An integrated sensing and backscatter communication method and apparatus based on phaseless extended rytov approximation

Abstract

The application discloses an integrated sensing and backscatter communication method and device based on a non-phase extended Rytov approximation, and relates to the technical field of communication, and comprises the following steps: generating a modulated signal by a transmitting antenna based on a dynamically modulated load impedance of a tag and backscattering to a receiving antenna to measure an RSSI vector, determining an environmental object tag structure power vector and a tag antenna power vector, and then according to a determined combined xRA relative permittivity contrast vector and a tag antenna xRA relative permittivity contrast vector and sensing and identifying the tag, transmitting communication data to the receiving antenna by the transmitting antenna activating the tag and adopting ASK modulation; demodulating the environmental object tag structure power vector, the tag antenna power vector and the RSSI vector corresponding to the communication data in combination to determine information bits. The sensing and backscatter communication are realized by adopting the non-phase inverse scattering based on the extended Rytov approximation, and the stable backscatter communication is overall assisted.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to an integrated sensing and backscattering communication method and apparatus based on the phaseless extended Rytov approximation. Background Technology

[0002] With the continuous development of smart cities, autonomous driving, transportation networks, and other fields, the number and density of wireless devices are surging, making energy consumption a key issue for wireless technology. Integrated Sensing and Communication (ISAC) systems can simultaneously complete information transmission and environmental perception by sharing spectrum, waveforms, and hardware resources, becoming one of the core directions of 6G. The key to its successful application lies in its ability to accurately locate backscatter tags.

[0003] Inverse scattering technology can extract spatial information from scattered or reflected electromagnetic signals, providing a good solution for the accurate location and characterization of scatterers. However, inverse scattering results are nonlinear and ill-posed, usually requiring complex reconstruction techniques to solve. Although methods such as Born approximation (BA), Rytov approximation (RA), contrast source inversion, and nonlinear deep learning have been proposed for reconstruction, their effectiveness is still limited to scatterers with relatively uniform permittivity, resulting in poor communication performance in complex environments. Summary of the Invention

[0004] This invention provides an integrated sensing and backscattering communication method and apparatus based on the phaseless extended Rytov approximation, which solves the technical problem that the effectiveness of existing integrated sensing and backscattering communication methods for reconstructing positioning tags using backscattering is limited to scatterers with relatively uniform permittivity, resulting in poor communication performance in complex environments.

[0005] The first aspect of this invention provides an integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation, comprising:

[0006] Pilot signals are transmitted to the tag via the transmitting antenna, and the load impedance of the tag is dynamically modulated to generate a corresponding modulated signal, which is then backscattered to the receiving antenna.

[0007] Based on the RSSI vector measured when the receiving antenna receives the modulated signal, the environmental object tag structure power vector and the tag antenna power vector are determined;

[0008] Using the environmental object tag structure power vector and the tag antenna power vector, determine the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector;

[0009] After the tag is sensed and identified based on the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector, the tag is activated by the transmitting antenna to transmit communication data to the receiving antenna using ASK modulation.

[0010] The information bits are determined by demodulating the power vector of the environmental object tag structure, the power vector of the tag antenna, and the RSSI vector corresponding to the communication data.

[0011] Optionally, the demodulation of the combined environmental object tag structure power vector, the tag antenna power vector, and the RSSI vector corresponding to the communication data to determine the information bits includes:

[0012] The RSSI vector measured when receiving the communication data using the receiving antenna is subtracted from the power vector of the environmental object tag structure to determine the received signal.

[0013] According to the preset signal model, based on the received signal and the tag antenna power vector, the output information bits are demodulated using the minimum mean square error detection algorithm.

[0014] Optionally, the step of sensing and identifying the tag based on the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector includes:

[0015] The combined location of the environmental object and the tag is perceived using the combined xRA relative permittivity comparison vector, and the tag is identified from the combined location based on the tag antenna xRA relative permittivity comparison vector.

[0016] Optionally, determining the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector using the environmental object tag structure power vector and the tag antenna power vector includes:

[0017] The underdetermined equations are constructed using the power vector of the environmental object tag structure through the compressed sensing algorithm, and the combined xRA relative permittivity comparison vector is determined by optimizing the solution based on the two-step iterative shrinkage threshold algorithm.

[0018] Based on the compressed sensing algorithm and the two-step iterative shrinkage threshold algorithm, the relative permittivity comparison vector of the tag antenna xRA is output by optimizing the tag antenna power vector.

[0019] Optionally, the demodulated output information bits based on the minimum mean square error detection algorithm include:

[0020] After channel estimation based on the minimum mean square error detection algorithm, bit detection is performed through threshold decision-making, and information bits are output.

[0021] Optionally, the signal model includes:

[0022]

[0023] In the formula, Indicates the first A communication data transmission symbol, Indicates receiving signal, This represents additive white Gaussian noise. This represents the tag antenna power vector.

[0024] A second aspect of the present invention provides an integrated sensing and backscattering communication device based on the phaseless extended Rytov approximation, comprising:

[0025] The pilot modulation module is used to transmit pilot signals to the tag through the transmitting antenna, dynamically modulate the tag's load impedance to generate a corresponding modulation signal, and backscatter it to the receiving antenna.

[0026] The power determination module is used to determine the environmental object tag structure power vector and the tag antenna power vector based on the RSSI vector measured when the receiving antenna receives the modulated signal;

[0027] The comparison vector determination module is used to determine the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector using the environmental object tag structure power vector and the tag antenna power vector;

[0028] The sensing and transmission module is used to sense and identify the tag based on the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector, and then activate the tag through the transmitting antenna to transmit communication data to the receiving antenna using ASK modulation.

[0029] The signal demodulation module is used to demodulate the environmental object tag structure power vector, the tag antenna power vector, and the RSSI vector corresponding to the communication data to determine the information bits.

[0030] A computer device provided in a third aspect of the present invention includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation as described in any of the preceding claims.

[0031] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation as described in any of the preceding claims.

[0032] The fifth aspect of the present invention provides a computer program product comprising a computer program / instruction, which, when executed by a processor, implements the integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation as described in any of the preceding claims.

[0033] As can be seen from the above technical solutions, the present invention has the following advantages:

[0034] The above-described solution of the present invention provides an integrated sensing and backscatter communication method based on the phaseless extended Rytov approximation, comprising: transmitting a pilot signal to a tag via a transmitting antenna, dynamically modulating the tag's load impedance to generate a corresponding modulated signal and backscattering it to a receiving antenna; determining the environmental object tag structure power vector and the tag antenna power vector based on the RSSI vector measured when the receiving antenna receives the modulated signal; determining the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector using the environmental object tag structure power vector and the tag antenna power vector; after sensing and identifying the tag based on the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector, activating the tag via the transmitting antenna to transmit communication data to the receiving antenna using ASK modulation; and demodulating the environmental object tag structure power vector, the tag antenna power vector, and the RSSI vector corresponding to the communication data to determine the information bits. Based on the above scheme, phaseless backscattering based on the extended Rytov approximation is used to realize sensing and backscatter communication. During the sensing process, the load impedance at the tag is dynamically modulated to decouple the RSSI components of the tag's structural mode and antenna mode. This supports the distinction between metals and dielectric objects with high relative permittivity. It can accurately locate and reliably identify single or multiple tags in complex scenarios. At the same time, it eliminates the dependence on phase information, reduces hardware complexity and cost, and helps to maintain stable backscatter communication overall. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1The flowchart illustrates the steps of an integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation provided in Embodiment 1 of the present invention.

[0037] Figure 2 This is a schematic diagram of the integrated sensing and backscatter communication system provided in Embodiment 1 of the present invention;

[0038] Figure 3 This is a schematic diagram of the xRA sensing results provided in Embodiment 1 of the present invention. Figure 1 ;

[0039] Figure 4 This is a schematic diagram of the RA sensing results provided in Embodiment 1 of the present invention;

[0040] Figure 5 This is a schematic diagram of the xRA sensing results provided in Embodiment 1 of the present invention. Figure 2 ;

[0041] Figure 6 This is a schematic diagram of BER performance results provided in Embodiment 1 of the present invention;

[0042] Figure 7 This is a structural block diagram of an integrated sensing and backscattering communication device based on the phaseless extended Rytov approximation provided in Embodiment 2 of the present invention. Detailed Implementation

[0043] This invention provides an integrated sensing and backscattering communication method and apparatus based on the phaseless extended Rytov approximation, which solves the technical problem that the effectiveness of existing integrated sensing and backscattering communication methods for reconstructing positioning tags using backscattering is limited to scatterers with nearly uniform relative permittivity, resulting in poor communication performance in complex environments.

[0044] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0045] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of an integrated sensing and backscatter communication method based on the phaseless extended Rytov approximation provided in Embodiment 1 of the present invention.

[0046] It should be noted that the method provided in this embodiment can be applied to integrated sensing scenarios related to electromagnetic scattering. For example, it can be applied to scenarios such as... Figure 2 The integrated sensing and backscatter communication system shown represents a typical indoor environment;

[0047] exist Figure 2 In this context, the Domain of Interest (DOI) is modeled as a two-dimensional scene, using... This indicates that in this scenario, the region of interest consists of metallic and dielectric objects with fixed reflectance coefficients, and tags whose reflectance coefficients can be adjusted to achieve reliable backscatter communication. All objects and tags are non-magnetic (vacuum permeability is 0.05%). Its characteristic is a complex relative permittivity. , Indicates location, This represents the real part of the relative permittivity. Represents the imaginary unit. Represents the imaginary part of the relative permittivity, or equivalently... , Indicates refractive index, Represents the real part of the refractive index. Indices represent refractive index; these materials exhibit low-loss characteristics and conform to... This is a typical characteristic of most objects at radio and microwave frequencies; the background medium within the DOI is uniform air (the relative permittivity of air is 1000 kJ / m²). (), representing typical features commonly found in indoor environments;

[0048] In this system, DOI is restricted to within the area, The dimension is in the x-axis direction. The dimension is along the y-axis and is divided into... Grid cells, where, and These represent the number of discrete spatial pixels along the x-axis and y-axis, respectively. The length per pixel along the x-axis. y is the length per pixel along the y-axis. The pixel area of ​​each pixel is... The total number of pixels within a DOI is determined by The result is that a specific pixel is represented by an integer. As a pixel index, where ;

[0049] To detect DOI, for example, set... Transceiver nodes with an operating frequency of 2.4 GHz are uniformly distributed along the DOI boundary. The number of transceiver nodes is indicated by... The transceiver nodes are evenly spaced to represent the boundary of the region of interest (DOI) and are used to collect scattering data within the DOI. Each transceiver node includes a transmitting antenna and a receiving antenna. In this embodiment... Indicates the first One transmitting antenna, Indicates the first The location of each transmitting antenna Indicates the first One receiving antenna, Indicates the first The position of the receiving antenna; when the first... When each node sends, the remaining Each node acts as a receiver (each node cannot simultaneously act as both a transmitting and receiving antenna). ,and ); although theoretically it is possible to obtain a total Each link measurement value, but due to reciprocity, the number of independent measurements is [missing information]. Reduce to .

[0050] Embodiment 1 of this invention provides an integrated sensing and backscatter communication method based on the phaseless extended Rytov approximation, realizing a novel ISAC system architecture that seamlessly integrates sensing, discrimination, identification, and backscatter communication functions into a single platform. Specifically, it includes:

[0051] Step 101: Transmit pilot signals to the tag through the transmitting antenna, dynamically modulate the tag's load impedance to generate corresponding modulation signals, and backscatter them to the receiving antenna.

[0052] It should be noted that a transmitting antenna will generate an incident electric field when there is no scattering body. Indicates the first Each transmitting antenna is located at... The incident electric field generated at that point Indicates the first The location of each pixel, when the incident field propagates through the DOI, interacts with the scatterer to generate the total field. Indicates the first Each transmitting antenna is located at... The total field generated at that location. and They satisfy the homogeneous and non-homogeneous Helmholtz wave equations respectively, as shown below:

[0053] (1)

[0054] (2)

[0055] In the formula, Represents the Laplace operator. Indicates wave number, Indicates the location The refractive index at the point; subtracting equation (1) from equation (2) yields the control scattering field. The wave equation:

[0056] (3)

[0057] (4)

[0058] In the formula, Indicates the first Each transmitting antenna is located at... The scattered field generated at that location, Indicates the location Comparison of relative permittivity at the location;

[0059] As previously mentioned, the DOI in the proposal system includes both environment objects and tags. For objects acting as environment objects, their scattered fields depend on their inherent physical properties. In contrast, the scattered field from tags consists of two parts: tag structure mode and tag antenna mode. The tag antenna mode depends on the tag's loading conditions, specifically its reflection coefficient. To identify labels from environmental objects based on features. This can be expressed by the following formula:

[0060] (5)

[0061] in, The sign indicates the complex conjugate operation. This indicates the antenna impedance of the tag. Indicates the load impedance of the label; when At that time, it appeared This is a special case; therefore, the scattered field generated by the tag, the load impedance By the Excitation by a single transmitting antenna is represented as:

[0062] (6)

[0063] in, This represents the scattering field of the label. This represents the structural mode scattering field of the tag. This represents the antenna mode scattering field of the tag. This represents the induced current at the tag under the conjugate matching condition caused by the incident field. This represents the radiation field under unit excitation conditions;

[0064] The scattered field of environmental objects is denoted as Then the scattered field It can be represented as:

[0065] (7)

[0066] because and Each of these is independent of the tag's load impedance, and they can be combined to form a constant combined scattered field. Thus, equation (7) can be expressed as:

[0067] (8)

[0068] However, and None of these can be directly measured, but in reality, these electric fields can be converted into measurable voltages at the output of the receiving antenna. Therefore, the scattered fields in equations (1)-(3) can be represented by measurable voltages:

[0069] (9)

[0070] in, Indicates the first The receiving antenna is due to the first The voltage recorded by scattering caused by a transmitting antenna. Represents the imaginary unit. Represents angular frequency. Represents the permeability of free space. It is a normalization factor defined through a standard calibration procedure. Indicates the region of interest. Indicates pixel index, Indicates the first The position of each pixel Indicating the first in unit incentives The electric field radiated by a receiving antenna in the transmission model.

[0071] Step 102: Determine the environmental object tag structure power vector and tag antenna power vector based on the RSSI vector measured when the receiving antenna receives the modulated signal.

[0072] It should be noted that if BA and RA are used in the detection and differentiation stage of scatterers, it will lead to significant limitations when dealing with high dielectric constants. Therefore, this embodiment considers using the extended Rytov approximation (xRA), which can be derived from RA.

[0073] For traditional RA, the total field The following formula represents:

[0074] (10)

[0075] in, Represents a complex scattering wavefront; the transformed total field is obtained through Rytov transform. :

[0076] (11)

[0077] By combining with equations (1) and (2), a nonlinear differential equation can be obtained:

[0078] (12)

[0079] in, The comparison functions defined under the Rytov transform include:

[0080] (13)

[0081] Based on the above equation, the wave equation controlling the scattered field can be further expressed as:

[0082] (14)

[0083] Traditional RA is achieved by ignoring This is derived, which implies the assumption of weak scattering. Under this simplification, the contrast function simplifies to:

[0084] (15)

[0085] However, this assumption is invalid for scenarios involving high dielectric constant comparisons. This is very important and cannot be ignored. As a result, RA is only applicable under weak scattering conditions and has high accuracy; xRA approximates the wave propagation in a lossy medium using a high-frequency approximation. This overcomes the limitation; specifically, the electric field in a lossy medium with a dielectric constant is expressed as:

[0086] (16)

[0087] (17)

[0088] in, and Representing the incident field and the transmitted field at position, respectively. Amplitude at that point and These represent the real and imaginary parts of the refractive index, respectively, and are directly related to the complex permittivity; they are unit vectors. , and These represent the directions of the incident wave, reflected wave, and normal vector to the interface, respectively; through equations (16) and (17), in In Substituting into equation (13), the xRA comparison function can be approximated as:

[0089] (18)

[0090] in, as well as These represent the incident angle and the scattering angle, respectively; an important feature of equation (18) is the contrast function. The imaginary part mainly depends on Not Importantly, due to the angle of incidence Essentially determined by the geometry of the object and independent of its dielectric constant, once the imaginary component is accurately reconstructed under low dielectric constant contrast, it can achieve accuracy over a relatively large range of dielectric constants. The imaginary components become the core focus of the reconstruction process, while the real components are usually not very accurate and can therefore be ignored;

[0091] It is understandable that, in order to eliminate the imaginary component in equation (18) with respect to angles... The explicit dependency of xRA is achieved by applying the angle averaging process of the incident angle. The final expression for the imaginary part of the xRA contrast function is:

[0092] (19)

[0093] xRA employs the same reconstruction process as RA, but requires additional steps in the final processing. xRA can perform accurate reconstructions on a wide range of everyday materials. Furthermore, this reconstruction incorporates both real and imaginary components of the dielectric constant, enabling it not only to accurately recover the shape of objects but also to distinguish different indoor objects based on their electromagnetic properties.

[0094] Since the real part of the dielectric constant has little impact on reconstruction accuracy within the xRA framework, while the imaginary part directly reflects the absorption and loss characteristics of the medium, this embodiment uses the aforementioned comparison function, namely the xRA relative dielectric constant comparison, in all subsequent modeling, reconstruction, and identification processes. All of these specifically refer to their imaginary components. ;

[0095] To complete the xRA formula, the derived comparison function must be combined with the measurable voltage of the receiving antenna. Within the xRA framework, replacing the comparison function in the measurable voltage expression with the xRA-derived comparison function yields the linearized voltage expression:

[0096] (20)

[0097] in, ;

[0098] In the formula, This represents the received voltage when there are no scatterers within the DOI. Indicates the first The incident wave emitted by the first transmitting antenna caused the first The voltage measured when a scatterer is present on the receiving antenna; this transformation effectively converts the nonlinear relationship between the received voltage and the contrast function into a linear equation, the only unknown in which is the voltage in the contrast equation. This simplifies the solution process;

[0099] To express equation (20) in a phase-free frame, we first express equation (20) in exponential form:

[0100] (twenty one)

[0101] Multiplying the above equation by its complex conjugate, and then converting the result to a logarithmic scale (base 10), we can obtain the expression for the change in received power in decibels:

[0102] (twenty two)

[0103] in, This represents the change in power, expressed in decibels. Let be a natural constant; this formula establishes a direct linear relationship between the RSSI variation and the spatial distribution of the relative permittivity within the DOI; rewriting the above formula as a set of linear equations for all L wireless links, we have:

[0104] (twenty three)

[0105] in, For all The RSSI vector received by each receiving antenna The number of independent measurements is determined by Composition, xRA relative permittivity comparison vector Includes the position of each pixel Comparison of relative permittivity of xRA As defined in equation (23), For each entry The measurement matrix is ​​given here:

[0106] (twenty four)

[0107] In the formula, This represents the measurement channel index; this linearized framework directly uses the corresponding vector of RSSI. Comparison functions derived from xRA Connecting them simplifies the inverse problem;

[0108] Using equations (8), (9), (21), (22), and (23), the received RSSI vector in equation (23) can be obtained. Further decomposed into the comprehensive changes in RSSI caused by environmental objects and tag structure patterns. and the contribution of tag antenna mode to RSSI variation Therefore, we can conclude that:

[0109] (25)

[0110] In the formula, This represents the power vector of the environmental object label structure. This represents the tag antenna power vector. Similarly, the relative permittivity is compared to the vector. It can be decomposed into:

[0111] (26)

[0112] In the formula, This indicates that the combined xRA relative permittivity comparison vector is related to the environment object and the tag structure pattern. The xRA represents the relative permittivity comparison vector of the tag antenna, which is related to the tag's antenna mode; by combining the decompositions in equations (25) and (26) into equation (23), we can obtain:

[0113] (27)

[0114] (28)

[0115] Therefore, to solve the inverse problem expressed by the above two equations, it is necessary to estimate the measurement RSSI. and Performing such an estimation can simultaneously detect and identify environment objects and markers in the DOI;

[0116] Understandably, on the one hand, the sensing capability utilizes backscatter communication to perform tag identification by using two different pilot signals from the tag, each pilot signal corresponding to a different reflection coefficient, denoted as the first reflection coefficient. Second reflection coefficient On the other hand, in the presence of environmental objects, accurate positioning and identification of tags can enhance the reliability of backscatter communication by improving the orientation of tag CSI (Channel State Information) and mitigating interference caused by environmental objects.

[0117] Therefore, the contribution in the RSSI vector is first extracted during the perception stage; according to equation (25), and the first RSSI vector under the first reflection coefficient. and the second RSSI vector under the second reflection coefficient The two relations can be derived and They are respectively:

[0118] (29)

[0119] (30)

[0120] Obviously, the tag reflectivity can be modulated. and Extract from the received RSSI vector and For example, the parameter can be set here as follows: and .

[0121] Step 103: Using the environmental object tag structure power vector and the tag antenna power vector, determine the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector.

[0122] In one specific embodiment of this example, step 103 includes the following sub-steps:

[0123] The underdetermined equations are constructed using the power vector of the environmental object tag structure through compressed sensing algorithm, and the combined xRA relative permittivity comparison vector is determined by optimizing the solution based on the two-step iterative shrinkage threshold algorithm.

[0124] Based on the compressed sensing algorithm and the two-step iterative shrinkage threshold algorithm, the relative permittivity comparison vector of the tag antenna xRA is output by optimizing the tag antenna power vector.

[0125] It should be noted that the combined xRA relative permittivity is compared to the vector. Power vector of environmental object label structure Reconstruction is performed and used to determine the location of environmental objects and identify labels based on Equation (27); considering the scarcity of environmental objects and labels within the DOI, the distribution is compared. Naturally, scarcity is evident; furthermore, the quantity available for measurement... Usually less than the number of unknown elements This makes equation (27) an ill-posed and uncertain inverse problem. To solve this problem, this embodiment can consider using compressed sensing technology to express the reconstruction as minimizing the norm. The optimization problem yields the following formula:

[0126] (31)

[0127] in, Representation norm To effectively address this optimization process, a two-step iterative shrinkage threshold holding algorithm (TwIST) was used.

[0128] Similarly, from RSSI measurement data China Reconstruction To achieve accurate label recognition, with The reconstruction The recovery is also configured to a norm. The minimization problem is as follows:

[0129] (32)

[0130] Furthermore, this optimization problem also utilizes the TwIST algorithm; because Specifically, this represents the contrast distribution associated with the tag's antenna pattern; therefore, accurate reconstruction helps in the accurate identification of the tag.

[0131] Step 104: After the tag is identified based on the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector, the tag is activated by the transmitting antenna to transmit communication data to the receiving antenna using ASK modulation.

[0132] In one specific embodiment of this example, the tag is sensed and identified based on the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector, including:

[0133] The combined location of environmental objects and tags is perceived by using the xRA relative permittivity comparison vector, and the tag is identified from the combined location based on the tag antenna xRA relative permittivity comparison vector.

[0134] It should be noted that after determining the combined xRA relative permittivity contrast vector and the tag antenna xRA relative permittivity contrast vector, the positions of environmental objects and tags are first reconstructed by combining the imaginary components represented by the combined xRA relative permittivity contrast vector. Furthermore, this reconstruction helps distinguish tags and environmental objects with different material distributions, such as metals or dielectrics. Next, the tag is identified based on the imaginary part represented by the tag antenna xRA relative permittivity contrast vector. After completing the sensing process, the tag switches to backscatter communication mode and uses ASK modulation to transmit communication data to the receiving antenna. This modulation technique utilizes two different load impedance states ( or Alternating between states, each state corresponds to a unique reflection coefficient. or Therefore, the first A single communication data transmission symbol can be mathematically represented as: .

[0135] Step 105: Demodulate the joint environmental object tag structure power vector, tag antenna power vector, and RSSI vector corresponding to the communication data to determine the information bits.

[0136] In one specific embodiment of this example, step 105 includes the following sub-steps:

[0137] The RSSI vector measured when receiving communication data using the receiving antenna is subtracted from the structural power vector of the environmental object tag to determine the received signal.

[0138] According to the preset signal model, based on the received signal and the tag antenna power vector, the output information bits are demodulated using the minimum mean square error detection algorithm.

[0139] In a more specific implementation of this embodiment, the demodulation of output information bits based on the minimum mean square error detection algorithm includes:

[0140] After channel estimation based on the minimum mean square error detection algorithm, bit detection is performed through threshold decision-making, and information bits are output.

[0141] It should be noted that during data transmission, the RSSI vector of the receiving antenna when receiving communication data is measured and recorded. Organization dimensional vector , For the first The RSSI vector of each communication data transmission symbol, For the first The power change is calculated based on the relationship established in the following formula, and the measured RSSI vector is... In mathematics, this can be represented as:

[0142] (33)

[0143] in, and The estimation has already been performed in the aforementioned sensing process, by eliminating interference components in the measured RSSI vector. The signal model is simplified to the following equation:

[0144] (34)

[0145] In the formula, Indicates receiving signal, This represents additive white Gaussian noise, specifically, noise with a mean of zero and a covariance matrix of zero. , Represents L-dimensional identical matrices. Let V be the noise variance; here, the vector It effectively served as a CSI for backscatter data transmission;

[0146] Understandably, from a communication perspective, a backscatter link can be conceptualized as a single-input multiple-output (SIMO) system, utilizing the CSI vector obtained during the sensing process. A minimum mean-squared-error (MMSE) detection algorithm was implemented, bringing the ASK modulation bit error rate close to the theoretical limit, thereby improving symbol detection accuracy and communication performance. The optimal linear combination vector under the MMSE criterion is calculated as follows:

[0147] (35)

[0148] In the formula, For linear combiners, This is a conjugate transpose operation; the received signal is processed using an MMSE-based combination vector, as shown below:

[0149] (36)

[0150] in, This represents a scalar signal after linear processing.

[0151] Symbolic recovery is performed using a threshold-based decision mechanism, with the threshold set between two reflection coefficients. and The midpoint between; specifically, when If the value is 1, the symbol will be interpreted as 1 in binary; otherwise, it will be interpreted as 0 in binary.

[0152] It is understood that this embodiment does not restrict other equivalent substitution detection algorithms using MMSE or other equivalent substitution methods for threshold decision-making; however, MMSE is preferred, mainly because: MMSE has strong robustness, is suitable for low-power scenarios, matches scenarios with low signal power and significant noise in backscatter communication links, and has low computational complexity but can achieve high accuracy.

[0153] To further illustrate the application performance of this embodiment, simulation experiments were conducted for verification:

[0154] This simulation used CST Studio Suite to analyze the performance of the integrated system. The system operates at a frequency of 2.4 GHz, and the DOI is a square region. Boundary dimension corresponds to And is discretized into Pixel grid; a total of 20 transceiver nodes, each equipped with a five-element Yagi antenna, wherein the copper dipole antenna serves as the tag, characterized by a radius of 10mm, a height of 56.5mm, an antenna impedance of 75 ohms, and two load impedances of 25 ohms and a short-circuited impedance, with reflection coefficients modulated as follows. and ;

[0155] 1. Regarding sensing performance:

[0156] In a single label, a copper object of the same size as the label, and dielectric constant With the configuration of a rectangular dielectric object, the response reconstruction results obtained using xRA are as follows: Figure 3 As shown, the response reconstruction results obtained using RA are as follows: Figure 4 As shown; Figure 3 This demonstrates that the positions of metals and dielectric objects, as well as the tags, can be accurately reconstructed, and that dielectric objects and metals can be clearly distinguished, conversely. Figure 4 This indicates that RA can successfully locate all environmental objects and identify tags, but it cannot reliably distinguish between copper and dielectric objects;

[0157] Three labels, copper objects of the same size as the labels, and dielectric constant. With the configuration of a rectangular dielectric object, the xRA-based reconstruction results are as follows: Figure 5 As shown, (a) represents the tag and the environmental object, (b)-(d) represent only one tag, (e)-(g) represent only two tags, and (h) represents three tags. It can be seen that the system accurately reconstructed the positions of all three tags, as well as the copper and dielectric objects, clearly distinguished the metal and dielectric objects during the detection and differentiation stage, and all tags were successfully identified.

[0158] In summary, the xRA system can not only accurately detect and locate copper objects, dielectric objects, and tags, but also distinguish between metallic and dielectric materials, and has high reliability in tag identification.

[0159] 2. Regarding backscatter communication performance:

[0160] A scene was set up containing a tag and copper and dielectric objects located within a DOI. The tag's load impedance is... Ohm and Alternating between them, theoretical reflection coefficient and ;

[0161] The main metric for evaluating the quality of backscatter communication is the bit error rate (BER). For this detection method, the theoretical BER is expressed as follows:

[0162] (37)

[0163] in, For bit error rate, For the Q function, The square of the norm;

[0164] The BER of the proposed integrated system was further investigated through extensive Monte Carlo simulations; MMSE detection was used to analyze the uplink BER performance to recover the transmitted signal, and the relationship between BER and signal-to-noise ratio was shown in the figure. Figure 6 As shown, ASK signals are used at different signal-to-noise ratios. The BER performance of the integrated system shows that the simulation results are in excellent agreement with the theoretical BER predictions, verifying the robustness and accuracy of the system.

[0165] In this embodiment of the invention, phaseless backscattering based on the extended Rytov approximation is used to realize sensing and backscatter communication. During the sensing process, the load impedance at the tag is dynamically modulated to decouple the RSSI components of the tag's structural mode and antenna mode. This supports the differentiation between metals and dielectric objects with high relative permittivity. In complex scenarios, it can accurately locate and reliably identify single or multiple tags. At the same time, it eliminates the dependence on phase information, reduces hardware complexity and cost, and reduces power consumption from milliwatts to nanowatts and microwatts, alleviating energy consumption problems and contributing to maintaining stable backscatter communication.

[0166] Please see Figure 7 Embodiment 2 of the present invention provides an integrated sensing and backscattering communication device without phase-extended Rytov approximation, comprising:

[0167] The pilot modulation module 701 is used to transmit pilot signals to the tag through the transmitting antenna, dynamically modulate the load impedance of the tag to generate a corresponding modulation signal and backscatter it to the receiving antenna;

[0168] The power determination module 702 is used to determine the environmental object tag structure power vector and the tag antenna power vector based on the RSSI vector measured when the receiving antenna receives the modulated signal;

[0169] The comparison vector determination module 703 is used to determine the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector by using the environmental object tag structure power vector and the tag antenna power vector;

[0170] The sensing and transmission module 704 is used to sense and identify the tag based on the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector, and then activate the tag through the transmitting antenna to transmit communication data to the receiving antenna using ASK modulation.

[0171] The signal demodulation module 705 is used to demodulate the combined environmental object tag structure power vector, tag antenna power vector, and RSSI vector corresponding to the communication data to determine the information bits.

[0172] In one specific embodiment of this example, the tag is sensed and identified based on the combined xRA relative permittivity comparison vector and the tag antenna xRA relative permittivity comparison vector, including:

[0173] The combined location of environmental objects and tags is perceived by using the xRA relative permittivity comparison vector, and the tag is identified from the combined location based on the tag antenna xRA relative permittivity comparison vector.

[0174] In one specific embodiment of this example, the comparison vector determination module 703 is specifically used for:

[0175] The underdetermined equations are constructed using the power vector of the environmental object tag structure through compressed sensing algorithm, and the combined xRA relative permittivity comparison vector is determined by optimizing the solution based on the two-step iterative shrinkage threshold algorithm.

[0176] Based on the compressed sensing algorithm and the two-step iterative shrinkage threshold algorithm, the relative permittivity comparison vector of the tag antenna xRA is output by optimizing the tag antenna power vector.

[0177] In one specific embodiment of this example, the signal demodulation module 705 is specifically used for:

[0178] The RSSI vector measured when receiving communication data using the receiving antenna is subtracted from the structural power vector of the environmental object tag to determine the received signal.

[0179] According to the preset signal model, based on the received signal and the tag antenna power vector, the output information bits are demodulated using the minimum mean square error detection algorithm.

[0180] In a more specific implementation of this embodiment, the demodulation of output information bits based on the minimum mean square error detection algorithm includes:

[0181] After channel estimation based on the minimum mean square error detection algorithm, bit detection is performed through threshold decision-making, and information bits are output.

[0182] In a more specific implementation of this embodiment, the signal model includes:

[0183]

[0184] In the formula, Indicates the first A communication data transmission symbol, Indicates receiving signal, This represents additive white Gaussian noise. This represents the tag antenna power vector.

[0185] Embodiment 3 of the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the integrated sensing and backscatter communication method without phase extended Rytov approximation as described in any of the above embodiments.

[0186] Embodiment 4 of the present invention also provides a computer-readable storage medium storing a computer program / instructions thereon, wherein the computer program / instructions, when executed by a processor, implement the steps of the integrated sensing and backscatter communication method without phase extended Rytov approximation as described in any of the above embodiments.

[0187] Embodiment 5 of the present invention also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the integrated sensing and backscatter communication method without phase extended Rytov approximation as described in any of the above embodiments.

[0188] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0189] In the several embodiments provided in this application, it should be understood that the disclosed apparatus 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.

[0190] 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; that is, 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.

[0191] Furthermore, the functional modules in the various embodiments of the present invention 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. The integrated modules described above can be implemented in hardware or as software functional modules.

[0192] If the integrated module is implemented as a software functional 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 the present invention, in essence, or the part that contributes to the prior art, or all or part 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 described in the various embodiments of the present invention. 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.

[0193] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 the present invention.

Claims

1. An integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation, characterized in that, include: Pilot signals are transmitted to the tag via the transmitting antenna, and the load impedance of the tag is dynamically modulated to generate a corresponding modulated signal, which is then backscattered to the receiving antenna. Based on the RSSI vector measured when the receiving antenna receives the modulated signal, the environmental object tag structure power vector and the tag antenna power vector are determined; Using the environmental object tag structure power vector and the tag antenna power vector, determine the combined extended Rytov approximate relative permittivity comparison vector and the tag antenna extended Rytov approximate relative permittivity comparison vector; After the tag is sensed and identified based on the combined extended Rytov approximate relative permittivity comparison vector and the tag antenna extended Rytov approximate relative permittivity comparison vector, the tag is activated by the transmitting antenna to transmit communication data to the receiving antenna using ASK modulation. Demodulate the information bits by combining the power vector of the environmental object tag structure, the power vector of the tag antenna, and the RSSI vector corresponding to the communication data; The step of determining the combined extended Rytov approximate relative permittivity comparison vector and the tag antenna extended Rytov approximate relative permittivity comparison vector using the environmental object tag structure power vector and the tag antenna power vector includes: The underdetermined equations are constructed using the power vector of the environmental object tag structure through the compressed sensing algorithm, and the combined extended Rytov approximation relative permittivity comparison vector is determined by optimizing the solution based on the two-step iterative shrinkage threshold algorithm. Based on the compressed sensing algorithm and the two-step iterative shrinkage threshold algorithm, the tag antenna extended Rytov approximate relative permittivity comparison vector is output by optimizing the tag antenna power vector.

2. The integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation according to claim 1, characterized in that, The demodulation of the combined environmental object tag structure power vector, the tag antenna power vector, and the RSSI vector corresponding to the communication data to determine the information bits includes: The RSSI vector measured when receiving the communication data using the receiving antenna is subtracted from the power vector of the environmental object tag structure to determine the received signal. According to the preset signal model, based on the received signal and the tag antenna power vector, the output information bits are demodulated using the minimum mean square error detection algorithm.

3. The integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation according to claim 1, characterized in that, The process of sensing and identifying the tag based on the combined extended Rytov approximate relative permittivity comparison vector and the tag antenna extended Rytov approximate relative permittivity comparison vector includes: The combined location of the environmental object and the tag is perceived using the combined extended Rytov approximate relative permittivity comparison vector, and the tag is identified from the combined location based on the tag antenna extended Rytov approximate relative permittivity comparison vector.

4. The integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation according to claim 2, characterized in that, The demodulated output information bits based on the minimum mean square error detection algorithm include: After channel estimation based on the minimum mean square error detection algorithm, bit detection is performed through threshold decision-making, and information bits are output.

5. The integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation according to claim 2, characterized in that, The signal model includes: In the formula, Indicates the first A communication data transmission symbol, Indicates receiving signal, This represents additive white Gaussian noise. This represents the tag antenna power vector.

6. An integrated sensing and backscattering communication device based on the phaseless extended Rytov approximation, characterized in that, include: The pilot modulation module is used to transmit pilot signals to the tag through the transmitting antenna, dynamically modulate the tag's load impedance to generate a corresponding modulation signal, and backscatter it to the receiving antenna. The power determination module is used to determine the environmental object tag structure power vector and the tag antenna power vector based on the RSSI vector measured when the receiving antenna receives the modulated signal; The comparison vector determination module is used to determine the combined extended Rytov approximate relative permittivity comparison vector and the tag antenna extended Rytov approximate relative permittivity comparison vector using the environmental object tag structure power vector and the tag antenna power vector; The sensing and transmission module is used to sense and identify the tag based on the combined extended Rytov approximate relative permittivity comparison vector and the tag antenna extended Rytov approximate relative permittivity comparison vector, and then activate the tag through the transmitting antenna to transmit communication data to the receiving antenna using ASK modulation. The signal demodulation module is used to demodulate the environmental object tag structure power vector, the tag antenna power vector, and the RSSI vector corresponding to the communication data to determine the information bits. The comparison vector determination module is specifically used for: The underdetermined equations are constructed using the power vector of the environmental object tag structure through the compressed sensing algorithm, and the combined extended Rytov approximation relative permittivity comparison vector is determined by optimizing the solution based on the two-step iterative shrinkage threshold algorithm. Based on the compressed sensing algorithm and the two-step iterative shrinkage threshold algorithm, the tag antenna extended Rytov approximate relative permittivity comparison vector is output by optimizing the tag antenna power vector.

7. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation as described in any one of claims 1-5.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the integrated sensing and backscattering communication method based on the phaseless extended Rytov approximation as described in any one of claims 1-5.

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