-to-noise ratio include the signal-to-noise ratio low subset, the signal-to-noise ratio medium subset, and the signal-to-noise ratio high subset; the universe of discourse of each fuzzy subset is different; For each input variable, calculate the membership degree of the input variable on the multiple fuzzy subsets corresponding to the input variable to obtain the membership degree vector of the input variable; The bandwidth scaling factor is determined based on the membership vectors corresponding to each of the multiple input variables.

[0043] In this embodiment, for each input variable, the membership degree of the input variable on the multiple fuzzy subsets corresponding to the input variable is calculated to obtain the membership degree vector corresponding to the input variable. This includes: for each input variable, calculating the membership degree of the input variable on the multiple fuzzy subsets corresponding to the input variable using the triangular membership function to obtain the membership degree vector corresponding to the input variable.

[0044] In this embodiment, determining the bandwidth scaling factor based on the membership vectors corresponding to each of the multiple input variables includes: selecting a target determination rule from multiple fuzzy determination rules that matches the membership vectors corresponding to each of the multiple input variables; The multiple fuzzy decision rules include a first decision rule, a second decision rule, a third decision rule, and a fourth decision rule. The first decision rule is that the membership degree of the frequency-locked loop residual on the zero subset of residuals is the lowest. The second decision rule is that the membership degree of the phase-locked loop phase error variance on the large subset of variances is the highest, and the membership degree of the integrated signal-to-noise ratio on the high subset of signal-to-noise ratios is the highest. The third decision rule is that the membership degree of the phase-locked loop phase error variance on the large subset of variances is the highest, and the membership degree of the integrated signal-to-noise ratio on the low subset of signal-to-noise ratios is the highest, and the membership degree of the frequency change rate loop residual on the zero subset of the rate of change is the highest. The fourth decision rule is that the membership degree of the frequency-locked loop residual on the zero subset of residuals is the highest, and the membership degree of the phase-locked loop phase error variance on the small subset of variances is the highest, and the integrated signal-to-noise ratio remains stable. Based on the target determination rule, a target fuzzy instruction is selected as the fuzzy inference result from multiple fuzzy instructions; all multiple fuzzy instructions are used to indicate the adjustment operation of the bandwidth scaling factor; The fuzzy inference results are defuzzified and converted into a bandwidth scaling factor vector.

[0045] In this embodiment, selecting a target determination rule from multiple fuzzy determination rules that matches the membership vectors corresponding to each of the multiple input variables includes: determining the membership vectors corresponding to each of the multiple input variables in the order of the first determination rule, the second determination rule, the third determination rule, and the fourth determination rule; if the membership vectors corresponding to each of the multiple input variables satisfy the current determination rule, then the current determination rule is used as the target determination rule matching the membership vectors corresponding to each of the multiple input variables; if the membership vectors corresponding to each of the multiple input variables do not satisfy the current determination rule, then the membership vectors corresponding to each of the multiple input variables are determined based on the next determination rule; if the membership vectors corresponding to each of the multiple input variables do not satisfy the first determination rule, the second determination rule, the third determination rule, and the fourth determination rule, then no bandwidth adjustment is performed, that is, no subsequent operation is performed, and a new round of signal tracking is restarted.

[0046] In this embodiment, the multiple fuzzy instructions include a first fuzzy instruction, a second fuzzy instruction, a third fuzzy instruction, and a fourth fuzzy instruction; The first fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the first bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the second bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the third bandwidth scaling factor interval; the median value of the first bandwidth scaling factor interval is greater than the median value of the second bandwidth scaling factor interval, the median value of the second bandwidth scaling factor interval is greater than the median value of the third bandwidth scaling factor interval, and the median value of the third bandwidth scaling factor interval is greater than 1. The second fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the fourth bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the fifth bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the third bandwidth scaling factor interval; the intermediate values ​​of the fourth and fifth bandwidth scaling factor intervals are both 1, and the endpoint values ​​of the fourth and fifth bandwidth scaling factor intervals are different. The third fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the fourth bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the second bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the third bandwidth scaling factor interval. The fourth fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the sixth bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the seventh bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the eighth bandwidth scaling factor interval; the median value of the sixth bandwidth scaling factor interval is greater than the median value of the eighth bandwidth scaling factor interval, the median value of the eighth bandwidth scaling factor interval is greater than the median value of the seventh bandwidth scaling factor interval, and the median value of the sixth bandwidth scaling factor interval is less than 1; Based on target determination rules, a target fuzzy instruction is selected as the fuzzy inference result from multiple fuzzy instructions, including: If the target determination rule is the first determination rule, then the first fuzzy instruction is selected as the fuzzy inference result; If the target determination rule is the second determination rule, then the second fuzzy instruction is selected as the fuzzy inference result; If the target determination rule is the third determination rule, then the third fuzzy instruction is selected as the fuzzy inference result; If the target determination rule is the fourth determination rule, then the fourth fuzzy instruction is selected as the fuzzy inference result.

[0047] In this embodiment, a fuzzy subset is a set of states that divides input variables according to their own characteristics. Different input variables correspond to different types of fuzzy subsets, used to characterize different state characteristics of the input variables. The subsets with large negative residuals, zero residuals, and large positive residuals are specific fuzzy subsets of the frequency-locked loop residuals, used to characterize their degree of abrupt change; the subsets with large negative rates of change, zero rates of change, and large positive rates of change are specific fuzzy subsets of the frequency rate of change loop residuals, used to characterize their residual magnitude; the subsets with small variance, medium variance, and large variance are specific fuzzy subsets of the phase-locked loop phase error variance, used to characterize their phase jitter degree; the subsets with low signal-to-noise ratios, medium signal-to-noise ratios, and high signal-to-noise ratios are specific fuzzy subsets of the integrated signal-to-noise ratio, used to characterize their channel quality. The universe of discourse is the numerical interval corresponding to each fuzzy subset, and the universe of discourse of different fuzzy subsets is set according to the characteristics of the input variables. The membership degree is the degree to which the input variable belongs to the corresponding fuzzy subset, used to quantify the degree of association between the input variable and the fuzzy subset.

[0048] The triangular membership function is a dedicated function for calculating membership degrees, characterized by low computational cost, and is used to obtain the membership degree of an input variable in each fuzzy subset. The membership vector is a vector formed by sequentially representing the membership degrees of the input variable in all corresponding fuzzy subsets; it is itself a fuzzy vector. The fuzzy decision rule is a pre-defined rule corresponding to the membership features of the input variable and bandwidth adjustment, including four categories of rules covering different typical signal scenarios. The fuzzy instruction is a command used to instruct the bandwidth scaling factor adjustment operation; different decision rules correspond to different fuzzy instructions. The fuzzy inference result is the target fuzzy instruction selected based on the target decision rule. Defuzzification is the operation of converting the fuzzy inference result into a precise bandwidth scaling factor. The bandwidth scaling factor vector is a vector formed by sequentially representing the bandwidth scaling factors corresponding to each level of the loop, corresponding one-to-one with each level of the multi-loop cascaded architecture.

[0049] This embodiment uses loop state parameters and the integrated signal-to-noise ratio as fuzzy input variables, adapting to the uncertainty of highly dynamic telemetry and control signal states and achieving flexible judgment rather than rigid threshold judgment. This embodiment divides each input variable into a specific fuzzy subset and sets different universes of discourse, accurately representing the different state characteristics of each input variable and conforming to the physical meaning of each parameter. This embodiment uses a triangular membership function to calculate membership, which has low computational cost and good linearity, adapting to the real-time requirements of high-speed signal processing in spaceborne telemetry and control transponders. This embodiment pre-sets four types of fuzzy judgment rules, covering all signal scenarios including high dynamics, dynamic fluctuations, noise fluctuations, and stable tracking, ensuring that bandwidth adjustment rules accurately match the actual signal state. This embodiment selects the matching target judgment rule through step-by-step judgment, ensuring the rationality of bandwidth adjustment. Defuzzification then transforms the fuzzy command into a precise bandwidth scaling factor vector, realizing the transition from fuzzy judgment to precise execution, balancing judgment flexibility and engineering execution accuracy, and adapting to the collaborative adjustment needs of multi-loop cascaded architectures.

[0050] In this embodiment, the fourth determination rule refers to the continuous stability of the signal-to-noise ratio after integration. The mean signal-to-noise ratio (SNR) after integration for each tracking period lies within the universe of discourse of a subset of the SNR or a high subset of the SNR, and this continuous The standard deviation of the signal-to-noise ratio after integration does not exceed a preset threshold. For example, suppose... =10. If the mean of the signal-to-noise ratio after integration for 10 consecutive tracking cycles is located in the universe of discourse of a subset of the signal-to-noise ratio or a high subset of the signal-to-noise ratio, and the standard deviation of the signal-to-noise ratio after integration for 10 consecutive cycles does not exceed 5dB (preset threshold), it is considered as continuous and stable tracking.

[0051] For example, this embodiment can define the universe of discourse and fuzzy subsets of the input variables. Based on the typical characteristics of high-dynamic measurement and control signals, a physical universe of discourse is set for the four input variables, and fuzzy subsets are defined: (1) Dynamic correlation quantities (frequency-locked loop residual and rate-of-change loop residual): Based on engineering experience, the universe of discourse for the frequency-locked loop residual is [-4kHz / s, 4kHz / s], and its universe of discourse is further divided into a large negative subset, a zero subset, and a large positive subset; the universe of discourse for the rate-of-change loop residual is [-4kHz / s, 4kHz / s]. 4kHz / Then, its universe of discourse is evenly divided into a subset with negative rate of change, a subset with zero rate of change, and a subset with positive rate of change.

[0052] (2) Precision-related quantities ( ): Reflects the severity of phase jitter, and its value is always greater than 0. The domain range is determined based on the maximum phase jitter that the phase-locked loop can tolerate. Generally speaking, it does not exceed 1 / 4 of the maximum traction range (180°) of the phase-locked loop. Therefore, within the range of [0°, 45°], the variance of the phase error of the phase-locked loop is uniformly divided into a small subset, a medium subset, and a large subset.

[0053] (3) Environmentally relevant quantities ( ): Reflects channel quality. The universe of discourse is determined based on the minimum signal-to-noise ratio (SNR) that enables stable tracking in each loop (approximately -25dB for a 1ms tracking period) and the maximum SNR of a typical received signal (5dB). Therefore, within the range of [-25dB, 5dB], the integrated SNR is uniformly divided into a low SNR subset, a medium SNR subset, and a high SNR subset.

[0054] Specifically, subsets with large negative residuals and large negative rates of change are assigned negative numerical ranges, subsets with large positive residuals and large rates of change are assigned positive numerical ranges, subsets with small variances and low signal-to-noise ratios are assigned low numerical ranges, and subsets with large variances and high signal-to-noise ratios are assigned high numerical ranges.

[0055] Considering the real-time requirements of telemetry and control transponders in high-speed signal processing, this embodiment adopts a triangular membership function and performs fuzzy mapping for each parameter, calculating its membership degree on the corresponding fuzzy subset according to the membership degree calculation rules. Fuzzy mapping is the process of converting precise input values ​​into fuzzy set membership degrees. Specifically, based on the specific position of the input value on the horizontal axis of the membership function, one or more corresponding fuzzy subsets are identified. For example, the triangular membership function is called to calculate membership degrees, substituting the actual value of the input variable into the function, and calculating the membership degree of that value on each specific fuzzy subset using linear interpolation. The closer the value is to the center of the fuzzy subset's domain, the higher the membership degree; the further it deviates, the lower the membership degree. The calculated membership degrees are then arranged according to the set order of the fuzzy subsets to obtain the membership degree vector corresponding to each input variable. The membership degree vectors of all input variables together constitute the fuzzy vector.

[0056] For example, this embodiment uses a preset collaborative inference engine to calculate the scaling factor vector K=[ for the bandwidth of the three rings (FRL, FLL, PLL) in real time. K FRL , K FLL , K PLL ],in, K FRL This is the bandwidth scaling factor for the frequency change rate loop. K FLL This is the bandwidth scaling factor for the frequency-locked loop. KPLL This is the bandwidth scaling factor for the phase-locked loop. The specific operation steps are as follows: This embodiment employs a multi-input multi-output (MIMO) fuzzy inference architecture, combining the four input quantities in parallel logic. The inference engine does not adjust a single loop in isolation, but rather treats the three loops as a whole, coordinating their adjustment according to the following strategy.

[0057] In this embodiment, the bandwidth scaling factor adjustment range for the frequency change rate loop can be set to 1 ± 0.2, the bandwidth scaling factor adjustment range for the frequency locking loop to 1 ± 0.12, and the bandwidth scaling factor adjustment range for the phase-locked loop to 1 ± 0.08. The first to eighth bandwidth scaling factor intervals are all uniformly divided based on the above adjustment ranges. For example, the median value of the first bandwidth scaling factor interval is 1.1, the median value of the second bandwidth scaling factor interval is 1.06, the median value of the third bandwidth scaling factor interval is 1.04, the median values ​​of the fourth and fifth bandwidth scaling factor intervals are both 1, the median value of the sixth bandwidth scaling factor interval is 0.9, the median value of the seventh bandwidth scaling factor interval is 0.94, and the median value of the eighth bandwidth scaling factor interval is 0.96.

[0058] For high-dynamic scenarios, a feedforward collaborative decision-making approach is implemented. When the input frequency change rate loop residual belongs to a large positive subset or a large negative subset of the change rate, the received signal is determined to be in a high-dynamic range, and the signal is immediately determined from the first bandwidth scaling factor interval. K FRL To quickly adapt to changes in acceleration; simultaneously, without waiting for subsequent loop errors to increase, the bandwidth scaling factor is determined synchronously from the second bandwidth scaling factor range. K FLL Determined from the third bandwidth scaling factor range K PLL This strategy predicts the dynamic characteristics of the signal and increases the tracking bandwidth of subsequent loops in advance, effectively compensating for the processing delay caused by the step-by-step propagation of errors in the cascaded architecture and preventing the loop from losing lock in high dynamic scenarios.

[0059] For scenarios with low signal-to-noise ratio and signal fluctuations, implement differentiated adjustment logic. If... Belongs to a subset with large variance and This belongs to the high signal-to-noise ratio subset, meaning the signal has a high signal-to-noise ratio but also significant signal fluctuations. In this case, the PLL is at risk of losing lock. Therefore, the FRL and FLL need to maintain their current loop parameters, and the PLL bandwidth needs to be increased to accommodate signal fluctuations; that is, it needs to be determined from the fourth bandwidth scaling factor range. K FRL Determined from the fifth bandwidth scaling factor interval K FLL Determined from the third bandwidth scaling factor range K PLL.

[0060] like It is a subset with large variance. This belongs to the low signal-to-noise ratio subset, and the frequency change rate loop residual belongs to the zero-rate-of-change subset. That is, the signal-to-noise ratio is low, but the frequency change rate is not large, indicating that the loop dynamics are not significant, only the signal quality is poor. Therefore, the FRL needs to maintain its current loop parameters, while the FLL and PLL bandwidths are increased to adapt to weak signal quality. This requires determining the bandwidth from the fourth bandwidth scaling factor range. K FRL Then determine from the second bandwidth scaling factor range K FLL Determined from the third bandwidth scaling factor range K PLL .

[0061] To address the need for improved accuracy after the loop enters a stable tracking state, a tracking accuracy smoothing optimization strategy is implemented. This strategy is applied when the frequency-locked loop residual belongs to a subset of residual zeros. Belongs to the small subset of variance and When the circuit remains stable, it is determined that the loop is completely locked. The value can be reduced simultaneously. K FRL , K FLL , K PLL That is, determined from the sixth bandwidth scaling factor interval. K FRL Determined from the seventh bandwidth scaling factor interval K FLL Determined from the eighth bandwidth scaling factor interval K PLL .

[0062] This embodiment can perform defuzzification on the obtained fuzzy inference results. For example, a weighted center method is used to match the preset output fuzzy subset center value to the bandwidth adjustment degree corresponding to the fuzzy inference results, transforming the fuzzy features of the fuzzy inference results into specific values. At the same time, combined with the hierarchical settings of the frequency change rate loop, frequency-locked loop, and phase-locked loop in the multi-loop cascade architecture, the corresponding accurate bandwidth scaling factor is calculated for each loop level. Then, the bandwidth scaling factors of each loop level are arranged in the loop hierarchy order to obtain the final bandwidth scaling factor vector, completing the entire process of determining the bandwidth scaling factor.

[0063] For example, such as Figure 2 As shown, the received signal first undergoes down-conversion (reducing the radio frequency signal to an intermediate frequency), and then despreading (removing the spreading code) to obtain the target baseband signal to be synchronized. Based on the target baseband signal, the three-stage loop operates sequentially to achieve accurate tracking, as detailed below: Stage 1: Frequency Rate of Change Loop (FRL). As the first stage of the multi-stage carrier synchronization loop, the FRL is specifically designed to handle the carrier Doppler rate of change (FCL), i.e., the second-order change of carrier frequency, caused by high-speed maneuvering of the carrier in high-dynamic scenarios. The FCL first uses the local carrier output by the FCL NCO to perform quadrature demodulation on the target baseband signal obtained after down-conversion despreading. After integral clearing of the quadrature demodulated signal, the FCL integral clearing result is obtained. This result is input to the FCL discriminator, and the output data of the discriminator is the state parameter of this stage of the loop. The FCL residual data output by the discriminator is filtered, smoothed, and noise-suppressed by the loop filter. The output loop control quantity is then fed back to the FCL NCO, adjusting the frequency rate of change parameter of the local carrier output by the NCO in real time, forming a complete negative feedback closed-loop control. This closed loop can quickly estimate and compensate for the large range of carrier frequency variation rates in the received signal, eliminate the carrier frequency drift trend caused by high dynamics, compress the range of carrier frequency variation of the received signal to the range that the subsequent frequency-locked loop can stably track, and complete the initial tracking process of carrier synchronization.

[0064] The second stage: Frequency Locked Loop (FLL). The FLL receives the output signal from the frequency rate of change loop (FRL). Addressing the residual carrier frequency difference (i.e., the first-order change in carrier frequency) that remains after initial compensation by the FRL, the FLL first uses the local carrier output from the FLL NCO to perform quadrature demodulation on the target baseband signal after quadrature demodulation by the FRL. The demodulated signal is then integrated and cleared to obtain the FLL integration clearing result. This result is input to the FLL discriminator, whose output data constitutes the state parameters of this stage of the loop. The residual data output from the discriminator is filtered, smoothed, and noise-suppressed by the loop filter. The resulting loop control input is then fed back to the FLL NCO, adjusting the frequency parameters of the local carrier output by the NCO in real time, forming a complete negative feedback closed-loop control. This closed loop accurately eliminates the residual carrier frequency difference, stabilizing the signal frequency deviation within the minimal error range that the phase-locked loop can lock, thus preparing for precise phase tracking in subsequent phase-locked loop stages.

[0065] The third stage: Phase-Locked Loop (PLL). As the final stage of carrier synchronization, the PLL receives the output signal from the frequency-locked loop (FLL). Addressing the remaining carrier phase deviation after compensation by the first two loop stages, the PLL first uses the local carrier output by the PLL NCO to perform quadrature demodulation on the target baseband signal, which has undergone quadrature demodulation by the rate-of-change loop and the FLL. The resulting quadrature demodulated signal is then integrated and cleared to obtain the PLL integrated clearing result. This result is input to the PLL phase discriminator. The variance of the PLL phase error, statistically obtained from the output data of the phase discriminator, is the state parameter of this stage of the loop. Simultaneously, the integrated signal-to-noise ratio (SNR) is calculated based on the integrated clearing result. The phase error data output by the phase discriminator is filtered, smoothed, and noise-suppressed by the loop filter. The output loop control quantity is then fed back to the PLL NCO, adjusting the phase parameters of the local carrier output by the NCO in real time, forming a complete negative feedback closed-loop control. This closed loop enables high-precision carrier phase locking, outputting the final signal that completes carrier synchronization.

[0066] Multi-level loop state parameter extraction refers to the simultaneous collection of key state information of three-level loops (such as phase error variance, frequency difference estimate, loop output signal-to-noise ratio, etc.) as a basis for judging the working status of the loop.

[0067] Collaborative adaptive reasoning decision-making refers to determining the current loop tracking state based on extracted state parameters using fuzzy reasoning algorithms, and then providing a decision on parameter adjustment.

[0068] State feature fuzzification refers to converting loop state parameters into fuzzy linguistic variables (such as "large / medium / small error" or "fast / slow change") to facilitate subsequent reasoning and decision-making. Loop parameter adaptive updating refers to dynamically adjusting key parameters such as the filter bandwidth of the three-stage loop based on the decision results.

[0069] Based on the same principle as the dynamically decomposed carrier adaptive tracking method provided in the embodiments of this application, the embodiments of this application also provide a dynamically decomposed carrier adaptive tracking device, such as... Figure 3As shown, the dynamically decomposed carrier adaptive tracking device 20 specifically includes: a multi-level loop state parameter extraction module 21, a loop bandwidth adjustment module 22, and a carrier tracking parameter update module 23. The multi-level loop state parameter extraction module 21 is used to down-convert and despread the received signal to obtain the target baseband signal. Based on the target baseband signal, it obtains the state parameters of each loop level and the integrated signal-to-noise ratio (SNR) through the multi-level loop. The multi-level loop includes a frequency rate-of-change loop, a frequency-locked loop, and a phase-locked loop. The state parameters of each loop level include the residual of the frequency rate-of-change loop, the residual of the frequency-locked loop, and the variance of the phase-locked loop phase error. The loop bandwidth adjustment module 22 is used to adjust the state parameters of each loop level based on the target baseband signal. The loop state parameters and the integrated signal-to-noise ratio determine the bandwidth scaling factor of each loop level, and obtain the pre-update bandwidth of each loop level for multiple tracking cycles. For each loop level, the pre-update bandwidth of the loop level is multiplied by the bandwidth scaling factor corresponding to the loop level to obtain the updated bandwidth of the loop level. Based on the maximum and minimum loop bandwidths corresponding to the loop level, the updated bandwidth of the loop level is corrected to obtain the target loop bandwidth of the loop level. The carrier tracking parameter update module 23 is used to configure the loop parameters of each loop level based on the target loop bandwidth of the multi-level loops, and perform carrier tracking for the next tracking cycle according to the configured loop parameters of each loop level.

[0070] In one embodiment of this application, when the multi-level loop state parameter extraction module 21 obtains the state parameters of each loop and the signal-to-noise ratio after integration based on the target baseband signal through the multi-level loop, it is specifically used to: perform frequency change rate loop quadrature demodulation and integration clearing on the target baseband signal through the frequency change rate loop to obtain the frequency change rate loop integration clearing result; The target baseband signal after orthogonal demodulation by the frequency rate change loop is subjected to frequency-locked loop orthogonal demodulation and integral clearing to obtain the frequency-locked loop integral clearing result. The target baseband signal, which has undergone quadrature demodulation by a rate-of-change loop and quadrature demodulation by a frequency-locked loop, is subjected to phase-locked loop quadrature demodulation and integration and clearing by a phase-locked loop to obtain the phase-locked loop integration and clearing result. The signal-to-noise ratio after integration is obtained based on the phase-locked loop integration and clearing result. Using the frequency change rate loop integral clearing result, the frequency-locked loop integral clearing result, and the phase-locked loop integral clearing result as input data, the output data of each loop discriminator is obtained through each loop discriminator; based on the output data of each loop discriminator, the loop state parameters of each loop are obtained.

[0071] In one embodiment of this application, the loop discriminators at each level include a rate of change loop discriminator, a phase-locked loop phase discriminator, and a frequency-locked loop discriminator; the multi-level loop state parameter extraction module 21, when using the rate of change loop integral clearing result, the frequency-locked loop integral clearing result, and the phase-locked loop integral clearing result as input data, and obtaining the output data of each level of loop discriminator through the loop discriminators at each level, is specifically used for: Using the frequency change rate loop integral clearing result as input data, the frequency change rate loop discriminator output data is obtained through the frequency change rate loop discriminator. Using the phase-locked loop integral clearing result as input data, the phase-locked loop phase discriminator output data is obtained through the phase-locked loop phase discriminator. Using the frequency-locked loop integral clearing result as input data, the frequency-locked loop discriminator output data is obtained through the frequency-locked loop discriminator. The output data of the frequency change rate loop discriminator, the output data of the phase-locked loop phase discriminator, and the output data of the frequency-locked loop discriminator are used as the output data of each loop discriminator.

[0072] In one embodiment of this application, the multi-level loop state parameter extraction module 21, when obtaining the loop state parameters at each level based on the output data of the loop discriminators at each level, is specifically used for: The output data of the frequency change rate loop discriminator is used as the frequency change rate loop residual. The output data of the frequency-locked loop discriminator is used as the frequency-locked loop residual. The phase error variance of the phase-locked loop is obtained based on the output data of the phase-locked loop phase discriminator. The residual of the frequency change rate loop, the residual of the frequency-locked loop, and the variance of the phase-locked loop phase error are used as the state parameters of each loop stage.

[0073] In one embodiment of this application, the loop state parameters at each level include the frequency change rate loop residual, the frequency locking loop residual, and the phase-locked loop phase error variance; when determining the bandwidth scaling factor of each loop level based on the loop state parameters at each level and the integrated signal-to-noise ratio, the loop bandwidth adjustment module 22 is specifically used to: use the loop state parameters at each level and the integrated signal-to-noise ratio as multiple input variables; and determine multiple fuzzy subsets corresponding to each of the multiple input variables; Among them, the multiple fuzzy subsets corresponding to the frequency-locked loop residuals include the residual large negative subset, the residual zero subset, and the residual large positive subset; the multiple fuzzy subsets corresponding to the frequency change rate loop residuals include the change rate large negative subset, the change rate zero subset, and the change rate large positive subset; the multiple fuzzy subsets corresponding to the phase-locked loop phase error variance include the variance small subset, the variance medium subset, and the variance large subset; the multiple fuzzy subsets corresponding to the integrated signal-to-noise ratio include the signal-to-noise ratio low subset, the signal-to-noise ratio medium subset, and the signal-to-noise ratio high subset; the universe of discourse of each fuzzy subset is different; For each input variable, calculate the membership degree of the input variable on the multiple fuzzy subsets corresponding to the input variable to obtain the membership degree vector of the input variable; The bandwidth scaling factor is determined based on the membership vectors corresponding to each of the multiple input variables.

[0074] In one embodiment of this application, when determining the bandwidth scaling factor based on the membership vectors corresponding to each of the multiple input variables, the loop bandwidth adjustment module 22 is specifically used to: select a target determination rule from multiple fuzzy determination rules that matches the membership vectors corresponding to each of the multiple input variables; The multiple fuzzy decision rules include a first decision rule, a second decision rule, a third decision rule, and a fourth decision rule. The first decision rule is that the membership degree of the frequency-locked loop residual on the zero subset of residuals is the lowest. The second decision rule is that the membership degree of the phase-locked loop phase error variance on the large subset of variances is the highest, and the membership degree of the integrated signal-to-noise ratio on the high subset of signal-to-noise ratios is the highest. The third decision rule is that the membership degree of the phase-locked loop phase error variance on the large subset of variances is the highest, and the membership degree of the integrated signal-to-noise ratio on the low subset of signal-to-noise ratios is the highest, and the membership degree of the frequency change rate loop residual on the zero subset of the rate of change is the highest. The fourth decision rule is that the membership degree of the frequency-locked loop residual on the zero subset of residuals is the highest, and the membership degree of the phase-locked loop phase error variance on the small subset of variances is the highest, and the integrated signal-to-noise ratio remains stable. Based on the target determination rule, a target fuzzy instruction is selected as the fuzzy inference result from multiple fuzzy instructions; all multiple fuzzy instructions are used to indicate the adjustment operation of the bandwidth scaling factor; The fuzzy inference results are defuzzified and converted into a bandwidth scaling factor vector.

[0075] In one embodiment of this application, the plurality of fuzzy instructions include a first fuzzy instruction, a second fuzzy instruction, a third fuzzy instruction, and a fourth fuzzy instruction; The first fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the first bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the second bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the third bandwidth scaling factor interval; the median value of the first bandwidth scaling factor interval is greater than the median value of the second bandwidth scaling factor interval, the median value of the second bandwidth scaling factor interval is greater than the median value of the third bandwidth scaling factor interval, and the median value of the third bandwidth scaling factor interval is greater than 1. The second fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the fourth bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the fifth bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the third bandwidth scaling factor interval; the intermediate values ​​of the fourth and fifth bandwidth scaling factor intervals are both 1, and the endpoint values ​​of the fourth and fifth bandwidth scaling factor intervals are different. The third fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the fourth bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the second bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the third bandwidth scaling factor interval. The fourth fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the sixth bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the seventh bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the eighth bandwidth scaling factor interval; the median value of the sixth bandwidth scaling factor interval is greater than the median value of the eighth bandwidth scaling factor interval, the median value of the eighth bandwidth scaling factor interval is greater than the median value of the seventh bandwidth scaling factor interval, and the median value of the sixth bandwidth scaling factor interval is less than 1; When the loop bandwidth adjustment module 22 selects the target fuzzy instruction as the fuzzy inference result from multiple fuzzy instructions based on the target determination rule, it is specifically used for: If the target determination rule is the first determination rule, then the first fuzzy instruction is selected as the fuzzy inference result; If the target determination rule is the second determination rule, then the second fuzzy instruction is selected as the fuzzy inference result; If the target determination rule is the third determination rule, then the third fuzzy instruction is selected as the fuzzy inference result; If the target determination rule is the fourth determination rule, then the fourth fuzzy instruction is selected as the fuzzy inference result.

[0076] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0077] Figure 4 A schematic diagram of the structure of an electronic device to which this application embodiment applies is shown, such as... Figure 4 As shown, the electronic device can be used to implement the methods provided in any embodiment of this application.

[0078] like Figure 4 As shown, the electronic device 300 mainly includes at least one processor 301. Figure 4 The diagram shows components such as a memory 302, a communication module 303, and an input / output interface 304. Optionally, these components can be connected and communicate with each other via a bus 305. It should be noted that... Figure 4 The structure of the electronic device 300 shown is merely illustrative and does not constitute a limitation on the electronic devices to which the methods provided in the embodiments of this application are applicable.

[0079] The memory 302 can be used to store operating systems and applications, etc. The applications can include computer programs that implement the methods shown in the embodiments of this application when invoked by the processor 301, and can also include programs for implementing other functions or services. The memory 302 can be ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices that can store information and computer programs, or it can be EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0080] Processor 301 is connected to memory 302 via bus 305 and implements corresponding functions by calling the application programs stored in memory 302. Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0081] Electronic device 300 can connect to a network via communication module 303 (which may include, but is not limited to, components such as a network interface) to communicate with other devices (such as user terminals or servers) through the network and achieve data interaction, such as sending data to or receiving data from other devices. Communication module 303 may include wired network interfaces and / or wireless network interfaces, meaning the communication module may include at least one of wired or wireless communication modules.

[0082] The electronic device 300 can connect to necessary input / output devices, such as a keyboard and display device, via the input / output interface 304. The electronic device 300 itself may have a display device, and other display devices can also be connected externally via the interface 304. Optionally, a storage device, such as a hard drive, can also be connected via the interface 304 to store data from the electronic device 300, read data from the storage device, or store data from the storage device in the memory 302. It is understood that the input / output interface 304 can be a wired interface or a wireless interface. Depending on the actual application scenario, the device connected to the input / output interface 304 can be a component of the electronic device 300 or an external device connected to the electronic device 300 when needed.

[0083] The bus 305 used to connect the components may include a path for transmitting information between the components. The bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Depending on its function, the bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0084] Optionally, for the solution provided in the embodiments of this application, the memory 302 can be used to store a computer program that executes the solution of this application, and the processor 301 runs the computer program. When the processor 301 runs the computer program, it implements the operation of the method or apparatus provided in the embodiments of this application.

[0085] Based on the same principle as the method provided in the embodiments of this application, the embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the corresponding content of the aforementioned method embodiments.

[0086] It should be noted that the terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0087] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0088] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0089] The above description is only an optional implementation method for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application without departing from the technical concept of this application also fall within the protection scope of the embodiments of this application.

Claims

1. A carrier adaptive tracking method with dynamic step-by-step decomposition, characterized in that, include: After down-conversion and despreading of the received signal, the target baseband signal is obtained. Based on the target baseband signal, the state parameters of each loop and the signal-to-noise ratio after integration are obtained through a multi-level loop. The multi-level loop includes a frequency change rate loop, a frequency locking loop, and a phase-locked loop. The state parameters of each loop include the residual of the frequency change rate loop, the residual of the frequency locking loop, and the variance of the phase error of the phase-locked loop. Based on the state parameters of each loop level and the integrated signal-to-noise ratio, the bandwidth scaling factor of each loop level is determined, and the pre-update bandwidth of each loop level corresponding to multiple tracking cycles is obtained. For each loop level, the pre-update bandwidth of the loop level is multiplied by the bandwidth scaling factor corresponding to the loop level to obtain the updated bandwidth of the loop level. Based on the maximum and minimum loop bandwidths corresponding to the loop level, the updated bandwidth of the loop level is corrected to obtain the target loop bandwidth of the loop level. Based on the target loop bandwidth of the multi-level loop, the loop parameters of each level of the loop are configured, and carrier tracking is performed in the next tracking cycle according to the configured loop parameters of each level of the loop.

2. The carrier adaptive tracking method with dynamic step-by-step decomposition as described in claim 1, characterized in that, The process of obtaining the state parameters of each loop level and the integrated signal-to-noise ratio based on the target baseband signal through a multi-level loop includes: The target baseband signal is subjected to frequency change rate loop quadrature demodulation and integration clearing by frequency change rate loop to obtain the frequency change rate loop integration clearing result. The target baseband signal after orthogonal demodulation by the frequency rate change loop is subjected to frequency-locked loop orthogonal demodulation and integral clearing to obtain the frequency-locked loop integral clearing result. The target baseband signal, which has undergone quadrature demodulation by a rate-of-change loop and quadrature demodulation by a frequency-locked loop, is subjected to phase-locked loop quadrature demodulation and integration and clearing by a phase-locked loop to obtain the phase-locked loop integration clearing result. The signal-to-noise ratio after integration is obtained based on the phase-locked loop integration clearing result. Using the frequency change rate loop integral clearing result, the frequency lock loop integral clearing result, and the phase lock loop integral clearing result as input data, the output data of each loop discriminator is obtained through each loop discriminator. The loop state parameters at each level are obtained based on the output data of the loop discriminators at each level.

3. The carrier adaptive tracking method with dynamic step-by-step decomposition as described in claim 2, characterized in that, The various levels of loop discriminators include a rate of change loop discriminator, a phase-locked loop phase discriminator, and a frequency-locked loop frequency discriminator; The process uses the frequency change rate loop integral clearing result, the frequency-locked loop integral clearing result, and the phase-locked loop integral clearing result as input data, and obtains the output data of each loop discriminator through each loop discriminator, including: Using the frequency change rate loop integral clearing result as input data, the frequency change rate loop discriminator output data is obtained through the frequency change rate loop discriminator. Using the phase-locked loop integral clearing result as input data, the phase-locked loop phase discriminator output data is obtained through the phase-locked loop phase discriminator; Using the frequency-locked loop integral clearing result as input data, the frequency-locked loop discriminator output data is obtained through the frequency-locked loop discriminator; The output data of the frequency change rate loop discriminator, the output data of the phase-locked loop phase discriminator, and the output data of the frequency-locked loop discriminator are used as the output data of each loop discriminator.

4. The carrier adaptive tracking method with dynamic step-by-step decomposition as described in claim 3, characterized in that, The process of obtaining loop state parameters at each level based on the output data of the loop discriminators at each level includes: The output data of the frequency change rate loop discriminator is used as the frequency change rate loop residual. The output data of the frequency-locked loop discriminator is used as the frequency-locked loop residual. The phase error variance of the phase-locked loop is obtained based on the output data of the phase-locked loop phase discriminator. The frequency change rate loop residual, the frequency-locked loop residual, and the phase-locked loop phase error variance are used as the state parameters of each loop stage.

5. The carrier adaptive tracking method with dynamic step-by-step decomposition as described in claim 1, characterized in that, The determination of the bandwidth scaling factor for each loop level based on the loop state parameters at each level and the integrated signal-to-noise ratio includes: The state parameters of each loop level and the integrated signal-to-noise ratio are used as multiple input variables; Determine multiple fuzzy subsets corresponding to each of the multiple input variables; The multiple fuzzy subsets corresponding to the frequency change rate loop residuals include a subset with large negative rates of change, a subset with zero rates of change, and a subset with large positive rates of change; the multiple fuzzy subsets corresponding to the frequency-locked loop residuals include a subset with large negative residuals, a subset with zero residuals, and a subset with large positive residuals; the multiple fuzzy subsets corresponding to the phase-locked loop phase error variances include a subset with small variances, a subset with medium variances, and a subset with large variances; the multiple fuzzy subsets corresponding to the integrated signal-to-noise ratios include a subset with low signal-to-noise ratios, a subset with medium signal-to-noise ratios, and a subset with high signal-to-noise ratios; and the universe of discourse for each fuzzy subset is different. For each input variable, calculate the membership degree of the input variable on the multiple fuzzy subsets corresponding to the input variable, and obtain the membership degree vector corresponding to the input variable; The bandwidth scaling factor is determined based on the membership vectors corresponding to each of the multiple input variables.

6. The carrier adaptive tracking method with dynamic step-by-step decomposition as described in claim 5, characterized in that, The step of determining the bandwidth scaling factor based on the membership vectors corresponding to each of the multiple input variables includes: Select the target determination rule that matches the membership vector corresponding to each of the multiple input variables from multiple fuzzy determination rules; The multiple fuzzy determination rules include a first determination rule, a second determination rule, a third determination rule, and a fourth determination rule. The first determination rule states that the membership degree of the frequency-locked loop residual on the zero subset of residuals is the lowest. The second determination rule states that the membership degree of the phase-locked loop phase error variance on the large subset of variances is the highest, and the membership degree of the integrated signal-to-noise ratio (SNR) on the high SNR subset is the highest. The third determination rule states that the membership degree of the phase-locked loop phase error variance on the large subset of variances is the highest, and the membership degree of the integrated SNR on the low SNR subset is the highest, and the membership degree of the frequency change rate loop residual on the zero change rate subset is the highest. The fourth determination rule states that the membership degree of the frequency-locked loop residual on the zero subset of residuals is the highest, and the membership degree of the phase-locked loop phase error variance on the small subset of variances is the highest, and the integrated SNR remains consistently stable. Based on the target determination rule, a target fuzzy instruction is selected from multiple fuzzy instructions as the fuzzy inference result; all of the multiple fuzzy instructions are used to indicate the adjustment operation of the bandwidth scaling factor; The fuzzy inference result is defuzzified and converted into a bandwidth scaling factor vector.

7. The carrier adaptive tracking method with dynamic step-by-step decomposition as described in claim 6, characterized in that, The multiple fuzzy instructions include a first fuzzy instruction, a second fuzzy instruction, a third fuzzy instruction, and a fourth fuzzy instruction; The first fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the first bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the second bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the third bandwidth scaling factor interval. The median value of the first bandwidth scaling factor interval is greater than the median value of the second bandwidth scaling factor interval, and the median value of the second bandwidth scaling factor interval is greater than the median value of the third bandwidth scaling factor interval; the median value of the third bandwidth scaling factor interval is greater than 1. The second fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the fourth bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the fifth bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the third bandwidth scaling factor interval; the intermediate value of the fourth bandwidth scaling factor interval and the fifth bandwidth scaling factor interval are both 1, and the endpoint values ​​of the fourth bandwidth scaling factor interval and the fifth bandwidth scaling factor interval are different. The third fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the fourth bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the second bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the third bandwidth scaling factor interval. The fourth fuzzy instruction is to determine the bandwidth scaling factor of the frequency change rate loop from the sixth bandwidth scaling factor interval, the bandwidth scaling factor of the frequency-locked loop from the seventh bandwidth scaling factor interval, and the bandwidth scaling factor of the phase-locked loop from the eighth bandwidth scaling factor interval. The median value of the sixth bandwidth scaling factor interval is greater than the median value of the eighth bandwidth scaling factor interval, and the median value of the eighth bandwidth scaling factor interval is greater than the median value of the seventh bandwidth scaling factor interval. The median value of the sixth bandwidth scaling factor interval is less than 1; The step of selecting a target fuzzy instruction as the fuzzy inference result from multiple fuzzy instructions based on the target determination rule includes: If the target determination rule is the first determination rule, then the first fuzzy instruction is selected as the fuzzy inference result; If the target determination rule is the second determination rule, then the second fuzzy instruction is selected as the fuzzy inference result; If the target determination rule is the third determination rule, then the third fuzzy instruction is selected as the fuzzy inference result; If the target determination rule is the fourth determination rule, then the fourth fuzzy instruction is selected as the fuzzy inference result.

8. A carrier adaptive tracking device with dynamic step-by-step decomposition, characterized in that, include: A multi-level loop state parameter extraction module is used to down-convert and despread the received signal to obtain the target baseband signal. Based on the target baseband signal, the state parameters of each loop level and the signal-to-noise ratio after integration are obtained through a multi-level loop. The multi-level loop includes a frequency change rate loop, a frequency locking loop, and a phase-locked loop. The state parameters of each loop level include the residual of the frequency change rate loop, the residual of the frequency locking loop, and the variance of the phase error of the phase-locked loop. The loop bandwidth adjustment module is used to determine the bandwidth scaling factor of each loop level based on the loop state parameters of each level and the integrated signal-to-noise ratio, and to obtain the pre-update bandwidth of each loop level corresponding to multiple tracking cycles; for each loop level, the pre-update bandwidth of the loop level is multiplied by the bandwidth scaling factor corresponding to the loop level to obtain the updated bandwidth of the loop level; the updated bandwidth of the loop level is corrected based on the maximum and minimum loop bandwidths corresponding to the loop level to obtain the target loop bandwidth of the loop level. The carrier tracking parameter update module is used to configure the loop parameters of each level of the loop based on the target loop bandwidth of the multi-level loop, and to perform carrier tracking for the next tracking cycle according to the configured loop parameters of each level of the loop.

9. The carrier adaptive tracking device with dynamic step-by-step decomposition as described in claim 8, characterized in that, When determining the bandwidth scaling factor for each loop level based on the loop state parameters and the integrated signal-to-noise ratio, the loop bandwidth adjustment module is specifically used for: The state parameters of each loop level and the integrated signal-to-noise ratio are used as multiple input variables; Determine multiple fuzzy subsets corresponding to each of the multiple input variables; The multiple fuzzy subsets corresponding to the frequency change rate loop residuals include a subset with large negative rates of change, a subset with zero rates of change, and a subset with large positive rates of change; the multiple fuzzy subsets corresponding to the frequency-locked loop residuals include a subset with large negative residuals, a subset with zero residuals, and a subset with large positive residuals; the multiple fuzzy subsets corresponding to the phase-locked loop phase error variances include a subset with small variances, a subset with medium variances, and a subset with large variances; the multiple fuzzy subsets corresponding to the integrated signal-to-noise ratios include a subset with low signal-to-noise ratios, a subset with medium signal-to-noise ratios, and a subset with high signal-to-noise ratios; and the universe of discourse for each fuzzy subset is different. For each input variable, calculate the membership degree of the input variable on the multiple fuzzy subsets corresponding to the input variable, and obtain the membership degree vector corresponding to the input variable; The bandwidth scaling factor is determined based on the membership vectors corresponding to each of the multiple input variables.

10. The carrier adaptive tracking device with dynamic step-by-step decomposition as described in claim 9, characterized in that, The multi-level loop state parameter extraction module, when determining the bandwidth scaling factor based on the membership vectors corresponding to the multiple input variables, is specifically used for: Select the target determination rule that matches the membership vector corresponding to each of the multiple input variables from multiple fuzzy determination rules; The multiple fuzzy determination rules include a first determination rule, a second determination rule, a third determination rule, and a fourth determination rule. The first determination rule states that the membership degree of the frequency-locked loop residual on the zero subset of residuals is the lowest. The second determination rule states that the membership degree of the phase-locked loop phase error variance on the large subset of variances is the highest, and the membership degree of the integrated signal-to-noise ratio (SNR) on the high SNR subset is the highest. The third determination rule states that the membership degree of the phase-locked loop phase error variance on the large subset of variances is the highest, and the membership degree of the integrated SNR on the low SNR subset is the highest, and the membership degree of the frequency change rate loop residual on the zero change rate subset is the highest. The fourth determination rule states that the membership degree of the frequency-locked loop residual on the zero subset of residuals is the highest, and the membership degree of the phase-locked loop phase error variance on the small subset of variances is the highest, and the integrated SNR remains consistently stable. Based on the target determination rule, a target fuzzy instruction is selected from multiple fuzzy instructions as the fuzzy inference result; all of the multiple fuzzy instructions are used to indicate the adjustment operation of the bandwidth scaling factor; The fuzzy inference result is defuzzified and converted into a bandwidth scaling factor vector.

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