An RFID phase data processing method for mountain landslide monitoring

Abstract

The application provides a RFID phase data processing method for mountain landslide monitoring, and relates to the field of mountain landslide monitoring.The method comprises the following steps: collecting original phase information of passive RFID tags in monitoring points by using a reader-writer, obtaining real phase information by phase unwrapping on the original phase information, and compensating for missing phase information of the real phase information by using a data grouping fusion compensation missing phase algorithm; processing the compensated phase by using an improved Kalman filtering algorithm based on phase change rate to obtain optimal phase estimation; and fusing multi-tag and multi-antenna data by using a RFID phase data fusion algorithm to obtain higher quality phase and realize surface displacement monitoring of landslide points.The application has the beneficial effects that, for the RFID positioning scheme for mountain landslide monitoring, a series of data processing schemes are used to reduce the interference of outdoor environment on the positioning scheme, improve the positioning precision, and further enhance the reliability of the mountain landslide monitoring scheme.

Description

Technical Field

[0001] This invention relates to the field of landslide monitoring, specifically to ultra-high frequency RFID positioning, and particularly to an RFID phase data processing method for landslide monitoring. Background Technology

[0002] Landslide monitoring utilizes various technological means to monitor and provide early warnings of landslides, thereby reducing the losses caused by landslide disasters. Landslide displacement typically ranges from a few centimeters to several meters per year, and is irregular in both time and space. Sometimes, landslides exhibit catastrophic acceleration, thus requiring monitoring for early warning.

[0003] Currently, landslide monitoring is mainly limited by the high cost of existing solutions and environmental constraints, such as weather, rugged terrain, or dense vegetation. Among various remote sensing methods, interferometric synthetic aperture radar (IAPR) is commonly used for surface monitoring of large landslides; however, whether via satellite or ground stations, it suffers from poor real-time performance or excessive cost. GNSS or wireless sensor network-based landslide monitoring methods offer high real-time performance, but require a power supply, significantly increasing initial and maintenance costs. Optical lidar measurement schemes are highly sensitive to the surrounding environment; laser light is easily scattered and reflected by rain, snow, and fog, and may even be absorbed, affecting accuracy. Considering the hazards of landslides and the shortcomings of the aforementioned methods, there is an urgent need for a low-cost, environmentally adaptable, and easy-to-maintain method to better achieve landslide monitoring. Summary of the Invention

[0004] To address the problems of high cost, poor environmental adaptability, and difficulty in maintenance of existing technologies, this invention provides an RFID phase data processing method for landslide monitoring, mainly including:

[0005] S1: Use a reader to collect the original phase information of passive RFID tags in the monitoring point, use the path integral method to unwrap the original phase information to obtain the true phase information, and use a data grouping and fusion compensation algorithm to compensate for the lack of true phase information.

[0006] S2: An improved Kalman filter algorithm based on the phase change rate is used to process the compensated phase to obtain the optimal phase estimate;

[0007] S3: Utilize RFID phase data fusion algorithm to fuse tag and multi-antenna data to obtain higher quality phase and realize surface displacement monitoring at landslide points.

[0008] A storage device that stores instructions and data for implementing an RFID phase data processing method for landslide monitoring.

[0009] An RFID phase data processing device for landslide monitoring includes a processor and a storage device; the processor loads and executes instructions and data in the storage device to implement an RFID phase data processing method for landslide monitoring.

[0010] The beneficial effects of the technical solution provided by this invention are as follows: This invention performs a series of data processing steps on the phase information collected by RFID positioning technology applied to landslide monitoring. It uses a reader to collect the original phase information of passive RFID tags at monitoring points, and uses the path integral method to unwrap the phases to obtain the true phase information. Based on this, a missing phase compensation algorithm is proposed to solve the phase missing problem and ensure the integrity of the unwrapped phases. A Kalman filter algorithm based on the phase change rate suppresses noise interference generated by the outdoor environment and improves the smoothness of the phase curve. The phase data fusion algorithm reduces the data gaps between phase data and improves the reliability of the phase data. This further improves the positioning accuracy of the RFID positioning scheme for landslide monitoring and enhances the monitoring and early warning functions of the landslide monitoring scheme, demonstrating significant progress. Attached Figure Description

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0012] Figure 1 This is a flowchart of an RFID phase data processing method for landslide monitoring according to an embodiment of the present invention.

[0013] Figure 2 This is a schematic diagram of landslide monitoring in an embodiment of the present invention.

[0014] Figure 3 This is a schematic diagram of the multi-antenna multi-tag method in an embodiment of the present invention.

[0015] Figure 4 This is a schematic diagram of the hardware device working in an embodiment of the present invention. Detailed Implementation

[0016] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0017] Example 1

[0018] Please refer to Figure 1 , Figure 1 This is a flowchart of an RFID phase data processing method for landslide monitoring according to an embodiment of the present invention, specifically including:

[0019] S1: The original phase information of passive RFID tags at monitoring points is collected using a reader / writer. The original phase information is unwrapped using the path integral method to obtain the true phase information. A data grouping and fusion compensation algorithm is then used to compensate for the missing true phase information. This algorithm solves the phase loss problem caused by insufficient phase signal data during signal sampling, correcting the unwrapped phase after the data gaps to restore the true phase curve and improve positioning accuracy. Positioning systems based on Passive Radio Frequency Identification (RFID) are easy to install, have low maintenance costs, provide dense measurements in both time and space, have low sensitivity to obstacles, and exhibit good weather robustness. Therefore, RFID positioning technology has significant development potential in the field of landslide monitoring. Furthermore, due to its advantages such as fine granularity and low susceptibility to interference, RFID phase information-based positioning technology has become a research hotspot in recent years.

[0020] S2: An improved Kalman filter algorithm based on the phase change rate is used to process the compensated phase; the improved Kalman filter algorithm based on the phase change rate improves the prediction equation in the Kalman filter algorithm based on the phase change rate, and uses the improved Kalman filter algorithm to filter the observed phase to suppress noise interference, thereby obtaining a smoother phase curve.

[0021] S3: The RFID phase data fusion algorithm is used to fuse tag and multi-antenna data after processing to obtain higher quality phase data, enabling surface displacement monitoring at landslide points. The RFID phase data fusion algorithm first fuses the dual-antenna wrapped phase data of a single tag through complex superposition and averaging. Further, it fuses the dual-antenna wrapped phase data of multiple tags at the same monitoring point through complex superposition and averaging. This improves the continuity and robustness of the resulting phase data, thereby enhancing the positioning accuracy of the monitoring scheme.

[0022] like Figure 1 As shown, this invention proposes an RFID phase data processing method for landslide monitoring. First, a missing phase compensation algorithm is proposed to solve the phase missing problem. Then, a Kalman filter algorithm based on the phase change rate is proposed to suppress noise interference caused by the outdoor environment. Finally, a phase data fusion algorithm is proposed to reduce data gaps and further improve the reliability of phase data.

[0023] Preferred embodiments: such as Figure 2As shown, RFID passive tags are bound to fiberglass rods, which are then deeply inserted into the soil of the mountain as monitoring points. Multiple RFID passive tags are set at each monitoring point. An RFID antenna is bound to a relatively stable location at the top of the landslide and connected to a reader. The host computer collects the phase information of the RFID passive tags collected by the reader. After a series of data processing steps, the tags are located, and finally, the surface displacement of the landslide point is monitored.

[0024] In the section on missing phase compensation algorithms, it's crucial to first mention the prerequisite for phase unwrapping: the signal acquisition rate must satisfy the Nyquist sampling theorem, meaning the acquisition frequency of the phase signal must be more than twice the highest frequency of the signal. Only by meeting this condition can the correctness and accuracy of the obtained unwrapped phase sequence be guaranteed. However, in actual signal sampling environments, RFID phase measurements may suffer from phase loss due to insufficient acquired phase signal data.

[0025] In RFID positioning scenarios for landslide monitoring, when a landslide occurs, the displacement characteristics of multiple tags often exhibit consistency. Therefore, to address the issue of missing phase data in landslide monitoring, a proposed approach is to group the tag phase data and fuse them into a reference phase sequence to compensate for the missing phases. The method involves grouping tags belonging to the same area together. If a tag experiences rapid displacement resulting in insufficient phase data for unwrapping, the phase data of that tag group is merged to create a reference phase sequence. Let... For the phase sequence of label t, by utilizing average rate of change To obtain the reference phase sequence fused from the tag group

[0026]

[0027] n t This represents the number of tags in the group. Reference phase sequence The derivative with respect to time, by... Integrating over time yields the reference phase sequence. Let the path integral method be U, then the phase unwrapping process is as follows:

[0028]

[0029] in The untangling phase is obtained by referring to the phase sequence. The measured phase that has a missing phase label.

[0030] In the Kalman filter algorithm section, it's important to first mention that in outdoor environments, the phase data collected by the reader contains significant noise, including multipath effects and random errors caused by thermal noise in the reader's receiving circuit. Because the effective phase signal and noise may overlap in the frequency spectrum, traditional filtering methods like low-pass filters are insufficient to filter out random interference signals. Therefore, the prediction equation in the Kalman filter algorithm is improved based on the phase change rate. Specifically, the prediction equation predicts the current phase value based on the best estimate of the phase from the previous moment and appropriate weights of the phase differences from multiple previous moments. The mathematical expression of the improved prediction equation in the improved Kalman filter algorithm is shown below:

[0031]

[0032] in, Let be the predicted value of the phase at time i. The optimal phase estimates for the first n time steps, k1, k2, k3, ..., k n-1 The phase difference is used as the weight. The phase values ​​from the first three time steps are selected for preprocessing. The specific design of the improved Kalman filter algorithm is as follows:

[0033] The process of initializing parameters and setting initial values ​​is as follows:

[0034] (1) Set the first three phase estimates to be: Set the weights of the phase difference to k1 and k2, and initialize the state observation matrix H, process noise covariance matrix Q, and measurement noise covariance matrix R.

[0035] (2) Calculate the predicted value of the phase at time i.

[0036] Based on the optimal phase estimate at time i-1 Optimal phase estimate at time i-2 Optimal phase estimate at time i-3 And with the corresponding weights, the phase prediction value at time i is obtained.

[0037]

[0038] Furthermore, the phase prediction value of the prior estimate of the covariance at time i is calculated.

[0039]

[0040] Furthermore, calculate the Kalman gain K at the current time. i :

[0041]

[0042] Furthermore, based on the phase prediction value at time i... Phase observations and Kalman gain K i The optimal phase estimate of the phase at time i is calculated.

[0043]

[0044] To reduce the prediction error of the optimal phase estimate, the phase estimate of the covariance prior estimate at time i is calculated as follows:

[0045]

[0046] in, This represents the prediction error. When the prediction error is within the error threshold range, the optimal phase estimate is the final optimal phase estimate.

[0047] Thus, the design of the improved Kalman filter algorithm based on the phase change rate is completed, which can effectively filter out noise errors that interfere with RFID phase information in the mountain environment.

[0048] In the phase data fusion algorithm section, it is important to first mention that when using RFID positioning technology to monitor landslides, the tag phase is easily affected by various factors. When there are large gaps in the phase data, the phase unwrapping of that segment of phase data will fail, resulting in phase loss and affecting the final positioning accuracy.

[0049] like Figure 3 As shown, a phase data fusion method using multiple antennas and multiple tags can be used to obtain better wrapped phase, with one tag corresponding to two antennas. When two antennas that are close together measure the phase of the same tag, their phase changes are usually very similar, so a complex superposition and averaging operation can be performed. Similarly, multiple tags that are close together within a single monitoring point and are all located on the same support usually have very similar phase changes, so a complex superposition and averaging operation can also be performed. The specific implementation is as follows.

[0050] Define a threshold σ, and the best phase estimate measured by antenna 1 at time t1 is... The optimal phase estimate measured by antenna 2 at time t2 is If the following relationship is satisfied between two moments:

[0051] |t1-t2|≤σ (9)

[0052] The two phase values ​​are fused using a complex summation and averaging operation:

[0053]

[0054] in, For multi-antenna phase fusion, z1 represents the complex form of the best phase estimate measured by antenna 1 at time t1, and z2 represents the complex form of the best phase estimate measured by antenna 2 at time t2. Phase fusion is performed in the complex form. If the phase data of one antenna contains a time t1, but the phase data of the other antenna does not contain a time t2 that satisfies equation (9), then the phase value at time t1 is directly added to the fused phase sequence.

[0055] After fusing the dual-antenna phase data of a single tag, the tag phase data from the same monitoring point are further subjected to complex superposition and averaging. Then, the fused phase sequence from that monitoring point is unwrapped to obtain the tag's radial displacement. This process fuses the available phase data of each tag to obtain a displacement index for monitoring the tag's radial displacement.

[0056] Example 2

[0057] An RFID phase data processing device 401 for landslide monitoring, such as Figure 4 As shown, it includes: a processor 402 and a storage device 403; the processor 402 loads and executes the instructions and data in the storage device 403 to implement an RFID phase data processing method for landslide monitoring.

[0058] Example 3

[0059] A storage device that stores instructions and data for implementing an RFID phase data processing method for landslide monitoring.

[0060] The above description is only a preferred embodiment of the present invention and is 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 RFID phase data processing method for mountain landslide monitoring, characterized in that: Comprise: S1: Collecting raw phase information of passive RFID tags in monitoring points by using a reader-writer, using path integral method to obtain real phase information by phase unwrapping the raw phase information, and using data grouping fusion compensation missing phase algorithm to compensate for the missing of the real phase information; The specific steps of step S1 are: The tags belonging to the same region are grouped, and in the case that there is insufficient phase data to solve the rapid displacement of the tags, the phase data of the tag group of the region is combined to synthesize a reference phase sequence; the derivative of the reference phase sequence with respect to time is set as the phase sequence of the tag , and the average change rate of the phase sequence is used to derive the reference phase sequence fused by the tag group . ​​​ (1) wherein, is the number of labels in the set; By integrating over time, a sequence of reference phases is obtained By integrating over time, a sequence of reference phases is obtained ; Let the path integral method be U, then the phase unwrapping process is as follows: (2) wherein, the unwrapped phase resulting from guiding the reference phase sequence, the measured phase tagged with an occurrence of a phase gap; S2: An improved Kalman filtering algorithm based on phase rate is used to process the compensated phase to obtain the best phase estimate value; In step S2, the improved Kalman filtering algorithm based on phase rate includes prediction equation improvement, initialization parameter and initial value setting, and Kalman gain calculation, and the prediction equation is to predict the phase value at the current time according to the best estimate value of the phase at the previous time and the appropriate weight of the phase difference value at the previous multiple times: S2.1: The improved prediction equation is: (3) wherein, is the first i is the predicted value of the phase at the instant, is the best estimate of the phase at the instant, is the weight of the phase difference; S2.2: The process of initialization parameter and initial value setting is: (1) Set the first three phase estimation values as 1, 2, 3, set the weight of the phase difference value as k 1, k 2, initialize the state observation matrix H , the process noise covariance matrix Q , the measurement noise covariance matrix R ; (2) calculating the predicted value of the time instant phase i ​ According to the first i -1st optimal phase estimate , the second i -2nd optimal phase estimate , the third i -3rd optimal phase estimate and the corresponding weights, the phase prediction value at the 4th time point is obtained i :​ (4) S2.3: According to the first i phase prediction value of the moment , the Kalman gain of the current moment is calculated : (5) According to the first i phase prediction value at the time t , the phase observation value and the Kalman gain K i get the optimal phase estimation value at the time t i :​ (6) At this point, the improved Kalman filtering algorithm based on phase rate is designed to filter out the noise error that interferes with the RFID phase information in the mountain environment; S3: Using RFID phase data fusion algorithm to fuse the best phase estimate value of multiple labels and multiple antennas to obtain higher quality phase and realize the ground displacement monitoring of landslide points; In step S3, the RFID phase data fusion algorithm fuses the double-antenna phase data of a single label through complex superposition averaging: Define threshold Antenna 1 in t The optimal phase estimate at time 1 is Antenna 2 in t The optimal phase estimate at time 2 is If the following relationship is satisfied at two moments: (8) Then the two phase values are fused through complex superposition averaging operation: (9) wherein, is the multi-antenna fused phase; z 1 represents the complex form of the optimal phase estimate of antenna 1 at t 1 time instant, z 2 represents the complex form of the optimal phase estimate of antenna 2 at t 2 time instant; If there is a time t 1 in the phase data of one antenna and there is no time t 2 in the phase data of the other antenna that satisfies equation (8), then the phase value at time t 1 is directly added to the fused phase sequence.

2. The RFID phase data processing method for landslide monitoring of mountains as claimed in claim 1, wherein: No. i The formula for calculating the phase prediction value of the prior estimate of the time covariance is as follows: (7) wherein A is the state transition matrix from i -1 to i -1.

3. The RFID phase data processing method for landslide monitoring of mountains as claimed in claim 1 wherein: In step S3, after fusing the double-antenna phase data of a single label, the label phase data from the same monitoring point is further subjected to complex superposition averaging operation, and then the double-antenna fusion data of multiple labels is fused, and then the fusion phase sequence of the monitoring point is subjected to phase unwrapping, so as to obtain the radial displacement of the label; This process fuses the available phase data of each label to obtain a displacement index for monitoring the radial displacement of the label.

4. A storage device, characterized by: The storage device stores instructions and data for realizing the RFID phase data processing method for mountain landslide monitoring according to any one of claims 1-3.

5. An RFID phase data processing device for landslide monitoring, characterized by: Comprise: A processor and a storage device; the processor loads and executes the instructions and data in the storage device to realize the RFID phase data processing method for mountain landslide monitoring according to any one of claims 1-3.

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