An RFID reader-based electronic tag position sensing and tracking method and system

Abstract

The application discloses an RFID reader-based electronic tag position sensing and tracking method and system, and particularly relates to the wireless radio frequency identification positioning technical field, and is used for solving the problem of the asynchronization of multi-reader signal collection caused by the anti-collision mechanism of the existing RFID system and the problem of insufficient positioning accuracy in a dynamic environment; the tag response signal is acquired and the channel coherence time parameter is estimated through a multi-reader synchronous triggering and signal receiving mechanism, then the parameter is subjected to symbolization processing and complexity analysis to identify the tag motion state, meanwhile, the measurement reliability is evaluated by analyzing the trend deviation degree of the channel parameter and the signal strength, finally, the distance measurement value is corrected based on the motion state and the reliability evaluation result, and the spatial coordinates are calculated by adopting a multi-lateral positioning algorithm, so that the accurate and stable positioning of the electronic tag in a dynamic high-density scene is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless radio frequency identification positioning technology, and particularly relates to an electronic tag position sensing and tracking method and system based on an RFID reader. BACKGROUND

[0002] Radio frequency identification (RFID) technology is widely used in object identification and tracking fields, and it realizes the identification and data collection of target objects through the wireless signal interaction between a reader and an electronic tag. In applications requiring spatial positioning, a positioning method based on the cooperation of multiple readers is often used, such as calculating the position of a tag by analyzing the time difference of arrival (TDOA) or the carrier phase difference (PDOA) of physical parameters to realize the continuous positioning of a moving or stationary target.

[0003] However, the existing RFID system generally uses a polling-based anti-collision mechanism to realize multi-tag identification, which causes the reading operations of different readers on the same tag to be performed asynchronously in time. This time dispersion destroys the spatiotemporal correlation of the signal parameters required for positioning between different receiving points, so that the precise positioning algorithm relying on the time difference or phase consistency assumption of the signal cannot work effectively due to the distortion of the basic data, which restricts the positioning accuracy and reliability of the RFID technology in dynamic high-density scenarios. SUMMARY

[0004] The present application provides an electronic tag position sensing and tracking method and system based on an RFID reader to solve the technical problems in the prior art.

[0005] The technical solution of the present application to solve the above technical problems is as follows:

[0006] An electronic tag position sensing and tracking method based on an RFID reader, comprising:

[0007] S1. Sending a synchronization trigger signal to an electronic tag in a target area through at least two readers, and synchronously receiving a response signal returned by the electronic tag by the at least two readers;

[0008] S2. Estimating the channel coherence time parameter of the wireless channel between each reader and the electronic tag according to the response signal received by each reader;

[0009] S3. Symbolizing the channel coherence time parameter to obtain a symbol sequence and calculating the complexity information of the symbol sequence, and identifying the motion state of the electronic tag according to the complexity information to obtain a motion state identification result;

[0010] S4. Generating a reliability evaluation result by analyzing the divergence between the channel coherence time parameter of each reader and the change trend of the signal strength of the response signal received by each reader.

[0011] S5、based on the channel coherence time parameter, calculating the relative distance between the electronic tag and each reader, and correcting the relative distance according to the motion state recognition result and the reliability evaluation result;

[0012] S6, after the relative distance is corrected, a multilateration algorithm is used to calculate the spatial coordinates of the electronic tag.

[0013] Further, at least two readers send a synchronization trigger signal to the electronic tag in the target area, and the at least two readers synchronously receive the response signal returned by the electronic tag, including:

[0014] The central controller generates a synchronization trigger signal based on the same high-precision clock source;

[0015] The central controller distributes the synchronization trigger signal to each reader through a synchronization trigger channel to control all readers to work synchronously;

[0016] After receiving the synchronization trigger signal, each reader sends a radio frequency trigger signal to the electronic tag within a pre-set same time slot;

[0017] The at least two readers synchronously receive the response signal returned by the electronic tag within a pre-set receiving time slot based on the same clock source.

[0018] Further, according to the response signal received by each reader, the channel coherence time parameter of the wireless channel between each reader and the electronic tag is estimated respectively, including:

[0019] Based on the response signal received by each reader, the change characteristics of the signal characteristics over time are analyzed;

[0020] According to the change characteristics of the signal characteristics, the coherence time characteristics of the wireless channel are determined;

[0021] Based on the coherence time characteristics, the channel coherence time parameter is calculated: the length of the time interval at which the change characteristics of the signal characteristics tend to be stable is quantified as the channel coherence time parameter.

[0022] Further, the channel coherence time parameter is symbolized to obtain a symbol sequence and calculate the complexity information of the symbol sequence, and according to the complexity information, the motion state of the electronic tag is recognized to obtain a motion state recognition result, including:

[0023] The channel coherence time parameter sequence is compared with a pre-set threshold, and according to the comparison result, each parameter value is mapped to a corresponding symbol to form a symbol sequence;

[0024] The occurrence law of different symbol combinations in the symbol sequence is analyzed, and the complexity information of the symbol sequence is calculated based on the occurrence law.

[0025] The complexity information is compared with a preset motion state judgment threshold, and a motion state of the electronic tag is determined according to a comparison result, so as to obtain a motion state recognition result.

[0026] Further, the occurrence law of different symbol combinations in the symbol sequence is analyzed, and the complexity information of the symbol sequence is calculated based on the occurrence law, including: the occurrence frequency of different length symbol combinations in the symbol sequence is counted, and the sequence complexity value is calculated according to the distribution characteristics of the occurrence frequency of each symbol combination; the complexity value is used to represent the pattern regularity degree of the symbol sequence.

[0027] Further, the reliability evaluation result is generated by analyzing the divergence between the channel coherence time parameter of each reader and the change trend of the signal strength of the received response signal, including:

[0028] The sequence of the change of the channel coherence time parameter with time and the sequence of the change of the signal strength with time are obtained respectively;

[0029] The divergence between the change trend of the channel coherence time parameter sequence and the change trend of the signal strength sequence is analyzed;

[0030] The reliability level of the distance measurement value of each reader is determined based on the size of the divergence;

[0031] The corresponding reliability evaluation result is generated according to the reliability level.

[0032] Further, the divergence between the change trend of the channel coherence time parameter sequence and the change trend of the signal strength sequence is analyzed, including: the consistency of the change direction of the two sequences at the same time is calculated respectively, the proportion of the number of inconsistent change directions in the total number is counted, and the corresponding proportion is taken as the quantitative index of the divergence.

[0033] Further, the relative distance between the electronic tag and each reader is calculated based on the channel coherence time parameter, and the relative distance is corrected according to the motion state recognition result and the reliability evaluation result, including:

[0034] The initial relative distance between the electronic tag and each reader is calculated based on the corresponding relationship between the channel coherence time parameter and the propagation distance;

[0035] The distance change constraint condition corresponding to the motion state of the electronic tag is determined according to the motion state recognition result;

[0036] The correction weight of each initial relative distance is determined according to the reliability evaluation result;

[0037] The initial relative distances are weighted and adjusted according to the distance change constraint condition and the correction weight to obtain the corrected relative distances: the effective range of each initial relative distance is determined according to the distance change constraint condition, and the initial relative distance is weighted and averaged in the effective range according to the correction weight to obtain the corrected relative distance.

[0038] Further, after the relative distance is corrected, a multilateration algorithm is used to calculate the spatial coordinates of the electronic tag, including:

[0039] A spatial spherical equation is constructed with the known spatial position of each reader as the center and the corrected relative distance as the radius;

[0040] The intersection of each spatial spherical equation is solved to determine the candidate spatial coordinates of the electronic tag;

[0041] The optimal spatial coordinates of the electronic tag are selected from the multiple intersections according to the geometric relationship between each reader and the candidate spatial coordinates;

[0042] The optimal spatial coordinates are output as the final spatial coordinates of the electronic tag.

[0043] On the other hand, the present application provides an RFID reader-based electronic tag position sensing and tracking system, including:

[0044] A signal synchronization module is configured to send a synchronization trigger signal to the electronic tag in the target area through at least two readers, and the at least two readers synchronously receive the response signal returned by the electronic tag;

[0045] A parameter estimation module is configured to estimate the channel coherence time parameter of the wireless channel between each reader and the electronic tag according to the response signal received by each reader;

[0046] A state recognition module is configured to symbolize the channel coherence time parameter to obtain a symbol sequence and calculate the complexity information of the symbol sequence, recognize the motion state of the electronic tag according to the complexity information to obtain a motion state recognition result;

[0047] A reliability evaluation module is configured to generate a reliability evaluation result by analyzing the deviation between the change trend of the channel coherence time parameter of each reader and the signal strength of the response signal received by each reader;

[0048] A distance correction module is configured to calculate the relative distance between the electronic tag and each reader based on the channel coherence time parameter, and correct the relative distance according to the motion state recognition result and the reliability evaluation result;

[0049] A positioning calculation module is configured to correct the relative distance and calculate the spatial coordinates of the electronic tag using a multilateration algorithm.

[0050] The beneficial effects of the present application are:

[0051] 1. Through the multi-reader synchronous triggering and receiving mechanism, the problem of signal collection asynchronization caused by the polling anti-collision mechanism in the traditional RFID system is effectively solved. By accurately estimating the channel coherence time parameter and performing symbolization processing and complexity analysis, the motion state of the electronic tag can be accurately identified. At the same time, by analyzing the deviation degree of the channel coherence time parameter and the signal strength change trend, the measurement reliability is accurately evaluated. Based on the motion state and reliability evaluation results, the distance measurement value is weighted and corrected, which significantly improves the accuracy and robustness of the multilateration algorithm. A complete technical system of signal collection, parameter estimation, state identification, reliability evaluation and distance correction is formed, which effectively improves the performance of the RFID positioning system in dynamic high-density scenarios.

[0052] 2. By establishing a correlation model between the wireless channel characteristics and the spatial position through the physical quantity of the channel coherence time parameter, the limitations of the traditional method on the signal time difference or phase consistency are overcome. By using the symbolization processing and complexity analysis method, the motion state of the electronic tag is accurately perceived, which provides an important basis for distance correction. Through multi-dimensional parameter fusion analysis, including channel characteristics, signal strength and motion state, a more perfect positioning evaluation system is constructed, which significantly improves the adaptability of the system to complex environments, enabling the RFID positioning technology to maintain stable positioning accuracy and reliability in dynamic scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 A flowchart of an electronic tag position perception and tracking method based on an RFID reader according to the present application is shown.

[0054] Figure 2 A structural schematic diagram of an electronic tag position perception and tracking system based on an RFID reader according to the present application is shown. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0056] Embodiment 1: Figure 1 A method for electronic tag position perception and tracking based on an RFID reader according to the present application is given, which includes:

[0057] S1, sending a synchronization trigger signal to the electronic tag in the target area through at least two readers, and synchronously receiving a response signal returned by the electronic tag by the at least two readers;

[0058] S2, estimating a channel coherence time parameter of a wireless channel between each reader and the electronic tag according to the response signal received by each reader;

[0059] S3, performing symbolization processing on the channel coherence time parameter to obtain a symbol sequence and calculating complexity information of the symbol sequence, identifying a motion state of the electronic tag according to the complexity information to obtain a motion state identification result;

[0060] S4, generating a reliability evaluation result by analyzing a deviation between the channel coherence time parameter of each reader and a change trend of signal strength of the response signal received by each reader;

[0061] S5, calculating a relative distance between the electronic tag and each reader based on the channel coherence time parameter, and correcting the relative distance according to the motion state identification result and the reliability evaluation result;

[0062] S6, calculating a spatial coordinate of the electronic tag by using a multilateral positioning algorithm after correcting the relative distance.

[0063] S1, sending a synchronization trigger signal to the electronic tag in the target area through at least two readers, and synchronously receiving a response signal returned by the electronic tag by the at least two readers, and the specific implementation includes:

[0064] In the implementation process, first, time synchronization of all readers needs to be realized. This is realized by configuring the same high-precision clock source for all readers, which can be a GPS clock or a high-stability atomic clock, to ensure that the clock references of all readers are completely consistent. Based on the same clock source, the central controller generates a synchronization trigger signal, which contains accurate timestamp information.

[0065] Then, the synchronization trigger signal is distributed to each reader through a dedicated wireless trigger channel. The wireless trigger channel uses an independent communication frequency band, for example, a 5.8 GHz frequency band, which is isolated from the communication frequency band between the reader and the electronic tag to avoid mutual interference. After receiving the synchronization trigger signal, each reader analyzes the timestamp information therein and adjusts the local clock accordingly, to ensure that all readers achieve microsecond-level time synchronization accuracy, for example, the time synchronization accuracy is controlled within 1 microsecond.

[0066] On the basis of time synchronization, each reader / writer sends a radio frequency query signal to the electronic tag in the same designated communication time slot according to the received synchronization trigger signal. The length of this communication time slot is set according to system requirements, for example, it can be set to 1 millisecond. Each reader / writer strictly follows the time division multiple access principle specified by the synchronization trigger signal when sending the radio frequency query signal, ensuring the time accuracy of all reader / writer signal transmissions.

[0067] After receiving the radio frequency query signal, the electronic tag generates a response signal and returns it. Each reader / writer synchronously receives the response signal returned by the electronic tag in the pre-set receiving time slot based on the same clock source. The start time and duration of the receiving time slot correspond to the sending time slot, for example, the receiving time slot is set to 2 milliseconds, ensuring that the response of the electronic tag can be completely captured. Each reader / writer records the arrival time, signal strength, and other parameters of the signal during the receiving process to provide raw data for subsequent processing.

[0068] Through the synchronization mechanism, all reader / writers work under the same time reference, making the subsequent signal processing and analysis comparable and consistent, laying an important foundation for accurate positioning calculation. During the entire synchronization process, the clock synchronization accuracy is maintained at the microsecond level, ensuring the time consistency requirements of the system. The central controller, as the core of the synchronization control, is responsible for the coordination and management of the entire synchronization process, ensuring that each reader / writer can complete the signal sending and receiving work according to the unified timing requirements.

[0069] S2, according to the response signal received by each reader / writer, respectively estimate the channel coherence time parameter of the wireless channel between each reader / writer and the electronic tag, including:

[0070] After obtaining the response signals synchronously received by each reader / writer, the estimation process of the channel coherence time parameter begins. First, based on the response signal received by each reader / writer, analyze the characteristics of the signal features over time. In specific implementation, extract multiple signal feature parameters including signal amplitude, signal phase, and signal delay from the response signal, arrange these signal feature parameters in time sequence to form time series data. The signal amplitude is obtained by measuring the strength value of the received signal, the signal phase is obtained by analyzing the phase information of the signal, and the signal delay is obtained by calculating the signal propagation time. Monitor and analyze the changes of these signal feature parameters over time, for example, observe the fluctuation of signal amplitude at consecutive time points, record the change law of signal phase over time, and track the time evolution trend of signal delay. The change characteristics of each signal feature parameter are quantitatively characterized by time series analysis methods, for example, use the sliding window method to calculate the variance of signal amplitude, use the difference method to calculate the change rate of signal phase, and use the time correlation analysis method to calculate the autocorrelation characteristics of signal delay.

[0071] The coherence time characteristic of the wireless channel is determined according to the change characteristic of the signal feature. On the basis of analyzing the change characteristic of the signal feature over time, it is necessary to determine the time range in which the wireless channel remains relatively stable. By observing the change law of the signal feature parameter, the time period in which the signal feature remains relatively stable and the time period in which the signal feature is unstable are identified. For example, when the signal amplitude fluctuation is within a small range and the signal phase change is relatively gentle, it is considered that the wireless channel is in a relatively stable state; when the signal amplitude appears a sharp fluctuation or the signal phase has a sudden change, it is considered that the wireless channel is in an unstable state. Based on these observation results, the coherence characteristic of the wireless channel in different time periods is determined, including the starting point, duration and change mode of the coherence time and the like. The determination of the coherence time characteristic needs to comprehensively consider the change of multiple signal feature parameters, for example, the stability of the signal amplitude and the stability of the signal phase are weighted and integrated, and the weight coefficient is set according to the requirements of the specific application scene, for example, a higher weight is given to the signal phase in the scene requiring phase stability.

[0072] The channel coherence time parameter is calculated based on the coherence time characteristic. After determining the coherence time characteristic of the wireless channel, it is necessary to quantify these characteristics into specific channel coherence time parameters. In specific implementation, the length of the time interval in which the change characteristic of the signal feature tends to be stable is determined, and this time interval length is taken as the basis for calculating the channel coherence time parameter. For example, by statistical analysis method, the longest continuous time period in which the signal feature parameter remains stable is found, and the length of this time period is taken as the channel coherence time parameter. In the calculation process, it is necessary to set a stability judgment standard, for example, when the signal amplitude fluctuation range is not more than 3 dB and the signal phase change is not more than 10 degrees, it is considered that the signal feature is in a stable state. According to this standard, all time periods meeting the stability requirement are identified, and the longest time period is selected, and its length is quantified as the channel coherence time parameter. The threshold of the stability judgment standard can be adjusted according to the actual application environment, for example, a relatively strict threshold can be used in indoor environment, and a relatively loose threshold can be used in outdoor environment.

[0073] In the quantization process, time units are used to represent the channel coherence time parameter, for example, using milliseconds as the unit of measurement. In order to ensure the accuracy of the calculation results, a comprehensive analysis of the stability of multiple time periods is needed, and the weighted average value is taken as the final channel coherence time parameter. The setting of the weighting coefficient is based on the length of the time period and the stability of the signal characteristics, the longer the time period and the higher the stability, the greater the weight. For example, the time period with a duration of more than 100 milliseconds is set to 0.6, the time period with a duration of 50 milliseconds to 100 milliseconds is set to 0.3, and the time period with a duration of less than 50 milliseconds is set to 0.1. Through this method, the channel coherence time parameter accurately reflecting the characteristics of the wireless channel is obtained, providing reliable basic data for subsequent positioning calculation. The entire calculation process ensures that the dimensions are unified, the time units remain consistent, and all parameters have clear physical meaning and implementability. An exception handling mechanism is also set in the calculation process, for example, when no time period meeting the stability requirements can be found, a default channel coherence time parameter value is used, which is obtained through historical data statistical analysis, ensuring the robustness of the system.

[0074] S3, symbolizing the channel coherence time parameter to obtain a symbol sequence and calculating the complexity information of the symbol sequence, identifying the motion state of the electronic tag according to the complexity information to obtain a motion state identification result, including:

[0075] After obtaining the channel coherence time parameter, symbolization processing is started to obtain a symbol sequence. The channel coherence time parameter sequence is compared with a preset threshold, and each parameter value is mapped to a corresponding symbol according to the comparison result to form a symbol sequence. The setting of the preset threshold is based on statistical analysis of historical channel coherence time parameter data, for example, by collecting a large number of channel coherence time parameter values under different environmental conditions, calculating the statistical distribution characteristics, and selecting a value with a distinguishing degree as the threshold. In specific implementation, two or three thresholds can be set to divide the parameter values into different intervals, for example, two thresholds are set to divide the parameter values into three intervals of low, medium and high, and each interval corresponds to a specific symbol. The symbol can be represented by a simple character, for example, A represents the low interval, B represents the medium interval, and C represents the high interval. Through this mapping method, the continuous channel coherence time parameter sequence is converted into a discrete symbol sequence, which is convenient for subsequent pattern analysis processing. The specific value of the threshold can be determined through experimental data, for example, in an indoor environment, the low interval threshold can be set to 10 milliseconds, the medium interval threshold can be set to 30 milliseconds, and the high interval threshold can be set to 50 milliseconds.

[0076] The occurrence law of different symbol combinations in the symbol sequence is analyzed, and the complexity information of the symbol sequence is calculated based on the occurrence law. In specific implementation, the occurrence frequency of different length symbol combinations in the symbol sequence is counted, and the sequence complexity value is calculated according to the distribution characteristics of the occurrence frequency of each symbol combination. The length of the symbol combination can be set according to actual needs, for example, the occurrence frequency of the symbol combination with a length of 2 (i.e. the combination of two continuous symbols) can be counted, or longer symbol combinations can be counted. In the counting process, the entire symbol sequence needs to be traversed, the number of occurrences of each symbol combination is recorded, and then the occurrence frequency is calculated. The calculation method of the occurrence frequency is that the number of occurrences of a certain symbol combination is divided by the total number of occurrences of all symbol combinations. According to the distribution characteristics of the occurrence frequency of each symbol combination, the information entropy method is used to calculate the sequence complexity value, and the complexity value is used to represent the degree of pattern regularity of the symbol sequence. The higher the complexity value, the more complex the pattern of the symbol sequence and the worse the regularity; the lower the complexity value, the simpler the pattern of the symbol sequence and the stronger the regularity.

[0077] In calculating the complexity information of the symbol sequence, information entropy is used as a quantitative index, and the calculation formula is as follows: ; wherein, represents the information entropy value of the symbol sequence, which is used to represent the complexity of the sequence; represents the set of all possible symbol combinations; represents a certain specific symbol combination in the set represents the occurrence frequency of the symbol combination in the sequence; represents the logarithm operation with 2 as the base. The larger the value, the stronger the randomness and the weaker the regularity of the symbol sequence, and the smaller the value, the stronger the regularity and the weaker the randomness of the symbol sequence.

[0078] ​The complexity information is compared with preset motion state judgment thresholds, and the motion state of the electronic tag is determined according to the comparison result to obtain a motion state recognition result. The preset motion state judgment thresholds are set based on statistical analysis of a large amount of experimental data, for example, by collecting the symbol sequence complexity values of the electronic tag under different motion states, a corresponding relationship model of the motion state and the complexity value is established. In specific implementation, one or more judgment thresholds can be set to distinguish different motion states, for example, a threshold is set to distinguish between static state and motion state, when the complexity value is lower than the threshold, it is judged as static state, and when the complexity value is higher than the threshold, it is judged as motion state. Multiple thresholds can also be set to distinguish more detailed motion states, for example, three thresholds are set to distinguish the motion state into three states of static, low-speed motion and high-speed motion. In the comparison process, the complexity value actually calculated is compared with these preset thresholds, and the current motion state of the electronic tag is determined according to the comparison result, so as to obtain the motion state recognition result. The specific value of the threshold can be determined by experiment, for example, the complexity threshold of the static state can be set to 0.5, the complexity threshold of the low-speed motion can be set to 1.2, and the complexity threshold of the high-speed motion can be set to 2.0.

[0079] In the specific implementation process, some special cases and processing mechanisms also need to be considered. For example, when the symbol sequence length is too short, the statistical result may not be accurate enough, at this time, interpolation method can be used to supplement data or default motion state judgment result can be directly used. The default value is set based on the statistical characteristics of historical data, for example, the average complexity value of the last 10 normal symbol sequences is taken as the default value. For example, when calculating the complexity value, the sliding window method can be used to segment the symbol sequence, and the complexity value of each segment is calculated respectively, and then the average value or weighted average value is taken as the final complexity value, so as to improve the accuracy and stability of the calculation. The weight coefficient is set based on the length and stability of each segment, and the segment with longer length and higher stability is given greater weight. For example, the weight coefficient of the segment with length more than 20 symbols is set to 0.6, the weight coefficient of the segment with length between 10 and 20 symbols is set to 0.3, and the weight coefficient of the segment with length less than 10 symbols is set to 0.1. Through these processing mechanisms, the accuracy and reliability of the motion state recognition result are ensured, and important state information is provided for subsequent positioning calculation. The whole processing process ensures consistent terminology, clear logic and reasonable parameter setting, and can be accurately implemented. At the same time, an abnormal processing mechanism is also set, when an abnormal situation occurs, for example, all symbols are the same or the symbol sequence is completely random, a specific processing strategy is adopted to ensure that the system can work normally under various conditions.

[0080] The step S3 can effectively extract the feature information of the motion state by converting the channel coherence time parameter sequence into a symbol sequence and conducting complexity analysis. Compared with directly processing the original parameter data, the symbolization processing method reduces the data dimension and enhances the anti-interference ability. At the same time, the complexity analysis can accurately quantify the change degree of the motion mode, avoid the limitations of relying on a single threshold judgment in the traditional method, and improve the accuracy and reliability of the motion state recognition through multi-level pattern recognition. It is especially suitable for motion state recognition in complex environments.

[0081] S4, generating a reliability evaluation result by analyzing the divergence between the change trend of the channel coherence time parameter of each reader and the signal strength of the response signal received by the reader, the specific implementation includes:

[0082] After completing the motion state recognition, the generation process of the reliability evaluation result is started. First, the sequence of the change of the channel coherence time parameter with time and the sequence of the change of the signal strength with time are obtained respectively. The channel coherence time parameter sequence is obtained by arranging the parameter values calculated in the previous step in chronological order, and the signal strength sequence is obtained by arranging the signal strength values recorded when the response signal is received by each reader in chronological order. The time stamps of the two sequences need to be strictly aligned to ensure that the parameter values at the same time can be correctly corresponded. The length of the sequence is set according to system requirements, for example, the last 100 sampling points can be taken to form the sequence to ensure that enough information is included for trend analysis. When obtaining the sequence, data quality check needs to be conducted to eliminate abnormal values and smoothing processing, for example, moving average method is used to eliminate the influence of random fluctuations, and the window size of the moving average can be set according to the data sampling frequency, for example, when the sampling frequency is 10 Hz, the window size is set to 5 points.

[0083] A divergence degree between the change trend of the channel coherence time parameter sequence and the change trend of the signal strength sequence is analyzed. In implementation, the consistency of the change direction of the two sequences at the same time is calculated respectively, and the proportion of the number of inconsistent change directions to the total number is counted, and the corresponding proportion is taken as the quantitative index of the divergence degree. The judgment of the change direction is realized by comparing the parameter values of adjacent time, for example, the current time value is greater than the previous time value, which is recorded as an upward trend, the current time value is less than the previous time value, which is recorded as a downward trend, and the current time value is equal to the previous time value, which is recorded as a stable trend. The number of times that the change direction of the two sequences is inconsistent at all time is counted, for example, the channel coherence time parameter presents an upward trend and the signal strength presents a downward trend, or the channel coherence time parameter presents a downward trend and the signal strength presents an upward trend. The total number is the number of all comparable time, and the proportion is the inconsistent number divided by the total number. The proportion value is taken as the quantitative index of the divergence degree, and the value range is between 0 and 1, and the greater the value is, the more inconsistent the change trends of the two sequences are. In the calculation process, special case processing needs to be considered, for example, when a sequence appears a continuous stable trend, the period does not participate in the divergence degree calculation.

[0084] The reliability level of each reader distance measurement value is determined based on the size of the divergence degree. There is a negative correlation between the size of the divergence degree and the reliability level, that is, the smaller the divergence degree is, the higher the reliability level is. The reliability level can be set to multiple levels, for example, three levels of high reliability, medium reliability and low reliability. The threshold of level division is determined based on statistical analysis of a large amount of experimental data, for example, by collecting a large amount of experimental data under different environmental conditions, analyzing the corresponding relationship between the divergence degree value and the actual measurement error, and determining reasonable level division threshold. When the divergence degree is less than 0.2, it is determined as a high reliability level, when the divergence degree is between 0.2 and 0.5, it is determined as a medium reliability level, and when the divergence degree is greater than 0.5, it is determined as a low reliability level. The specific value of the threshold can be adjusted according to the actual application environment, for example, a stricter threshold can be used in a stable indoor environment, and a relatively loose threshold can be used in an outdoor environment with variable environment. The adjustment is based on the quantitative analysis of environmental characteristics, for example, the threshold adjustment range is determined by the environmental complexity index.

[0085] A corresponding reliability evaluation result is generated according to the reliability level. The reliability evaluation result is represented by a quantitative numerical value, for example, a high reliability level is mapped to a numerical value of 1, a medium reliability level is mapped to a numerical value of 0.6, and a low reliability level is mapped to a numerical value of 0.3. The reliability level can also be represented by a continuous probability value, and the probability value is calculated based on a continuous mapping of the divergence value, for example, by a linear transformation or a nonlinear function to map the divergence value to a reliability probability value of 0-1. The generation of the evaluation result also needs to consider the continuity in the time dimension, for example, using a sliding window to calculate the reliability level of multiple time points in the recent period, and then taking the weighted average value as the final reliability evaluation result. The weight coefficient is set based on the time decay principle, and the weight of the more recent time point is larger, and the specific weight distribution can be calculated by an exponential decay function, for example, the weight of the current time is 0.5, the weight of the previous time is 0.3, and the weight of the previous two times is 0.2. The reliability evaluation result generated in this way can accurately reflect the reliability of each reader distance measurement value, providing an important basis for subsequent distance correction. The entire processing process ensures consistent terminology, clear logic, and reasonable parameter settings, and can be accurately implemented. At the same time, an exception handling mechanism is set, which adopts a default reliability level when data anomalies or calculation errors occur, and the default value is determined based on the statistical characteristics of historical data to ensure the stable operation of the system. The update frequency of the reliability evaluation result is consistent with the data sampling frequency, ensuring the timeliness of the evaluation result.

[0086] S5, based on the channel coherence time parameter, the relative distance between the electronic tag and each reader is calculated, and the relative distance is corrected according to the motion state recognition result and the reliability evaluation result, and the specific implementation includes:

[0087] After obtaining the channel coherence time parameter, the motion state recognition result and the reliability evaluation result, the calculation and correction process of the relative distance is started. Based on the correspondence between the channel coherence time parameter and the propagation distance, the initial relative distance between the electronic tag and each reader is calculated. The correspondence between the channel coherence time parameter and the propagation distance is obtained by experiment calibration, specifically by measuring a large number of channel coherence time parameter sample values under the condition of known distance, and establishing a mapping relationship model of parameter value and distance. The mapping relationship can be realized by function fitting or lookup table, for example, using a polynomial function to fit the relationship between parameter value and distance, or establishing a corresponding lookup table of parameter value interval and distance value. When calculating the initial relative distance, the currently measured channel coherence time parameter value is input into the mapping model to obtain the corresponding distance estimation value. The influence of environmental factors needs to be considered in the calculation process, for example, by using temperature sensors and humidity sensors to obtain environmental parameters, establishing an environmental compensation coefficient table, and querying the corresponding compensation coefficient according to the real-time environmental parameters to correct the distance estimation value.

[0088] According to the motion state recognition result, the distance change constraint condition corresponding to the motion state of the electronic tag is determined. The motion state includes a static state, a low-speed motion state, and a high-speed motion state, and each state corresponds to different distance change constraint conditions. The distance change constraint condition includes a maximum change rate constraint and a reasonable change range constraint. For example, the maximum change rate constraint of the static state is set to 0.1 meters per second, the maximum change rate constraint of the low-speed motion state is set to 0.5 meters per second, and the maximum change rate constraint of the high-speed motion state is set to 2 meters per second. The change range constraint is determined based on the statistical characteristics of the historical distance data, for example, taking the average value of the last 10 distance measurements plus or minus 3 times the standard deviation as the reasonable change range at the current time. The parameter value of the constraint condition is dynamically adjusted according to the motion state characteristics, for example, a gradual constraint condition adjustment strategy is adopted during state transition to avoid distance correction abnormalities caused by sudden changes in constraint conditions.

[0089] According to the reliability evaluation result, the correction weight of each initial relative distance is determined. The correction weight is positively correlated with the reliability evaluation result, that is, the higher the reliability evaluation result, the greater the correction weight. The calculation of the correction weight is realized through a weight mapping function, for example, a linear mapping function is used to map the reliability evaluation result to the weight interval of 0-1, or a S-shaped function is used to realize nonlinear mapping. The correction weights of multiple readers need to be normalized to make the sum of all weights equal to 1. The normalization processing adopts the calculation method of weight divided by weight sum to ensure the comparability of each weight coefficient in weighted calculation. Time correlation is also considered in the weight calculation process, for example, historical weight values are smoothed to avoid weight mutation.

[0090] In the determination of the correction weight, a weight mapping function is used for calculation, and the linear mapping function formula is as follows: ; wherein, represents the calculated correction weight; represents the reliability evaluation result; and represents the linear mapping parameter, which is determined by experimental data calibration, The value range of is generally 0.8 to 1.2, The value range of is generally -0.2 to 0.2.

[0091] Nonlinear mapping uses a S-shaped function, and the formula is as follows: ; wherein, represents the calculated correction weight; represents the reliability evaluation result; represents the base of natural logarithm; represents the steepness parameter of the curve, which controls the steepness of the function, and the value range is generally 5 to 15; The center point parameters of the function represent the control center of the function, and the value range is generally 0.4 to 0.6. The specific values of these parameters are determined by a large amount of experimental data optimization to ensure the accuracy and reasonableness of the weight mapping.

[0092] The initial relative distances are weighted and adjusted according to the distance change constraint condition and the correction weight to obtain the corrected relative distances. In specific implementation, first, the effective range of each initial relative distance is determined according to the distance change constraint condition, for example, by combining the current initial relative distance value with the change range determined by the constraint condition, the effective interval of each distance value is calculated. In the effective range, the initial relative distances are weighted and averaged according to the correction weight. In the weighted average calculation, each initial relative distance value is multiplied by its corresponding correction weight, and then the sum is obtained to get the weighted average value. In the calculation process, an iterative optimization method is used, for example, by adjusting the weight distribution through multiple iterations, so that the corrected distance value meets the constraint condition and maintains the optimal weighting effect. The final corrected relative distance is used as the input data for positioning calculation, ensuring the accuracy and reliability of the distance data. The entire processing process establishes a perfect quality control mechanism, including data validity check, calculation process monitoring and result verification, etc. to ensure the accurate implementation of distance correction. At the same time, an exception handling process is set up, when data exception or calculation error occurs, a backup calculation scheme is used, for example, using the sliding average value of historical distance value as the correction result, to ensure the stable operation of the system. The frequency of distance correction is consistent with the data acquisition frequency, ensuring the real-time of the correction result, providing reliable guarantee for the subsequent accurate positioning. All parameters in the correction process have clear physical meaning and operability, which can be adjusted and optimized according to the actual application requirements.

[0093] S6, after correcting the relative distances, a multilateral positioning algorithm is used to calculate the spatial coordinates of the electronic tag, including:

[0094] After obtaining the corrected relative distances, the multi-lateration algorithm is used to calculate the spatial coordinates of the electronic tag. A spatial spherical equation is constructed with the known spatial position of each reader as the center and the corrected relative distance as the radius. The spatial position of each reader is obtained by pre-precise measurement, and the coordinate values of each reader are recorded in a three-dimensional rectangular coordinate system, for example, the x, y, and z coordinate values of each reader are recorded in a rectangular coordinate system. The corrected relative distance is used as the radius parameter of the spherical equation, and the spherical equation is constructed in the form of the distance from each sphere center to the undetermined point being equal to the corresponding radius. Specifically, for the i-th reader, the spherical equation is represented as (x-xi)²+(y-yi)²+(z-zi)²=ri², where xi, yi, and zi are the known coordinates of the reader, and ri is the corresponding corrected relative distance. The spherical equations constructed by all readers form a system of equations, and the solution of the system of equations is the possible spatial position of the electronic tag. The number of equations in the system of equations is equal to the number of readers participating in positioning.

[0095] The candidate spatial coordinates of the electronic tag are determined by solving the intersection points of the spatial spherical equations. The solving process uses numerical calculation methods, such as using the least squares method to solve the overdetermined system of equations, or using iterative optimization algorithms to find the optimal solution. Due to the existence of measurement errors and calculation errors, multiple spherical equations may not have an exact intersection point, so it is necessary to find the optimal approximate solution. Reasonable initial values are set during solving, such as using the geometric center of all reader coordinates as the initial point of iteration, and using optimization algorithms such as gradient descent or Newton method to gradually approach the optimal solution. The numerical stability of the system of equations needs to be considered during calculation, such as using regularization methods to handle ill-conditioned systems of equations, or using robust estimation methods to reduce the influence of outliers. Finally, multiple possible candidate spatial coordinates are obtained, which form a set of possible positions of the electronic tag, and the number of candidate coordinates depends on the solution of the system of equations, for example, 1, 2, or multiple candidate solutions may be obtained.

[0096] The optimal spatial coordinates of the electronic tag are selected from the multiple intersection points according to the geometric relationship between each reader and the candidate spatial coordinates. The geometric relationship includes distance consistency, angle distribution rationality, and other evaluation indicators. For example, the deviation of the distance from each candidate coordinate to each reader from the corrected relative distance is calculated, and the candidate coordinate with the smallest deviation is selected as the optimal coordinate. The included angle between each reader and the candidate coordinate can also be considered, and the candidate coordinate with the most reasonable included angle distribution is selected. The weights of the evaluation indicators are set according to the reliability evaluation results, and the readers with higher reliability are given higher weights, for example, the weight of a reader with a reliability of 0.9 is set to 0.3, and the weight of a reader with a reliability of 0.6 is set to 0.2. Reasonable threshold conditions are set during the screening process, for example, the maximum allowed deviation threshold is set to 0.5 meters, and when the deviations of all candidate coordinates exceed the threshold, re-calculation or alternative solutions are needed.

[0097] The optimal spatial coordinates are output as the final spatial coordinates of the electronic tag. Before output, the coordinate values are verified and optimized, such as checking whether the coordinate values are within a reasonable spatial range and whether they meet the motion state constraint conditions. The coordinate output adopts a standard three-dimensional coordinate format, such as (x, y, z) form, and is accompanied by an accuracy estimation value. The accuracy estimation is calculated based on factors such as residual size and geometric dilution accuracy during the solving process, such as evaluating the positioning accuracy by calculating the residual sum of squares. The final output spatial coordinates are used for subsequent positioning applications, and historical coordinate data is saved for trajectory analysis and state monitoring. The entire positioning process establishes a complete quality control mechanism, including coordinate validity check, accuracy evaluation and exception handling, etc., to ensure the accuracy and reliability of the positioning results. The implementation of the multilateration algorithm takes into account various factors in practical applications, and through a systematic processing flow, the positioning accuracy is ensured to meet the requirements, providing technical support for the accurate positioning of electronic tags.

[0098] In the embodiments, the reader and the writer both refer to an RFID reader and writer.

[0099] Embodiment 2: Figure 2 A structure diagram of an electronic tag position perception and tracking system based on an RFID reader and writer is given, and the electronic tag position perception and tracking system based on the RFID reader and writer comprises:

[0100] A signal synchronization module is configured to send a synchronization trigger signal to an electronic tag in a target area through at least two readers, and the at least two readers synchronously receive a response signal returned by the electronic tag;

[0101] A parameter estimation module is configured to estimate a channel coherence time parameter of a wireless channel between each reader and the electronic tag according to the response signal received by each reader;

[0102] A state identification module is configured to perform symbolization processing on the channel coherence time parameter to obtain a symbol sequence and calculate complexity information of the symbol sequence, identify a motion state of the electronic tag according to the complexity information, and obtain a motion state identification result;

[0103] A reliability evaluation module is configured to generate a reliability evaluation result by analyzing a deviation degree between a change trend of the channel coherence time parameter of each reader and a change trend of a signal strength of the response signal received by each reader;

[0104] A distance correction module is configured to calculate a relative distance between the electronic tag and each reader based on the channel coherence time parameter, and correct the relative distance according to the motion state identification result and the reliability evaluation result;

[0105] The positioning calculation module is configured to calculate the spatial coordinates of the electronic tag by using a multilateral positioning algorithm after correcting the relative distance.

[0106] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and threshold values in the calculations are set by a person skilled in the art according to actual conditions.

[0107] It should be noted that the application can be deployed on a device itself to realize embedded applications, or run on a PC or other terminal with a user interface, thereby meeting various hardware environments and use requirements.

[0108] The above embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above embodiments can be realized in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another through wireless or wired transmission, including optical fiber, twisted pair, coaxial cable, etc. Wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium sets. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0109] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and module can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0110] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiment is merely an example, and there can be other division manners. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or modules, and can be in electrical, mechanical or other forms.

[0111] The modules illustrated as separated components can or can not be physically separated, and the components illustrated as modules can or can not be physical modules, and can be located in one place, or can be distributed on multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0112] In addition, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can be physically present alone, or two or more modules can be integrated in one module.

[0113] If the functions are realized in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that makes a contribution or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the method of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program codes that can be stored in the medium.

[0114] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0115] Finally: the above only for the preferred embodiments of the present application, and not for limiting the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application, should be included in the scope of protection of the present application.

Claims

1. An RFID reader-based electronic tag location awareness and tracking method, characterized by, The method comprises the following steps: S1, sending a synchronization trigger signal to an electronic tag in a target area through at least two readers, and synchronously receiving a response signal returned by the electronic tag through the at least two readers; S2, estimating a channel coherence time parameter between each reader and the electronic tag according to the response signal received by each reader, comprising: analyzing the change characteristics of the signal characteristics over time based on the response signal received by each reader; determining the coherence time characteristics of the wireless channel according to the change characteristics of the signal characteristics; calculating the channel coherence time parameter based on the coherence time characteristics: determining the length of the time interval at which the change characteristics of the signal characteristics tend to be stable, and quantifying the corresponding time interval length as the channel coherence time parameter; S3, symbolizing the channel coherence time parameter to obtain a symbol sequence and calculating the complexity information of the symbol sequence, identifying the motion state of the electronic tag according to the complexity information to obtain a motion state identification result, comprising: comparing the channel coherence time parameter sequence with a preset threshold, mapping each parameter value to a corresponding symbol according to the comparison result to form a symbol sequence; analyzing the occurrence law of different symbol combinations in the symbol sequence, and calculating the complexity information of the symbol sequence based on the occurrence law; comparing the complexity information with a preset motion state judgment threshold, and determining the motion state of the electronic tag according to the comparison result to obtain a motion state identification result; S4, generating a reliability evaluation result by analyzing the divergence between the channel coherence time parameter of each reader and the change trend of the signal strength of the response signal received by each reader; S5, calculating the relative distance between the electronic tag and each reader based on the channel coherence time parameter, and correcting the relative distance according to the motion state identification result and the reliability evaluation result; S6, after correcting the relative distance, calculating the spatial coordinates of the electronic tag by using a multilateral positioning algorithm.

2. The RFID reader-based electronic tag location awareness and tracking method of claim 1, wherein, S1, sending a synchronization trigger signal to an electronic tag in a target area through at least two readers, and synchronously receiving a response signal returned by the electronic tag through the at least two readers, comprising: generating a synchronization trigger signal based on the same high-precision clock source by a central controller; distributing the synchronization trigger signal to each reader through a synchronization trigger channel to control all readers to work synchronously by the central controller; sending a radio frequency trigger signal to the electronic tag within a pre-set same time slot after receiving the synchronization trigger signal by each reader; synchronously receiving the response signal returned by the electronic tag within a pre-set receiving time slot based on the same clock source by the at least two readers.

3. The RFID reader-based electronic tag location awareness and tracking method of claim 1, wherein, The method for analyzing the occurrence law of different symbol combinations in the symbol sequence and calculating the complexity information of the symbol sequence comprises: counting the occurrence frequency of different length symbol combinations in the symbol sequence, and calculating a sequence complexity value according to the distribution characteristics of the occurrence frequency of each symbol combination; the complexity value is used to represent the pattern regularity degree of the symbol sequence.

4. The RFID reader-based electronic tag location awareness and tracking method of claim 1, wherein, The method for generating a reliability evaluation result by analyzing the divergence between the channel coherence time parameter of each reader and the change trend of the signal strength of the response signal received by each reader comprises: respectively obtaining a sequence of the channel coherence time parameter changing over time and a sequence of the signal strength changing over time; analyze a divergence degree between a change trend of the channel coherence time parameter sequence and a change trend of the signal strength sequence; determine a reliability level of each reader distance measurement value based on the divergence degree; generate a corresponding reliability evaluation result according to the reliability level.

5. The RFID reader-based electronic tag location awareness and tracking method of claim 4, wherein, The analysis of the divergence degree between the change trend of the channel coherence time parameter sequence and the change trend of the signal strength sequence includes: calculating the consistency of the change direction of the two sequences at the same time respectively, counting the proportion of the number of inconsistent change directions in the total number, and taking the corresponding proportion as the quantitative index of the divergence degree.

6. The RFID reader-based electronic tag location awareness and tracking method of claim 1, wherein, The relative distance between the electronic tag and each reader is calculated based on the channel coherence time parameter, and the relative distance is corrected according to the motion state recognition result and the reliability evaluation result, including: The initial relative distance between the electronic tag and each reader is calculated based on the corresponding relationship between the channel coherence time parameter and the propagation distance. The distance change constraint condition corresponding to the motion state of the electronic tag is determined according to the motion state recognition result. The correction weight of each initial relative distance is determined according to the reliability evaluation result. The initial relative distance is weighted and adjusted in combination with the distance change constraint condition and the correction weight to obtain the corrected relative distance: the effective range of each initial relative distance is determined according to the distance change constraint condition, and the initial relative distance is weighted and averaged within the effective range according to the correction weight to obtain the corrected relative distance.

7. The RFID reader-based electronic tag location awareness and tracking method of claim 1, wherein, After the relative distance is corrected, the spatial coordinates of the electronic tag are calculated by using a multilateral positioning algorithm, including: A spatial spherical equation is constructed with the known spatial position of each reader as the center and the corrected relative distance as the radius. The intersection points of each spatial spherical equation are determined to obtain the candidate spatial coordinates of the electronic tag. The optimal spatial coordinates of the electronic tag are selected from the multiple intersection points according to the geometric relationship between each reader and the candidate spatial coordinates. The optimal spatial coordinates are output as the final spatial coordinates of the electronic tag.

8. An RFID reader-based electronic tag location awareness and tracking system for implementing the RFID reader-based electronic tag location awareness and tracking method of any one of claims 1-7, characterized by, It includes: A signal synchronization module for sending a synchronization trigger signal to the electronic tag in the target area through at least two readers, and the at least two readers synchronously receive the response signal returned by the electronic tag; A parameter estimation module for estimating the channel coherence time parameter of the wireless channel between each reader and the electronic tag according to the response signal received by each reader; A state recognition module for symbolizing the channel coherence time parameter to obtain a symbol sequence and calculating the complexity information of the symbol sequence, and recognizing the motion state of the electronic tag according to the complexity information to obtain a motion state recognition result; A reliability evaluation module for generating a reliability evaluation result by analyzing the divergence degree between the change trend of the channel coherence time parameter and the signal strength of the response signal received by each reader; A distance correction module for calculating the relative distance between the electronic tag and each reader based on the channel coherence time parameter, and correcting the relative distance according to the motion state recognition result and the reliability evaluation result; A positioning calculation module for calculating the spatial coordinates of the electronic tag by using a multilateral positioning algorithm after correcting the relative distance.

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