High-speed refreshing positioning algorithm and system based on wireless signal processing

Abstract

The invention discloses a high-speed refresh positioning algorithm and system based on wireless signal processing, and relates to the technical field of high-speed refresh positioning, and the algorithm specifically comprises the following steps: analyzing the cycle number of a signal data unit in a current refresh cycle, judging whether there is a signal data unit with the cycle number not equal to the current cycle number, and if yes, executing the following steps; when the signal data units exist and the number ratio of the signal data units exceeds a preset threshold value, a cross-cycle data synchronization state judgment process is started; and on the basis of the time label distribution, the historical data proportion and the source period number of the signal data units in the current refresh period, extracting feature information used for representing time consistency and a periodic structure, and inputting the feature information into a classification discrimination function to determine a cross-period data synchronization state corresponding to the current refresh period. According to the method, the problem of positioning mistaken refreshing caused by cross-cycle data dislocation is solved, refreshing control based on synchronous state judgment is realized, and the positioning accuracy and the system stability are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of high-speed refresh positioning technology, and in particular to a high-speed refresh positioning algorithm and system based on wireless signal processing. Background Art

[0002] Real-time, high-precision positioning has become a key supporting technology for scenarios requiring high spatial awareness, such as the Internet of Things (IoT), smart transportation, smart manufacturing, and augmented reality. Wireless signals, with their advantages of flexible deployment, stable transmission, and wide sensing range, are widely used in indoor and outdoor positioning systems. By analyzing signal characteristics from multiple wireless transmitters, such as received signal strength (RSSI), time of arrival (ToA), time difference of arrival (TDoA), phase information, or spectral characteristics, the position of a moving target can be estimated. To accommodate high-speed moving targets, continuous tracking scenarios, and tasks requiring frequent location data updates, positioning systems must possess high-speed acquisition, rapid processing, and high-frequency refresh capabilities. High-speed refresh positioning technology based on wireless signal processing is becoming an important development for highly dynamic applications. This technology typically requires an algorithmic framework that integrates multi-source signal fusion, low-latency computation, and dynamic prediction capabilities, combined with a corresponding system architecture, to meet the requirements for stable, continuous, and high-precision positioning in complex environments.

[0003] Existing high-speed refresh positioning algorithms based on wireless signal processing typically deploy multiple wireless signal transmitters (such as Wi-Fi routers, Bluetooth beacons, UWB base stations, or 5G small cells). The target device's receiving end continuously collects wireless signal data from each transmitter and pre-processes the signal parameters using time synchronization mechanisms or a known coordinate reference. Positioning algorithms generally include the following core components: First, a signal acquisition and pre-processing module filters, normalizes, and performs time / frequency domain analysis on received RSSI, ToA, TDoA, phase, or CSI data. Second, a positioning engine calculates position estimates based on geometric positioning models (such as trilateration and multilateration) or statistical models (such as Kalman filtering, particle filtering, and deep learning models). Third, a data refresh and update mechanism typically combines sliding windows, prediction algorithms, or edge computing frameworks to achieve high-frequency output updates to meet real-time requirements. Finally, positioning results can be transmitted locally or via a network to remote systems for navigation control or visualization services. The entire process involves multiple technical links such as signal decoding, feature extraction, positioning calculation, error correction and result feedback, forming an integrated wireless positioning processing framework.

[0004] The existing technology has the following deficiencies: In a high-speed refresh positioning algorithm that uses a cross-cycle distributed sampling strategy, the system merges and processes signal data received in multiple consecutive refresh cycles to improve anti-interference capabilities and result stability. However, in the event of signal source congestion, data packet delays, or short-term loss, data from some cycles may not enter the processing flow of the current cycle in a timely manner, but instead are added to the queue in subsequent cycles. This causes the system to mix the lagged signals of the previous two cycles with some new data of the current cycle within one refresh cycle, forming a specific state of "cross-cycle input time misalignment." Because this misalignment occurs in the underlying data reception and queue scheduling links, the refresh logic cannot sense whether the input data comes from the same timing reference. As a result, the data with time offset is incorrectly used for current position estimation during processing, ultimately outputting an "outdated positioning result" that does not match the current actual position. The existing high-speed refresh positioning algorithm based on wireless signal processing cannot determine whether the current positioning refresh should be executed based on the "cross-cycle data synchronization status" when the "distributed sampling input is delayed and misaligned at the cycle boundary". As a result, the incorrect refresh not only causes the system output trajectory to "jump back" or "drift", but also causes incorrect judgment of the forward direction and distorted path prediction. In severe cases, it may even affect the normal decision-making of the navigation control and dynamic obstacle avoidance modules, destroying the responsiveness and accuracy of the entire system.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a high-speed refresh positioning algorithm and system based on wireless signal processing to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a high-speed refresh positioning algorithm based on wireless signal processing, specifically comprising the following steps: Processing the wireless signal data received in each refresh cycle, adding timestamp information and cycle number information, generating signal data units containing time information and cycle information, and storing all signal data units in a data buffer; Analyze the cycle numbers of the signal data units in the current refresh cycle to determine whether there are signal data units with cycle numbers that are not equal to the current cycle number, and if such signal data units exist and their proportion exceeds a preset threshold, start the cross-cycle data synchronization status determination process; Based on the time tag distribution of signal data units in the current refresh cycle, the proportion of historical data, and the number of source cycles, feature information used to characterize time consistency and cycle structure is extracted, and the feature information is input into the classification discriminant function to determine the cross-cycle data synchronization status corresponding to the current refresh cycle. The cross-cycle data synchronization status is synchronization consistency, synchronization uncertainty, or synchronization failure; According to the cross-cycle data synchronization status, determine whether the current positioning refresh should be executed and perform the corresponding refresh control operation; During the continuous refresh cycle after the cross-cycle data synchronization status is synchronization failure, only the wireless signal data with a cycle number equal to the current cycle number is accessed and used for positioning calculation, and the synchronization failure status is recorded for use in sampling control strategy adjustment.

[0008] Preferably, the process of processing the wireless signal data received in each refresh cycle includes: Adding timestamp information and cycle number information to each wireless signal data, where the timestamp information records the reception time of the wireless signal data, and the cycle number information is an identifier generated according to the refresh cycle number; Generate a signal data unit based on the timestamp information and the cycle number information, the signal data unit including a signal content field, a timestamp field and a cycle number field; The signal data units generated in each refresh cycle are stored in a data buffer divided according to the refresh cycle, and the data buffer classifies and manages the signal data units using the refresh cycle number as an index.

[0009] Preferably, the process of analyzing the cycle number of the signal data unit in the current refresh cycle and starting the cross-cycle data synchronization state determination process when a specific condition is met includes: Obtaining the current refresh cycle number as the current cycle number, and constructing a current cycle data set, where the current cycle data set includes all signal data units received in the current refresh cycle; Traverse each signal data unit in the current cycle data set, extract the cycle number field contained in each signal data unit, mark the signal data unit whose cycle number is not equal to the current cycle number as a lagging data unit, and construct a lagging data set; The ratio between the number of hysteresis data sets and the total number of data sets in the current cycle is calculated as the hysteresis ratio. When the hysteresis ratio is higher than the preset threshold, it is determined that there is a risk of cycle number misalignment in the current refresh cycle. When it is determined that there is a risk of cycle number misalignment in two consecutive refresh cycles, the cross-cycle data synchronization status judgment process is started; Based on the period number deviation degree of each signal data unit in the lag data set, period deviation distribution information is constructed as the input basis for subsequent synchronization state classification and judgment.

[0010] Preferably, extracting feature information representing temporal consistency includes: Extract the receiving time tag of each signal data unit and construct a time tag sequence; Calculate the range, standard deviation, skewness, kurtosis and time window uniformity based on the time tag sequence to characterize the time distribution characteristics; Divide the refresh cycle time axis into equal time segments, count the number of signal data units in each time segment, and calculate the Shannon entropy as the time distribution entropy value; All time distribution features are uniformly constructed into a time consistency feature set for subsequent synchronization state identification.

[0011] Preferably, extracting characteristic information representing the historical data structure includes: Obtaining the cycle number of each signal data unit in the current refresh cycle and comparing it with the current cycle number to identify the historical data unit; Determine that the signal data units with a cycle number smaller than the current cycle number are historical data, calculate the ratio between the number of historical data and the total number of data in the current cycle, and construct the historical data ratio; Construct a cycle source distribution vector based on the number of signals from each cycle in the historical data, and calculate the maximum source cycle concentration and historical cycle span; Count the number of unique cycle numbers that appear in historical data to determine the diversity of historical cycle sources; The historical data proportion, source concentration, period span and period source diversity are combined into a feature information set that characterizes the historical data structure.

[0012] Preferably, the process of extracting characteristic information related to the number of source cycles includes: Extract the cycle numbers of all signal data units in the current refresh cycle; Construct a set containing unique period numbers, and determine the number of different period numbers in the set as the number of source periods; Calculating source cycle redundancy based on the number of source cycles, the source cycle redundancy being the number of source cycles minus one; The number of source cycles and the redundancy of source cycles are used as characteristic indicators to measure the complexity of the periodic structure; Combining the source cycle redundancy with the historical data proportion to construct a composite feature vector to enhance the ability to identify periodic structures; The periodic source structural features are used as input to participate in the synchronization state discrimination.

[0013] Preferably, the process of determining the cross-cycle data synchronization state based on the characteristic information includes: Normalize the feature information extracted in the current cycle and map all feature values ​​to a unified numerical range; Construct a classification discriminant function and input the normalized feature information into the classification discriminant function; Determine the cross-cycle data synchronization status of the current cycle based on the output of the classification discriminant function. The status types include synchronization consistency, synchronization uncertainty, or synchronization failure. When the confidence level of the classification discriminant function output is lower than the threshold, the current judgment is marked as uncertain and the final refresh judgment is delayed to the next cycle; A sliding average process is performed on the judgment results of multiple consecutive refresh cycles to suppress the judgment jitter caused by short-term fluctuations in the synchronization state.

[0014] Preferably, the process of constructing the classification discriminant function includes: Normalize the feature information used to characterize time consistency, historical data composition, and periodic source structure, unifying all feature values ​​into a preset value range; The normalized feature information is combined into a multi-dimensional input vector as input data for the classification discriminant function; A classification discriminant function is constructed based on a three-class support vector machine, where the support vector machine uses a nonlinear kernel function to achieve feature space mapping and boundary fitting; The output results of the classification discriminant function are set to correspond to three synchronization status types: synchronization consistency, synchronization uncertainty and synchronization failure; The classification discriminant function is trained using labeled historical period sample data, and the cross-validation method is used to optimize the model parameters to improve the generalization ability of the classification discriminant function.

[0015] Preferably, according to the cross-cycle data synchronization state, it is determined whether the current positioning refresh should be executed, and the corresponding refresh control operation is performed, specifically: Match the cross-cycle data synchronization status with the predefined synchronization consistency, synchronization uncertainty, and synchronization failure status types, and determine whether the conditions for performing positioning refresh are met based on the matching results; When it is judged to be synchronous and consistent, the wireless signal data in the current period is considered to have time continuity and period source consistency, and the positioning refresh operation of the current position is performed and the refresh result is output; When synchronization is determined to be uncertain, it is considered that a certain proportion of historical data is mixed in the current cycle or the cycle source is not completely consistent. Positioning refresh is suspended, the wireless signal data of the current cycle is stored in the delayed data buffer area, and the synchronization status is re-determined in the next cycle in combination with the newly received data; When synchronization failure is determined, it is considered that there is a serious time offset or period source redundancy in the wireless signal data in the current cycle, and the positioning refresh operation of the current cycle is canceled and the wireless signal data of the current cycle is cleared; When the synchronization status is synchronization uncertain or synchronization failure, the current position refresh status is marked as incomplete, and the trajectory continuity estimation strategy is called to generate alternative trajectory points in the path output for navigation continuity maintenance.

[0016] Preferably, the high-speed refresh positioning system based on wireless signal processing includes a unit construction module, a number screening module, a state determination module, a refresh decision module and an access isolation module; a unit construction module, processing the wireless signal data received in each refresh cycle, adding timestamp information and cycle number information, generating a signal data unit containing time information and cycle information, and storing all signal data units in a data buffer; The number screening module analyzes the cycle numbers of the signal data units in the current refresh cycle, determines whether there are signal data units with cycle numbers that are not equal to the current cycle number, and starts the cross-cycle data synchronization status judgment process if such signal data units exist and the proportion of the number exceeds a preset threshold; The state discrimination module extracts characteristic information used to characterize time consistency and cycle structure based on the time tag distribution of signal data units in the current refresh cycle, the proportion of historical data, and the number of source cycles. The characteristic information is input into the classification discrimination function to determine the cross-cycle data synchronization state corresponding to the current refresh cycle. The cross-cycle data synchronization state is synchronized, uncertain, or failed. The refresh decision module determines whether the current positioning refresh should be executed based on the cross-cycle data synchronization status and performs the corresponding refresh control operation; The access isolation module only accesses and uses the wireless signal data with a cycle number equal to the current cycle number for positioning calculation during the continuous refresh cycle after the cross-cycle data synchronization status is synchronization failure, and records the synchronization failure status for use in sampling control strategy adjustment.

[0017] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention realizes the periodic structured management of wireless signal data by constructing a dual labeling mechanism of timestamp and cycle number, significantly enhancing the system's ability to perceive the phenomenon of cross-cycle data mixing. In high-speed refresh scenarios, wireless signals often have transmission delays caused by network congestion or channel jitter. This solution accurately marks the reception time and cycle affiliation of each piece of data, allowing the system to finely identify whether there is "historical lagging data" interference within the refresh cycle, thereby avoiding the use of data with disordered time base for current positioning calculations. This mechanism effectively improves the timing consistency of the data processing process and lays a highly reliable data foundation for subsequent judgment and control.

[0018] 2. The present invention introduces three types of quantifiable indicators, namely "time tag distribution characteristics", "historical data proportion" and "source cycle redundancy", to establish a cross-cycle synchronization state discrimination model based on multi-dimensional feature extraction and classification function identification, thereby realizing an intelligent assessment of the health of the signal data time series structure. In this model, the system can dynamically output whether the current cycle is suitable for refresh execution based on the degree of data distribution skewness, historical mixing intensity and cycle source complexity. Compared with traditional algorithms that blindly refresh by cycle or static threshold judgment, this method has higher dynamic adaptability and anti-interference ability, and can effectively suppress phenomena such as positioning refresh false triggering, positioning rebound and trajectory drift, ensuring that the system always makes highly reliable refresh decisions based on structural consistency data.

[0019] 3. The present invention uses the identified synchronization status to implement a hierarchical refresh control strategy: immediate refresh when synchronization is consistent to ensure timeliness; buffering delays when synchronization is uncertain to retain potentially available information; and rejection of refresh when synchronization fails. In subsequent cycles, data access standards are tightened to achieve rapid isolation and recovery control of misaligned cycles. This refresh control strategy not only ensures the continuity and accuracy of positioning results, but also effectively prevents periodic interference from causing chain reactions to the system's overall decision-making chain (such as path prediction and navigation planning). Overall, this solution has significant technical advantages in handling complex wireless data environments such as high-frequency refreshes, periodic mixing, and data lags. It is particularly suitable for scenarios where multi-cycle fusion, distributed sampling, and real-time control are highly coupled. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0021] Figure 1 The figure is a flow chart of the high-speed refresh positioning algorithm and system based on wireless signal processing of the present invention.

[0022] Figure 2 This is a module diagram of the high-speed refresh positioning algorithm and system based on wireless signal processing of the present invention. DETAILED DESCRIPTION

[0023] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0024] The present invention provides Figure 1 The high-speed refresh positioning algorithm based on wireless signal processing shown in the figure specifically includes the following processes: Processing the wireless signal data received in each refresh cycle, adding timestamp information and cycle number information, generating signal data units containing time information and cycle information, and storing all signal data units in a data buffer; A high-speed refresh positioning algorithm based on wireless signal processing is applicable to positioning terminal devices with multiple wireless signal receivers, such as smart wearable devices, vehicle-mounted positioning modules, and mobile robotic platforms. The algorithm relies on precise periodic structuring of wireless signal data received within continuous refresh cycles. By introducing time tags and a period numbering mechanism, it converts the raw received signals into structured signal data units, providing the foundation for refresh-level data buffering control and subsequent synchronization state modeling.

[0025] In its implementation, the system periodically samples external wireless signal sources (such as Wi-Fi routers, UWB base stations, Bluetooth beacons, and 5G small cells). The sampling frequency can be dynamically adjusted based on the refresh rate requirements of the application scenario. At the beginning of each refresh cycle, the system master clock initiates a sampling task, which runs continuously from the current cycle until the next cycle is triggered, continuously receiving signal information from the wireless interface. Each piece of received wireless signal data typically includes, but is not limited to, parameters such as signal strength (RSSI), time of arrival (ToA), carrier phase, and subcarrier frequency response (such as CSI). Upon receipt, this raw signal data is immediately cached in the local raw receive buffer and marked as "pending."

[0026] To implement structured processing at the refresh cycle level, the system appends two key pieces of information to each received wireless signal: a timestamp and a cycle number. The timestamp records the actual moment the signal data was received, typically provided by a local high-precision timer with microsecond accuracy. The cycle number identifies the refresh cycle to which the data belongs and is generated by the refresh counter of the system's master clock. For example, if the system refresh cycle is Tms, the cycle number can be defined as floor(received timestamp / T).

[0027] After appending time and cycle number information, signal data is structured as a standard data unit. This signal data unit includes at least: a signal content field (used to store raw data such as RSSI, ToA, and CSI); a time tag field (used to store the absolute timestamp of reception); a cycle number field (used to store the refresh cycle number to which it is assigned); and an optional source identification field (used to record the transmitter or channel from which the data originated). This structure enables the system to classify data based on cycle number and perform timing consistency analysis based on time tags. It serves as the fundamental input format for the subsequent discrimination algorithm.

[0028] At the end of each refresh cycle, the system transfers all signal data units constructed within the current cycle from the original receive buffer to the periodic data buffer. The periodic data buffer uses a tagged structured ring storage structure to implement data caching and cleanup control divided by cycle. Each refresh cycle corresponds to a logical buffer segment, which contains all completed structured data units in that cycle. In an embodiment, the system can set the periodic data buffer to only store data within the most recent N refresh cycles (N is usually dynamically adjusted based on the length of the algorithm window) to achieve memory control and fast data retrieval capabilities under high-frequency refresh conditions.

[0029] In order to support the subsequent detection of "cross-cycle input misalignment status", the system can also attach intra-cycle statistical indexes in the data buffer, such as: the number of data units corresponding to each cycle number, the earliest and latest receiving times, the source distribution structure, etc. These index information can be used as one of the criteria for determining whether there is data from a non-current cycle number and whether it exceeds the threshold in subsequent steps.

[0030] To reduce system drift in time stamps, timestamp generation should be performed immediately after the signal is physically received. These timestamps must be converted to a unified reference to the system master clock to avoid time drift caused by the use of local timers within multiple modules. In distributed architecture systems, time synchronization mechanisms can utilize NTP, PTP, or a high-precision synchronization protocol based on 5G NR to ensure consistent cycle numbering logic across all sampling endpoints.

[0031] The system can also sort signal data units within the same cycle number by reception time or group them by source ID as a preprocessing step for signal clustering, location fitting, or consistency assessment in subsequent processing stages. All data units that have completed structured processing and are stored in the buffer within the current refresh cycle will serve as the basic data set for determining the "cross-cycle data synchronization status" in subsequent steps.

[0032] In summary, by attaching a unified timestamp and cycle number to received wireless signal data within a refresh cycle, we structure it into manageable and analyzable signal data units, and then categorize and store them in a periodic data buffer, thus establishing a data organization framework at the refresh cycle level. This mechanism not only ensures data access stability in high-speed refresh positioning environments but also provides a solid structural support for subsequent synchronization state modeling, misalignment state identification, and refresh strategy selection.

[0033] Analyze the cycle numbers of the signal data units in the current refresh cycle to determine whether there are signal data units with cycle numbers that are not equal to the current cycle number, and if such signal data units exist and their proportion exceeds a preset threshold, start the cross-cycle data synchronization status determination process; To ensure positioning accuracy under high-speed refresh conditions, after the system completes the construction of signal data units in each refresh cycle, it uniformly analyzes the cycle number field of all signal data units within that refresh cycle to identify whether there are lagged data units from cycles other than the current one. If the number of lagged data exceeds a set threshold ratio within a certain cycle, the system determines that there may be a risk of time misalignment in cross-cycle data input and immediately triggers a special "cross-cycle data synchronization status determination process" to determine whether to allow the positioning refresh of the current cycle.

[0034] In a specific implementation, the system master clock identifies the current cycle number as C_cur when entering each refresh cycle. The system then scans the cycle data buffer for the data unit set D_cur that belongs to the current cycle refresh window. This set contains all signal data units that have been received and structured during the current refresh cycle.

[0035] When traversing each signal data unit in the set D_cur, the system reads its bound cycle number field C_i. For each signal data unit, if C_i ≠ C_cur, it is marked as a delayed data unit. This means that the data was not generated in the current refresh cycle, but rather came from the previous cycle or an earlier cycle. However, due to transmission delays, scheduling congestion, or buffer overflow, it was delayed and injected into the current refresh cycle for processing. All such delayed data units are aggregated to form the set D_late.

[0036] After all traversals are completed, the system calculates the total number of data units in D_cur (N_all) and the number of delayed data units in D_late (N_late). From this, the delayed data ratio indicator R_late = N_late / N_all can be calculated. This indicator is used to indicate the severity of cross-period data injection within the current period.

[0037] To ensure system error tolerance, the system introduces a preset lag threshold, T_sync. Its value is initialized based on the system's sensitivity to synchronization (for example, it can be set to a lower value in positioning scenarios with significant environmental interference). When R_late > T_sync, it indicates that a large number of data units with numbers other than the current cycle have been mixed into the current cycle, and the system may have entered a cycle misalignment state.

[0038] To prevent false triggers caused by occasional network jitter or individual signal fluctuations, the system can also introduce a continuity condition mechanism. That is, if R_late > T_sync is observed twice consecutively in the current cycle and the previous cycle, the "cross-cycle data synchronization status judgment process" is triggered. This design can effectively suppress temporary misalignment misjudgments and improve the overall robustness of the algorithm.

[0039] In a preferred embodiment, the system can classify and count lagged data units based on the absolute value of the difference between their cycle numbers and the current cycle number to form a cycle offset distribution vector. For example, one type of data may have C_i = C_cur-1, another type may have C_i = C_cur-2, and so on. This distribution vector can be further used as one of the input features of a subsequent discriminant model to assist in determining the severity of the synchronization status.

[0040] To improve processing efficiency and discrimination accuracy, the system can also introduce a cycle number index structure during the signal data unit construction phase. The cycle data buffer updates a cycle number statistical index table with each new data entry. This index table maintains the occurrence count of all cycle numbers within the current cycle window, enabling real-time identification of cycle mixing without requiring individual scans.

[0041] When the system determines that there is a risk of cycle number misalignment in the current refresh cycle based on the proportion of lagging data, cycle offset characteristics, or continuity conditions, a dedicated synchronization status analysis process is activated. In subsequent steps, this process will further analyze indicators such as timing consistency, data stability, and cycle structure integrity through advanced modeling methods to determine whether to perform a position refresh operation in this cycle.

[0042] In addition, to avoid excessive processing overhead or waste of system resources, if R_late < T_sync and no lag data units appear for multiple consecutive cycles, the system can enter a "cycle number consistency stable state". In this state, the synchronization state discrimination process is not triggered every cycle, but instead, a trigger interval is set or it is only activated when abnormal traffic appears in the buffer, further reducing the system's computational load.

[0043] In summary, in this embodiment, by performing a structured analysis on the cycle number field of the signal data units within the current refresh cycle, quantifying the proportion of lag data mixed in, and introducing a threshold comparison, continuous judgment, and offset distribution modeling mechanism, a cycle misalignment detection capability at the cycle number level is established. As a pre-filtering logic for subsequent synchronization state modeling, this detection mechanism not only improves the ability to capture cross-cycle mis-refresh problems but also enhances the system's tolerance to network jitter and signal delay, ensuring the stability and accuracy of the high-speed refresh positioning algorithm in complex environments.

[0044] Based on the time tag distribution, historical data proportion, and number of source cycles of the signal data units within the current refresh cycle, extract feature information for characterizing time consistency and cycle structure, and input the feature information into a classification discrimination function to determine the cross-cycle data synchronization state corresponding to the current refresh cycle. The cross-cycle data synchronization state is synchronization consistency, synchronization uncertainty, or synchronization failure. To achieve automatic discrimination and control of the cross-cycle data input state, the system sets up a processing mechanism based on feature information extraction and classification function recognition to identify the synchronization consistency of the data structure within the refresh cycle. The key to this mechanism is that for each signal data unit received within a refresh cycle, according to the basic information such as the time tag and cycle number attached to the signal, feature information is extracted from three specific dimensions, namely: time tag distribution, historical data proportion, and number of source cycles. These three together are used to reflect the time consistency and cycle structure integrity of the signal data in the current cycle. Finally, this feature information is used as input and sent into the classification discrimination function to determine the cross-cycle data synchronization state corresponding to the current cycle, which can be synchronization consistency, synchronization uncertainty, or synchronization failure.

[0045] First, in the dimension of time tag distribution, the system establishes a time distribution model within the refresh cycle based on the time stamps bound to each signal data unit. All signal data units are assigned a reception time tag when received, generated using a unified clock source (such as a local high-precision timer, a synchronous network clock, etc.). For a refresh cycle T, the system first obtains the time stamp sequence {t1, t2, ..., t n} in the set D_T of all signal data units within this cycle. Based on this time sequence, the system performs a series of statistical processing operations to extract the following distribution features: Range R_t: The difference between the maximum time and the minimum time, which reflects the time span covered by the signal. If R_t is too large, it indicates that some data is very likely to be injected with a delay from the previous cycle.

[0046] Standard deviation σ_t: Characterizes the degree of dispersion of the data in terms of time distribution. The larger the value, the worse the time concentration.

[0047] Skewness Sk_t and kurtosis Ku_t: Used to determine whether the data distribution deviates from the center and whether there is abnormal concentration. For example, obvious positive skewness or high peaks may indicate batch injection of signals and non - typical periodic sampling.

[0048] Time window uniformity U_t: The system divides the period into equal - time - length intervals (such as 10 ms), counts the number of data in each interval, and calculates the standard deviation to quantify the degree of distribution uniformity.

[0049] Time - tag entropy H_t: Calculates the Shannon entropy based on the proportion of the number of data in each time period. The larger the entropy value, the more uniform the distribution, and the smaller the value, the higher the data concentration.

[0050] The above five types of time features are the key indicators for measuring whether the data in the current refresh cycle is sampled naturally and continuously. If the time - tag distribution shows skewness, high peaks, local concentration or large - range extension, it is highly suspected that there is a situation of mixing historical data or misalignment of cycle boundaries.

[0051] Secondly, the system analyzes the proportion of historical data. Each signal data unit also附带 its original sampling - cycle number. In the current cycle T, the system extracts the sequence of cycle numbers {p1, p2, ..., p n}, where p_i represents the cycle number of the i - th signal data unit. Comparing all p_i with the current cycle number T, if there is a situation where p_i < T, these data units are determined to be historical data. The system further calculates the following indicators: Proportion of historical data R_h: Defined as the ratio of the number of historical - data units Nh to the total number Nt, that is, R_h = Nh / Nt. This value is used to reflect the intensity of historical mixing. The larger the value, the more serious the misalignment degree.

[0052] Maximum historical - source - cycle concentration C_h: Among all historical data, the one with the highest proportion of data from a certain specific historical cycle is the dominant source. This value reflects whether the historical interference comes from a single delayed cycle.

[0053] Historical - data cycle span S_h: The difference between the farthest historical - cycle number and the current - cycle number, which represents the "depth" of the delayed injection of historical data.

[0054] Historical - cycle - source diversity D_h: The number of cycle types of historical - data sources, which can reflect the complexity of the data - delay sources.

[0055] By quantifying these parameters, the system can further characterize the structure of historical data in the current cycle. For example, if R_h exceeds a certain threshold and C_h is close to 1, it indicates that the current cycle may be controlled by delayed data input from the previous cycle. If S_h is greater than 2, it indicates that the historical data is not simply lagging behind by one cycle, but may be subject to severe queue accumulation or data retransmission.

[0056] The third dimension is the number of source cycles. The system extracts the cycle numbers of all signal data units in the current cycle T and constructs a unique number set P = {p∈D_T|p is a unique cycle number}. The number of source cycles S_p = |P| indicates the number of different signal source cycles that appear in that cycle. If S_p = 1, it means that all data comes from the current cycle, which is the ideal state of synchronization and consistency. If S_p > 1, it indicates that historical data is mixed. The system further defines the source cycle redundancy R_d = S_p-1 as an evaluation metric for the complexity of the periodic structure.

[0057] In order to enhance the collaborative characterization effect of indicators, the system also introduces a combined feature construction mechanism. For example, R_h and R_d are constructed as a feature pair.<R_h, R_d> It is used to identify abnormal periodic structures with strong historical proportions and multiple sources, or to identify typical skewed batch delayed injection phenomena by combining time label entropy H_t and historical source concentration C_h.

[0058] After completing the above feature extraction, the system normalizes all original and derived features to prevent bias in the classification function caused by different dimensions or value ranges. Normalization can use Z-score standardization or minimum-maximum mapping to map all feature values ​​to the range [0, 1] or [-1, 1].

[0059] The system inputs the standardized feature set generated during each refresh cycle into a classification discriminant function. In a preferred embodiment, this classification function is constructed using a three-class support vector machine (SVM) model, which uses a nonlinear kernel function (such as a radial basis kernel) to fit the dividing line in the high-dimensional feature space. The model accepts three synchronization states as output labels: Synchronous consistency: The current period data is evenly distributed in time, with no obvious mixing of historical data, and a single source period; Synchronization uncertainty: The time distribution is slightly deviated, the proportion of historical data is small but exists, and the source period is small; Synchronization failure: highly discrete or concentrated time distribution, high proportion of historical data, diverse source periods or large spans.

[0060] The above status labels are obtained through supervised annotation based on known samples during the training phase. The system training data consists of cycle samples from real-world operating environments, combined with manual review results to construct a status annotation dataset. Cross-validation is used to optimize model parameters, ensuring strong generalization and low false positive rates.

[0061] During each refresh cycle, the system feeds the extracted features into the classification model, which then determines whether to proceed with the location refresh operation based on the status label output by the model. The confidence level of the classification result is also calculated and output. If the confidence level is low, the system marks the result as "delayed decision" and re-judges it in the next cycle using older data to avoid misjudgments at critical values.

[0062] Furthermore, to ensure classification stability, the system establishes a classification output cache queue and performs a sliding average of the classification results over multiple consecutive cycles to determine whether classification oscillation exists. If the system repeatedly fluctuates between synchronization states over multiple consecutive cycles, it can temporarily adjust the classification threshold or activate an alternative judgment strategy (such as rule-based template judgment) to enhance system robustness.

[0063] The synchronization state discrimination mechanism described in the present invention is designed based on clear and quantifiable feature dimensions, covering multiple dimensions of time, structure and historical interference. It has feasibility and algorithmic innovation in each link of feature selection, standardization processing, model construction and strategy response, and can effectively avoid the problem of false triggering of positioning refresh due to confusion of periodic boundary input.

[0064] According to the cross-cycle data synchronization status, determine whether the current positioning refresh should be executed and perform the corresponding refresh control operation; In order to improve the judgment accuracy and refresh decision rationality of the high-speed refresh positioning algorithm based on wireless signal processing when processing cross-cycle data, the system needs to further decide whether to perform the current position refresh operation based on the synchronization status after completing the classification and identification of the cross-cycle data synchronization status in the current refresh cycle, and adopt corresponding refresh control strategies accordingly to prevent refresh false triggering, trajectory deviation and mispositioning problems caused by data misalignment, lag or inconsistency.

[0065] To achieve this, this embodiment first categorizes synchronization states into three types: consistent, uncertain, and failed. These three states represent a high-level, abstract reflection of the stability and timing consistency of the signal data structure received during the current cycle, representing the system's level of feasibility for positioning refresh operations during the current cycle. The system implements a differentiated refresh decision process based on the synchronization state type, ensuring a high refresh rate while balancing data quality and positioning accuracy.

[0066] If the synchronization status is consistent, this indicates that all signal data units in the current cycle originate from the current cycle number, or contain a very small proportion of data from adjacent cycles without any abnormal disturbances in the time tag distribution or source structure. In this state, the system determines that the signal data is highly consistent in time and structure, meeting the basic conditions for effective positioning refresh.

[0067] Therefore, in a synchronized state, the system directly inputs the data set within the current cycle into the positioning calculation module to perform the position information refresh operation for this cycle. The positioning calculation module can perform algorithmic processes such as trilateration, multipath fusion, Kalman filtering, or depth model estimation based on real-time wireless signal characteristics (such as RSSI, ToF, CSI, etc.) to generate the current position estimate and update it to the system positioning cache and output interface for downstream navigation and control modules to call.

[0068] To avoid refresh interference or false triggering, the system can also perform a "refresh confirmation" action in the synchronous and consistent state. This action reviews the key characteristic indicators used to support the "synchronous consistency" judgment in the completed classification judgment (such as time distribution entropy and historical proportion). When these indicators are within the safe range and the current refresh operation does not have a significant position deviation from the previous cycle refresh (low jump variable), it is considered a normal refresh cycle. Otherwise, the system can still issue a warning mark for subsequent algorithm self-checking.

[0069] If the synchronization status is "Uncertain," this is typically caused by the following: a certain proportion of data units outside the current cycle exist in the current cycle, the time distribution is partially uneven but not completely chaotic, and the number of source cycles is greater than one but still within the tolerance range. In this case, the system considers the input data in the current cycle to have certain timing and structural risks. Directly refreshing the data may cause positioning offset, while completely abandoning the refresh may result in loss of continuity and track breakage.

[0070] In the state of synchronization uncertainty, the system will adopt a delayed refresh control strategy, that is, suspending the positioning refresh of the current cycle and temporarily storing all signal data units of the current cycle in the "delayed refresh buffer". This buffer is a high-priority storage area specifically used to retain data sets that may still be effectively used in subsequent cycles.

[0071] In the next refresh cycle, the system first checks whether there is delayed buffered data. If so, it combines this data with the signal data units received in the new cycle and enters the synchronization status judgment process. Based on the second comprehensive judgment result, it decides whether to perform a refresh. If the subsequent judgment status is synchronous and consistent, the system performs a "fusion refresh" by combining the delayed cycle and the current cycle data. If the status is still uncertain, the system allows one more buffering cycle and sets a maximum number of buffering cycles to avoid infinite loops. If the status becomes invalid, the delayed data is cleared, the refresh is abandoned, and the recovery mechanism is entered.

[0072] In addition, the system will also perform a marking operation during the delay control process, that is, marking the current cycle with an "unrefreshed" label and inserting a "smooth jump reference point" in the system path trajectory for subsequent path correction and speed estimation model input adjustment to avoid navigation misjudgment or dynamic obstacle avoidance failure due to refresh interruption.

[0073] If the synchronization status is synchronization failure, this indicates that there is a serious structural misalignment or time anomaly in the signal data within the current period. For example, the proportion of historical data far exceeds the threshold, there are more than two source periods, and the time tags are highly concentrated or present a bimodal distribution. This is most likely due to data delay accumulation, communication congestion, or data scheduling anomalies.

[0074] In this state, the system determines that the data structure within the current cycle no longer meets the basic consistency requirements for a positioning refresh operation. Forcing a refresh could cause serious trajectory "jumps," path regressions, or positioning drift, affecting the stability and accuracy of the entire navigation link. Therefore, in the synchronization failure state, the system adopts the strategy of terminating the refresh operation.

[0075] Specifically, the system will immediately abandon the positioning refresh operation in this cycle and perform the following three control measures: Clear the data buffer of this cycle: to prevent the current invalid data from being mistakenly transferred to the subsequent cycle, affecting the judgment and estimation of the next cycle; Refresh failure tag mounting: The system writes the refresh failure tag of this cycle to the status recording interface and records the failure reason (such as high historical ratio, abnormal redundant source structure, etc.) for system log and subsequent analysis calls; The state recovery mechanism is activated: After the failure state, the system enters a "refresh suppression mode" with a limited period. That is, in the next few cycles, only data units with source cycle numbers that are exactly the same as the current cycle number are allowed to be received. All lagging or inconsistent data will be directly discarded to restore the stability of the data structure.

[0076] It is worth noting that within several cycles after synchronization failure, the system significantly tightens data access criteria, strengthening the discriminator's lower tolerance limit to prevent multiple consecutive positioning failures due to misidentification. At the same time, the system performs short-term prediction interpolation based on the historical trajectory fit of the velocity vector and direction estimation results from the previous valid cycle to fill refresh gaps and maintain trajectory consistency.

[0077] This refresh control mechanism uses hierarchical determination and differentiated control based on synchronization status. This not only achieves the dual goals of high-frequency refresh and data quality assurance, but also effectively identifies and isolates spurious updates caused by cycle misalignment, improving the dynamic responsiveness and robustness of the entire wireless positioning system. In actual deployments, this mechanism can adapt to different refresh cycle settings (such as 10ms, 20ms, etc.) and data density scenarios, ensuring good system compatibility and engineering feasibility.

[0078] In summary, this implementation provides a refresh determination and control mechanism based on synchronization state identification results. Its core is to implement three refresh responses based on the state identification results: direct refresh (synchronization consistency), delayed refresh (synchronization uncertainty), and terminated refresh (synchronization failure), corresponding to normal, safety protection, and abnormal isolation scenarios, respectively. This mechanism can operate independently within the algorithm chain or be integrated with the positioning estimation engine and path smoothing module to achieve reliable refresh decisions in complex dynamic environments.

[0079] During the continuous refresh cycle after the cross-cycle data synchronization status is synchronization failure, only the wireless signal data with a cycle number equal to the current cycle number is accessed and used for positioning calculation, and the synchronization failure status is recorded for use in sampling control strategy adjustment.

[0080] To enhance the system's resilience and decision-making stability after handling cross-cycle data anomalies, when a cross-cycle data synchronization failure is identified, the system proactively enters a continuous refresh cycle window, tightening controls on data access and computation. Within this window, the system restricts the inclusion of signal data units with a cycle number equal to the current refresh cycle number for positioning calculations. A state recording mechanism incorporates this failure into subsequent adjustments to the sampling control strategy.

[0081] Specifically, when any refresh cycle is identified as a synchronization failure, the system immediately triggers the "refresh conservative window mechanism." This mechanism stipulates that in multiple consecutive refresh cycles following the failed cycle, the refresh logic will only accept data units with exactly the same number as the current cycle. Signal data units from other source cycles (i.e., with numbers smaller than the current cycle number) are directly judged as delayed data and discarded, even if their reception time falls within the current cycle. This cycle number consistency determination behavior can isolate historical data accumulated or delayed from previous cycles within a certain time range, forcing subsequent calculations to be based solely on the raw data obtained from the current sampling period, thus laying the foundation for reestablishing the system's timing consistency.

[0082] To achieve this, after the signal data receiving module completes data reception within the current cycle, the system performs cycle number filtering before distributing the data to the processing module. Specifically, the system extracts all received signal data units from the data buffer and compares them one by one with the attached cycle number field, filtering out any data units with cycle numbers that do not equal the current cycle number. This process does not rely on the relative age of the timestamp or the location of the data source; instead, it uses the number field as the sole basis for judgment, fundamentally preventing the infiltration of historical lagged data into the positioning estimation process.

[0083] In the positioning calculation module, to prevent accuracy degradation due to reduced data volume, the system implements redundancy enhancement strategies during the recovery cycle. For example, these strategies dynamically adjust the minimum dataset size threshold required in the signal processing algorithm or appropriately reduce the positioning output frequency to enhance tolerance to data jitter. Furthermore, the system can temporarily call upon historical trajectory vectors stored in the path smoothing module to participate in position information correction, maintaining the continuity and credibility of the navigation trajectory.

[0084] It's worth noting that to prevent valid data from being discarded due to cycle numbering, the system also incorporates a time redundancy tolerance mechanism. If received data meets the cycle number requirement but its time stamp is significantly out of date (e.g., more than a few milliseconds behind the current cycle window), the system will automatically disregard it, thus ensuring the freshness and currency of refreshed data.

[0085] During this conservative refresh window, the system not only restricts data access but also continuously records information such as the duration of this state, the amount of data discarded, and the number of consecutive synchronization failures. All status information is written to an internal "synchronization anomaly status log" and regularly transmitted to the system's sampling control strategy analysis unit for dynamic adjustment of subsequent signal sampling behavior.

[0086] The sampling control strategy can automatically implement the following strategy adjustments based on the log analysis results: Extended refresh cycle: If continuous failure states occur frequently, the system can temporarily increase the length of the refresh cycle to alleviate the problem of data queue accumulation; Pre-cache expansion: Dynamically expand the data receiving buffer capacity to improve the historical data diversion capability; The signal source rotation mechanism is triggered: an attempt is made to switch some data source base stations or channel frequency bands to avoid interference on specific channels; Sampling rate adjustment: If redundant data causes frequent failures, the system can appropriately reduce the sampling rate to balance data density and processing delay.

[0087] This strategy forms a closed-loop feedback process: identification of failure states → refreshing behavior restrictions → state recording → strategy adjustment → data structure optimization → state recovery, ensuring that the system can achieve robust and efficient continuous positioning capabilities in dynamic and complex wireless communication environments.

[0088] The recovery and refresh mechanism after synchronization failure provided in this embodiment not only avoids the risk of "contamination" of subsequent cycles by data from the failure period, but also realizes the adaptive evolution of the sampling strategy while ensuring system recovery stability through a parallel design of control and strategy. This is of great significance for improving the fault tolerance and long-term stability of the high-speed refresh positioning algorithm under non-ideal network conditions.

[0089] like Figure 2 The high-speed refresh positioning system based on wireless signal processing shown includes a unit construction module, a number screening module, a state determination module, a refresh decision module and an access isolation module; a unit construction module, processing the wireless signal data received in each refresh cycle, adding timestamp information and cycle number information, generating a signal data unit containing time information and cycle information, and storing all signal data units in a data buffer; The number screening module analyzes the cycle numbers of the signal data units in the current refresh cycle, determines whether there are signal data units with cycle numbers that are not equal to the current cycle number, and starts the cross-cycle data synchronization status judgment process if such signal data units exist and the proportion of the number exceeds a preset threshold; The state discrimination module extracts characteristic information used to characterize time consistency and cycle structure based on the time tag distribution of signal data units in the current refresh cycle, the proportion of historical data, and the number of source cycles. The characteristic information is input into the classification discrimination function to determine the cross-cycle data synchronization state corresponding to the current refresh cycle. The cross-cycle data synchronization state is synchronized, uncertain, or failed. The refresh decision module determines whether the current positioning refresh should be executed based on the cross-cycle data synchronization status and performs the corresponding refresh control operation; The access isolation module only accesses and uses the wireless signal data with a cycle number equal to the current cycle number for positioning calculation during the continuous refresh cycle after the cross-cycle data synchronization status is synchronization failure, and records the synchronization failure status for use in sampling control strategy adjustment.

[0090] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0091] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0092] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0093] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0095] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0096] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0097] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. High-speed refresh positioning algorithm based on wireless signal processing, characterized by: The specific steps include: Processing the wireless signal data received in each refresh cycle, adding timestamp information and cycle number information, generating signal data units containing time information and cycle information, and storing all signal data units in a data buffer; Analyze the cycle numbers of the signal data units in the current refresh cycle to determine whether there are signal data units with cycle numbers that are not equal to the current cycle number, and if such signal data units exist and their proportion exceeds a preset threshold, start the cross-cycle data synchronization status determination process; Based on the time tag distribution of signal data units in the current refresh cycle, the proportion of historical data, and the number of source cycles, feature information used to characterize time consistency and cycle structure is extracted, and the feature information is input into the classification discriminant function to determine the cross-cycle data synchronization status corresponding to the current refresh cycle. The cross-cycle data synchronization status is synchronization consistency, synchronization uncertainty, or synchronization failure; According to the cross-cycle data synchronization status, determine whether the current positioning refresh should be executed and perform the corresponding refresh control operation; During the continuous refresh cycle after the cross-cycle data synchronization status is synchronization failure, only the wireless signal data with a cycle number equal to the current cycle number is accessed and used for positioning calculation, and the synchronization failure status is recorded for use in sampling control strategy adjustment.

2. The high-speed refresh positioning algorithm based on wireless signal processing according to claim 1, characterized in that: The process of processing the wireless signal data received in each refresh cycle includes: Adding timestamp information and cycle number information to each wireless signal data, where the timestamp information records the reception time of the wireless signal data, and the cycle number information is an identifier generated according to the refresh cycle number; Generate a signal data unit based on the timestamp information and the cycle number information, the signal data unit including a signal content field, a timestamp field and a cycle number field; The signal data units generated in each refresh cycle are stored in a data buffer divided according to the refresh cycle, and the data buffer classifies and manages the signal data units using the refresh cycle number as an index.

3. The high-speed refresh positioning algorithm based on wireless signal processing according to claim 2, characterized in that: The process of analyzing the cycle number of the signal data unit in the current refresh cycle and starting the cross-cycle data synchronization state determination process when specific conditions are met includes: Obtaining the current refresh cycle number as the current cycle number, and constructing a current cycle data set, where the current cycle data set includes all signal data units received in the current refresh cycle; Traverse each signal data unit in the current cycle data set, extract the cycle number field contained in each signal data unit, mark the signal data unit whose cycle number is not equal to the current cycle number as a lagging data unit, and construct a lagging data set; The ratio between the number of hysteresis data sets and the total number of data sets in the current cycle is calculated as the hysteresis ratio. When the hysteresis ratio is higher than the preset threshold, it is determined that there is a risk of cycle number misalignment in the current refresh cycle. When it is determined that there is a risk of cycle number misalignment in two consecutive refresh cycles, the cross-cycle data synchronization status judgment process is started; Based on the period number deviation degree of each signal data unit in the lag data set, period deviation distribution information is constructed as the input basis for subsequent synchronization state classification and judgment.

4. The high-speed refresh positioning algorithm based on wireless signal processing according to claim 3 is characterized in that: The feature information extracted to represent temporal consistency includes: Extract the receiving time tag of each signal data unit and construct a time tag sequence; Calculate the range, standard deviation, skewness, kurtosis and time window uniformity based on the time tag sequence to characterize the time distribution characteristics; Divide the refresh cycle time axis into equal time segments, count the number of signal data units in each time segment, and calculate the Shannon entropy as the time distribution entropy value; All time distribution features are uniformly constructed into a time consistency feature set for subsequent synchronization state identification.

5. The high-speed refresh positioning algorithm based on wireless signal processing according to claim 4 is characterized in that: Extracting characteristic information that characterizes the historical data structure includes: Obtaining the cycle number of each signal data unit in the current refresh cycle and comparing it with the current cycle number to identify the historical data unit; Determine that the signal data units with a cycle number smaller than the current cycle number are historical data, calculate the ratio between the number of historical data and the total number of data in the current cycle, and construct the historical data ratio; Construct a cycle source distribution vector based on the number of signals from each cycle in the historical data, and calculate the maximum source cycle concentration and historical cycle span; Count the number of unique cycle numbers that appear in historical data to determine the diversity of historical cycle sources; The historical data proportion, source concentration, period span and period source diversity are combined into a feature information set that characterizes the historical data structure.

6. The high-speed refresh positioning algorithm based on wireless signal processing according to claim 5, characterized in that: The process of extracting characteristic information related to the number of source cycles includes: Extract the cycle numbers of all signal data units in the current refresh cycle; Construct a set containing unique period numbers, and determine the number of different period numbers in the set as the number of source periods; Calculating source cycle redundancy based on the number of source cycles, the source cycle redundancy being the number of source cycles minus one; The number of source cycles and the redundancy of source cycles are used as characteristic indicators to measure the complexity of the periodic structure; Combining the source cycle redundancy with the historical data proportion to construct a composite feature vector to enhance the ability to identify periodic structures; The periodic source structural features are used as input to participate in the synchronization state discrimination.

7. The high-speed refresh positioning algorithm based on wireless signal processing according to claim 6, characterized in that: The process of determining the cross-cycle data synchronization status based on feature information includes: Normalize the feature information extracted in the current cycle and map all feature values ​​to a unified numerical range; Construct a classification discriminant function and input the normalized feature information into the classification discriminant function; Determine the cross-cycle data synchronization status of the current cycle based on the output of the classification discriminant function. The status types include synchronization consistency, synchronization uncertainty, or synchronization failure. When the confidence level of the classification discriminant function output is lower than the threshold, the current judgment is marked as uncertain and the final refresh judgment is delayed to the next cycle; A sliding average process is performed on the judgment results of multiple consecutive refresh cycles to suppress the judgment jitter caused by short-term fluctuations in the synchronization state.

8. The high-speed refresh positioning algorithm based on wireless signal processing according to claim 7, characterized in that: The process of constructing a classification discriminant function includes: Normalize the feature information used to characterize time consistency, historical data composition, and periodic source structure, unifying all feature values ​​into a preset value range; The normalized feature information is combined into a multi-dimensional input vector as input data for the classification discriminant function; A classification discriminant function is constructed based on a three-class support vector machine, where the support vector machine uses a nonlinear kernel function to achieve feature space mapping and boundary fitting; The output results of the classification discriminant function are set to correspond to three synchronization status types: synchronization consistency, synchronization uncertainty and synchronization failure; The classification discriminant function is trained using labeled historical period sample data, and the cross-validation method is used to optimize the model parameters to improve the generalization ability of the classification discriminant function.

9. The high-speed refresh positioning algorithm based on wireless signal processing according to claim 8, characterized in that: According to the cross-cycle data synchronization status, determine whether the current positioning refresh should be executed and perform the corresponding refresh control operation, specifically: Match the cross-cycle data synchronization status with the predefined synchronization consistency, synchronization uncertainty, and synchronization failure status types, and determine whether the conditions for performing positioning refresh are met based on the matching results; When it is judged to be synchronous and consistent, the wireless signal data in the current period is considered to have time continuity and period source consistency, and the positioning refresh operation of the current position is performed and the refresh result is output; When synchronization is determined to be uncertain, it is considered that a certain proportion of historical data is mixed in the current cycle or the cycle source is not completely consistent. Positioning refresh is suspended, the wireless signal data of the current cycle is stored in the delayed data buffer area, and the synchronization status is re-determined in the next cycle in combination with the newly received data; When synchronization failure is determined, it is considered that there is a serious time offset or period source redundancy in the wireless signal data in the current cycle, and the positioning refresh operation of the current cycle is canceled and the wireless signal data of the current cycle is cleared; When the synchronization status is synchronization uncertain or synchronization failure, the current position refresh status is marked as incomplete, and the trajectory continuity estimation strategy is called to generate alternative trajectory points in the path output for navigation continuity maintenance.

10. A high-speed refresh positioning system based on wireless signal processing, used to implement the high-speed refresh positioning algorithm based on wireless signal processing according to any one of claims 1 to 9, characterized in that: It includes unit construction module, number screening module, status identification module, refresh decision module and access isolation module; a unit construction module, processing the wireless signal data received in each refresh cycle, adding timestamp information and cycle number information, generating a signal data unit containing time information and cycle information, and storing all signal data units in a data buffer; The number screening module analyzes the cycle numbers of the signal data units in the current refresh cycle, determines whether there are signal data units with cycle numbers that are not equal to the current cycle number, and starts the cross-cycle data synchronization status judgment process if such signal data units exist and the proportion of the number exceeds a preset threshold; The state discrimination module extracts characteristic information used to characterize time consistency and cycle structure based on the time tag distribution of signal data units in the current refresh cycle, the proportion of historical data, and the number of source cycles. The characteristic information is input into the classification discrimination function to determine the cross-cycle data synchronization state corresponding to the current refresh cycle. The cross-cycle data synchronization state is synchronized, uncertain, or failed. The refresh decision module determines whether the current positioning refresh should be executed based on the cross-cycle data synchronization status and performs the corresponding refresh control operation; The access isolation module only accesses and uses the wireless signal data with a cycle number equal to the current cycle number for positioning calculation during the continuous refresh cycle after the cross-cycle data synchronization status is synchronization failure, and records the synchronization failure status for use in sampling control strategy adjustment.

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