Regional imaging system construction method based on RIS assistance

Abstract

The invention discloses a region imaging system construction method based on RIS assistance. The method comprises the following steps: (1) constructing a scene model; (2) constructing an RIS channel model; (3) constructing a received signal model; and (4) carrying out validity experiment verification. By constructing an RIS channel model and a receiving signal model, the purposes of improving the resolution ratio of RIS-assisted RF imaging and improving the imaging effect are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of imaging system application, and in particular to a method for constructing a regional imaging system based on RIS assistance. Background Art

[0002] In the existing technology, RF wireless radio frequency imaging systems can be used in many fields, such as detection, security and privacy, human-computer interaction, smart home, telemedicine and augmented virtual reality. Compared with traditional optical and infrared imaging systems, the advantage of RF imaging is that it can be used in non-line-of-sight (NLOS) scenarios or scenarios with obstructions.

[0003] In communication sensor networks, array antennas estimate angles by receiving signal phase differences. The existing large array antenna technology can provide rich signal feature dimensions for single-station positioning. However, the existing three-dimensional spatial imaging technology has low imaging resolution and poor imaging effect.

[0004] How to improve the three-dimensional imaging resolution and imaging effect is a technical problem that needs to be solved urgently at this stage.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a method for constructing a regional imaging system based on RIS assistance, so as to achieve the purpose of improving the resolution of RIS-assisted RF imaging and enhancing the imaging effect.

[0007] To achieve the above object, the technical solution of the present invention is as follows:

[0008] A method for constructing a regional imaging system based on RIS assistance includes the following steps:

[0009] (1) Build a scene model;

[0010] (2) Construct RIS channel model;

[0011] (3) Construct a signal receiving model;

[0012] (4) Conduct effectiveness experiments to verify the results.

[0013] The present invention provides a method for constructing a regional imaging system based on RIS assistance, which achieves the purpose of improving the resolution of RIS-assisted RF imaging and enhancing the imaging effect by constructing a RIS channel model and a receiving signal model.

[0014] It is further worth noting that step (1) is specifically as follows:

[0015] Each RIS is an n-element uniform rectangular array (URA) with each element spaced λ / 2, where λ represents the wavelength;

[0016] Each RIS operates with a discrete phase shift of one bit, serving a single receiver;

[0017] There is an NLoS path between the SOI and the receiver, and the sensing link is provided as a reflection through the RIS;

[0018] The three-dimensional SoI is divided into M subspaces;

[0019] The complex-valued signal s from the SoI is represented by a vector;

[0020] The receiver and RIS operate passively at f=5.8 GHz.

[0021] It is further worth noting that the step (2) is specifically as follows:

[0022] The M grid points of the SoI are considered as event signal sources; considering that RIS is a UPA, the channel of a single RIS-assisted perception is modeled as follows:

[0023] h=h s a s ΩA i H i ;

[0024] in, is the propagation channel matrix of M far-field sources to RIS, whose phase and amplitude vary with distance

[0025] change;

[0026] The distance from RIS to the mth source is recorded as

[0027] The propagation channel between RIS and the receiver is denoted by

[0028] The distance from RIS to the receiver is denoted as r s ;

[0029] A i is the incident steering vector of the RIS unit array, expressed as:

[0030]

[0031] in represents the steering vector of the reflected wave of the RIS array element;

[0032] is the spatial location of each RIS unit;

[0033] u m =[sinθ m cosφ m , sinθ m sinφ m , cosθ m ], m = 1, ..., M;

[0034] It is a RIS unit configuration.

[0035] It is further worth noting that the step (3) is specifically as follows:

[0036] There are M signals on RIS From different grid points ((θ1, φ1, r1), …, (θ M ,φ M , r M )), where s m is the signal amplitude, α m is the signal phase;

[0037] The signal received by the space-fed RISs can be expressed as:

[0038] y s =hs i +n;

[0039] Assuming that the signal strength distribution of SoI in a short sensing time is approximately constant; by generating T encoding matrices Ω to sense the event signal, the received signal matrix of T samples can be expressed as:

[0040]

[0041] The interactions between multiple RISs are negligible;

[0042] Therefore, for a single receiver serving a single RIS distributed system, the T sampled signals received by the receivers of the K systems are:

[0043]

[0044] It is further worth noting that the received signal matrix is ​​transformed with constraints into the following form:

[0045]

[0046] Using the received signal The signal strength distribution of SoI is recovered and the problem is transformed into a phase recovery problem;

[0047]

[0048] The amplitude constraint is transformed into an intensity-based empirical loss function optimization problem as follows:

[0049]

[0050] In the equation, z = x; the minimization problem is solved using an iterative optimization method, which is divided into two parts:

[0051]

[0052] The solution is transformed into a reweighted Wirtinger flow model using an iterative reweighted LS algorithm to improve the computational performance.

[0053]

[0054] in,

[0055]

[0056] These weights are based on z k-1 Adaptively calculated from the algorithm, where z k-1 is the result of (k-1) iterations; η i is a parameter, and at each iteration k, we will solve an optimization problem:

[0057]

[0058] in,

[0059] It is further worth noting that step (4) is specifically as follows: data from two real-world scenes are collected by a general-purpose software radio peripheral, and RISs changes the phase shift through FPGA to complete a single measurement of the SoI; computational imaging is performed on the two-dimensional SoI.

[0060] It is further worth noting that the state of each column of the RIS is set to be the same so that a RIS containing 32×10 elements can be treated as a 32×1 row array:

[0061] In the first scenario, the SoI covers a 7m×8m range at 1m intervals in the microwave chamber; T=500 randomly samples from RIS1 to complete the data collection of the SoI, and similarly, RIS2 is activated;

[0062] In the second scenario, the SoI covers a 5m×6m area with a spacing of 1m. The image is reconstructed when the phase of the complex-valued signal is unknown.

[0063] The present invention has the following advantages:

[0064] 1. The present invention provides a method for constructing a regional imaging system based on RIS assistance, which achieves the purpose of improving the resolution of RIS-assisted RF imaging and enhancing the imaging effect by constructing a RIS channel model and a receiving signal model. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

[0066] Figure 1 A schematic diagram of the layout of a scene model disclosed in an embodiment of the present invention;

[0067] Figure 2 A schematic diagram of an imaging layout in a darkroom disclosed in an embodiment of the present invention;

[0068] Figure 3 A schematic diagram of an imaging layout in an office disclosed in an embodiment of the present invention;

[0069] Figure 4 This is a diagram of the imaging reconstruction result in a darkroom disclosed in an embodiment of the present invention;

[0070] Figure 5 This is a diagram of the imaging reconstruction result in the office disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0072] The present invention provides a method for constructing a regional imaging system based on RIS assistance, and its working principle is to achieve the purpose of improving the resolution of RIS-assisted RF imaging and enhancing the imaging effect by constructing a RIS channel model and a receiving signal model.

[0073] The present invention is further described in detail below with reference to examples and specific implementation modes.

[0074] A method for constructing a regional imaging system based on RIS assistance includes the following steps:

[0075] (1) Build a scene model, such as Figure 1 ;

[0076] Each RIS is an n-element uniform rectangular array (URA) with each element spaced λ / 2, where λ represents the wavelength;

[0077] Each RIS operates with a discrete phase shift of one bit, serving a single receiver;

[0078] There is an NLoS path between the SOI and the receiver, and the sensing link is provided as a reflection through the RIS;

[0079] The three-dimensional SoI is divided into M subspaces;

[0080] The complex-valued signal s from the SoI is represented by a vector;

[0081] The receiver and RIS operate passively at f=5.8 GHz.

[0082] (2) Construct RIS channel model;

[0083] The M grid points of the SoI are considered as event signal sources; considering that RIS is a UPA, the channel of a single RIS-assisted perception is modeled as follows:

[0084] h=h s a s ΩA i H i ;

[0085] in, is the propagation channel matrix of M far-field sources to RIS, whose phase and amplitude vary with distance

[0086] change;

[0087] The distance from RIS to the mth source is recorded as

[0088] The propagation channel between RIS and the receiver is denoted as

[0089] The distance from RIS to the receiver is denoted as r s ;

[0090] A i is the incident steering vector of the RIS unit array, expressed as:

[0091]

[0092] in represents the steering vector of the reflected wave of the RIS array element;

[0093] is the spatial location of each RIS unit;

[0094] u m =[sinθ m , cosφ m , sinθ m , sinφ m , cosθ m ], m = 1, ..., M;

[0095] It is a RIS unit configuration.

[0096] (3) Construct a signal receiving model;

[0097] There are M signals on RIS From different grid points ((θ1, φ1, r1), …, (θ M ,φ M , r M )), where s m is the signal amplitude, α m is the signal phase;

[0098] The signal received by the space-fed RISs can be expressed as:

[0099] y s =hs i +n;

[0100] Assuming that the signal strength distribution of SoI in a short sensing time is approximately constant; by generating T encoding matrices Ω to sense the event signal, the received signal matrix of T samples can be expressed as:

[0101]

[0102] The interactions between multiple RISs are negligible;

[0103] Therefore, for a single receiver serving a single RIS distributed system, the T sampled signals received by the receivers of the K systems are:

[0104]

[0105] It is further worth noting that the received signal matrix is ​​transformed with constraints into the following form:

[0106]

[0107] Using the received signal The signal strength distribution of SoI is recovered and the problem is transformed into a phase recovery problem;

[0108]

[0109] The amplitude constraint is transformed into an intensity-based empirical loss function optimization problem as follows:

[0110]

[0111] In the equation, z = x; the minimization problem is solved using an iterative optimization method, which is divided into two parts:

[0112]

[0113] The solution is transformed into a reweighted Wirtinger flow model using an iterative reweighted LS algorithm to improve the computational performance.

[0114]

[0115] in,

[0116]

[0117] These weights are based on z k-1 Adaptively calculated from the algorithm, where z k-1 is the result of (k-1) iterations; η i is a parameter, and at each iteration k, we will solve an optimization problem:

[0118]

[0119] in,

[0120] (4) Conduct effectiveness experiments to verify the results.

[0121] Data from two real-world scenarios were collected by a general-purpose software radio peripheral [e.g. Figure 2 and Figure 3 】, RISs completes a single measurement of SoI by changing the phase shift through FPGA; and performs computational imaging on a two-dimensional SoI.

[0122] Set the state of each column of RIS to be the same so that a RIS containing 32×10 elements can be treated as a 32×1 row array:

[0123] In the first scenario, SoI covers a 7m×8m range in a microwave chamber at 1m intervals; T=500 randomly samples from RIS1 to complete the data acquisition of SoI, and similarly, RIS2 is activated; the imaging results are shown in Figure 4 .

[0124] In the second scenario, SoI covers a range of 5m×6m with a spacing of 1m. The imaging results are as follows: Figure 5 .

[0125] Through the above method, the present invention provides a method for constructing a regional imaging system based on RIS assistance, and its working principle is to improve the resolution of RIS-assisted RF imaging and enhance the imaging effect by constructing a RIS channel model and a receiving signal model.

[0126] The above is only a preferred implementation of a method for constructing a regional imaging system based on RIS assistance disclosed in the present invention. It should be pointed out that, for ordinary technicians in this field, several modifications and improvements can be made without departing from the creative concept of the present invention, which all fall within the protection scope of the present invention.

Claims

1. A method for constructing a regional imaging system based on RIS, characterized in that: It includes the following steps: (1) Build a scene model; (2) Construct RIS channel model; (3) Construct a signal receiving model; (4) Conduct effectiveness experiments to verify the results.

2. The method for constructing a regional imaging system based on RIS assistance according to claim 1, characterized in that: Step (1) is as follows: Each RIS is an n-element uniform rectangular array (URA) with each element spaced λ / 2, where λ represents the wavelength; Each RIS operates with a discrete phase shift of one bit, serving a single receiver; There is an NLoS path between the SOI and the receiver, and the sensing link is provided as a reflection through the RIS; The three-dimensional SoI is divided into M subspaces; The complex-valued signal s from the SoI is represented by a vector; The receiver and RIS operate passively at f=5.8 GHz.

3. The method for constructing a regional imaging system based on RIS assistance according to claim 1, characterized in that: The step (2) is specifically as follows: The M grid points of the SoI are considered as event signal sources; considering that RIS is a UPA, the channel of a single RIS-assisted perception is modeled as follows: h=h s a s ΩA i H i ; in, is the propagation channel matrix of M far-field sources to RIS, whose phase and amplitude vary with distance; The distance from RIS to the mth source is recorded as The propagation channel between RIS and the receiver is denoted as The distance from RIS to the receiver is denoted as r s ; A i is the incident steering vector of the RIS unit array, expressed as: in represents the steering vector of the reflected wave of the RIS array element; is the spatial location of each RIS unit; u m =[sinθ m cosφ m ,sinθ m sinφ m ,cosθ m ],m=1,…,M; It is a RIS unit configuration.

4. The method for constructing a regional imaging system based on RIS assistance according to claim 2, characterized in that: The step (3) is specifically as follows: There are M signals on RIS From different grid points ((θ1, φ1, r1), …, (θ M ,φ M , r M )), where s m is the signal amplitude, α m is the signal phase; The signal received by the space-fed RISs can be expressed as: y s =hs i +n; Assuming that the signal strength distribution of SoI in a short sensing time is approximately constant; by generating T encoding matrices Ω to sense the event signal, the received signal matrix of T samples can be expressed as: The interactions between multiple RISs are negligible; Therefore, for a single receiver serving a single RIS distributed system, the T sampled signals received by the receivers of the K systems are:

5. The method for constructing a regional imaging system based on RIS assistance according to claim 4, characterized in that: The received signal matrix is ​​transformed into the following form with constraints: Using the received signal The signal strength distribution of SoI is recovered and the problem is transformed into a phase recovery problem; The amplitude constraint is transformed into an intensity-based empirical loss function optimization problem as follows: In the equation, z = x; the minimization problem is solved using an iterative optimization method, which is divided into two parts: The solution is transformed into a reweighted Wirtinger flow model using an iterative reweighted LS algorithm to improve the computational performance. in, These weights are based on z k-1 Adaptively calculated from the algorithm, where z k-1 is the result of (k-1) iterations; η is a parameter, and for each iteration k, we will solve an optimization problem: in, 6. The method for constructing a regional imaging system based on RIS assistance according to claim 1, characterized in that: Step (4) is as follows: data from two real-world scenes are collected by a general-purpose software radio peripheral, and RISs changes the phase shift through FPGA to complete a single measurement of the SoI; computational imaging is performed on the two-dimensional SoI.

7. A method for constructing a regional imaging system based on RIS assistance according to claim 6, characterized in that: Set the state of each column of RIS to be the same so that a RIS containing 32×10 elements can be treated as a 32×1 row array: In the first scenario, the SoI covers a 7m×8m range at 1m intervals in the microwave chamber; T=500 randomly samples from RIS1 to complete the data collection of the SoI, and similarly, RIS2 is activated; In the second scenario, the SoI covers a 5m×6m area with a spacing of 1m; the image is reconstructed when the phase of the complex-valued signal is unknown.

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