Building Deformation Monitoring Method Based on Intelligent Metasurface-Assisted Sensing Technology

Abstract

This application belongs to the field of integrated communication and sensing technology, and provides a building deformation monitoring method based on intelligent metasurface-assisted integrated communication and sensing technology. The method includes: deploying multiple Reflection Arrays (RIS) on the building surface, acquiring the reflected signals modulated by the RIS using a transceiver, suppressing background multipath interference through phase switching of adjacent time slices and differential processing, extracting relevant path signals of the RIS, obtaining angle, delay, and amplitude parameters based on a channel estimation algorithm, constructing a positioning equation, and using weighted least squares method to solve for the current spatial coordinates of each RIS, then comparing these coordinates with the initial coordinates to calculate the building rotation angle, thereby achieving high-precision monitoring of building attitude changes. This application utilizes RIS phase modulation and differential processing to suppress multipath interference, combined with weighted least squares positioning to achieve high-precision attitude estimation, and has the advantages of strong anti-interference capability, high positioning accuracy, and high adaptability.

Description

Technical Field

[0001] This application belongs to the field of integrated communication and sensing technology, and in particular relates to a method for monitoring building deformation based on intelligent metasurface-assisted integrated communication and sensing technology. Background Technology

[0002] With the increasing number of high-rise and complex-structure buildings, these structures may tilt or rotate under wind loads, earthquakes, and foundation settlement, impacting structural safety and operational reliability. Therefore, high-precision, continuous monitoring of building attitude changes is crucial. Commonly used methods include total station measurements, GNSS positioning, tilt sensor monitoring, and deformation detection methods based on vision or lidar, acquiring displacement or angular change information of the building through the deployment of sensors or external measuring equipment.

[0003] However, existing methods are susceptible to environmental obstruction, electromagnetic interference, or multipath effects, and the equipment deployment is complex and costly, making it difficult to achieve high-precision and stable building rotation monitoring in complex urban environments. Summary of the Invention

[0004] This application provides a building deformation monitoring method based on intelligent metasurface-assisted integrated sensing technology, which can solve the above-mentioned problems.

[0005] In a first aspect, embodiments of this application provide a building deformation monitoring method based on intelligent metasurface-assisted sensing integration technology, comprising: S1, installing multiple intelligent metasurface RIS on the building surface, and establishing a global coordinate system based on the initial spatial coordinates of each RIS and a communication transceiver; the communication transceiver and the RIS form a line-of-sight (LOS) path; the communication transceiver includes at least one transmitter and at least three receivers; S2, designing the phase mode of the RIS based on the initial spatial coordinates, and controlling the RIS to switch to multiple phase states in adjacent time slices, so that the transmitter-RIS-receiver path signal formed by the RIS reflection in adjacent time slices produces a distinguishable phase change, and the signal received by each receiver in adjacent time slices is analyzed. S2 is performed by differential processing to suppress multipath interference from the environmental background unrelated to the RIS and extract the target path signal corresponding to the RIS; S3 is performed by processing the target path signal according to a preset channel estimation algorithm to obtain channel parameters such as angle and time delay; S4 is performed by constructing a positioning equation based on the channel parameters corresponding to multiple receivers and scene geometric constraints, and solving the positioning equation using the weighted least squares method to obtain the spatial coordinates of each RIS at the current monitoring time; S5 is performed by comparing the spatial coordinates of each RIS at the current monitoring time with the initial spatial coordinates of each RIS, calculating the rotation angle of the building, and repeating steps S2 to S5 at preset time intervals to form a long-term monitoring closed loop of building deformation based on the spatial position change of the RIS.

[0006] In one possible implementation of the first aspect, the antenna arrays of the transmitter and receiver are standard uniform planar arrays (UPA) or standard uniform linear arrays (ULA), and are orthogonal frequency division multiplexing (OFDM) systems.

[0007] Optionally, in another possible implementation of the first aspect, in step S2 above, the phase mode of the RIS is designed based on the initial spatial coordinates. By controlling the RIS to switch to multiple phase states in adjacent time slices, the transmitter-RIS-receiver path signal formed by the reflection of the RIS in adjacent time slices produces a distinguishable phase change. Differential processing is performed on the signals received by each receiver in adjacent time slices to suppress environmental background multipath interference unrelated to the RIS and extract the target path signal corresponding to the RIS. Specifically, this includes:

[0008] Based on the initial spatial coordinates corresponding to the m-th RIS unit, calculate the phase response of the incident direction from the transmitter to the m-th RIS unit. Phase response of the m-th RIS unit to the receiver output direction ;

[0009] Phase response based on the incident direction from the transmitter to the m-th RIS unit Phase response of the m-th RIS unit to the receiver output direction Determine the optimal phase of the m-th RIS unit at adjacent times i and i-1. and = ;

[0010] Based on the optimal phase of the m-th RIS unit at adjacent times i and i-1, construct the composite phase configuration matrix for adjacent times i and i-1 respectively. and ,in, Represents a diagonal matrix. This represents the complex representation of the phase shift applied by the Mth RIS unit at time i. The composite phase configuration matrix is ​​used to control the RIS to alternately load different phase states in adjacent time slices.

[0011] The receiver receives signals at adjacent times i and i-1 respectively. and , respectively represented as:

[0012]

[0013]

[0014] in, The channel representing the RIS contribution at adjacent time i. The non-RIS contribution channel at adjacent time i includes line-of-sight (LOS) and non-line-of-sight (NLOS) channels; The channel representing the RIS contribution at adjacent time i-1, This represents the non-RIS contribution channel at adjacent time i-1; Define the beamforming vector, where x is the transmitted signal. and This represents additive white Gaussian noise, whose elements are independent and all obey zero mean and variance. Gaussian distribution;

[0015] For signal and Perform differential analysis to obtain the differential signal. :

[0016]

[0017]

[0018] And in conjunction with the definition:

[0019]

[0020]

[0021]

[0022] Obtain the target path signal .

[0023] Optionally, in another possible implementation of the first aspect, step S3 above involves processing the target path signal according to a preset channel estimation algorithm to obtain channel parameters such as angle and time delay, specifically including:

[0024] target path signal This can be represented by the following parameterized model:

[0025]

[0026] in, Indicates path loss. Indicates propagation delay; the system has There are subcarriers, with a subcarrier frequency spacing of . The phase difference between adjacent subcarriers is The complex amplitude ratio of adjacent subcarriers is expressed as steering vector in the subcarrier domain for:

[0027] ;

[0028] These represent the receiver UPA steering vector, the RIS transmit steering vector, the RIS receive steering vector, and the transmitter UPA steering vector, respectively. This indicates the azimuth angle of the signal incident at the receiver UPA. This indicates the signal incident elevation angle of the receiver UPA. This indicates the direction angle of the emitted signal from the RIS array element. This indicates the elevation angle of the signal emitted by the RIS array element. Indicates the direction angle of the signal received by the RIS array element. This indicates the elevation angle of the signal received by the RIS array element. This indicates the signal transmission azimuth angle of the transmitter UPA. This indicates the signal transmission elevation angle of the transmitter UPA. Indicates time The phase configuration matrix of the time RIS, let ,Will Defined as:

[0029]

[0030] in, This represents the guide vector in the vertical direction of the UPA. , This represents the guide vector in the horizontal direction of the UPA. , This indicates the number of cells in the vertical direction of the UPA. Indicates the number of units in the horizontal direction of the UPA;

[0031] Define scalar for , convert target path signal for:

[0032]

[0033] definition , convert target path signal for:

[0034]

[0035] definition Rewrite the target path signal The standard model form of the NOMP algorithm:

[0036]

[0037] Where L represents the number of wireless signal paths, This represents zero-mean Gaussian complex Gaussian noise;

[0038] Using the NOMP algorithm from The channel parameters for obtaining angle and time delay are: .

[0039] Optionally, in another possible implementation of the first aspect, step S4 above involves constructing a positioning equation based on the channel parameters and scene geometric constraints corresponding to multiple receivers, and solving the positioning equation using the weighted least squares method to obtain the spatial coordinates of each RIS at the current monitoring time. Specifically, this includes:

[0040] S41. Define the propagation length of the signal received by the nth receiver as... :

[0041]

[0042] in, , The speed of electromagnetic wave propagation. The propagation delay of the signal received by the nth receiver is given by... For transmitter coordinates, Let n be the coordinates of the receiver. RIS coordinates;

[0043] Define the difference in the propagation length of the signal received by the nth receiver and the 1st receiver as . :

[0044] ;

[0045] For any RIS, integration from The channel parameters of each receiver form the positioning observation vector. :

[0046]

[0047] in, , They represent the first The azimuth and elevation angles obtained from each receiver;

[0048] S42. Constructing RIS coordinates based on scene geometric constraints The positioning equation:

[0049]

[0050] Among them, the observation vector sum coefficient matrix Its structure is as follows:

[0051]

[0052]

[0053] in, It is the transpose matrix. , , , Let each represent a coefficient in the constructed equation. , , and They are defined as follows:

[0054]

[0055]

[0056]

[0057]

[0058] in, Let be the unit direction vector from the first receiver to the RIS. and A constraint vector defined by an angle;

[0059] S43. Define the positioning observation vector. The estimation error is It follows a mean of zero and a covariance matrix of... The Gaussian distribution, that is:

[0060]

[0061] Wherein, the covariance matrix It is a diagonal matrix, and its diagonal elements are determined by the Cramer-Rao lower bound (CRLB) of each channel parameter under a specific signal-to-noise ratio condition;

[0062] The observation error vector in the positioning equation Approximately expressed as estimation error Linear functions:

[0063]

[0064] Where B is the Jacobian matrix for error propagation;

[0065] Based on estimation error A linear function, calculating the weight matrix required for weighted least squares. , which is the error The inverse of the covariance matrix:

[0066] ;

[0067] in, This represents the mathematical expectation operation;

[0068] S44. Using the weight matrix The coordinates of RIS are solved by weighted least squares method. ,Right now The estimated value :

[0069] ;

[0070] S45. The transmit beam and RIS phase configuration are controlled through a time-division multiplexed frame structure to coordinate the measurement of the positions of multiple RIS; the frame structure includes a pilot band, a RIS phase configuration section, a channel estimation section, and a data processing section; in the RIS phase configuration section, one of the following two modes is used for operation:

[0071] Single beam mode: Control the transmit beam to be aligned with each RIS in sequence, and make the beam of that RIS aligned with each receiver in sequence for time division measurement;

[0072] Multi-beam mode: Controls the transmit beam to be aligned sequentially with each RIS, and enables that RIS to simultaneously generate multiple beams covering all receivers for concurrent measurement;

[0073] S46. Repeat steps S41 to S44 for all RIS and output the spatial coordinates of each RIS at the current monitoring time.

[0074] Optionally, in another possible implementation of the first aspect, step S5 above compares the spatial coordinates of each RIS at the current monitoring time with the initial spatial coordinates of each RIS to calculate the building's rotation angle, specifically including:

[0075] Obtain the initial spatial coordinate set of b RIS. And the set of spatial coordinates for the current monitoring time obtained through step S4. ;

[0076] definition Let be a rotation matrix. The rotation angle is... Using the translation vector, we obtain the rotation-translation observation model for the point set:

[0077]

[0078] Subtracting the transpose of the rotation-translation observation model of the point set yields the rotation constraint equations for the relative displacement:

[0079]

[0080] The rotation constraint equations of relative displacement are written in matrix form to obtain the matrix form of the rotation constraint equations of relative displacement, and the matrix to be estimated is defined. ,get ,in:

[0081]

[0082] Vectorizing the rotation constraint equations of relative displacement in matrix form yields the standard linear form that can be directly solved by the weighted least squares algorithm:

[0083]

[0084] in, Represents a vectorized operator. Represents the identity matrix;

[0085] The weighted least squares estimation of the rotation matrix parameters can be obtained by using the weighted least squares algorithm to estimate the rotation constraint equations of the relative displacements in vectorized matrix form.

[0086]

[0087] Wherein, N is the weight matrix, which is constructed using the lower bound of the position error PEB of each RIS coordinate;

[0088] right Reorganize to obtain a 3×3 matrix. Then transpose it to get According to the rotation angle With rotation matrix Relationship

[0089]

[0090] get , , Thus, the rotation angle ϵ of the building is obtained.

[0091] Beneficial Effects: This application's method deploys multiple Reflection Arrays (RIS) on the building surface and combines them with a communication transceiver system to accurately extract and estimate the parameters of the RIS reflection path signals, thereby achieving high-precision inversion of the building's rotation angle. Compared to traditional monitoring methods based on GNSS, total stations, or tilt sensors, this application does not rely on complex external measurement equipment or large-scale sensor deployment, resulting in a simpler system structure and greater deployment flexibility. By designing the RIS to switch different phase states within adjacent time slices and performing differential processing on the received signals, multipath interference from the environmental background is effectively suppressed, improving the signal-to-noise ratio of the target path signal and the accuracy of parameter estimation. A weighted least squares algorithm is introduced in the positioning stage, and a weight matrix is ​​constructed based on the error covariance matrix to achieve optimal fusion of observation information from multiple receivers, further improving the accuracy and stability of spatial coordinate estimation. Finally, the building's rotation angle is calculated by comparing the coordinates of multiple RIS, enabling continuous, real-time, and non-contact attitude monitoring. This method has the advantages of strong anti-interference capability, high positioning accuracy, and applicability to complex electromagnetic and urban environments. Attached Figure Description

[0092] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0093] Figure 1 This is a schematic flowchart of a building deformation monitoring method based on intelligent metasurface-assisted synesthesia technology provided in an embodiment of this application;

[0094] Figure 2This is a schematic diagram of a building deformation monitoring method based on intelligent metasurface-assisted sensing integration technology provided in an embodiment of this application;

[0095] Figure 3 This is a schematic diagram of the single-beam mode RIS frame structure of a building deformation monitoring method based on intelligent metasurface-assisted synsensory integration technology provided in an embodiment of this application;

[0096] Figure 4 This is a schematic diagram of the multi-beam mode RIS frame structure of a building deformation monitoring method based on intelligent metasurface-assisted synesthesia integration technology provided in an embodiment of this application;

[0097] Figure 5 This is a measurement RIS coordinate simulation accuracy diagram of a building deformation monitoring method based on intelligent metasurface-assisted sensing integration technology provided in an embodiment of this application;

[0098] Figure 6 This is a simulation accuracy diagram of the building rotation angle measurement method based on intelligent metasurface-assisted integrated sensing technology provided in an embodiment of this application. Detailed Implementation

[0099] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0100] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0101] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0102] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0103] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0104] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0105] The following is a detailed description of a building deformation monitoring method based on intelligent metasurface-assisted integrated sensing technology provided in this application, with reference to the accompanying drawings.

[0106] Figure 1 The illustration shows a flowchart of a building deformation monitoring method based on intelligent metasurface-assisted sensing integration technology provided in this application embodiment.

[0107] like Figure 1 As shown, the building deformation monitoring method based on intelligent metasurface-assisted integrated sensing technology includes the following steps:

[0108] S1. Install multiple intelligent metasurfaces (RIS) on the building surface and establish a global coordinate system based on the initial spatial coordinates of each RIS and the communication transceiver; the communication transceiver and the RIS form a line-of-sight (LOS) path; the communication transceiver includes at least one transmitter and at least three receivers;

[0109] It should be noted that the antenna arrays of the transmitter and receiver mentioned above adopt the standard uniform planar array UPA or the standard uniform linear array ULA, and adopt the orthogonal frequency division multiplexing (OFDM) system.

[0110] S2. Design the phase mode of RIS based on the initial spatial coordinates. By controlling RIS to switch to multiple phase states in adjacent time slots, the transmitter-RIS-receiver path signal formed by RIS reflection in adjacent time slots will produce distinguishable phase changes. Differential processing will be performed on the signals received by each receiver in adjacent time slots to suppress environmental background multipath interference unrelated to RIS and extract the target path signal corresponding to RIS.

[0111] Furthermore, in this embodiment of the application, step S2 includes:

[0112] Based on the initial spatial coordinates corresponding to the m-th RIS unit, calculate the phase response of the incident direction from the transmitter to the m-th RIS unit. Phase response of the m-th RIS unit to the receiver output direction ;

[0113] Phase response based on the incident direction from the transmitter to the m-th RIS unit Phase response of the m-th RIS unit to the receiver output direction Determine the optimal phase of the m-th RIS unit at adjacent times i and i-1. and = ;

[0114] Based on the optimal phase of the m-th RIS unit at adjacent times i and i-1, construct the composite phase configuration matrix for adjacent times i and i-1 respectively. and ,in, Represents a diagonal matrix. This represents the complex representation of the phase shift applied by the Mth RIS unit at time i. The composite phase configuration matrix is ​​used to control the RIS to alternately load different phase states in adjacent time slices.

[0115] The receiver receives signals at adjacent times i and i-1 respectively. and , respectively represented as:

[0116]

[0117]

[0118] in, The channel representing the RIS contribution at adjacent time i. The non-RIS contribution channel at adjacent time i includes line-of-sight (LOS) and non-line-of-sight (NLOS) channels; The channel representing the RIS contribution at adjacent time i-1, This represents the non-RIS contribution channel at adjacent time i-1; Define the beamforming vector, where x is the transmitted signal. and This represents additive white Gaussian noise, whose elements are independent and all obey zero mean and variance. Gaussian distribution;

[0119] For signal and Perform differential analysis to obtain the differential signal. :

[0120]

[0121]

[0122] And in conjunction with the definition:

[0123]

[0124]

[0125]

[0126] Obtain the target path signal .

[0127] S3. Process the target path signal according to the preset channel estimation algorithm to obtain the channel parameters of angle and time delay;

[0128] Furthermore, in this embodiment of the application, step S3 includes:

[0129] target path signal This can be represented by the following parameterized model:

[0130]

[0131] in, Indicates path loss. Indicates propagation delay; the system has There are subcarriers, with a subcarrier frequency spacing of . The phase difference between adjacent subcarriers is The complex amplitude ratio of adjacent subcarriers is expressed as steering vector in the subcarrier domain for:

[0132] ;

[0133] These represent the receiver UPA steering vector, the RIS transmit steering vector, the RIS receive steering vector, and the transmitter UPA steering vector, respectively. This indicates the azimuth angle of the signal incident at the receiver UPA. This indicates the signal incident elevation angle of the receiver UPA. This indicates the direction angle of the emitted signal from the RIS array element. This indicates the elevation angle of the signal emitted by the RIS array element. Indicates the direction angle of the signal received by the RIS array element. This indicates the elevation angle of the signal received by the RIS array element. This indicates the signal transmission azimuth angle of the transmitter UPA. This indicates the signal transmission elevation angle of the transmitter UPA. Indicates time The phase configuration matrix of the time RIS, let ,Will Defined as:

[0134]

[0135] in, This represents the guide vector in the vertical direction of the UPA. , This represents the guide vector in the horizontal direction of the UPA. , This indicates the number of cells in the vertical direction of the UPA. Indicates the number of units in the horizontal direction of the UPA;

[0136] Define scalar for , convert target path signal for:

[0137]

[0138] definition , convert target path signal for:

[0139]

[0140] definition Rewrite the target path signal The standard model form of the NOMP algorithm:

[0141]

[0142] Where L represents the number of wireless signal paths, This represents zero-mean Gaussian complex Gaussian noise;

[0143] Using the NOMP algorithm from The channel parameters for obtaining angle and time delay are: .

[0144] S4. Based on the channel parameters and scene geometric constraints corresponding to multiple receivers, a positioning equation is constructed, and the weighted least squares method is used to solve the positioning equation to obtain the spatial coordinates of each RIS at the current monitoring time.

[0145] Furthermore, in this embodiment of the application, step S4 includes:

[0146] S41. Define the propagation length of the signal received by the nth receiver as... :

[0147]

[0148] in, , The speed of electromagnetic wave propagation. The propagation delay of the signal received by the nth receiver is given by... For transmitter coordinates, Let n be the coordinates of the receiver. RIS coordinates;

[0149] Define the difference in the propagation length of the signal received by the nth receiver and the 1st receiver as . :

[0150] ;

[0151] For any RIS, integration from The channel parameters of each receiver form the positioning observation vector. :

[0152]

[0153] in, , They represent the first The azimuth and elevation angles obtained from each receiver;

[0154] S42. Constructing RIS coordinates based on scene geometric constraints The positioning equation:

[0155]

[0156] Among them, the observation vector sum coefficient matrix Its structure is as follows:

[0157]

[0158]

[0159] in, It is the transpose matrix. , , , Let each represent a coefficient in the constructed equation. , , and They are defined as follows:

[0160]

[0161]

[0162]

[0163]

[0164] in, Let be the unit direction vector from the first receiver to the RIS. and A constraint vector defined by an angle;

[0165] S43. Define the positioning observation vector. The estimation error is It follows a mean of zero and a covariance matrix of... The Gaussian distribution, that is:

[0166]

[0167] Wherein, the covariance matrix It is a diagonal matrix, and its diagonal elements are determined by the Cramer-Rao lower bound (CRLB) of each channel parameter under a specific signal-to-noise ratio condition;

[0168] The observation error vector in the positioning equation Approximately expressed as estimation error Linear functions:

[0169]

[0170] Where B is the Jacobian matrix for error propagation;

[0171] Based on estimation error A linear function, calculating the weight matrix required for weighted least squares. , which is the error The inverse of the covariance matrix:

[0172] ;

[0173] in, This represents the mathematical expectation operation;

[0174] S44. Using the weight matrix The coordinates of RIS are solved by weighted least squares method. ,Right now The estimated value :

[0175] ;

[0176] S45. The transmit beam and RIS phase configuration are controlled through a time-division multiplexed frame structure to coordinate the measurement of the positions of multiple RIS; the frame structure includes a pilot band, a RIS phase configuration section, a channel estimation section, and a data processing section; in the RIS phase configuration section, one of the following two modes is used for operation:

[0177] Single beam mode: Control the transmit beam to be aligned with each RIS in sequence, and make the beam of that RIS aligned with each receiver in sequence for time division measurement;

[0178] Multi-beam mode: Controls the transmit beam to be aligned sequentially with each RIS, and enables that RIS to simultaneously generate multiple beams covering all receivers for concurrent measurement;

[0179] S46. Repeat steps S41 to S44 for all RIS and output the spatial coordinates of each RIS at the current monitoring time.

[0180] S5. Compare the spatial coordinates of each RIS at the current monitoring time with the initial spatial coordinates of each RIS, calculate the rotation angle of the building, and repeat steps S2 to S5 at preset time intervals to form a long-term monitoring closed loop of building deformation based on the spatial position change of RIS.

[0181] Furthermore, in this embodiment of the application, step S5 includes:

[0182] Obtain the initial spatial coordinate set of b RIS. And the set of spatial coordinates for the current monitoring time obtained through step S4. ;

[0183] definition Let be a rotation matrix. The rotation angle is... Using the translation vector, we obtain the rotation-translation observation model for the point set:

[0184]

[0185] Subtracting the transpose of the rotation-translation observation model of the point set yields the rotation constraint equations for the relative displacement:

[0186]

[0187] The rotation constraint equations of relative displacement are written in matrix form to obtain the matrix form of the rotation constraint equations of relative displacement, and the matrix to be estimated is defined. ,get ,in:

[0188]

[0189] Vectorizing the rotation constraint equations of relative displacement in matrix form yields the standard linear form that can be directly solved by the weighted least squares algorithm:

[0190]

[0191] in, Represents a vectorized operator. Represents the identity matrix;

[0192] The weighted least squares estimation of the rotation matrix parameters can be obtained by using the weighted least squares algorithm to estimate the rotation constraint equations of the relative displacements in vectorized matrix form.

[0193]

[0194] Wherein, N is the weight matrix, which is constructed using the lower bound of the position error PEB of each RIS coordinate;

[0195] right Reorganize to obtain a 3×3 matrix. Then transpose it to get According to the rotation angle With rotation matrix Relationship

[0196]

[0197] get , , Thus, the rotation angle ϵ of the building is obtained.

[0198] This application provides a building deformation monitoring method based on intelligent metasurface-assisted sensing integration technology. Multiple Reference Indicators (RIS) are deployed on the building surface, and transceivers acquire reflected signals modulated by the RIS. Background multipath interference is suppressed through phase switching of adjacent time slices and differential processing. Corresponding path signals of the RIS are extracted, and angle, delay, and amplitude parameters are obtained based on a channel estimation algorithm. A positioning equation is constructed, and the current spatial coordinates of each RIS are solved using weighted least squares method. These coordinates are then compared with the initial coordinates to calculate the building's rotation angle, achieving high-precision monitoring of building attitude changes. This application utilizes RIS phase modulation and differential processing to suppress multipath interference, combined with weighted least squares positioning to achieve high-precision attitude estimation, offering advantages such as strong anti-interference capability, high positioning accuracy, and high adaptability.

[0199] The following is based on Figure 2-6 The solution provided in this application will be illustrated by another embodiment.

[0200] Step (1): Install multiple RIS on the surface of the building and record the initial coordinates of each RIS and the communication transceiver to establish a global coordinate system.

[0201] It should be noted that the RIS can be configured as a reflective, transmissive, or transflective-reflective integrated structure according to the needs of the scenario, in order to adapt to the signal transmission and sensing requirements in different environments. In this embodiment, the RIS used includes 256 programmable reflective units.

[0202] like Figure 2 As shown, the RIS (Real-Installation Array) is installed on the rigid outer surface of the building, and the spatial distribution of multiple RIS on this surface is not on the same plane, in order to create spatial geometric differences, enhance the solution stability of the positioning equation, and improve the 3D positioning accuracy. A nearby communication receiver capable of forming a LOS (Low-Installation Path) with the RIS is selected, and a global coordinate system is established based on the initial coordinates of the receiver and the RIS deployed on the building surface. This global coordinate system serves as prior information for subsequent RIS positioning calculations. The transceiver, as the basic communication unit, consists of at least one transmitter and three receivers. The antenna arrays of the transmitter and receivers adopt UPA (Upper-Installation Array) or ULA (Upper-Installation Array), using an OFDM (Optical Frequency Division Multiplexing) system.

[0203] In this embodiment, a UPA is used as the antenna array for the transmitter and receiver, wherein the UPA equipped with the transmitter has Unit, denoted as the coordinates of the transmitter. The coordinates of RIS are And the electromagnetic unit size of RIS is In this embodiment, there is a Each receiver is equipped with a UPA of size [size missing]. The coordinates of the j-th receiver are According to the definition of AOD, we can obtain: , .

[0204] Step (2): Design the phase mode of RIS according to the initial position. By controlling RIS to switch to different phase states in adjacent time periods, differential suppression of background multipath interference is performed on the received signal in adjacent time periods, so that the receiver can extract the signal components of the "transmitter-RIS-receiver" path in a continuous time slice.

[0205] It should be noted that for the m-th RIS unit, the optimal phases at adjacent times i and i-1 are denoted as follows: and Its value satisfies: , , and Let represent the phase of the guiding vector corresponding to the m-th element generated by the incident and exit RIS, respectively. and Based on the initial coordinates of the transceiver and the RIS, the phase response is calculated to represent the phase response from the transmitter to the m-th RIS unit along the incident direction and the phase response from the m-th RIS unit to the receiver along the output direction, respectively. The RIS phase mode is designed based on the spatial positions of the transmitter and receiver to maximize the received signal energy. Simultaneously, it introduces phase changes between adjacent moments within a continuous time slice. The RIS alternates between these phase modes within the time slice. and Two phase configurations, and Let each represent a composite phase configuration matrix, where is defined as follows: , Therefore, the matrix The m-th element of the diagonal is equal to This ensures that the RIS reflected signal remains phase-aligned with the transmitted and received signals at adjacent moments.

[0206] By employing different RIS phase states in adjacent time slices and performing differential processing on the received signal, the target signal component of the "transmitter-RIS-receiver" path is extracted, thereby effectively suppressing background interference and improving channel estimation accuracy in a multipath environment.

[0207] At time i, the channel received by the receiver is denoted as . , in: The channel representing the contribution of RIS This indicates non-RIS contributing channels, including LOS and non-line-of-sight (NLOS) channels.

[0208] time The received signal is:

[0209]

[0210] The beamforming vector is defined as follows: x is the transmitted signal, and the vector is... This represents additive white Gaussian noise, whose elements are independent and all obey zero mean and variance. The Gaussian distribution.

[0211] The RIS phase configuration uses programmable features to set two different phase states in adjacent time intervals, ensuring that only the "transmitter-RIS-receiver" path channel changes between adjacent time intervals. The differential signal is then extracted from this path signal and defined as follows:

[0212]

[0213] in, And there are ;

[0214] definition ,because We can obtain:

[0215] .

[0216] Step (3): The receiver processes the extracted target path signal to obtain channel parameters such as angle, time delay and amplitude.

[0217] A standard-compliant wireless channel model is established for target monitoring scenarios. This channel model includes the direct path, the reflection path, and the noise component, which serve as the prior basis for subsequent channel parameter estimation.

[0218] At time i, the channel model for the "transmitter-RIS-receiver" path is: ,definition for:

[0219]

[0220] in, Indicates path loss. Indicates the propagation delay; the system has There are subcarriers, with a subcarrier frequency spacing of . The phase difference between adjacent subcarriers is The complex amplitude ratio of adjacent subcarriers is expressed as The steering vector in the subcarrier domain is:

[0221]

[0222] in, Indicates the guide vector. Indicates time The phase configuration matrix of the RIS. For example, the guide vector is defined as:

[0223]

[0224] in:

[0225] ,

[0226]

[0227] Then there is,

[0228] in, .

[0229] because Let be a scalar, and define this scalar as We can obtain:

[0230]

[0231] definition We can obtain:

[0232]

[0233] When processing the extracted target path signal, the channel estimation algorithms used include compressed sensing algorithms (such as OMP, NOMP, LASSO) and subspace / spectral estimation algorithms (such as MUSIC) to achieve joint estimation of channel parameters such as angle, time delay, and amplitude.

[0234] In this embodiment, the NOMP algorithm is used to achieve joint estimation of angle, time delay, and amplitude; that is, the NOMP algorithm is used to estimate the signal... Medium estimate Four parameters.

[0235] because Indicates time and The difference observation is essentially still about the parameter to be estimated. The equivalent observation vector. In the subsequent parameter extraction stage, the NOMP algorithm relies only on the parameterized representation of the signal, without distinguishing whether the observation comes from an absolute channel or a differential channel. Therefore, when the model is rewritten as ,in To avoid symbolic burden and highlight the model structure, the differences are superscripted. It is incorporated into the definition of the observation vector, making it the default input data, and no longer needs to be explicitly labeled for each signal term. .

[0236] Step (4): Based on the channel parameters of multiple receivers and scene geometric constraints, construct the positioning equation, use the weighted least squares method or other estimation algorithms to solve the RIS position, and combine the frame structure design to realize multi-RIS cooperative positioning.

[0237] Scene geometric constraints include a LOS propagation model based on the transmitter, receiver, and RIS, which defines the feasible solution space of the RIS in the global coordinate system. Multiple receivers are employed, and channel parameters from multiple different signal paths are fused to enhance the redundancy and geometric accuracy of the localization equations, thereby improving the robustness and accuracy of RIS localization.

[0238] In this embodiment, the weighted least squares method is used to solve for the RIS coordinates. The specific solution method is as follows:

[0239] It is equipped with one transmitter. A receiver generates... The estimated parameters obtained in the previous stage are denoted as follows:

[0240]

[0241] definition , where represents the propagation length of the signal received by the nth receiver;

[0242] definition , representing the difference in propagation length between the signal received by the nth receiver and the first receiver;

[0243] Rearrange terms, square them, and substitute. get A set of linear equations:

[0244]

[0245] in , This represents the unit direction vector from the first receiver to the RIS.

[0246] Based on the definition of angle, we can also obtain Group linear relationship

[0247]

[0248]

[0249] in , , ;

[0250] Construct a system of linear equations:

[0251]

[0252] in, , , ;

[0253] And there are:

[0254]

[0255]

[0256]

[0257] According to the weighted least squares algorithm It can be estimated as follows:

[0258]

[0259] In fact, the observed values ,Right now:

[0260]

[0261] in, It is the error of the observation. for The inverse of the covariance matrix, i.e. .

[0262] definition The mean is The covariance matrix is , where: is a Gaussian noise vector.

[0263]

[0264] The weights in the weighted least squares method are determined based on the CRLB calculated for the channel parameters of each signal path under specific signal-to-noise ratio conditions, to achieve adaptive weight allocation. In this application, the CRLB of the estimated parameters is used instead. . for The inverse of the covariance matrix, given Given the nonlinearity of the expression, obtaining the weighting matrix W is often difficult. By performing a Taylor expansion and neglecting the second and higher-order noise terms, its linear terms can be approximated as... When Approximate to its linear noise term hour, With covariance matrix Therefore, the weighted matrix can be calculated as follows: .

[0265] The frame structure design for multi-RIS collaboration includes a pilot band, a multi-RIS phase configuration section, a channel estimation section, and a data processing section. The multi-RIS phase configuration section, based on the programmable nature of RIS, alternates between two different phase states within adjacent time slices. The multi-RIS phase configuration section has two design methods:

[0266] ① For example Figure 3 As shown, when the RIS is configured in single-beam mode, that is, each phase configuration generates only one beam direction, the RIS beam direction is adjusted sequentially in a time-division manner so that it is aligned with different receivers. That is, when the transmitter beam is aligned with a certain RIS, the phase configuration of that RIS is switched at adjacent time points, and multiple receivers are sequentially completed to measure the RIS through time division. Then the transmitter is aligned with other RIS in sequence and the above steps are repeated.

[0267] ② For example Figure 4 As shown, when the RIS is configured in multi-beam mode, that is, each phase configuration can generate multiple beams to simultaneously cover multiple receivers, when the transmitter is aligned with a certain RIS, the phase configuration of that RIS is switched at adjacent times. At this time, all receivers complete the measurement of that RIS at the same time. Then the transmitter is aligned with other RIS in turn and the above steps are repeated.

[0268] Figure 5 The simulation accuracy of RIS coordinate measurement at various signal-to-noise ratio levels [-20dB, -10dB, 0dB, 10dB, 20dB] using RIS single-beam operating mode is shown.

[0269] Step (5) compares the position information of multiple RIS at different times, calculates the rotation angle and deformation trend of the building, thereby realizing long-term monitoring and safety assessment of the building structure.

[0270] The rotation angle and deformation trend are calculated by comparing the currently measured smart metasurface coordinates with the initial coordinates recorded in step (1).

[0271] The rotation angle is calculated based on the coordinate changes of the RIS at different times. The rotation matrix between the two sets of coordinate points is calculated, and the rotation angle is calculated based on the rotation matrix. The rotation matrix is ​​calculated based on the weighted least squares method or the point cloud matching algorithm. The weights of the weighted least squares method are determined based on the lower bound of the localization variance of each RIS under a specific signal-to-noise ratio condition.

[0272] In this example, the weighted least squares method is used to solve for the rotation angle. The specific implementation plan is as follows: Suppose there are b RIS, and their coordinates before rotation are... The rotated coordinates are .

[0273] definition Let be a rotation matrix. The rotation angle is... Given the translation vector, we can obtain:

[0274]

[0275] After transposing and subtracting the above equation, we get:

[0276]

[0277] Rewrite the above system of equations in matrix form and define the matrix to be estimated. We can obtain:

[0278]

[0279] in:

[0280]

[0281]

[0282] After vectorizing the above system of equations in matrix form, we get:

[0283]

[0284] Further estimation using the weighted least squares algorithm yields:

[0285]

[0286] Where N is the weight matrix, which is composed of the lower bound of the position error of each RIS under a specific signal-to-noise ratio condition. Furthermore, by reorganizing the estimation results, we obtain... Inverse solution .

[0287] In the simulation scenario, the system is configured with 6 receiving base stations collaboratively estimating the rotation angle of 7 RIS (Radio Reflectors) arranged on the building surface. Each base station and user terminal uses an 8×8 unit UPA (Unified Base Station Assembly), while each RIS module is designed with 64×64 units. The system operates in a single-beam mode in the frame structure and its performance is evaluated under various signal-to-noise ratio (SNR) levels [-20dB, -10dB, 0dB, 10dB, 20dB]. The simulation focuses on examining the system's accuracy in estimating the building rotation angle under different noise environments. K independent experiments are conducted at each SNR level. The accuracy evaluation index RMSE is defined as:

[0288]

[0289] in The rotation angle estimation error of the kth independent experiment. Figure 6 The simulation accuracy diagram for the rotation angle estimation is shown.

[0290] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0291] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A building deformation monitoring method based on intelligent metasurface-assisted all-sensing integration technology, characterized in that, Includes the following steps: S1. Install multiple intelligent metasurfaces (RIS) on the surface of a building, and establish a global coordinate system based on the initial spatial coordinates of each RIS and the communication transceiver; the communication transceiver and the RIS form a line-of-sight (LOS) path; the communication transceiver includes at least one transmitter and at least three receivers; S2. Design the phase mode of RIS according to the initial spatial coordinates. By controlling RIS to switch to multiple phase states in adjacent time slots, the transmitter-RIS-receiver path signal formed by RIS reflection in adjacent time slots will produce distinguishable phase changes. Differential processing will be performed on the signals received by each receiver in adjacent time slots to suppress environmental background multipath interference unrelated to RIS and extract the target path signal corresponding to RIS. S3. Process the target path signal according to the preset channel estimation algorithm to obtain channel parameters such as angle and time delay; S4. Based on the channel parameters and scene geometric constraints corresponding to multiple receivers, a positioning equation is constructed, and the weighted least squares method is used to solve the positioning equation to obtain the spatial coordinates of each RIS at the current monitoring time. S5. Compare the spatial coordinates of each RIS at the current monitoring time with the initial spatial coordinates of each RIS, calculate the rotation angle of the building, and repeat steps S2 to S5 at preset time intervals to form a long-term monitoring closed loop for building deformation based on the spatial position changes of RIS.

2. The method of claim 1, wherein, The antenna arrays of the transmitter and receiver adopt a standard uniform planar array (UPA) or a standard uniform linear array (ULA), and employ an orthogonal frequency division multiplexing (OFDM) system.

3. The method of claim 1, wherein, In step S2, the phase mode of the RIS is designed based on the initial spatial coordinates. By controlling the RIS to switch to multiple phase states in adjacent time slots, the transmitter-RIS-receiver path signal reflected by the RIS in adjacent time slots produces distinguishable phase changes. Differential processing is performed on the signals received by each receiver in adjacent time slots to suppress environmental background multipath interference unrelated to the RIS and extract the target path signal corresponding to the RIS. Specifically, this includes: According to the initial spatial coordinates corresponding to the mth RIS unit, the phase response of the incident direction of the transmitter to the mth RIS unit is calculated And the phase response of the exit direction of the mth RIS unit to the receiver ; a phase response of an exit direction of the mth RIS element to the receiver a phase response of an exit direction of the mth RIS element to the receiver determining an optimal phase of the mth RIS element at adjacent time instants i and i-1 and = ; construct a complex phase configuration matrix of adjacent time i and i-1 based on the optimal phase of the mth RIS unit at adjacent time i and i-1 and wherein, denotes a diagonal matrix, denotes a complex number corresponding to the phase shift amount applied by the Mth RIS unit at the i th time, and the complex phase configuration matrix is used to control the RIS to alternately load different phase states in adjacent time slices; The receiver receives the signal at adjacent time instants i and i-1 and are denoted by ； ； wherein, denotes the RIS-contributed channel at adjacent time i, denotes the non-RIS-contributed channel at adjacent time i, including line-of-sight (LOS) and non-line-of-sight (NLOS) channels; denotes the RIS-contributed channel at adjacent time i-1, denotes the non-RIS-contributed channel at adjacent time i-1; is the beamforming vector, x is the transmitted signal, and denotes the additive white Gaussian noise, each element of which is mutually independent and subject to a Gaussian distribution with zero mean and variance . For signals and Perform differential analysis to obtain the differential signal. : ； ； And in conjunction with the definition: ； ； ； The target path signal is obtained. .

4. The method according to claim 3, characterized in that, In step S3, the target path signal is processed according to a preset channel estimation algorithm to obtain channel parameters such as angle and time delay, specifically including: The target path signal This can be represented by the following parameterized model: ； in, Indicates path loss. Indicate the propagation delay; assume the system has There are subcarriers, with a subcarrier frequency spacing of . The phase difference between adjacent subcarriers is The complex amplitude ratio of adjacent subcarriers is expressed as steering vector in the subcarrier domain for: ； These represent the receiver UPA steering vector, the RIS transmit steering vector, the RIS receive steering vector, and the transmitter UPA steering vector, respectively. This indicates the azimuth angle of the signal incident at the receiver UPA. This indicates the signal incident elevation angle of the receiver UPA. This indicates the direction angle of the emitted signal from the RIS array element. This indicates the elevation angle of the signal emitted by the RIS array element. Indicates the direction angle of the signal received by the RIS array element. This indicates the elevation angle of the signal received by the RIS array element. This indicates the signal transmission azimuth angle of the transmitter UPA. This indicates the signal transmission elevation angle of the transmitter UPA. Indicates time The phase configuration matrix of the time RIS, let ,Will Defined as: ； in, This represents the guide vector in the vertical direction of the UPA. , This represents the guide vector in the horizontal direction of the UPA. , This indicates the number of cells in the vertical direction of the UPA. Indicates the number of units in the horizontal direction of the UPA; Define scalar for Convert the target path signal for: ； definition Convert the target path signal for: ； definition Rewrite the target path signal The standard model form of the NOMP algorithm: ; Where L represents the number of wireless signal paths, This represents zero-mean Gaussian complex Gaussian noise; Using the NOMP algorithm from The channel parameters for obtaining angle and time delay are: .

5. The method according to claim 4, characterized in that, In step S4, based on the channel parameters and scene geometric constraints corresponding to multiple receivers, a positioning equation is constructed, and the weighted least squares method is used to solve the positioning equation to obtain the spatial coordinates of each RIS at the current monitoring time. Specifically, this includes: S41. Define the propagation length of the signal received by the nth receiver as... : ； in, , The speed of electromagnetic wave propagation. The propagation delay of the signal received by the nth receiver is given by the following information. For transmitter coordinates, Let n be the coordinates of the receiver. RIS coordinates; Define the difference in the propagation length of the signal received by the nth receiver and the 1st receiver as . : ； For any RIS, integration from The channel parameters of each receiver form the positioning observation vector. : ； in, , They represent the first The azimuth and elevation angles obtained from each receiver; S42. Constructing RIS coordinates based on scene geometric constraints The positioning equation: ； Among them, the observation vector sum coefficient matrix Its structure is as follows: ； ； in, It is the transpose matrix. , , , Let each represent a coefficient in the constructed equation. , , and They are defined as follows: ； ； ； ； in, Let be the unit direction vector from the first receiver to the RIS. and A constraint vector defined by an angle; S43. Define the positioning observation vector. The estimation error is It follows a mean of zero and a covariance matrix of... The Gaussian distribution, that is: ； Wherein, the covariance matrix It is a diagonal matrix, and its diagonal elements are determined by the Cramer-Rao lower bound (CRLB) of each channel parameter under a specific signal-to-noise ratio condition; The observation error vector in the positioning equation Approximately expressed as estimation error Linear functions: ； Where B is the Jacobian matrix for error propagation; Based on the estimation error A linear function, calculating the weight matrix required for weighted least squares. , which is the error The inverse of the covariance matrix: ； in, Represents the mathematical expectation operation; S44. Using the weight matrix The coordinates of RIS are solved by weighted least squares method. ,Right now The estimated value : ； S45. The transmit beam and RIS phase configuration are controlled by a time-division multiplexed frame structure to coordinate the measurement of the positions of multiple RIS; the frame structure includes a pilot band, a RIS phase configuration section, a channel estimation section, and a data processing section; in the RIS phase configuration section, one of the following two modes is used for operation: Single beam mode: Control the transmit beam to be aligned with each RIS in sequence, and make the beam of that RIS aligned with each receiver in sequence for time division measurement; Multi-beam mode: Controls the transmit beam to be aligned sequentially with each RIS, and enables that RIS to simultaneously generate multiple beams covering all receivers for concurrent measurement; S46. Repeat steps S41 to S44 for all RIS and output the spatial coordinates of each RIS at the current monitoring time.

6. The method according to claim 5, characterized in that, In step S5, the spatial coordinates of each RIS at the current monitoring time are compared with the initial spatial coordinates of each RIS to calculate the building's rotation angle, specifically including: Obtain the initial spatial coordinate set of b RIS. And the set of spatial coordinates for the current monitoring time obtained through step S4. ; definition Let be a rotation matrix. The rotation angle is... Using the translation vector, we obtain the rotation-translation observation model for the point set: ； Subtracting the transpose of the rotation-translation observation model of the point set yields the rotation constraint equations for the relative displacement: ； The rotation constraint equations of relative displacement are written in matrix form to obtain the matrix form of the rotation constraint equations of relative displacement, and the matrix to be estimated is defined. ,get ,in: ； Vectorizing the rotation constraint equations of relative displacement in matrix form yields the standard linear form that can be directly solved by the weighted least squares algorithm: ； in, Represents a vectorized operator. Represents the identity matrix; The weighted least squares estimation of the rotation matrix parameters can be obtained by using the weighted least squares algorithm to estimate the rotation constraint equations of the relative displacements in vectorized matrix form. ； Wherein, N is the weight matrix, which is constructed using the lower bound of the position error PEB of each RIS coordinate; right Reorganize to obtain a 3×3 matrix. Then transpose it to get According to the rotation angle With rotation matrix Relationship ； get , , Thus, the rotation angle ϵ of the building is obtained.

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