Electronic tag position sensing and tracking method and system based on RFID reader-writer

Abstract

The invention discloses an RFID reader-writer-based electronic tag position sensing and tracking method and system, particularly relates to the technical field of radio frequency identification and positioning, and is used for solving the problems of asynchronous signal acquisition of multiple reader-writers and insufficient positioning precision in a dynamic environment caused by an anti-collision mechanism of an existing RFID system. A label response signal is obtained through a multi-reader-writer synchronous triggering and signal receiving mechanism, a channel coherence time parameter is estimated, then symbolization processing and complexity analysis are carried out on the parameter to identify a label motion state, and meanwhile the measurement reliability is evaluated by analyzing the trend deviation degree of the channel parameter and the signal strength. And finally, correcting a distance measurement value based on the motion state and a reliability evaluation result, and calculating a space coordinate by adopting a multilateral positioning algorithm, thereby realizing accurate and stable positioning of the electronic tag in a dynamic high-density scene.

Description

Technical Field

[0001] This invention relates to the field of radio frequency identification (RFID) positioning technology, and in particular to a method and system for electronic tag location sensing and tracking based on an RFID reader. Background Technology

[0002] Radio Frequency Identification (RFID) technology is widely used in object identification and tracking. It achieves target object identification and data collection through radio signal interaction between the reader and the electronic tag. In applications requiring spatial positioning, a multi-reader collaborative positioning method is often used. For example, the tag position is calculated by analyzing physical parameters such as the time difference of arrival (TDOA) or the phase difference of carrier (PDOA) to achieve continuous positioning of moving or stationary targets.

[0003] However, existing RFID systems generally adopt a polling-based anti-collision mechanism to achieve multi-tag identification. This mechanism causes different readers to read the same tag asynchronously in time. This temporal discreteness disrupts the spatiotemporal correlation of the signal parameters required for positioning between different receiving points. As a result, precise positioning algorithms that rely on the assumption of signal time difference or phase consistency cannot work effectively due to the distortion of basic data, which restricts the positioning accuracy and reliability of RFID technology in dynamic high-density scenarios. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method and system for electronic tag location sensing and tracking based on RFID readers.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A method for location sensing and tracking of electronic tags based on RFID readers includes: S1. At least two readers send a synchronization trigger signal to the electronic tag in the target area, and at least two readers simultaneously receive the response signal returned by the electronic tag; S2. Based on the response signal received by each reader, estimate the channel coherence time parameters of the wireless channel between each reader and the electronic tag. S3. Symbolize the channel coherence time parameters to obtain a symbol sequence and calculate the complexity information of the symbol sequence. Based on the complexity information, identify the motion state of the electronic tag to obtain the motion state identification result. S4. By analyzing the divergence between the changing trends of the channel coherence time parameter of each reader and the signal strength of its received response signal, a reliability assessment result is generated. S5. 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 identification result and the reliability assessment result; S6. After correcting the relative distance, the spatial coordinates of the electronic tag are calculated using a polygonal positioning algorithm.

[0006] Furthermore, at least two readers send synchronization trigger signals to the electronic tags in the target area, and at least two readers synchronously receive response signals returned by the electronic tags, including: The central controller generates a synchronous trigger signal based on the same high-precision clock source; The central controller distributes the synchronization trigger signal to each reader / writer through the synchronization trigger channel to control all readers / writers to work synchronously. After receiving the synchronization trigger signal, each reader sends an RF trigger signal to the electronic tag within the same pre-set time slot; 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.

[0007] Furthermore, based on the response signal received by each reader, the channel coherence time parameters of the wireless channel between each reader and the electronic tag are estimated, including: Based on the response signal received by each reader, the characteristics of the signal change over time are analyzed; The coherence time characteristics of the wireless channel are determined based on the changing characteristics of the signal features; The channel coherence time parameters are calculated based on the coherence time characteristics: the length of the time interval in which the signal characteristics tend to stabilize is determined, and the corresponding time interval length is quantized into the channel coherence time parameters.

[0008] Furthermore, the channel coherence time parameters are symbolized to obtain a symbol sequence, and the complexity information of the symbol sequence is calculated. Based on the complexity information, the motion state of the electronic tag is identified to obtain the motion state identification result, including: The channel coherence time parameter sequence is compared with a preset threshold, and each parameter value is mapped to a corresponding symbol based on the comparison result to form a symbol sequence. Analyze the occurrence patterns of different symbol combinations in a symbol sequence, and calculate the complexity information of the symbol sequence based on the occurrence patterns; The complexity information is compared with a preset motion state judgment threshold, and the motion state of the electronic tag is determined based on the comparison result to obtain the motion state identification result.

[0009] Furthermore, the occurrence patterns of different symbol combinations in the symbol sequence are analyzed, and the complexity information of the symbol sequence is calculated based on the occurrence patterns. This includes: statistically analyzing the occurrence frequency of symbol combinations of different lengths in the symbol sequence, and calculating the sequence complexity value based on the distribution characteristics of the occurrence frequency of each symbol combination; the complexity value is used to characterize the degree of regularity of the symbol sequence pattern.

[0010] Furthermore, by analyzing the divergence between the changing trends of the channel coherence time parameter of each reader and the signal strength of its received response signal, a reliability assessment result is generated, including: Sequences of channel coherence time parameters and signal strength over time were obtained respectively. Analyze the degree of divergence between the changing trends of the channel coherence time parameter sequence and the changing trends of the signal strength sequence; The reliability level of each reader distance measurement is determined based on the degree of divergence. Generate corresponding reliability assessment results based on the reliability level.

[0011] Furthermore, the deviation between the changing trend of the channel coherence time parameter sequence and the changing trend of the signal strength sequence is analyzed, including: calculating the consistency of the changing direction of the two sequences at the same time, counting the proportion of inconsistent changing directions to the total number of times, and using the corresponding proportion as a quantitative indicator of the deviation.

[0012] Furthermore, 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 identification results and reliability assessment results, including: The initial relative distance between the electronic tag and each reader is calculated based on the correspondence between the channel coherence time parameter and the propagation distance. Determine the distance change constraints corresponding to the motion state of the electronic tag based on the motion state identification results; The correction weights for each initial relative distance are determined based on the reliability assessment results;

[0013] By combining distance change constraints and correction weights, the initial relative distances are weighted and adjusted to obtain the corrected relative distances. The effective range of each initial relative distance is determined according to the distance change constraints. Within the effective range, the initial relative distances are weighted and averaged according to the correction weights to obtain the corrected relative distances.

[0014] Furthermore, after correcting for the relative distance, a multilateral positioning algorithm is used to calculate the spatial coordinates of the electronic tag, including: A spatial sphere equation is constructed with the known spatial location of each reader as the center and the corrected relative distance as the radius; The candidate spatial coordinates of the electronic tag are determined by solving the intersection points of the equations of each spatial sphere. The optimal spatial coordinates of the electronic tag are selected from multiple intersection points based on 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.

[0015] On the other hand, the present invention provides an electronic tag location sensing and tracking system based on an RFID reader, comprising: The signal synchronization module is used to send a synchronization trigger signal to the electronic tag in the target area through at least two readers and to receive the response signal returned by the electronic tag synchronously through at least two readers. The parameter estimation module is used to estimate the channel coherence time parameters of the wireless channel between each reader and the electronic tag based on the response signal received by each reader. The state identification module is used to symbolize the channel coherence time parameters to obtain a symbol sequence and calculate the complexity information of the symbol sequence. Based on the complexity information, the motion state of the electronic tag is identified to obtain the motion state identification result. The reliability assessment module is used to generate reliability assessment results by analyzing the deviation between the changing trends of the channel coherence time parameter of each reader and the signal strength of its received response signal. The distance correction module is used to calculate the relative distance between the electronic tag and each reader based on the channel coherence time parameter, and to correct the relative distance according to the motion state identification results and reliability assessment results; The positioning calculation module is used to calculate the spatial coordinates of the electronic tag after correcting for the relative distance using a polygonal positioning algorithm.

[0016] The beneficial effects of this invention are: 1. By employing a multi-reader synchronous triggering and receiving mechanism, the asynchronous signal acquisition problem caused by the polling anti-collision mechanism in traditional RFID systems is effectively solved. By accurately estimating the channel coherence time parameter and performing symbolic processing and complexity analysis, the motion state of the electronic tag can be accurately identified. At the same time, by analyzing the deviation between the channel coherence time parameter and the signal strength change trend, a precise assessment of measurement reliability is achieved. Based on the motion state and reliability assessment results, the distance measurement value is weighted and corrected, significantly improving the accuracy and robustness of the multilateral positioning algorithm. This forms a complete technical system for signal acquisition, parameter estimation, state recognition, reliability assessment, and distance correction, effectively improving the performance of the RFID positioning system in dynamic high-density scenarios.

[0017] 2. By using the physical quantity of channel coherence time parameter, a correlation model between wireless channel characteristics and spatial location was established, overcoming the limitations of traditional methods that require signal time difference or phase consistency. By using symbolic processing and complexity analysis, accurate perception of the movement state of electronic tags was achieved, providing an important basis for distance correction. Through multi-dimensional parameter fusion analysis, including channel characteristics, signal strength, and movement state, a more complete positioning evaluation system was constructed, significantly improving the system's adaptability to complex environments and enabling RFID positioning technology to maintain stable positioning accuracy and reliability in dynamic scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart of an electronic tag location sensing and tracking method based on an RFID reader / writer according to the present invention; Figure 2 This is a schematic diagram of the structure of an electronic tag location sensing and tracking system based on an RFID reader / writer according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: Figure 1 This invention presents a method for location sensing and tracking of electronic tags based on RFID readers, comprising: S1. At least two readers send a synchronization trigger signal to the electronic tag in the target area, and at least two readers simultaneously receive the response signal returned by the electronic tag; S2. Based on the response signal received by each reader, estimate the channel coherence time parameters of the wireless channel between each reader and the electronic tag. S3. Symbolize the channel coherence time parameters to obtain a symbol sequence and calculate the complexity information of the symbol sequence. Based on the complexity information, identify the motion state of the electronic tag to obtain the motion state identification result. S4. By analyzing the divergence between the changing trends of the channel coherence time parameter of each reader and the signal strength of its received response signal, a reliability assessment result is generated. S5. 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 identification result and the reliability assessment result; S6. After correcting the relative distance, the spatial coordinates of the electronic tag are calculated using a polygonal positioning algorithm.

[0021] S1. At least two readers send synchronization trigger signals to the electronic tags in the target area, and at least two readers synchronously receive the response signals returned by the electronic tags. Specific implementation includes: During implementation, the first step is to synchronize the time of all readers. This is achieved by configuring all readers with the same high-precision clock source, which can be a GPS clock or a highly stable atomic clock, ensuring that the clock references of all readers are completely consistent. Based on this common clock source, the central controller generates a synchronization trigger signal containing precise timestamp information.

[0022] Next, the synchronization trigger signal is distributed to each reader via a dedicated wireless trigger channel. This wireless trigger channel uses an independent communication frequency band, such as the 5.8 GHz band, 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 parses the timestamp information and adjusts its local clock accordingly to ensure that all readers achieve microsecond-level time synchronization accuracy, for example, within 1 microsecond.

[0023] Based on time synchronization, each reader sends an RFID query signal to the electronic tag within 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. When sending the RFID query signal, each reader strictly follows the time division multiple access principle specified by the synchronization trigger signal to ensure the time accuracy of all reader signal transmissions.

[0024] After receiving a radio frequency query signal, the electronic tag generates a response signal and returns it. Each reader, based on the same clock source, synchronously receives the response signal returned by the electronic tag within a pre-set receive time slot. The start time and duration of the receive time slot correspond to the transmit time slot; for example, a receive time slot set to 2 milliseconds ensures complete capture of the electronic tag's response. Each reader records parameters such as signal arrival time and signal strength during the reception process, providing raw data for subsequent processing.

[0025] The synchronization mechanism ensures that all readers operate on the same time base, making subsequent signal processing and analysis comparable and consistent, laying an important foundation for accurate positioning calculations. Throughout the synchronization process, clock synchronization accuracy is maintained at the microsecond level, ensuring the system's time consistency requirements. The central controller, as the core of synchronization control, is responsible for coordinating and managing the entire synchronization process, ensuring that each reader can complete signal transmission and reception according to unified timing requirements.

[0026] S2. Based on the response signal received by each reader, estimate the channel coherence time parameters of the wireless channel between each reader and the electronic tag. Specific implementation includes: After receiving the synchronously received response signals from each reader, the estimation process of the channel coherence time parameters begins. First, based on the response signals received by each reader, the characteristics of the signal variations over time are analyzed. Specifically, multiple signal characteristic parameters, including signal amplitude, signal phase, and signal delay, are extracted from the response signals and arranged in chronological order to form time-series data. Signal amplitude is obtained by measuring the strength of the received signal, signal phase is obtained by analyzing the signal's phase information, and signal delay is obtained by calculating the signal propagation time. The changes of these signal characteristic parameters over time are monitored and analyzed; for example, the fluctuations in signal amplitude at continuous time points are observed, the changes in signal phase over time are recorded, and the temporal evolution trend of signal delay is tracked. The variation characteristics of each signal characteristic parameter are quantitatively characterized using time-series analysis methods, such as using the sliding window method to calculate the variance of the signal amplitude, the finite difference method to calculate the rate of change of the signal phase, and time correlation analysis to calculate the autocorrelation characteristics of the signal delay.

[0027] The coherence time characteristics of a wireless channel are determined based on the changing characteristics of signal features. After analyzing the changes in signal features over time, it is necessary to determine the time range within which the wireless channel remains relatively stable. By observing the changing patterns of signal feature parameters, periods of relative stability and periods of instability are identified. For example, when signal amplitude fluctuations are within a small range and signal phase changes are relatively gentle, the wireless channel is considered to be in a relatively stable state; when signal amplitude fluctuates drastically or signal phase changes abruptly, the wireless channel is considered to be in an unstable state. Based on these observations, the coherence characteristics of the wireless channel in different time periods are determined, including the start point, duration, and change pattern of coherence time. Determining coherence time characteristics requires comprehensive consideration of the changes in multiple signal feature parameters. For example, the stability of signal amplitude and the stability of signal phase can be weighted and combined, with the weighting coefficients set according to the specific application scenario requirements. For instance, in scenarios requiring phase stability, a higher weight is given to the signal phase.

[0028] Channel coherence time parameters are calculated based on coherence time characteristics. After determining the coherence time characteristics of the wireless channel, these characteristics need to be quantified into specific channel coherence time parameters. In practice, the length of the time interval during which the signal characteristics tend to stabilize is determined, and this time interval length is used as the basis for calculating the channel coherence time parameters. For example, through statistical analysis, the longest continuous time period during which the signal characteristic parameters remain stable is identified, and the length of this time period is used as the channel coherence time parameter. During the calculation process, a stability judgment standard needs to be set. For example, when the signal amplitude fluctuation range does not exceed 3dB and the signal phase change does not exceed 10 degrees, the signal characteristics are considered to be in a stable state. According to this standard, all time periods that meet the stability requirements are identified, and the longest duration of this 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 stricter threshold can be used in indoor environments, while a relatively lenient threshold can be used in outdoor environments.

[0029] In the quantization process, time units are used to characterize the channel coherence time parameters, such as milliseconds. To ensure the accuracy of the calculation results, the stability of multiple time periods needs to be comprehensively analyzed, and the weighted average is taken as the final channel coherence time parameter. The weighting coefficients are set based on the duration of the time period and the stability of the signal characteristics; longer duration and higher stability time periods are assigned greater weights. For example, time periods with a duration exceeding 100 milliseconds have a weighting coefficient of 0.6, time periods with a duration between 50 and 100 milliseconds have a weighting coefficient of 0.3, and time periods with a duration less than 50 milliseconds have a weighting coefficient of 0.1. This method yields channel coherence time parameters that accurately reflect the characteristics of the wireless channel, providing reliable basic data for subsequent positioning calculations. The entire calculation process ensures uniformity of units of measurement and time, and all parameters have clear physical meaning and feasibility. An anomaly handling mechanism is also included in the calculation process. For example, when a time period meeting the stability requirements cannot be found, a default channel coherence time parameter value is used. This default value is obtained based on historical data statistical analysis, ensuring the robustness of the system.

[0030] S3. Symbolize the channel coherence time parameters to obtain a symbol sequence and calculate the complexity information of the symbol sequence. Based on the complexity information, identify the motion state of the electronic tag to obtain the motion state identification result. Specific implementation includes: After obtaining the channel coherence time parameters, symbolization processing begins 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 based on the comparison result, forming a symbol sequence. The preset threshold is set 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 their statistical distribution characteristics, and selecting values ​​with discriminative power as thresholds. In practice, two or three thresholds can be set to divide the parameter values ​​into different intervals. For example, setting two thresholds divides the parameter values ​​into three intervals: low, medium, and high, with each interval corresponding to a specific symbol. The symbols can be represented by simple characters, such as A for the low interval, B for the medium interval, and C for the high interval. This mapping method converts the continuous channel coherence time parameter sequence into a discrete symbol sequence, facilitating subsequent mode analysis processing. The specific values ​​of the thresholds can be determined through experimental data. For example, in an indoor environment, the threshold for the low interval can be set to 10 milliseconds, the threshold for the medium interval to 30 milliseconds, and the threshold for the high interval to 50 milliseconds.

[0031] This study analyzes the occurrence patterns of different symbol combinations in a symbol sequence and calculates the sequence's complexity based on these patterns. Specifically, it involves statistically analyzing the frequency of symbol combinations of different lengths within the sequence and calculating the sequence complexity value based on the distribution characteristics of these frequencies. The length of a symbol combination can be set according to actual needs; for example, the frequency of combinations with a length of 2 (i.e., two consecutive symbols) can be analyzed, as can the frequency of even longer combinations. During the statistical analysis, the entire symbol sequence is traversed, recording the frequency of each symbol combination and then calculating its frequency. The frequency is calculated by dividing the number of times a particular symbol combination appears by the total number of times all symbol combinations appear. Based on the distribution characteristics of the frequency of each symbol combination, the information entropy method is used to calculate the sequence complexity value. The complexity value characterizes the degree of regularity in the symbol sequence's pattern. A higher complexity value indicates a more complex and less regular pattern in the symbol sequence; a lower complexity value indicates a simpler and more regular pattern in the symbol sequence.

[0032] When calculating the complexity information of a symbol sequence, information entropy is used as a quantitative indicator, and its calculation formula is as follows: ;in, The information entropy value represents the sequence of symbols and is used to characterize the complexity of the sequence; The set representing all possible combinations of symbols; Represents a set A specific combination of symbols in; Symbol combination Frequency of occurrence in the sequence; This represents a logarithmic operation with base 2. The larger the value, the stronger the randomness and the weaker the regularity of the symbol sequence; the smaller the value, the stronger the regularity and the weaker the randomness of the symbol sequence.

[0033] The complexity information is compared with preset motion state judgment thresholds. Based on the comparison result, the motion state of the electronic tag is determined, thus obtaining the motion state identification result. The preset motion state judgment thresholds are set based on statistical analysis of a large amount of experimental data. For example, by collecting symbol sequence complexity values ​​of electronic tags under different motion states, a correspondence model between motion states and complexity values ​​is established. In specific implementation, one or more judgment thresholds can be set to distinguish different motion states. For example, a threshold can be set to distinguish between a stationary state and a moving state. When the complexity value is lower than the threshold, it is judged as a stationary state; when the complexity value is higher than the threshold, it is judged as a moving state. Multiple thresholds can also be set to distinguish more refined motion states. For example, three thresholds can be set to distinguish motion states into three states: stationary, low-speed motion, and high-speed motion. During the comparison process, the actual calculated complexity value is compared with these preset thresholds. Based on the comparison result, the current motion state of the electronic tag is determined, thus obtaining the motion state identification result. The specific values ​​of the thresholds can be determined experimentally. For example, the complexity threshold for a stationary state can be set to 0.5, the complexity threshold for low-speed motion can be set to 1.2, and the complexity threshold for high-speed motion can be set to 2.0.

[0034] 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 results may not be accurate enough. In this case, interpolation can be used to supplement the data or the default motion state judgment result can be used directly. The default value is set based on the statistical characteristics of historical data. For example, the average complexity of the most recent 10 normal symbol sequences can be taken as the default value. Furthermore, when calculating the complexity value, a sliding window method can be used to segment the symbol sequence, calculate the complexity value of each segment separately, and then take the average or weighted average as the final complexity value to improve the accuracy and stability of the calculation. The weight coefficient is set based on the length and stability of each segment. Longer segments with higher stability are given larger weights. For example, the weight coefficient of segments longer than 20 symbols is set to 0.6, the weight coefficient of segments between 10 and 20 symbols is set to 0.3, and the weight coefficient of segments shorter than 10 symbols is set to 0.1. Through these processing mechanisms, the accuracy and reliability of the motion state identification results are ensured, providing important state information for subsequent positioning calculations. The entire process ensures consistent terminology, clear logic, reasonable parameter settings, and accurate implementation. In addition, an exception handling mechanism is set up. When an abnormal situation occurs, such as when all symbols are the same or the symbol sequence is completely random, a specific handling strategy is adopted to ensure that the system can work normally under various conditions.

[0035] Step S3 effectively extracts motion state feature information by converting the channel coherence time parameter sequence into a symbol sequence and performing complexity analysis. Compared to directly processing the original parameter data, the symbolization method reduces data dimensionality and enhances anti-interference capabilities. Simultaneously, complexity analysis accurately quantifies the degree of change in motion patterns, avoiding the limitations of relying on a single threshold judgment in traditional methods. Through multi-level pattern recognition, it improves the accuracy and reliability of motion state identification, making it particularly suitable for motion state identification in complex environments.

[0036] S4. By analyzing the divergence between the changing trends of the channel coherence time parameter of each reader and the signal strength of its received response signal, a reliability assessment result is generated. Specific implementation includes: After completing motion state identification, the reliability assessment results are generated. First, sequences of channel coherence time parameters and signal strength over time are acquired. The channel coherence time parameter sequence is derived from parameter values ​​calculated in previous steps, arranged chronologically. The signal strength sequence is derived from signal strength values ​​recorded by each reader when receiving response signals, also arranged chronologically. The timestamps of the two sequences must be strictly aligned to ensure correct correspondence of parameter values ​​at the same time. The sequence length is set according to system requirements; for example, data from the most recent 100 sampling points can be used to form the sequence, ensuring sufficient information for trend analysis. During sequence acquisition, data quality checks are performed to remove outliers and smooth the data. For example, a moving average method can be used to eliminate the influence of random fluctuations. The window size of the moving average can be set according to the data sampling frequency; for example, a window size of 5 points can be used when the sampling frequency is 10Hz.

[0037] This analysis examines the divergence between the changing trends of the channel coherence time parameter sequence and the signal strength sequence. Specifically, it calculates the consistency of the changing directions of both sequences at the same time point, and counts the proportion of inconsistent changing directions out of the total number of occurrences. This proportion serves as the quantitative indicator of the divergence. The direction of change is determined by comparing parameter values ​​at adjacent times. For example, a value greater than the previous value is considered an upward trend, a value less than the previous value is considered a downward trend, and a value equal to the previous value is considered a stable trend. The analysis counts the number of times the two sequences' changing directions are inconsistent across all times, such as when the channel coherence time parameter shows an upward trend while the signal strength shows a downward trend, or vice versa. The total number of occurrences is the total number of comparable times, and the proportion is the number of inconsistent occurrences divided by the total number of occurrences. This proportion serves as the quantitative indicator of the divergence, ranging from 0 to 1; a larger value indicates a greater inconsistency in the changing trends of the two sequences. Special cases need to be considered during the calculation; for example, if a sequence exhibits a continuous stable trend, that time period is excluded from the divergence calculation.

[0038] The reliability level of each reader's distance measurement is determined based on the magnitude of the deviation. There is a negative correlation between the deviation and the reliability level; the smaller the deviation, the higher the reliability level. Multiple reliability levels can be set, such as high, medium, and low reliability. The thresholds for level classification are determined based on statistical analysis of extensive experimental data. For example, by collecting large amounts of experimental data under different environmental conditions, the correlation between deviation values ​​and actual measurement errors is analyzed to determine reasonable level classification thresholds. A deviation less than 0.2 is classified as high reliability, a deviation between 0.2 and 0.5 as medium reliability, and a deviation greater than 0.5 as low reliability. The specific value of the threshold can be adjusted according to the actual application environment. For example, a stricter threshold can be used in stable indoor environments, while a more lenient threshold can be used in variable outdoor environments. The adjustment is based on quantitative analysis of environmental characteristics, such as using environmental complexity indicators to determine the threshold adjustment range.

[0039] Reliability assessment results are generated based on the reliability level. These results are represented numerically, for example, mapping high reliability to 1, medium reliability to 0.6, and low reliability to 0.3. Alternatively, continuous probability values ​​can be used to represent reliability. These probability values ​​are calculated based on a continuous mapping of deviation values, such as mapping deviation values ​​to a 0-1 reliability probability value using linear transformations or nonlinear functions. The generation of assessment results also considers the continuity over time. For example, a sliding window approach is used to calculate the reliability levels at the most recent time points, and then a weighted average is taken as the final reliability assessment result. Weighting coefficients are set based on the time decay principle, with more recent time points having higher weights. The specific weight distribution can be calculated using an exponential decay function; for example, the current time point has a weight of 0.5, the previous time point has a weight of 0.3, and the two time points before that have a weight of 0.2. Reliability assessment results generated in this way accurately reflect the reliability of each reader's distance measurement, providing crucial information for subsequent distance correction. The entire process ensures consistent terminology, clear logic, and reasonable parameter settings, enabling accurate implementation. An anomaly handling mechanism is also implemented. When data anomalies or calculation errors occur, a default reliability level is adopted. The default value is determined based on the statistical characteristics of historical data to ensure stable system operation. The update frequency of the reliability assessment results is consistent with the data sampling frequency to ensure the timeliness of the assessment results.

[0040] S5. 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 identification results and reliability assessment results. Specific implementation includes: After obtaining the channel coherence time parameters, motion state identification results, and reliability assessment results, the relative distance calculation and correction process begins. The initial relative distance between the electronic tag and each reader is calculated based on the correspondence between the channel coherence time parameters and propagation distance. This correspondence is obtained through experimental calibration, specifically by measuring a large number of channel coherence time parameter sample values ​​under known distance conditions to establish a mapping model between parameter values ​​and distance. This mapping relationship can be achieved using function fitting or a lookup table, for example, using a polynomial function to fit the relationship between parameter values ​​and distance, or establishing a lookup table corresponding to parameter value intervals and distance values. During the initial relative distance calculation, the currently measured channel coherence time parameter values ​​are input into the mapping model to obtain the corresponding distance estimate. The calculation process needs to consider the influence of environmental factors, such as obtaining environmental parameters through temperature and humidity sensors, establishing an environmental compensation coefficient lookup table, and correcting the distance estimate by querying the corresponding compensation coefficient based on real-time environmental parameters.

[0041] The distance change constraints corresponding to the movement state of the electronic tag are determined based on the motion state identification results. Motion states include stationary, low-speed, and high-speed motion states, each with different distance change constraints. These constraints include a maximum rate of change constraint and a reasonable range of change constraint. For example, the maximum rate of change constraint for a stationary state is set at 0.1 m / s, for a low-speed motion state at 0.5 m / s, and for a high-speed motion state at 2 m / s. The range of change constraint is determined based on the statistical characteristics of historical distance data; for example, the reasonable range of change at the current moment is the average of the most recent 10 distance measurements plus or minus three standard deviations. The parameter values ​​of the constraints are dynamically adjusted according to the motion state characteristics. For example, a gradual adjustment strategy is used during state transitions to avoid abrupt changes in constraints that could lead to abnormal distance corrections.

[0042] The correction weights for each initial relative distance are determined based on the reliability assessment results. The correction weights are positively correlated with the reliability assessment results; that is, the higher the reliability assessment result, the larger the correction weight. The correction weights are calculated using a weight mapping function, such as a linear mapping function to map the reliability assessment results to a weight range of 0-1, or a sigmoid function to achieve a non-linear mapping. The correction weights for multiple readers need to be normalized so that the sum of all weights equals 1. Normalization is performed by dividing each weight by its sum, ensuring the comparability of weight coefficients during weighted calculation. Time-related factors are also considered during weight calculation, such as smoothing by incorporating historical weight values ​​to avoid abrupt weight changes.

[0043] When determining the correction weights, a weight mapping function is used for calculation. The formula for the linear mapping function is as follows: ;in, This represents the calculated corrected weights; This indicates the reliability assessment results; and The linear mapping parameters are determined through experimental data calibration. The value range is generally from 0.8 to 1.2. The value range is generally from -0.2 to 0.2.

[0044] The nonlinear mapping uses a sigmoid function, as shown in the following formula: ;in, This represents the calculated corrected weights; This indicates the reliability assessment results; The base of the natural logarithm; This parameter represents the steepness of the curve, controlling the steepness of the function, and its value ranges from 5 to 15. The parameter representing the center point of the function controls the center of symmetry of the function, and its value typically ranges from 0.4 to 0.6. The specific values ​​of these parameters are determined through optimization using a large amount of experimental data to ensure the accuracy and rationality of the weight mapping.

[0045] The initial relative distances are weighted and adjusted based on distance variation constraints and correction weights to obtain the corrected relative distances. In practice, the effective range of each initial relative distance is first determined according to the distance variation constraints. For example, the effective interval for each distance value is calculated by combining the current initial relative distance value with the variation range determined by the constraints. Within the effective range, a weighted average of the initial relative distances is calculated based on the correction weights. The weighted average is obtained by multiplying each initial relative distance value by its corresponding correction weight and then summing the results. An iterative optimization method is used during the calculation process, such as adjusting the weight distribution through multiple iterations, to ensure that the corrected distance values ​​satisfy the constraints while maintaining the optimal weighting effect. The final corrected relative distance is used as input data for positioning calculations, ensuring the accuracy and reliability of the distance data. A comprehensive quality control mechanism is established throughout the process, including data validity checks, calculation process monitoring, and result verification, to ensure the accurate implementation of distance correction. An exception handling process is also set up. When data anomalies or calculation errors occur, a backup calculation scheme is used, such as using the moving average of historical distance values ​​as the correction result, to ensure the stable operation of the system. The distance correction frequency is kept consistent with the data acquisition frequency to ensure the real-time nature of the correction results, providing a reliable guarantee for subsequent accurate positioning. All parameters in the correction process have clear physical meaning and operability, and can be adjusted and optimized according to actual application requirements.

[0046] S6. After correcting the relative distance, the spatial coordinates of the electronic tag are calculated using a multilateral positioning algorithm. Specific implementation includes: After obtaining the corrected relative distance, a multilateral positioning 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 through pre-measured precision, and the coordinate values ​​of each reader are recorded using a three-dimensional Cartesian coordinate system, for example, using a Cartesian coordinate system to record the x, y, and z coordinates of each reader. The corrected relative distance is used as the radius parameter of the spherical equation, and the constructed spherical equation takes the form that the distance from each sphere's center to the point to be determined is equal to the corresponding radius. Specifically, for the i-th reader, its spherical equation is expressed 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 to this 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 the positioning.

[0047] Candidate spatial coordinates of the electronic tag are determined by solving for the intersection points of the equations of various spherical surfaces. The solution process employs numerical computation methods, such as using the least squares method to solve overdetermined equations or using iterative optimization algorithms to find the optimal solution. Due to measurement and computational errors, multiple spherical equations may not have precise intersection points, thus requiring the search for optimal approximate solutions. Reasonable initial values ​​are set during the solution process, such as using the geometric center of all reader coordinates as the initial iteration point, and optimization algorithms such as gradient descent or Newton's method are used to gradually approximate the optimal solution. The numerical stability of the equation system needs to be considered during the calculation process, for example, by using regularization methods to handle ill-conditioned equation systems or by employing robust estimation methods to reduce the impact of outliers. Finally, multiple possible candidate spatial coordinates are obtained, forming a set of possible locations for the electronic tag. The number of candidate coordinates depends on the solution to the equation system; for example, one, two, or more candidate solutions may be obtained.

[0048] The optimal spatial coordinates of electronic tags are selected from multiple intersection points based on the geometric relationship between each reader and the candidate spatial coordinates. Geometric relationships include evaluation indicators such as distance consistency and the rationality of angle distribution. For example, the deviation between the distance from each candidate coordinate to each reader and the corrected relative distance is calculated, and the candidate coordinate with the smallest sum of deviations is selected as the optimal coordinate. Alternatively, the angle between the lines connecting each reader and the candidate coordinates can be considered, and the candidate coordinate with the most reasonable angle distribution can be selected. The weights of the evaluation indicators are set based on the reliability assessment results, with readers having higher reliability assigned larger weights. For example, a reader with a reliability of 0.9 has a weight of 0.3, and a reader with a reliability of 0.6 has a weight of 0.2. Reasonable threshold conditions are set during the selection process, such as a maximum allowable deviation threshold of 0.5 meters. When the deviation of all candidate coordinates exceeds the threshold, recalculation or alternative solutions need to be adopted.

[0049] The optimal spatial coordinates are output as the final spatial coordinates of the electronic tag. Before output, the coordinate values ​​are verified and optimized, for example, by checking whether the coordinate values ​​are within a reasonable spatial range and whether they meet the motion state constraints. The coordinate output adopts a standard three-dimensional coordinate format, such as (x, y, z), and includes an accuracy estimate. The accuracy estimate is calculated based on factors such as the magnitude of residuals and geometric dilution accuracy during the solution process; for example, the positioning accuracy is evaluated by calculating the sum of squared residuals. The final output spatial coordinates are used for subsequent positioning applications, while historical coordinate data is saved for trajectory analysis and status monitoring. A comprehensive quality control mechanism is established throughout the positioning process, including coordinate validity checks, accuracy assessments, and anomaly handling, to ensure the accuracy and reliability of the positioning results. The implementation of the multilateral positioning algorithm considers various influencing factors in practical applications, and a systematic processing flow ensures that the positioning accuracy meets the requirements, providing technical support for the precise positioning of electronic tags.

[0050] In the embodiments, the reader / writer refers to an RFID reader / writer.

[0051] Example 2: Figure 2 A schematic diagram of an electronic tag location sensing and tracking system based on an RFID reader is provided. The system includes: The signal synchronization module is used to send a synchronization trigger signal to the electronic tag in the target area through at least two readers and to receive the response signal returned by the electronic tag synchronously through at least two readers. The parameter estimation module is used to estimate the channel coherence time parameters of the wireless channel between each reader and the electronic tag based on the response signal received by each reader. The state identification module is used to symbolize the channel coherence time parameters to obtain a symbol sequence and calculate the complexity information of the symbol sequence. Based on the complexity information, the motion state of the electronic tag is identified to obtain the motion state identification result. The reliability assessment module is used to generate reliability assessment results by analyzing the deviation between the changing trends of the channel coherence time parameter of each reader and the signal strength of its received response signal. The distance correction module is used to calculate the relative distance between the electronic tag and each reader based on the channel coherence time parameter, and to correct the relative distance according to the motion state identification results and reliability assessment results; The positioning calculation module is used to calculate the spatial coordinates of the electronic tag after correcting for the relative distance using a polygonal positioning algorithm.

[0052] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0053] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0054] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A 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, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0055] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0057] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0058] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0059] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0061] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for location sensing and tracking of electronic tags based on RFID readers, characterized in that, include: S1. At least two readers send a synchronization trigger signal to the electronic tag in the target area, and at least two readers simultaneously receive the response signal returned by the electronic tag; S2. Based on the response signal received by each reader, estimate the channel coherence time parameter of the wireless channel between each reader and the electronic tag. S3. Symbolize the channel coherence time parameters to obtain a symbol sequence and calculate the complexity information of the symbol sequence. Based on the complexity information, identify the motion state of the electronic tag to obtain the motion state identification result. S4. By analyzing the divergence between the changing trends of the channel coherence time parameter of each reader and the signal strength of the received response signal, a reliability assessment result is generated. S5. 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 identification result and the reliability assessment result; S6. After correcting the relative distance, the spatial coordinates of the electronic tag are calculated using a polygonal positioning algorithm.

2. The method for location sensing and tracking of electronic tags based on RFID readers according to claim 1, characterized in that, At least two readers send synchronization trigger signals to the electronic tags in the target area, and at least two readers synchronously receive response signals returned by the electronic tags, including: The central controller generates a synchronous trigger signal based on the same high-precision clock source; The central controller distributes the synchronization trigger signal to each reader / writer through the synchronization trigger channel to control all readers / writers to work synchronously. After receiving the synchronization trigger signal, each reader sends an RF trigger signal to the electronic tag within the same pre-set time slot; 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.

3. The method for location sensing and tracking of electronic tags based on RFID readers according to claim 1, characterized in that, Based on the response signal received by each reader, estimate the channel coherence time parameters of the wireless channel between each reader and the electronic tag, including: Based on the response signal received by each reader, the characteristics of the signal change over time are analyzed; The coherence time characteristics of the wireless channel are determined based on the changing characteristics of the signal features; The channel coherence time parameters are calculated based on the coherence time characteristics: the length of the time interval in which the signal characteristics tend to stabilize is determined, and the corresponding time interval length is quantized into the channel coherence time parameters.

4. The method for location sensing and tracking of electronic tags based on RFID readers according to claim 1, characterized in that, The channel coherence time parameters are symbolized to obtain a symbol sequence, and the complexity information of the symbol sequence is calculated. Based on the complexity information, the motion state of the electronic tag is identified to obtain the motion state identification result, including: The channel coherence time parameter sequence is compared with a preset threshold, and each parameter value is mapped to a corresponding symbol based on the comparison result to form a symbol sequence; Analyze the occurrence patterns of different symbol combinations in a symbol sequence, and calculate the complexity information of the symbol sequence based on the occurrence patterns; The complexity information is compared with a preset motion state judgment threshold, and the motion state of the electronic tag is determined based on the comparison result to obtain the motion state identification result.

5. The method for location sensing and tracking of electronic tags based on RFID readers according to claim 4, characterized in that, Analyze the occurrence patterns of different symbol combinations in a symbol sequence, and calculate the complexity information of the symbol sequence based on the occurrence patterns. This includes: statistically analyzing the occurrence frequency of symbol combinations of different lengths in the symbol sequence, and calculating the sequence complexity value based on the distribution characteristics of the occurrence frequency of each symbol combination. The complexity value is used to characterize the degree of regularity of the symbol sequence pattern.

6. The method for location sensing and tracking of electronic tags based on RFID readers according to claim 1, characterized in that, By analyzing the divergence between the changing trends of the channel coherence time parameter of each reader and the signal strength of its received response signal, a reliability assessment result is generated, including: Sequences of channel coherence time parameters and signal strength over time were obtained respectively. Analyze the degree of divergence between the changing trends of the channel coherence time parameter sequence and the changing trends of the signal strength sequence; The reliability level of each reader distance measurement is determined based on the degree of divergence. Generate corresponding reliability assessment results based on the reliability level.

7. The method for location sensing and tracking of electronic tags based on RFID readers according to claim 6, characterized in that, The analysis of the divergence between the changing trends of the channel coherence time parameter sequence and the changing trends of the signal strength sequence includes: calculating the consistency of the changing directions of the two sequences at the same time, counting the proportion of times the changing directions are inconsistent to the total number of times, and using the corresponding proportion as a quantitative indicator of the divergence.

8. The method for location sensing and tracking of electronic tags based on RFID readers according to claim 1, characterized in that, 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 identification results and reliability assessment results, including: The initial relative distance between the electronic tag and each reader is calculated based on the correspondence between the channel coherence time parameter and the propagation distance. Determine the distance change constraints corresponding to the motion state of the electronic tag based on the motion state identification results; The correction weights for each initial relative distance are determined based on the reliability assessment results; By combining distance change constraints and correction weights, the initial relative distances are weighted and adjusted to obtain the corrected relative distances. The effective range of each initial relative distance is determined according to the distance change constraints. Within the effective range, the initial relative distances are weighted and averaged according to the correction weights to obtain the corrected relative distances.

9. The method for location sensing and tracking of electronic tags based on RFID readers according to claim 1, characterized in that, After correcting for the relative distance, a multilateral positioning algorithm is used to calculate the spatial coordinates of the electronic tag, including: A spatial sphere equation is constructed with the known spatial location of each reader as the center and the corrected relative distance as the radius; The candidate spatial coordinates of the electronic tag are determined by solving the intersection points of the equations of each spatial sphere. The optimal spatial coordinates of the electronic tag are selected from multiple intersection points based on 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.

10. An electronic tag location sensing and tracking system based on an RFID reader / writer, used to implement the electronic tag location sensing and tracking method based on an RFID reader / writer as described in any one of claims 1-9, characterized in that, include: The signal synchronization module is used to send a synchronization trigger signal to the electronic tag in the target area through at least two readers and to receive the response signal returned by the electronic tag synchronously through at least two readers. The parameter estimation module is used to estimate the channel coherence time parameters of the wireless channel between each reader and the electronic tag based on the response signal received by each reader. The state identification module is used to symbolize the channel coherence time parameters to obtain a symbol sequence and calculate the complexity information of the symbol sequence. Based on the complexity information, the motion state of the electronic tag is identified to obtain the motion state identification result. The reliability assessment module is used to generate reliability assessment results by analyzing the deviation between the changing trends of the channel coherence time parameter of each reader and the signal strength of its received response signal. The distance correction module is used to calculate the relative distance between the electronic tag and each reader based on the channel coherence time parameter, and to correct the relative distance according to the motion state identification results and reliability assessment results; The positioning calculation module is used to calculate the spatial coordinates of the electronic tag after correcting for the relative distance using a polygonal positioning algorithm.

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