VSAT-TDMA time delay tracking method based on decoding verification

Abstract

The invention discloses a VSAT-TDMA time delay tracking method based on decoding verification, and belongs to the technical field of satellite communication. According to the method, demodulation and signaling analysis are carried out on a main station receiving signal, a small station frame plan is generated, and a signaling initial time delay estimation value is obtained by comparing burst theoretical occurrence time with actual receiving time; receiving a small station burst signal and completing decoding verification, and reserving a preorder burst signal conforming to a set rule; differential correlation operation is carried out on the effective signal to obtain a correlation peak position, a time delay change trend is obtained through smooth fitting, a subsequent burst initial position is predicted based on the trend, a receiving window is dynamically adjusted, and time delay tracking is completed. Effective samples are screened through decoding verification, time delay extraction accuracy is improved, detection jitter is filtered out, channel time-varying characteristics are adapted, uplink access synchronization stability of the system is guaranteed, the method can be deployed in an existing satellite communication system, and communication reliability is improved.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to a VSAT-TDMA delay tracking method based on decoding verification. Background Technology

[0002] The Very Small Aperture Satellite (SAT) system, with its wide-area coverage, flexible deployment, low access cost, and strong resilience, has become a core communication technology for scenarios such as supplementing coverage blind spots in terrestrial communication networks, providing emergency communication support, maritime shipping communication, and building private networks for industries in remote areas. It has been widely and extensively applied in both civilian and industrial communication fields. Time Division Multiple Access (TDMA), as the mainstream multi-user channel-sharing access system in VSAT satellite communication systems, achieves parallel access for multiple users by allocating non-overlapping time slot resources to different remote terminals. It boasts advantages such as flexible time slot resource scheduling, high channel utilization, and strong adaptability to multi-user access, making it the preferred access technology for current medium- and low-speed VSAT satellite communication systems. With the rapid development of low-Earth orbit satellite communication constellations and the continuous expansion of satellite communication application scenarios, the operating scenarios of VSAT-TDMA systems are gradually extending to complex environments with high dynamics, low signal-to-noise ratio, and dense multi-user access. As a core technology to ensure the uplink synchronization performance of TDMA systems, delay tracking technology for uplink burst signals has been continuously deepened in related technical research and engineering applications. Various delay tracking processing methods based on unique code correlation detection, sequence synchronization estimation, and digital signal filtering fitting have become standardized core links in the signal processing flow of the VSAT-TDMA system receiver.

[0003] In current VSAT-TDMA system delay tracking technology applications, most existing solutions directly perform delay detection and estimation on all uplink burst signals acquired at the receiver, without effectively screening and identifying the burst signal samples involved in delay calculation. When uplink burst signals experience transmission errors due to noise, interference, and multipath effects during satellite channel transmission, invalid signal samples that fail to decode will directly participate in the delay estimation calculation, introducing additional detection bias and reducing the accuracy of delay parameter extraction. Furthermore, existing solutions lack a systematic smoothing fitting and trend extraction process for discrete single-shot delay detection results, making it difficult to accurately capture the time-varying characteristics of satellite channel transmission delay. When the channel environment experiences dynamic fluctuations, random jitter in delay tracking results can easily occur, failing to provide a stable prediction benchmark for subsequent burst reception. Furthermore, in existing technologies, the adjustment of the receiving window is mostly based on the result of a single delay detection, lacking the ability to dynamically adapt based on the overall trend of delay changes. The connection between the initial delay estimation and the subsequent continuous tracking is insufficient. In complex channel environments and scenarios with dense access by multiple users, situations such as misalignment of the receiving window and incomplete reception of sudden signals are prone to occur, making it difficult to guarantee the synchronization stability and communication reliability of the uplink multiple access of the VSAT-TDMA system. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a VSAT-TDMA delay tracking method based on decoding verification.

[0005] The objective of this invention is achieved through the following technical solution: A VSAT-TDMA delay tracking method based on decoding verification is provided, which includes the following steps: S1. Demodulate the received signal from the main station to obtain the main station demodulated data, perform signaling parsing on the main station demodulated data, and generate a small station frame plan; S2. Read the small station frame plan, compare the theoretical occurrence time of the burst contained in the small station frame plan with the actual reception time of the burst, and obtain the signaling initial delay estimate; S3. Receive burst signals sent by small stations, decode the burst signals, verify the decoding results, and retain preceding burst signals whose decoding verification results conform to the set verification rules. S4. Perform differential correlation operation on the preceding burst signal whose decoding verification result conforms to the set verification rules to obtain the position of the correlation peak. Perform smooth fitting processing on the position of the correlation peak to obtain the time delay change trend. Predict the starting position of the subsequent burst based on the time delay change trend. Dynamically adjust the receiving window based on the predicted starting position to complete the time delay tracking.

[0006] Furthermore, the signaling parsing and small station frame plan generation in step S1 includes the following sub-steps: S1.1. Perform carrier synchronization and symbol synchronization processing on the master station demodulated data, complete the frame synchronization processing of the master station demodulated data, locate the frame start position of the master station demodulated data, and extract the signaling field at a fixed position in the frame structure of the master station demodulated data; S1.2. Perform channel decoding processing on the signaling field to obtain the small station identifier, time slot start position information, and time slot duration information contained in the signaling field. Perform time sequence matching on the obtained information and integrate it to obtain the small station time slot allocation information. S1.3. Based on the small station time slot allocation information, arrange the frames according to the frame structure time sequence to generate a small station frame plan that includes the small station identifier, the theoretical occurrence time of the burst, and the duration of the burst.

[0007] Furthermore, obtaining the initial signaling delay estimate in step S2 includes the following sub-steps: S2.1. Read the small station frame plan, extract the burst theoretical occurrence time of the corresponding small station identifier in the small station frame plan, and sort and organize the burst theoretical occurrence time according to the frame time sequence; S2.2. Perform burst arrival time detection on the signal received by the main station, count the actual burst reception time of the corresponding small station, and match the actual burst reception time with the corresponding theoretical burst occurrence time; S2.3. Calculate the time difference between the theoretical occurrence time of the burst and the actual reception time of the burst after matching is completed, and determine the time difference as the initial delay estimate of the signaling.

[0008] Furthermore, the burst signal decoding and verification process in step S3 includes the following sub-steps: S3.1. Read the initial delay estimate of the signaling, determine the starting position of the receiving window for the burst signal based on the initial delay estimate of the signaling, read the burst duration of the corresponding small station identifier in the small station frame plan, set the duration of the receiving window, and complete the reception of the burst signal of the corresponding small station identifier. S3.2. Perform frequency offset compensation and phase offset compensation on the received burst signal to complete the baseband demodulation of the burst signal. Decode the data after baseband demodulation to obtain the decoding result. S3.3. Verify the decoding result based on the set verification rules, determine whether the decoding result conforms to the set verification rules, retain the preceding burst signal corresponding to the decoding result that conforms to the set verification rules, and discard the burst signal corresponding to the decoding result that does not conform to the set verification rules.

[0009] Furthermore, the differential correlation operation and correlation peak position acquisition in step S4 include the following sub-steps: S4.1. Read the preceding burst signal whose decoding verification result conforms to the set verification rules, extract the fixed-length preceding part sequence from the preceding burst signal whose decoding verification result conforms to the set verification rules, and normalize the preceding part sequence. S4.2. Read the unique code sequence stored locally, perform sliding differential correlation operation between the normalized preorder sequence and the unique code sequence stored locally, and obtain the correlation function result of the corresponding sliding offset; S4.3. Take the modulus of the correlation function results corresponding to all sliding offsets to generate a modulus sequence. Perform peak search on the modulus sequence and determine the sliding offset corresponding to the maximum value in the modulus sequence as the correlation peak position.

[0010] Furthermore, in step S4, the smoothing and fitting of the relevant peak positions adopts a first-order linear fitting method. A first-order linear fitting function is constructed based on the burst sequence number and the corresponding relevant peak position. The fitting coefficient of the first-order linear fitting function is solved by the least squares method. The relevant peak positions are sorted according to time order, and a corresponding burst sequence number is assigned to the sorted relevant peak positions. A set of calculation equations for the first-order linear fitting function is constructed based on the burst sequence number and the corresponding relevant peak position. The set of calculation equations is solved by the least squares method to obtain the slope coefficient and intercept coefficient of the first-order linear fitting function. The first-order linear fitting function is determined based on the slope coefficient and intercept coefficient, and the time delay change trend is characterized by the first-order linear fitting function.

[0011] Furthermore, in step S4, the smoothing fitting of the relevant peak positions is performed using a moving average filtering method. Based on the set sliding window length, the mean of the relevant peak positions within the sliding window is calculated to obtain the time delay change trend. The relevant peak positions are sorted according to time order to generate a relevant peak position sequence. The sliding window length is set, and a sliding calculation window is generated based on the sliding window length. The sliding calculation window is slid along the relevant peak position sequence. The arithmetic mean of all relevant peak positions contained in each sliding calculation window is calculated to obtain the smoothed time delay value corresponding to each sliding position. The sequence composed of all smoothed time delay values ​​represents the time delay change trend.

[0012] Furthermore, in step S3, the decoding result is verified using either Cyclic Redundancy Check (CRC) or Forward Error Correction (FEC). The verification is performed based on predefined verification rules. When using CRC, a CRC code field at a fixed position is extracted from the decoding result. A CRC generator polynomial is used to calculate the information field in the decoding result to obtain a calculated check code. The calculated check code is then compared to the content of the CRC code field. The verification of the decoding result is completed based on the comparison result. When using FEC, the error correction verification result obtained during the decoding process is used to determine if there are any uncorrectable errors in the decoding result. The verification of the decoding result is completed based on the determination result.

[0013] Furthermore, in step S4, based on the rate of change of the time delay trend, the fitting order or filtering parameters of the smoothing fitting process are adaptively adjusted to match the dynamic characteristics of the time delay change. The difference between adjacent values ​​of the time delay change trend is calculated, and the rate of change of the time delay change trend is calculated based on the difference. The rate of change is compared with the set rate of change threshold. When the rate of change is less than or equal to the set rate of change threshold, the fitting order of the smoothing fitting process is reduced or the sliding window length of the moving average filter is increased. When the rate of change is greater than the set rate of change threshold, the fitting order of the smoothing fitting process is increased or the sliding window length of the moving average filter is decreased.

[0014] Furthermore, in step S4, based on the predicted starting position of the subsequent burst, the starting position and length of the receiving window are adjusted to complete the reception and delay tracking of the subsequent burst signal. The predicted starting position corresponding to the subsequent burst is calculated based on the delay change trend, and the predicted starting position is set as the starting position of the receiving window. The burst duration corresponding to the small station identifier in the small station frame plan is read, and the basic length of the receiving window is set. The length of the receiving window is adjusted based on the rate of change of the delay change trend. The reception of the subsequent burst signal is completed based on the adjusted receiving window. The delay change trend is iteratively updated based on the reception result of the subsequent burst signal to complete continuous delay tracking.

[0015] The beneficial effects of this invention are: (1) Valid signal samples are screened through the decoding and verification process, and time delay deviation information is extracted by combining differential correlation operation to complete smooth fitting and dynamic adjustment of the receiving window, so as to realize continuous and stable time delay tracking of uplink burst signals. (2) Smooth fitting is performed on the discrete time delay detection results to obtain the overall pattern of time delay change, effectively filter out the detection jitter caused by random noise and channel interference, and reduce the false alarm probability of the time delay estimation results; (3) Based on the dynamic characteristics of time delay change, the processing parameters are adaptively adjusted to adapt to different channel transmission environments. No additional hardware overhead is required, and it can be deployed and applied directly in the existing communication system. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the steps of a VSAT-TDMA delay tracking method based on decoding verification; Figure 2 The following is a flowchart illustrating the specific steps of a VSAT-TDMA delay tracking method based on decoding verification, provided as an example. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 See Figure 1 This embodiment provides a VSAT-TDMA delay tracking method based on decoding verification, which includes the following steps: S1. Demodulate the received signal from the main station to obtain the main station demodulated data, perform signaling parsing on the main station demodulated data, and generate a small station frame plan; S2. Read the small station frame plan, compare the theoretical occurrence time of the burst contained in the small station frame plan with the actual reception time of the burst, and obtain the signaling initial delay estimate; S3. Receive burst signals sent by small stations, decode the burst signals, verify the decoding results, and retain preceding burst signals whose decoding verification results conform to the set verification rules. S4. Perform differential correlation operation on the preceding burst signal whose decoding verification result conforms to the set verification rules to obtain the position of the correlation peak. Perform smooth fitting processing on the position of the correlation peak to obtain the time delay change trend. Predict the starting position of the subsequent burst based on the time delay change trend. Dynamically adjust the receiving window based on the predicted starting position to complete the time delay tracking.

[0019] In some embodiments, the signaling parsing and small station frame plan generation in step S1 includes the following sub-steps: S1.1. Perform carrier synchronization and symbol synchronization processing on the master station demodulated data, complete the frame synchronization processing of the master station demodulated data, locate the frame start position of the master station demodulated data, and extract the signaling field at a fixed position in the frame structure of the master station demodulated data; S1.2. Perform channel decoding processing on the signaling field to obtain the small station identifier, time slot start position information, and time slot duration information contained in the signaling field. Perform time sequence matching on the obtained information and integrate it to obtain the small station time slot allocation information. S1.3. Based on the small station time slot allocation information, arrange the frames according to the frame structure time sequence to generate a small station frame plan that includes the small station identifier, the theoretical occurrence time of the burst, and the duration of the burst.

[0020] In some embodiments, obtaining the initial signaling delay estimate in step S2 includes the following sub-steps: S2.1. Read the small station frame plan, extract the burst theoretical occurrence time of the corresponding small station identifier in the small station frame plan, and sort and organize the burst theoretical occurrence time according to the frame time sequence; S2.2. Perform burst arrival time detection on the signal received by the main station, count the actual burst reception time of the corresponding small station, and match the actual burst reception time with the corresponding theoretical burst occurrence time; S2.3. Calculate the time difference between the theoretical occurrence time of the burst and the actual reception time of the burst after matching is completed, and determine the time difference as the initial delay estimate of the signaling.

[0021] In some embodiments, the burst signal decoding and verification process in step S3 includes the following sub-steps: S3.1. Read the initial delay estimate of the signaling, determine the starting position of the receiving window for the burst signal based on the initial delay estimate of the signaling, read the burst duration of the corresponding small station identifier in the small station frame plan, set the duration of the receiving window, and complete the reception of the burst signal of the corresponding small station identifier. S3.2. Perform frequency offset compensation and phase offset compensation on the received burst signal to complete the baseband demodulation of the burst signal. Decode the data after baseband demodulation to obtain the decoding result. S3.3. Verify the decoding result based on the set verification rules, determine whether the decoding result conforms to the set verification rules, retain the preceding burst signal corresponding to the decoding result that conforms to the set verification rules, and discard the burst signal corresponding to the decoding result that does not conform to the set verification rules.

[0022] In some embodiments, the differential correlation operation and correlation peak position acquisition in step S4 include the following sub-steps: S4.1. Read the preceding burst signal whose decoding verification result conforms to the set verification rules, extract the fixed-length preceding part sequence from the preceding burst signal whose decoding verification result conforms to the set verification rules, and normalize the preceding part sequence. S4.2. Read the unique code sequence stored locally, perform sliding differential correlation operation between the normalized preorder sequence and the unique code sequence stored locally, and obtain the correlation function result of the corresponding sliding offset; S4.3. Take the modulus of the correlation function results corresponding to all sliding offsets to generate a modulus sequence. Perform peak search on the modulus sequence and determine the sliding offset corresponding to the maximum value in the modulus sequence as the correlation peak position.

[0023] In some embodiments, in step S4, the smoothing fitting of the relevant peak positions adopts a first-order linear fitting method. A first-order linear fitting function is constructed based on the burst sequence number and the corresponding relevant peak position. The fitting coefficient of the first-order linear fitting function is solved by the least squares method. The relevant peak positions are sorted according to time order, and a corresponding burst sequence number is assigned to the sorted relevant peak positions. A set of calculation equations for the first-order linear fitting function is constructed based on the burst sequence number and the corresponding relevant peak position. The set of calculation equations is solved by the least squares method to obtain the slope coefficient and intercept coefficient of the first-order linear fitting function. The first-order linear fitting function is determined based on the slope coefficient and intercept coefficient, and the first-order linear fitting function is used to characterize the time delay change trend.

[0024] In some embodiments, in step S4, the smoothing fitting of the relevant peak positions adopts a moving average filtering method. Based on the set sliding window length, the mean of the relevant peak positions within the sliding window is calculated to obtain the time delay change trend. The relevant peak positions are sorted according to time order to generate a relevant peak position sequence. The sliding window length is set, and a sliding calculation window is generated based on the sliding window length. The sliding calculation window is slid along the relevant peak position sequence. The arithmetic mean of all relevant peak positions contained in each sliding calculation window is calculated to obtain the smoothed time delay value corresponding to each sliding position. The sequence composed of all smoothed time delay values ​​represents the time delay change trend.

[0025] In some embodiments, in step S3, the decoding result is verified using either Cyclic Redundancy Check (CRC) or Forward Error Correction (FEC). The verification of the decoding result is completed based on predefined verification rules. When using CRC, a CRC code field at a fixed position in the decoding result is extracted. A CRC generator polynomial is used to calculate the information field in the decoding result to obtain a calculated check code. The content of the calculated check code is compared with that of the CRC code field. The verification of the decoding result is completed based on the comparison result. When using FEC, the error correction verification result obtained during the decoding process is used to determine whether there are any uncorrectable errors in the decoding result. The verification of the decoding result is completed based on the determination result.

[0026] In some embodiments, in step S4, based on the rate of change of the time delay trend, the fitting order or filtering parameters of the smoothing fitting process are adaptively adjusted to match the dynamic characteristics of the time delay change. The difference between adjacent values ​​of the time delay change trend is calculated, and the rate of change of the time delay change trend is calculated based on the difference. The rate of change is compared with a set rate of change threshold. When the rate of change is less than or equal to the set rate of change threshold, the fitting order of the smoothing fitting process is reduced or the sliding window length of the moving average filter is increased. When the rate of change is greater than the set rate of change threshold, the fitting order of the smoothing fitting process is increased or the sliding window length of the moving average filter is decreased.

[0027] In some embodiments, in step S4, based on the predicted starting position of the subsequent burst, the starting position and length of the receiving window are adjusted to complete the reception and delay tracking of the subsequent burst signal. The predicted starting position corresponding to the subsequent burst is calculated based on the delay change trend, and the predicted starting position is set as the starting position of the receiving window. The burst duration corresponding to the small station identifier in the small station frame plan is read, the basic length of the receiving window is set, the length of the receiving window is adjusted based on the rate of change of the delay change trend, the reception of the subsequent burst signal is completed based on the adjusted receiving window, and the delay change trend is iteratively updated based on the reception result of the subsequent burst signal to complete continuous delay tracking.

[0028] Example 2 This embodiment provides a specific implementation process of a VSAT-TDMA delay tracking method based on decoding verification. This embodiment is applied to the synchronization processing flow of uplink multiple access in a satellite communication system. Based on the standard frame structure and signal processing flow of the time division multiple access system, it filters valid signal samples through a decoding verification stage, extracts delay deviation information by combining differential correlation operations, and obtains the delay change pattern through smoothing fitting processing. Ultimately, it achieves continuous delay tracking of uplink burst signals from remote terminals. This embodiment completes the entire signal processing and time slot scheduling process based on the standard frame structure of this system. The master station is the central control station of the VSAT-TDMA satellite communication system, responsible for the time slot scheduling, signal reception, and synchronization control of the entire system. The signal processing flow is completed at the master station receiving end. The small station is the remote user terminal of the VSAT-TDMA satellite communication system, which sends uplink burst signals according to the time slots allocated by the master station and is the target object of delay tracking in this embodiment. Figure 2 As shown, the specific implementation process is as follows: Step 1. Received signal demodulation and frame plan generation: Step 1.1. Perform synchronization preprocessing on the baseband signal received by the master station, complete carrier synchronization, symbol synchronization and frame synchronization processing, locate the start position of the frame structure, and extract the signaling fields at fixed positions within the frame: Carrier synchronization refers to the basic signal processing process of recovering the carrier signal with the same frequency and phase as the transmitter from the received signal to achieve coherent demodulation of the received signal. In this embodiment, carrier synchronization processing eliminates the carrier frequency and phase deviations introduced by channel transmission in the received signal, ensuring the accuracy of subsequent demodulation processing. Symbol synchronization refers to the process of extracting a timing clock signal with the same symbol rate as the transmitter from the received signal and determining the optimal sampling decision time for each symbol. In this embodiment, symbol synchronization processing achieves accurate symbol sampling and decision of the received signal. Frame synchronization refers to the process of identifying the starting position of the frame structure in the received signal after completing symbol synchronization, and realizing the processing of dividing the received data according to the frame structure. In this embodiment, frame synchronization processing locates the frame boundary of the received data, providing an accurate position reference for subsequent signaling field extraction.

[0029] The baseband signal of the satellite uplink radio frequency signal received by the master station after downconversion is demodulated to obtain the master station demodulated data. The master station demodulated data is then subjected to carrier synchronization and symbol synchronization processing in sequence to complete the frame synchronization processing of the master station demodulated data. The frame start position of the master station demodulated data is located, and the signaling field at a fixed position in the frame structure of the master station demodulated data is extracted.

[0030] Step 1.2. Perform channel decoding processing on the extracted signaling fields to obtain time slot scheduling related information. After completing time sequence matching, integrate the information to obtain the small station time slot allocation information: Channel decoding refers to the inverse processing of received data after channel coding to recover the original information from the transmitting end, while simultaneously detecting and correcting errors introduced during data transmission. It is a core component of digital communication systems ensuring transmission reliability. In this embodiment, channel decoding is used to recover the time slot scheduling and control information carried in the signaling field. The small station identifier is a unique identification information used to distinguish different remote user terminals in a VSAT-TDMA satellite communication system. The master station uses the small station identifier to distinguish uplink signals sent by different terminals, enabling independent scheduling and management of multiple users. The time slot start position information refers to the start time information of the uplink burst signal transmission time slot allocated by the master station to the corresponding small station in the frame structure. The time slot duration information refers to the duration information of the uplink burst signal transmission time slot allocated by the master station to the corresponding small station. Channel decoding is performed on the extracted signaling field to obtain the small station identifier, time slot start position information, and time slot duration information contained in the signaling field. Timing matching is then performed on the obtained small station identifier, time slot start position information, and time slot duration information to integrate and obtain the small station time slot allocation information.

[0031] Step 1.3. Based on the integrated small station time slot allocation information, arrange the frames according to the frame structure timing to generate a small station frame plan containing complete scheduling information: A small station frame plan is a timing schedule generated by the master station based on time slot allocation information. It indicates the theoretical transmission and arrival times of uplink burst signals from each small station. The master station determines the expected reception window for each small station's uplink burst signals based on the small station frame plan, enabling targeted reception of uplink signals. Based on the integrated small station time slot allocation information, the frames are arranged according to the frame structure timing of the VSAT-TDMA satellite communication system to generate a small station frame plan containing the small station identifier, the theoretical occurrence time of the burst, and the burst duration.

[0032] In some specific implementations, the VSAT-TDMA system uses a fixed-duration superframe structure for time slot scheduling. Each superframe contains 24 consecutive TDMA frames, each with a duration of 20ms. Each TDMA frame is evenly divided into 32 independent time slots, each with a duration of 625μs. The first two time slots of each TDMA frame are fixed as common signaling time slots, used to carry time slot scheduling signaling sent from the master station to all small stations. The signaling field uses a fixed-length encoding structure, with a total length of 256 bits for a single signaling field. 16 bits are used for the small station identifier to distinguish different terminals, 32 bits are used for the time slot start position information indicating the start time of the time slot, 16 bits are used for the time slot duration information indicating the effective duration of the time slot, and the remaining bits are used to fill the frame synchronization check code and reserved extension bits.

[0033] During signaling parsing, the time slot scheduling information within the signaling fields is only decoded after the extracted signaling fields pass frame synchronization verification. After decoding, the time slot allocation information corresponding to each small station is time-matched according to the three-level timing structure of superframe, TDMA frame, and time slot. The start time of each time slot is converted into the theoretical occurrence time of the corresponding burst, and finally a small station frame plan containing the small station identifier, the theoretical occurrence time of the burst, and the duration of the burst is generated. This implementation method can effectively avoid errors in time slot scheduling information caused by signaling errors, improve the timing accuracy of the small station frame plan, and provide a reliable theoretical time reference for subsequent initial delay estimation.

[0034] In some embodiments, the extraction of signaling fields can be completed based on a preset fixed offset position in the frame structure. The offset position can be updated through preset configuration information of the system to adapt to frame structures of different lengths and signaling arrangement methods.

[0035] Step 2. Calculation of initial time delay baseline value: Step 2.1. Read the generated small station frame plan, extract the burst theoretical occurrence time of the corresponding small station identifier, and sort and organize them according to the frame time sequence: Read the generated small station frame plan, extract the burst theoretical occurrence time of the corresponding small station identifier in the small station frame plan, sort and organize the burst theoretical occurrence time according to the frame timing of the VSAT-TDMA satellite communication system, and provide an accurate theoretical time reference for subsequent delay difference calculation.

[0036] Step 2.2. Perform burst arrival time detection on the signal received by the main station, and statistically analyze the actual reception time of the bursts identified by the corresponding small stations to complete the matching with the theoretical time: Burst arrival time detection (BAT) is a signal processing procedure that identifies and statistically analyzes the initial arrival times of uplink signals in burst form within the received signal. It is a fundamental processing step in a TDMA system receiver for determining the actual arrival time of the uplink signal. In this embodiment, BAT is used to obtain the actual reception time of the uplink signal from the small station, providing a real time reference for initial delay calculation. BAT is performed on the received signal from the master station, and the actual reception times of the bursts identified by the corresponding small stations are statistically analyzed. These actual reception times are then matched with the corresponding theoretical burst occurrence times to establish a one-to-one correspondence between theoretical and actual reception times.

[0037] Step 2.3. Calculate the difference between the theoretical occurrence time of the burst after matching and the actual reception time, and determine the difference as the estimated initial signaling delay: The initial signaling delay estimate refers to the time difference between the theoretical and actual transmission delay of the uplink burst signal from the small station. This difference reflects the initial delay deviation introduced by the satellite channel transmission and provides an initial delay compensation benchmark for setting the subsequent burst signal reception window. The time difference between the theoretical occurrence time of the matched burst and the actual reception time of the burst is calculated, and this time difference is determined as the initial signaling delay estimate.

[0038] In some specific implementations, during the calculation of the initial delay estimate, the theoretical occurrence time and actual reception time corresponding to eight consecutive uplink bursts from the same small station are selected as calculation samples. The theoretical occurrence time interval between each adjacent burst is set to 40ms, and the detection accuracy of the actual reception time of the burst is set to 1μs. During the sample screening process, the time difference between the theoretical occurrence time and the actual reception time corresponding to each burst is first calculated. Then, all differences are compared with a preset outlier threshold of ±20μs, and outlier samples with differences exceeding this threshold are eliminated to avoid interference from single burst detection errors on the initial estimation results. The time differences corresponding to the remaining valid samples after screening are arithmetically averaged, and the calculated average value is determined as the initial signaling delay estimate for that small station. If the number of valid samples after screening is less than four, new uplink bursts from that small station are continuously received, and the initial delay estimate is recalculated after supplementing the calculation samples. This implementation can effectively reduce the initial delay estimation deviation caused by burst arrival time detection error in low signal-to-noise ratio environments, provide an accurate delay compensation benchmark for setting the subsequent burst signal receiving window, and avoid the receiving window misalignment problem caused by inaccurate initial estimation.

[0039] In some embodiments, the initial delay estimate can be calculated based on the average difference between the theoretical time and the actual reception time of multiple consecutive bursts, thereby reducing the impact of single burst detection bias on the initial estimate result.

[0040] Step 3. Decoding the burst signal and screening the effective samples: Step 3.1. Read the initial signaling delay estimate and the small station frame plan to determine the starting position and duration of the burst signal reception window, and complete the reception of the corresponding small station burst signal: A receiving window refers to a fixed-length signal reception interval set by the master station in the receiving timing to receive uplink burst signals from a specific small station. The master station only receives and processes the uplink signals from the corresponding small station within this receiving window interval, reducing interference from non-target signals. In this embodiment, the starting position of the receiving window is compensated using an initial delay estimate to improve the accuracy of burst signal reception. The determined initial signaling delay estimate is read, and the starting position of the receiving window for the burst signal is determined based on this estimate. The burst duration corresponding to the small station identifier in the small station frame plan generated in the previous step is read, the receiving window duration is set, and the reception of the burst signal corresponding to the small station identifier is completed.

[0041] Step 3.2. Perform frequency offset compensation, phase offset compensation, and baseband demodulation on the received burst signal to complete the decoding process of the demodulated data and obtain the decoding result: Frequency offset compensation refers to the signal processing process of estimating and compensating for carrier frequency deviations in the received signal caused by factors such as inconsistency between the carrier frequencies of the transmitting and receiving ends and the satellite Doppler effect. Phase offset compensation refers to the signal processing process of estimating and compensating for carrier phase deviations in the received signal caused by channel transmission. In this embodiment, frequency offset compensation and phase offset compensation are used to eliminate the influence of channel transmission on the signal carrier and improve the accuracy of subsequent demodulation processing. Decoding processing refers to the process of inversely processing the received burst data after channel coding to recover the original service data from the transmitting end. It corresponds to the channel coding process and is the core processing link of the receiving end of the digital communication system. Frequency offset compensation and phase offset compensation are performed on the received burst signal. After the frequency offset compensation and phase offset compensation are completed, the baseband demodulation processing of the burst signal is completed. The data after baseband demodulation processing is then decoded to obtain the decoding result.

[0042] Step 3.3. Based on the preset verification rules, verify the correctness of the decoding results, and filter and retain the preceding burst signals corresponding to the decoding results that meet the verification rules: A preceding burst signal refers to a signal segment in a burst signal structure that precedes the service data field. This segment contains a fixed, known sequence of elements, such as a synchronization sequence and a unique code, used for signal synchronization and detection. This signal segment has a fixed sequence structure and can be used for subsequent differential correlation operations and delay deviation extraction. Cyclic redundancy check (CRC) is a widely used error checking method in existing data communication. It calculates a fixed-length checksum using a preset generator polynomial on the transmitted information field. The receiving end recalculates and compares the checksum to determine if errors have occurred in the transmitted data. Forward error correction (FEC) is another widely used error control method in existing data communication. By encoding error correction in the data at the sending end, the receiving end can detect and correct a limited number of errors introduced during transmission using the error correction code's checksum information. It can also determine if there are any uncorrectable errors in the data.

[0043] The decoding results are verified based on the set verification rules. The results are judged to determine whether they conform to the set verification rules. The preceding burst signals corresponding to the decoding results that conform to the set verification rules are retained, and the burst signals corresponding to the decoding results that do not conform to the set verification rules are discarded.

[0044] The decoding results are verified using either Cyclic Redundancy Check (CRC) or Forward Error Correction (FEC). When using CRC, a CRC code field at a fixed position is extracted from the decoding result. A predefined CRC generator polynomial is used to calculate the checksum in the information fields of the decoding result. The calculated checksum is then compared to the content of the CRC field to verify the decoding result. When using FEC, the error correction results obtained during decoding are used to determine if any uncorrectable errors exist in the decoding result. This process effectively filters out invalid signals with uncorrectable errors during transmission, preventing these signals from interfering with subsequent delay estimation results.

[0045] In some specific implementations, the decoding verification stage uses Cyclic Redundancy Check (CRC) to determine the correctness of the decoding result. CRC uses the CRC-16 standard generator polynomial, and the checksum field has a fixed length of 16 bits, located at the end of each burst decoding result. The total length of the decoding data block for each uplink burst signal is set to 1024 bits, of which the information field carrying the service content occupies 1008 bits, and the CRC field for error verification occupies 16 bits. After decoding, the last 16 bits of the checksum are extracted first, and then the first 1008 bits of the information field are recalculated based on the CRC-16 generator polynomial to generate a locally calculated checksum. The locally calculated checksum is compared with the extracted checksum to determine whether the decoding result conforms to the preset verification rules. During the effective sample selection process, only when the number of consecutively acquired correctly decoded preceding burst signals is not less than 16, is the batch of burst signals considered as effective samples and sent to the subsequent differential correlation operation stage. If the number of consecutively correct bursts is less than 16, new uplink burst signals from the small station are continuously received and decoded and verified until the number of effective samples meets the requirement. This implementation method can strictly select effective signals without uncorrectable errors during transmission, avoid interference from incorrectly decoded burst signals on subsequent time delay estimation results, and ensure sufficient effective samples for subsequent fitting stages, thereby improving the stability of time delay trend extraction.

[0046] In some embodiments, the verification of the decoding result can be completed by combining cyclic redundancy check and forward error correction check. Only when both verification methods determine that the decoding result meets the preset rules will the corresponding burst signal be retained as a valid sample, thereby improving the screening accuracy of valid samples.

[0047] Step 4. Delay Deviation Extraction and Tracking Closed-Loop Processing: Step 4.1. Read the filtered valid preceding burst signals, extract the fixed-length preceding sequence, and complete the normalization preprocessing of the sequence: Normalization refers to standardizing the amplitude of a signal sequence, adjusting it to a preset fixed range to eliminate the impact of amplitude fluctuations on subsequent correlation calculations and improve the stability of these calculations. In this embodiment, normalization unifies the amplitude benchmark between the effective sequence and the local reference sequence, ensuring the reliability of the differential correlation calculation results. The preceding burst signal whose decoding verification results conform to the set verification rules is read, and a fixed-length preceding sequence is extracted from this preceding burst signal. This preceding sequence is then normalized.

[0048] Step 4.2. Read the locally stored unique code sequence, perform a sliding differential correlation operation between the preprocessed preorder sequence and the unique code sequence, and generate the correlation function result corresponding to the sliding offset: Differential correlation is a common processing method in the field of digital signal processing for sequence synchronization and position detection. By performing a sliding correlation operation between the received signal sequence and a locally known reference sequence, the position corresponding to the peak of the correlation result is found, thereby determining the accurate position of the reference sequence in the received sequence. In this embodiment, differential correlation is used to extract the accurate start position deviation of the burst signal. The unique code sequence refers to a known pseudo-random sequence pre-set in the pre-sequence part of the burst signal in the VSAT-TDMA satellite communication system for burst synchronization detection. This sequence has good autocorrelation characteristics and can be accurately identified in the received signal through correlation operation. It is a universal reference sequence for burst synchronization detection in TDMA systems. The locally stored unique code sequence is read, and the normalized pre-sequence part sequence is performed with the locally stored unique code sequence to obtain the correlation function result corresponding to the sliding offset. The calculation formula is: ; Where r(n) is the received pre-sequence sequence, which is the normalized sampled sequence of the effective burst signal pre-sequence; p*(n) is the locally stored unique code sequence, which is a pre-set known pseudo-random sequence used for burst synchronization detection; N is the length of the unique code sequence; m is the offset of the sequence sliding; R(m) is the differential correlation function result corresponding to the sliding offset m.

[0049] In some specific implementations, the locally stored unique code sequence adopts a 32-bit m-sequence, which has good autocorrelation characteristics. The autocorrelation sidelobe peak is lower than 1 / 10 of the main lobe peak, which can effectively reduce the false alarm problem of correlation peak caused by multipath interference. In the differential correlation operation, the search range of the sliding offset is set to ±32 symbols, the number of sampling points corresponding to each symbol is set to 8, and the step size of the sliding operation is set to 1 sampling point. After the correlation operation is completed for each sliding offset, the correlation function result of the corresponding offset is generated. After all offset operations are completed, a correlation function result sequence with a total length of 512 points is generated. After taking the modulus of all correlation function results, a modulus sequence of corresponding length is generated. In the peak search process, the average noise power of the modulus sequence is calculated first, and then the peak detection threshold is set to 6 times the average noise power. Only when the peak in the modulus sequence exceeds the detection threshold is the sliding offset corresponding to the peak determined as the correlation peak position. If there is no peak in the modulus sequence that exceeds the detection threshold, the correlation operation result corresponding to the burst is discarded and not included in the subsequent fitting stage. This implementation method can effectively suppress noise peak interference in low signal-to-noise ratio environments, improve the accuracy of correlation peak position detection, and reduce the false alarm probability of time delay deviation extraction.

[0050] Step 4.3. Take the modulus of the correlation function results corresponding to all sliding offsets to generate a modulus sequence, complete the peak search of the modulus sequence, and determine the offset corresponding to the maximum modulus value as the correlation peak position: The modulus of the correlation function results corresponding to all the obtained sliding offsets is taken to generate a modulus sequence. Peak search is performed on the modulus sequence, and the sliding offset corresponding to the maximum value in the modulus sequence is determined as the correlation peak position. The correlation peak position corresponds to the time delay deviation between the actual starting position of the received burst signal and the theoretical starting position, which is the basic data for subsequent time delay trend fitting.

[0051] Step 4.4. Perform smoothing fitting on the positions of the relevant peaks corresponding to multiple effective bursts to obtain the time delay variation trend characterizing the time delay variation law: The least squares method is a commonly used optimization method for data fitting in existing mathematical fields. It finds the best matching function for the data by minimizing the sum of squared errors. It can fit a continuous function reflecting the trend of data change based on discrete observation data. In this embodiment, the least squares method is used to solve for the coefficients of the fitting function to achieve trend fitting of discrete time-delay observations. The obtained multiple sets of related peak positions are then smoothed to obtain the time-delay change trend. The smoothing process can use a first-order linear fitting method or a moving average filtering method. When using a first-order linear fitting method, a first-order linear fitting function is constructed based on the burst sequence number and the corresponding related peak position. The fitting coefficients of the first-order linear fitting function are solved using the least squares method. The calculation formula is: ; Where k is the burst sequence number, which is the sequential number assigned to effective bursts according to time order; a is the slope coefficient of the first-order linear fitting function, which represents the rate of change of time delay; b is the intercept coefficient of the first-order linear fitting function, which represents the initial time delay reference value. Let k be the fitted time delay value for the burst corresponding to sequence number k. The positions of the relevant peaks are sorted chronologically, and corresponding burst sequences are assigned to the sorted peak positions. Based on the burst sequence number and the corresponding relevant peak position, a system of calculation equations for the first-order linear fitting function is constructed. The system of calculation equations is solved using the least squares method to obtain the slope coefficient and intercept coefficient of the first-order linear fitting function. Based on the slope coefficient and intercept coefficient, the first-order linear fitting function is determined, and the first-order linear fitting function is used to characterize the time delay variation trend.

[0052] When using the moving average filtering method, based on the set moving window length, the mean value of the relevant peak positions within the moving window is calculated to obtain the time delay variation trend. The calculation formula is as follows: ; Where L is the length of the sliding window, and is the preset number of consecutive correlation peak positions participating in the mean calculation; Let be the position of the relevant peak corresponding to the i-th valid burst, i.e., the actual detected delay observation value. The relevant peak positions are sorted chronologically to generate a relevant peak position sequence. A sliding window length is set, and a sliding calculation window is generated based on this length. The sliding calculation window is slid along the relevant peak position sequence, and the arithmetic mean of all relevant peak positions contained within each sliding calculation window is calculated to obtain the smoothed delay value corresponding to each sliding position. The sequence composed of all smoothed delay values ​​represents the delay change trend. This process can effectively filter out delay detection jitter caused by random noise and channel interference, improving the stability of delay trend extraction.

[0053] In some specific implementations, when using a first-order linear fitting method to smooth the relevant peak positions, the relevant peak positions corresponding to 32 consecutive effective bursts from the same small station are selected as fitting samples. Each fitting sample is assigned a consecutive burst number from 1 to 32 according to chronological order. Based on the mapping relationship between the burst number and the corresponding relevant peak position, a system of equations for calculating the first-order linear fitting function is constructed. The slope coefficient 'a' and intercept coefficient 'b' of the fitting function are obtained by solving the system of equations using the least squares method. After fitting, the fitting residuals for each sample are calculated. When the mean square value of the fitting residuals exceeds a preset threshold, the earliest 8 samples are discarded, and the latest 8 effective relevant peak positions are added. The system of equations is then reconstructed to update the fitting coefficients. The update cycle of the fitting results is consistent with the transmission cycle of the small station bursts. After the relevant peak position of each burst is obtained, an iterative update of the fitting results is performed. When using a moving average filter for smoothing, the length of the moving window is set to 16 consecutive correlation peak positions, and the moving step size is set to 1 burst. After each window movement, the arithmetic mean of the 16 correlation peak positions within the window is calculated to obtain the smoothed delay value at the corresponding position. The sequence of all smoothed delay values ​​represents the delay variation trend, characterizing the delay variation pattern. This implementation effectively filters out random jitter in the correlation peak positions, improves the smoothness and accuracy of delay variation trend extraction, and adapts to slow time-varying transmission delay variations caused by satellite on-orbit perturbations.

[0054] In some embodiments, the smoothing fitting process can be completed using a second-order polynomial fitting method. A second-order polynomial fitting function is constructed based on the burst sequence number and the corresponding correlation peak position. The coefficients of each term of the fitting function are solved by the least squares method to adapt to nonlinear time delay scenarios.

[0055] Step 4.5. Based on the rate of change of the time delay trend, adaptively adjust the fitting order or filtering parameters of the smoothing fitting process to match the dynamic characteristics of the time delay change: The difference between adjacent values ​​of the time delay change trend is calculated, and the rate of change of the time delay change trend is calculated based on the difference. The rate of change is compared with the set rate of change threshold. When the rate of change is less than or equal to the set rate of change threshold, the fitting order of the smoothing fitting process is reduced or the sliding window length of the moving average filter is increased to improve the smoothness of the time delay estimation result. When the rate of change is greater than the set rate of change threshold, the fitting order of the smoothing fitting process is increased or the sliding window length of the moving average filter is decreased to improve the response speed of time delay tracking.

[0056] In some specific implementations, when adaptively adjusting the fitting parameters and receiving window parameters based on the rate of change of the latency trend, eight consecutive smoothed latency values ​​obtained from fitting are selected as calculation samples. First, the difference between two adjacent smoothed latency values ​​is calculated, and then the latency change rate is calculated based on the arithmetic mean of the eight differences. The preset threshold for the rate of change is set to 0.5 μs / burst. When the detected latency change rate is less than or equal to 0.5 μs / burst, the current latency is determined to be in a slow-changing state. The number of samples for first-order linear fitting is increased from 32 to 64, or the window length of the moving average filter is increased from 16 to 32 to improve the smoothness of the fitting results and reduce the interference of random noise. When the detected latency change rate is greater than 0.5 μs / burst, the current latency is determined to be in a fast-changing state. The number of samples for first-order linear fitting is reduced from 32 to 16, or the window length of the moving average filter is reduced from 16 to 8 to improve the response speed of latency tracking and avoid tracking lag problems caused by sudden changes in latency. Meanwhile, the base length of the receiving window is set to 1.2 times the duration of the corresponding burst. When the delay change rate exceeds a preset threshold, the length of the receiving window is adjusted to 1.5 times the duration of the burst, expanding the signal reception range and avoiding incomplete reception of burst signals due to sudden delay changes. This implementation method can balance the smoothness and response speed of delay tracking, adapt to delay change scenarios with different dynamic characteristics, and improve the system's environmental adaptability.

[0057] Step 4.6. Based on the latency change trend, predict the starting position of subsequent bursts, dynamically adjust the relevant parameters of the receiving window, and complete the continuous latency tracking closed loop: Based on the obtained latency change trend, calculate the predicted starting position corresponding to the subsequent burst, set the predicted starting position as the starting position of the receiving window, read the burst duration of the corresponding small station identifier in the generated small station frame plan, set the basic length of the receiving window, adjust the length of the receiving window based on the rate of change of the latency change trend, complete the reception of the subsequent burst signal based on the adjusted receiving window, iteratively update the latency change trend based on the reception results of the subsequent burst signal, and complete continuous latency tracking.

[0058] In some embodiments, the prediction of the subsequent burst start position can be completed by combining the weighted results of the time delay change trends obtained from multiple previous fittings. The weights can be set based on the magnitude of the relevant peak modulus values ​​of the corresponding fitting results to improve the stability of the prediction results.

[0059] This embodiment achieves stable delay tracking of uplink burst signals in a VSAT-TDMA satellite communication system through a closed-loop processing flow encompassing complete signal demodulation, initial delay estimation, effective sample selection, differential correlation calculation, delay trend fitting, and dynamic tracking. By selecting effective signal samples through a decoding verification stage, this embodiment eliminates interference from decoded burst signals on the delay estimation results, improving the accuracy of delay deviation extraction. Smoothing fitting processes the discrete correlation peak positions, filtering out delay detection jitter caused by random noise and channel interference, reducing the false alarm probability of the delay estimation results. Fitting and predicting delay change trends adapts to the time-varying characteristics of satellite channel transmission delay, enhancing the system's adaptability to dynamic channel environments. The processing flow of this embodiment is based on the standard frame structure and signal processing flow design of the existing VSAT-TDMA communication system, requiring no additional hardware overhead. It can be directly deployed and applied at the receiver end of existing satellite communication systems, effectively improving the synchronization stability of uplink multiple access in satellite communication systems, reducing the probability of time slot collisions and signal reception errors caused by delay deviations, and enhancing the overall communication reliability of the system.

[0060] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A VSAT-TDMA delay tracking method based on decoding verification, characterized in that, Includes the following steps: S1. Demodulate the received signal from the main station to obtain the main station demodulated data, perform signaling parsing on the main station demodulated data, and generate a small station frame plan; S2. Read the small station frame plan, compare the theoretical occurrence time of the burst contained in the small station frame plan with the actual reception time of the burst, and obtain the signaling initial delay estimate; S3. Receive burst signals sent by small stations, decode the burst signals, verify the decoding results, and retain preceding burst signals whose decoding verification results conform to the set verification rules. S4. Perform differential correlation operation on the preceding burst signal whose decoding verification result conforms to the set verification rules to obtain the position of the correlation peak. Perform smooth fitting processing on the position of the correlation peak to obtain the time delay change trend. Predict the starting position of the subsequent burst based on the time delay change trend. Dynamically adjust the receiving window based on the predicted starting position to complete the time delay tracking.

2. The method according to claim 1, characterized in that, The signaling parsing and small station frame plan generation in step S1 include the following sub-steps: S1.

1. Perform carrier synchronization and symbol synchronization processing on the master station demodulated data, complete the frame synchronization processing of the master station demodulated data, locate the frame start position of the master station demodulated data, and extract the signaling field at a fixed position in the frame structure of the master station demodulated data; S1.

2. Perform channel decoding processing on the signaling field to obtain the small station identifier, time slot start position information, and time slot duration information contained in the signaling field. Perform time sequence matching on the obtained information and integrate it to obtain the small station time slot allocation information. S1.

3. Based on the small station time slot allocation information, arrange the frames according to the frame structure time sequence to generate a small station frame plan that includes the small station identifier, the theoretical occurrence time of the burst, and the duration of the burst.

3. The method according to claim 1, characterized in that, The initial signaling delay estimate obtained in step S2 includes the following sub-steps: S2.

1. Read the small station frame plan, extract the burst theoretical occurrence time of the corresponding small station identifier in the small station frame plan, and sort and organize the burst theoretical occurrence time according to the frame time sequence; S2.

2. Perform burst arrival time detection on the signal received by the main station, count the actual burst reception time of the corresponding small station, and match the actual burst reception time with the corresponding theoretical burst occurrence time; S2.

3. Calculate the time difference between the theoretical occurrence time of the burst and the actual reception time of the burst after matching is completed, and determine the time difference as the initial delay estimate of the signaling.

4. The method according to claim 1, characterized in that, The burst signal decoding and verification process in step S3 includes the following sub-steps: S3.

1. Read the initial delay estimate of the signaling, determine the starting position of the receiving window for the burst signal based on the initial delay estimate of the signaling, read the burst duration of the corresponding small station identifier in the small station frame plan, set the duration of the receiving window, and complete the reception of the burst signal of the corresponding small station identifier. S3.

2. Perform frequency offset compensation and phase offset compensation on the received burst signal to complete the baseband demodulation of the burst signal. Decode the data after baseband demodulation to obtain the decoding result. S3.

3. Verify the decoding results based on the set verification rules, determine whether the decoding results conform to the set verification rules, retain the preceding burst signals corresponding to the decoding results that conform to the set verification rules, and discard the burst signals corresponding to the decoding results that do not conform to the set verification rules.

5. The method according to claim 1, characterized in that, Step S4, the differential correlation operation and the acquisition of correlation peak positions, includes the following sub-steps: S4.

1. Read the preceding burst signal whose decoding verification result conforms to the set verification rules, extract the preceding part sequence of fixed length from the preceding burst signal whose decoding verification result conforms to the set verification rules, and normalize the preceding part sequence. S4.

2. Read the unique code sequence stored locally, perform sliding differential correlation operation between the normalized preorder sequence and the unique code sequence stored locally, and obtain the correlation function result of the corresponding sliding offset; S4.

3. Take the modulus of the correlation function results corresponding to all sliding offsets to generate a modulus sequence. Perform peak search on the modulus sequence and determine the sliding offset corresponding to the maximum value in the modulus sequence as the correlation peak position.

6. The method according to claim 1, characterized in that, In step S4, the relevant peak positions are smoothed and fitted using a first-order linear fitting method. A first-order linear fitting function is constructed based on the burst sequence number and the corresponding relevant peak position. The fitting coefficients of the first-order linear fitting function are solved using the least squares method. The relevant peak positions are sorted according to time order, and corresponding burst sequences are assigned to the sorted relevant peak positions. A set of calculation equations for the first-order linear fitting function is constructed based on the burst sequence number and the corresponding relevant peak position. The set of calculation equations is solved using the least squares method to obtain the slope coefficient and intercept coefficient of the first-order linear fitting function. The first-order linear fitting function is determined based on the slope coefficient and intercept coefficient, and the first-order linear fitting function is used to characterize the time delay change trend.

7. The method according to claim 1, characterized in that, In step S4, the smoothing fitting of the relevant peak positions is performed using a moving average filtering method. Based on the set sliding window length, the mean of the relevant peak positions within the sliding window is calculated to obtain the time delay change trend. The relevant peak positions are sorted according to time order to generate a relevant peak position sequence. The sliding window length is set, and a sliding calculation window is generated based on the sliding window length. The sliding calculation window is slid along the relevant peak position sequence. The arithmetic mean of all relevant peak positions contained in each sliding calculation window is calculated to obtain the smoothed time delay value corresponding to each sliding position. The sequence composed of all smoothed time delay values ​​represents the time delay change trend.

8. The method according to claim 1, characterized in that, In step S3, the decoding result is verified using either Cyclic Redundancy Check (CRC) or Forward Error Correction (FEC). Verification is performed based on predefined rules. When using CRC, a CRC code field at a fixed position in the decoding result is extracted. A CRC generator polynomial is used to calculate the information field in the decoding result to obtain a calculated check code. The calculated check code is then compared with the content of the CRC code field. Verification of the decoding result is completed based on the comparison result. When using FEC, the error correction result obtained during the decoding process is used to determine if there are any uncorrectable errors in the decoding result. Verification of the decoding result is completed based on the determination result.

9. The method according to claim 1, characterized in that, In step S4, based on the rate of change of the time delay trend, the fitting order or filtering parameters of the smoothing fitting process are adaptively adjusted to match the dynamic characteristics of the time delay change. The difference between adjacent values ​​of the time delay change trend is calculated, and the rate of change of the time delay change trend is calculated based on the difference. The rate of change is compared with the set rate of change threshold. When the rate of change is less than or equal to the set rate of change threshold, the fitting order of the smoothing fitting process is reduced or the sliding window length of the moving average filter is increased. When the rate of change is greater than the set rate of change threshold, the fitting order of the smoothing fitting process is increased or the sliding window length of the moving average filter is decreased.

10. The method according to claim 1, characterized in that, In step S4, based on the predicted starting position of the subsequent burst, the starting position and length of the receiving window are adjusted to complete the reception and delay tracking of the subsequent burst signal. The predicted starting position corresponding to the subsequent burst is calculated based on the delay change trend, and the predicted starting position is set as the starting position of the receiving window. The burst duration corresponding to the small station identifier in the small station frame plan is read, and the basic length of the receiving window is set. The length of the receiving window is adjusted based on the rate of change of the delay change trend. The reception of the subsequent burst signal is completed based on the adjusted receiving window. The delay change trend is iteratively updated based on the reception result of the subsequent burst signal to complete continuous delay tracking.

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