Imaging system and method of double-RIS auxiliary radio complementary mode

Abstract

The invention belongs to the technical field of wireless imaging, and discloses an imaging system and method of a double-RIS auxiliary radio complementary mode, and the system comprises a transmitter Tx, a first receiver Rx1, a second receiver Rx2, a forward RIS, a backward RIS, and a deep learning network. The forward RIS performs beam regulation and control on a signal transmitted by the Tx; one part of the wave beam which passes through the target sensing area SoI and is regulated and controlled is reflected by a target to form a reflection signal and is received by the Rx1, and the other part of the wave beam passes through the SoI and is used as a transmission signal to be received by the backward RIS and focused on the Rx2 after being subjected to wave beam regulation and control; and the deep learning network is used for carrying out feature extraction on the reflection signal and the transmission signal, correspondingly obtaining features for representing a reflection coefficient and a transmission coefficient, and carrying out feature fusion to generate a contour map of the target. Wherein the forward and backward RIS calls a beamforming control mode which is configured in advance when acquiring electromagnetic data, and sequentially scans and senses the space. The method can improve the quality of the reconstructed image.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless imaging, and more specifically, relates to an imaging system and method of a dual RIS-assisted radio complementary mode. Background Art

[0002] In contemporary society, radio frequency (RF) imaging is becoming increasingly popular in the field of electromagnetic (EM) sensing. RF imaging technology has demonstrated its ability to achieve high-resolution imaging under different lighting conditions and in all-weather situations. It can be applied to environmental monitoring, medical monitoring, autonomous driving, and security inspection. Traditional RF imaging systems rely on frequency diversity or spatial diversity to achieve accurate sensing. Specifically, specific signal modulation techniques broaden the bandwidth of the imaging signal and improve the degree of freedom of perception through frequency diversity, such as ultra-wideband (UWB) technology, linear frequency modulation (LFM), and orthogonal frequency division multiplexing (OFDM). In addition, antenna diversity techniques such as multiple-input multiple-output (MIMO) antennas can achieve super-resolution imaging in the angular domain, providing significant spatial diversity. Although these operations of expanding the signal bandwidth and increasing the number of RF antenna units can improve image resolution, they also bring problems such as high power consumption, complexity of the RF transceiver system, and increased cost.

[0003] Reconfigurable smart surface RIS, as an active member of the metamaterial family, has the advantages of low energy consumption, cost-effectiveness, and easy deployment. Combining RIS-based passive antennas with computational imaging (CI) expands the freedom of imaging.

[0004] There have been some studies on single RIS-assisted reflection mode imaging systems, which have demonstrated their feasibility by improving the multiplexing capability of spatial information. However, the complexity of the target structure and the effect of the target surface roughness relative to the illumination wavelength often lead to multiple scattering, producing weak and complex scattered waves. It is challenging to extract information from such complex, low signal-to-noise ratio reflection signals, especially from high-loss objects. Such imaging data usually results in blurred and incomplete image outputs from reconstruction algorithms. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides an imaging system and method of a dual RIS assisted radio complementary mode, which aims to improve imaging quality.

[0006] To achieve the above object, according to a first aspect of the present invention, there is provided an imaging system of a dual RIS-assisted radio complementary mode, comprising: a transmitter Tx, a first receiver Rx1, a second receiver Rx2, a forward RIS and a backward RIS placed on both sides of a target sensing area SoI, and an imaging network based on deep learning;

[0007] The transmitter Tx points to the forward RIS and is used to transmit a linear frequency modulation wave signal;

[0008] The forward RIS is used to perform beam control on the received linear frequency modulation wave signal so that the controlled beam passes through the target sensing area SoI; wherein a part of the beam passing through the target sensing area SoI is reflected by the target to form a reflected signal and is received by the first receiver Rx1, and the other part passes through the target sensing area SoI and is received by the backward RIS as a transmitted signal, and after the backward RIS beam control, is focused on the second receiver Rx2;

[0009] The deep learning-based imaging network is used to extract features of the reflection signal received by the first receiver Rx1 and the transmission signal received by the second receiver Rx2, obtain corresponding features for characterizing the reflection coefficient and the transmission coefficient, and generate a contour map of the target after feature fusion.

[0010] Furthermore, the forward RIS and the backward RIS use a configured radiation mode to control the corresponding beam; wherein, for the beam pointing to the target sensing area SoI, the configuration strategy of the forward RIS and the backward RIS radiation mode specifically includes:

[0011] The target sensing area SoI is divided into multiple blocks. For each block, the radiation mode of the forward RIS is adjusted to focus on each block in turn. With the goal of maximizing the signal power received by the current block, the greedy algorithm is used to traverse the states of the reflection units of the forward RIS, and a codebook consisting of the states of the reflection units of the forward RIS corresponding to the current block is obtained to determine the radiation mode of the forward RIS.

[0012] Based on the determined radiation mode of the forward RIS, with the goal of maximizing the power of the transmitted signal received by the second receiver Rx2, a greedy algorithm is used to traverse the states of the reflection units of the backward RIS, and a corresponding codebook composed of the states of the reflection units of the backward RIS is obtained to determine the radiation mode of the backward RIS.

[0013] Furthermore, for the beam directed to the rear RIS area, the configuration strategy for the forward RIS and the rearward RIS radiation modes specifically includes:

[0014] According to the area of ​​the backward RIS, the backward RIS is divided into a plurality of blocks; the radiation mode of the forward RIS is adjusted to focus on the block corresponding to the backward RIS, and the state of each reflection unit of the forward RIS is traversed by a greedy algorithm with the goal of maximizing the signal power received by the current block, so as to obtain a codebook corresponding to the current block and composed of the states of each reflection unit of the forward RIS, so as to determine the radiation mode of the forward RIS;

[0015] Based on the determined radiation mode of the forward RIS, with the goal of maximizing the power of the transmitted signal received by the second receiver Rx2, a greedy algorithm is used to traverse the states of the reflection units of the backward RIS, and a corresponding codebook composed of the states of the reflection units of the backward RIS is obtained to determine the radiation mode of the backward RIS.

[0016] Furthermore, for the radiation beam generated by the random phase control of the preceding RIS, the configuration strategy for the forward RIS and the backward RIS radiation modes specifically includes:

[0017] The radiation mode of the forward RIS is random. Under each random radiation mode of the forward RIS, with the goal of maximizing the power of the transmitted signal received by the second receiver Rx2, a greedy algorithm is used to traverse the states of the reflection units of the backward RIS, and a corresponding code book composed of the states of the reflection units of the backward RIS is obtained to determine the radiation mode of the backward RIS.

[0018] Furthermore, the length of each block divided in the target sensing area SoI is consistent with the main lobe width of the forward RIS.

[0019] Furthermore, the deep learning-based imaging network includes:

[0020] An encoder, used to encode the reflection signal and the transmission signal, and to generate features representing the reflection coefficient and the transmission coefficient respectively;

[0021] The fusion module is used to fuse the features generated by the encoder using an external attention mechanism to obtain enhanced features that focus on the target contour;

[0022] The decoder is used to decode the enhanced features to obtain a contour map of the target.

[0023] Furthermore, the encoder and the decoder are Res-CNN.

[0024] Furthermore, the central operating frequency band of the transmitter Tx is 5.8 GHz.

[0025] According to a second aspect of the present invention, a dual RIS assisted radio complementary mode imaging method is provided, comprising: using the imaging system described in any one of the first aspects to perform contour imaging of a target in a target perception area SoI to obtain a contour map of the target.

[0026] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0027] (1) The dual RIS-assisted radio complementary mode imaging system of the present invention establishes two radio receiving modes: reflection mode and transmission mode by placing forward RIS and backward RIS on both sides of the target sensing area SoI opposite to each other, and reconstructs the target contour based on the two complementary modes to improve the quality of the reconstructed image. Specifically, the forward RIS and backward RIS are used as electromagnetic control means, the first receiver Rx1 receives the reflection signal of the target object, and the second receiver Rx2 receives the transmission signal passing through the target sensing area SoI, and the reflection signal and transmission signal of the target object are obtained at the same time. The reflection signal and the transmission signal are respectively extracted, and the features representing the reflection coefficient and the transmission coefficient are correspondingly obtained. After fusion, the target contour is reconstructed. The present invention combines the two forms of electromagnetic signals of reflection and transmission, and has the function of complementary imaging. Compared with the existing method of reconstructing images only using reflection coefficients, the present invention reconstructs the target contour based on the reflection and transmission coefficient features of the target object. This method of assisting imaging through forward and backward RIS makes full use of the information of the beam of the target sensing area SoI, and greatly improves the quality of the generated image.

[0028] (2) Furthermore, the present invention adopts different scanning strategies for the beam focused on the target sensing area SoI, the beam focused on the backward RIS area, and the radiation beam generated by the random phase control of the previous RIS, so as to enhance the reflected signal and the transmitted signal. Specifically, by adjusting the radiation mode of the forward RIS, the signal power of the beam focused on different blocks of the target sensing area SoI is maximized, so as to mainly enhance the reflected signal; by adjusting the radiation mode of the forward RIS, it is focused on the block corresponding to the backward RIS, so as to mainly enhance the transmitted signal; and, the radiation beam generated by the random phase control of the previous RIS is enhanced. At the same time, for the backward RIS, the radiation mode of the backward RIS is adjusted based on the electric field distribution of each forward RIS, so as to finally make the electric field strength of the receiver Rx2 the highest, and respectively achieve the enhancement of the reflected signal and the transmitted signal under each strategy. In this way, the scanning of the beam focused on the target sensing area SoI, the beam focused on the backward RIS area, and / or the radiation beam area generated by the random phase control of the previous RIS is covered, so that the spatial resolution of the system is improved. Moreover, RF data (reflection and transmission signals) with various modes are obtained under different scanning strategies, thereby increasing spatial diversity; while ensuring imaging accuracy, a variety of electromagnetic data can be obtained with fewer sampling times.

[0029] (3) Furthermore, the deep learning-based imaging network designed in the present invention introduces an external attention mechanism into RF data imaging to fuse the features corresponding to the reflection signal and the transmission signal using the external attention mechanism, thereby achieving enhanced features that focus more on the target contour. Compared with the existing imaging method that directly uses a codec network, the present invention combines the external attention mechanism to improve the recognition of data features, achieve super-resolution imaging results, and improve the robustness and effect of imaging.

[0030] (4) Preferably, the length of each block divided within the target sensing area SoI is consistent with the main lobe width of the forward RIS to maximize the power of the received signal.

[0031] In summary, the present invention combines the perception signal reflection mode signal and the transmission mode signal to achieve imaging. This operation alleviates the situation of signal instability when only the reflection signal is used for imaging. The hybrid mode dual RIS system of the present invention shows the synergistic and complementary value of this multi-modal combination. In addition, RIS, as a large-aperture antenna of the passive relay "antenna" array, can help Rx achieve a wider field of view and precise perception. Compared with the deployment of large-scale transceiver equipment, the deployment of multiple RIS can obtain higher spatial diversity characteristics, realizing an economical and high-freedom imaging method, so that the system of the present invention exhibits a high degree of freedom configuration characteristics similar to dual static radar. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of an imaging system in a dual RIS-assisted radio complementary mode in an embodiment of the present invention.

[0033] Figure 2 Schematic diagram of the hardware structure of the imaging system of the dual RIS assisted radio complementary mode in an embodiment of the present invention.

[0034] Figure 3 Schematic diagram of three types of scanning strategies in an embodiment of the present invention; wherein, Figure 3 (a)-(c) in the figure are respectively the scanning strategy of focusing the beam on the target sensing area SoI, the scanning strategy of pointing the beam to the RIS, and the scanning strategy of the radiation beam generated by the random phase control of the RIS.

[0035] Figure 4 This is a schematic diagram of the deep learning imaging network structure in an embodiment of the present invention; Figure 4 (a)-(c) in the figure are the main network structure, external attention mechanism and Res-CNN network structure respectively.

[0036] Figure 5 It is a visualization diagram of data collection in an embodiment of the present invention.

[0037] Figure 6 Schematic diagram of imaging results and data visualization corresponding to different original images in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0039] In the present invention, the terms "first", "second", etc. in the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0040] Example 1

[0041] like Figure 1 and Figure 2As shown, an embodiment of the present invention provides an imaging system of a dual RIS-assisted radio complementary mode, including: a transmitter Tx, a first receiver Rx1, a second receiver Rx2, a forward reconfigurable intelligent surface RIS, a rearward reconfigurable intelligent surface RIS, and an imaging network based on deep learning; wherein the forward RIS and the rearward RIS are placed oppositely on both sides of the target perception area SoI.

[0042] The transmitter Tx points to the forward RIS and is used to transmit a linear frequency modulation wave signal;

[0043] The forward RIS is used to perform beam control on the received linear frequency modulation wave signal, so that the controlled beam passes through the target sensing area SoI; wherein, a part of the beam passing through the target sensing area SoI is reflected by the target to form a reflected signal and is received by the first receiver Rx1, and the other part passes through the target sensing area SoI and is received by the backward RIS as a transmitted signal and after the backward RIS beam control, the controlled electromagnetic signal beam is focused on the second receiver Rx2;

[0044] The deep learning-based imaging network is used to extract features of the reflection signal received by the first receiver Rx1 and the transmission signal received by the second receiver Rx2, and obtain corresponding features for characterizing the reflection coefficient and the transmission coefficient, and after feature fusion, generate a contour map of the target.

[0045] In the embodiment of the present invention, the central operating frequency band of the system is 5.8 GHz, which is also the current operating frequency band of indoor Wi-Fi.

[0046] The dual RIS-assisted radio complementary mode imaging system of the present invention establishes two radio receiving modes: reflection mode and transmission mode by placing forward RIS and backward RIS on both sides of the target sensing area SoI opposite to each other, and reconstructs the target contour based on the two complementary modes to improve the quality of the reconstructed image. Specifically, the forward RIS and backward RIS are used as electromagnetic control means, the first receiver Rx1 receives the reflection signal of the target object, and the second receiver Rx2 receives the transmission signal passing through the target sensing area SoI, and the reflection signal and transmission signal of the target object are obtained at the same time. The reflection signal and the transmission signal are respectively extracted, and the features characterizing the reflection coefficient and the transmission coefficient are correspondingly obtained. After fusion, the target contour is reconstructed. The present invention combines the two forms of electromagnetic signals of reflection and transmission, and has the function of complementary imaging. Compared with the existing method of reconstructing images only using the reflection coefficient, the present invention reconstructs the target contour based on the reflection and transmission coefficient features of the target object. This method of assisting imaging through the forward and backward RIS makes full use of the information of the SoI beam in the target sensing area, and greatly improves the quality of the generated image.

[0047] Experiments have also proved that the system of the present invention can achieve a higher super-resolution result than the existing 5.8 GHz electromagnetic imaging.

[0048] As a further design of the present invention, when the forward RIS and the backward RIS perform beam control, they call the configured radiation mode to scan and sense the space in turn to perform beam forming.

[0049] In the embodiment of the present invention, three types of scanning strategies are used to configure the radiation modes of the forward RIS and the backward RIS to enhance the reflected signal and the transmitted signal. Specifically:

[0050] The beams controlled by the previous RIS can be roughly divided into three categories: beams focused on the target sensing area SoI, beams focused on the backward RIS area, and radiation beams generated by the previous RIS random phase control. Correspondingly, the illumination strategies of the three types of beams are as follows: Figure 3 As shown in (a)-(c) in .

[0051] like Figure 3 As shown in (a) in the figure, the first type of beam scanning strategy is a scanning strategy that focuses the beam on the target sensing area SoI (mainly for enhancing the reflected signal). The target sensing area SoI is divided into blocks, and each block corresponds to a scanning strategy. The radiation mode of the forward RIS is regulated to focus on each block in turn. The greedy algorithm is used to traverse the states of each reflection unit of the forward RIS (including 0 and 1). The signal power received by the current block is maximized, and the codebook corresponding to the current block, which is composed of the states of each reflection unit of the forward RIS, is obtained. The radiation mode of the forward RIS is determined by the codebook corresponding to each block.

[0052] For the backward RIS, the control strategy is set according to the electric field distribution of the forward RIS to reshape the backward electromagnetic field, and finally the electric field strength of the receiver Rx2 is maximized. Specifically, based on the determined radiation mode of the forward RIS, a greedy algorithm is used to traverse the states of each reflection unit of the backward RIS, with the goal of maximizing the signal power received by the second receiver Rx2, and the corresponding codebook composed of the states of each reflection unit of the backward RIS is obtained to determine the radiation mode of the backward RIS.

[0053] like Figure 3As shown in (b), the second type of scanning strategy is a beam scanning strategy that points the beam to the rear RIS (mainly for enhancing the transmitted signal). According to the area of ​​the rear RIS, the rear RIS is divided into multiple blocks, and the radiation mode of the forward RIS is adjusted to focus on the block corresponding to the rear RIS. The greedy algorithm is used to traverse the states of the reflection units of the forward RIS, with the goal of maximizing the signal power received by the current block, and the codebook corresponding to the current block consisting of the states of the reflection units of the forward RIS is obtained. The radiation mode of the forward RIS is determined by the codebook corresponding to each block. Based on the determined radiation mode of the forward RIS, the greedy algorithm is used to traverse the states of the reflection units of the rear RIS, with the goal of maximizing the signal power received by the second receiver Rx2, and the corresponding codebook consisting of the states of the reflection units of the rear RIS is obtained to determine the radiation mode of the rear RIS.

[0054] like Figure 3 As shown in (c) in the figure, the third type of scanning strategy is the radiation beam scanning strategy generated by the random phase control of the previous RIS. The radiation mode of the forward RIS is random. Under each random radiation mode of the forward RIS, a greedy algorithm is used to traverse the states of each reflection unit of the backward RIS, with the goal of maximizing the signal power received by the second receiver Rx2, and the corresponding codebook composed of the states of each reflection unit of the backward RIS is obtained to determine the radiation mode of the backward RIS.

[0055] In the embodiment of the present invention, the target sensing area SoI is divided into 35 blocks, each block corresponds to a scanning strategy, a total of 35 types, each focus point of the beam focus area is as follows: Figure 3 As shown in (a) in the figure. Each beam points to each block in the sensing area. The size of the block is 0.3m*0.3m, which just corresponds to the main lobe width of the forward RIS, so as to maximize the power of the received signal. The second scanning strategy has a total of 25 types, and the spatial beam focus points pointed to are as follows: Figure 3 As shown in (b), the size of the RIS used in the embodiment of the present invention is 1.4m*1.4m. The third is a scanning strategy for 10 random radiations, such as Figure 3As shown in (c) in the figure. A greedy traversal algorithm is used to realize the actual acquisition of the beam. In an embodiment of the present invention, under three categories of 70 scanning modes, the system can complete the acquisition of the corresponding RF data (reflection signal and transmission signal) under each scanning mode. For one cycle (70 types of sequential calls and traversals are one cycle), the acquisition time is controlled within 1s. By collecting the corresponding RF data at different times, continuous and real-time reconstruction of the target can be achieved. In an embodiment of the present invention, a codebook of 70 beam scans is designed. This beam joint control and the obtained RIS control scheme not only ensure the reliability of the collected samples, but also ensure the accurate acquisition of radio data with less perception sampling. In an embodiment of the present invention, the label corresponding to the data collected in each cycle is the image contour of the target in the perception area.

[0056] That is to say, this scanning method of the present invention not only takes into account the effect of energy gain brought by beam focusing, but also improves the spatial resolution of the system in different scanning areas. Specifically, by adjusting the radiation mode of the forward RIS, the signal power of the beam focused on different blocks of the target sensing area SoI is maximized, so as to mainly enhance the reflected signal; by adjusting the radiation mode of the forward RIS, it is focused on the block corresponding to the backward RIS, so as to mainly enhance the transmitted signal; and, the radiation beam generated by the random phase control of the front RIS is enhanced. At the same time, for the backward RIS, the electromagnetic field of the backward RIS is reshaped based on the electric field distribution of each forward RIS, so that the electric field strength of the receiver Rx2 is finally the highest, and the reflected signal and the transmitted signal under each strategy are enhanced respectively. In this way, the scanning of the beam focused on the target sensing area SoI, the beam focused on the backward RIS area, and the radiation beam area generated by the random phase control of the front RIS is covered, so that the spatial resolution of the system is improved. Moreover, RF data (reflection and transmission signals) with various modes are obtained under different scanning strategies, thereby increasing spatial diversity; while ensuring imaging accuracy, a variety of electromagnetic data can be obtained with fewer sampling times.

[0057] As a further design of the present invention, Figure 4 As shown, the deep learning-based imaging network in the embodiment of the present invention includes an encoder, a fusion module and a decoder.

[0058] The encoder is used to encode the reflection signal and the transmission signal, and correspondingly generate features representing the reflection coefficient and the transmission coefficient;

[0059] The fusion module is used to fuse the features generated by the encoder using an external attention mechanism to generate enhanced features that focus more on the target contour;

[0060] The decoder is used to decode the enhanced features to obtain the contour map of the target.

[0061] As a preferred implementation, the encoder and decoder are Res-CNN.

[0062] The deep learning-based imaging network designed in the present invention introduces an external attention mechanism into RF data imaging to fuse the features corresponding to the reflection signal and the transmission signal using the external attention mechanism, thereby achieving enhanced features that focus more on the target contour. Compared with the existing method of directly using a codec network for imaging, the present invention combines the external attention mechanism to improve the recognition of data features, can achieve super-resolution imaging results, and improve the robustness and effect of imaging.

[0063] The imaging effect of the present invention is described below with reference to specific examples.

[0064] like Figure 5 The figure is a visualization diagram of data collection in an embodiment of the present invention. Figure 5 The first to third rows in the figure represent the original image and the corresponding reflection signal and transmission signal respectively.

[0065] In the implementation of the present invention, different deep learning networks are used to reconstruct the target contour for the acquired reflection signal and transmission signal data, and the target contour is reconstructed under different evaluation indicators (SSIM, IoU, MAE, F β , BEC), the corresponding experimental data are shown in Table 1 below.

[0066] Table 1 Comparison of ablation experiment results

[0067]

[0068] Among them, SA in Table 1 is a self-attention mechanism network, EA-I is QV as an external attention parameter, and PSD2ImageNet is an imaging network based on deep learning in the embodiment of the present invention that uses K as an external attention parameter. SSIM is a structural similarity index, IoU is an evaluation index used to measure the overlap between the predicted border and the true border, MAE is the mean absolute error, and F β BEC is a binary cross entropy that combines the two metrics of precision and recall into a single score calculated by harmonic mean.

[0069] It can be seen that the PSD2ImageNet proposed in the present invention achieves the best performance in the structural similarity index as the main evaluation index, and also achieves relatively good performance in other indicators.

[0070] In the embodiment of the present invention, better performance can be obtained for different actual images, such as Figure 6 As shown, Figure 6 The first row in the figure is the actual image data. The second, third and fourth rows are the imaging results using the transmission + reflection signal, reflection signal and transmission signal outputs. It can be seen that the reconstruction image in the second row (the imaging result obtained using the transmission + reflection signal) is the best. The bottom two rows are the actually collected electromagnetic signals (reflection signal and transmission signal).

[0071] Example 2

[0072] The embodiment of the present invention provides an imaging method of a dual RIS assisted radio complementary mode, comprising: using the imaging system of the dual RIS assisted radio complementary mode in Embodiment 1 to perform contour imaging on a target in a target sensing area SoI to obtain a contour map of the target.

[0073] For the related technical solutions, please refer to the corresponding description in Example 1, which will not be repeated here.

[0074] In general, the present invention proposes a deployment scheme and system for dual RIS-assisted imaging. The joint regulation of forward and backward RIS can achieve more precise capture of electromagnetic signals. A deep learning network model based on an external attention encoding and decoding structure is proposed. By using images to guide the conversion of radio data to target contours, the embodiments of the present invention achieve the optimal wireless imaging in the current 5.8 GHz frequency band and complete super-resolution imaging.

[0075] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dual RIS-assisted radio complementary mode imaging system, characterized in that: include: A transmitter Tx, a first receiver Rx1, a second receiver Rx2, a forward RIS and a backward RIS placed on both sides of the target sensing area SoI, and an imaging network based on deep learning; The transmitter Tx points to the forward RIS and is used to transmit a linear frequency modulation wave signal; The forward RIS is used to perform beam control on the received linear frequency modulation wave signal so that the controlled beam passes through the target sensing area SoI; wherein a part of the beam passing through the target sensing area SoI is reflected by the target to form a reflected signal and is received by the first receiver Rx1, and the other part passes through the target sensing area SoI and is received by the backward RIS as a transmitted signal, and after the backward RIS beam control, is focused on the second receiver Rx2; The deep learning-based imaging network is used to extract features of the reflection signal received by the first receiver Rx1 and the transmission signal received by the second receiver Rx2, obtain corresponding features for characterizing the reflection coefficient and the transmission coefficient, and generate a contour map of the target after feature fusion.

2. The imaging system according to claim 1, characterized in that The forward RIS and the backward RIS use a configured radiation mode to control the corresponding beam; wherein, for the beam pointing to the target sensing area SoI, the configuration strategy of the radiation mode of the forward RIS and the backward RIS specifically includes: The target sensing area SoI is divided into multiple blocks. For each block, the radiation mode of the forward RIS is adjusted to focus on each block in turn. With the goal of maximizing the signal power received by the current block, the greedy algorithm is used to traverse the states of the reflection units of the forward RIS, and a codebook consisting of the states of the reflection units of the forward RIS corresponding to the current block is obtained to determine the radiation mode of the forward RIS. Based on the determined radiation mode of the forward RIS, with the goal of maximizing the power of the transmitted signal received by the second receiver Rx2, a greedy algorithm is used to traverse the states of the reflection units of the backward RIS, and a corresponding codebook composed of the states of the reflection units of the backward RIS is obtained to determine the radiation mode of the backward RIS.

3. The imaging system according to claim 2, characterized in that For the beam directed to the rear RIS area, the configuration strategy for the forward RIS and the rear RIS radiation modes specifically includes: According to the area of ​​the backward RIS, the backward RIS is divided into a plurality of blocks; the radiation mode of the forward RIS is adjusted to focus on the block corresponding to the backward RIS, and the state of each reflection unit of the forward RIS is traversed by a greedy algorithm with the goal of maximizing the signal power received by the current block, so as to obtain a codebook corresponding to the current block and composed of the states of each reflection unit of the forward RIS, so as to determine the radiation mode of the forward RIS; Based on the determined radiation mode of the forward RIS, with the goal of maximizing the power of the transmitted signal received by the second receiver Rx2, a greedy algorithm is used to traverse the states of the reflection units of the backward RIS, and a corresponding codebook composed of the states of the reflection units of the backward RIS is obtained to determine the radiation mode of the backward RIS.

4. The imaging system according to claim 2 or 3, characterized in that: With respect to the radiation beam generated by the random phase control of the preceding RIS, the configuration strategy for the forward RIS and the backward RIS radiation modes specifically includes: The radiation mode of the forward RIS is random. Under each random radiation mode of the forward RIS, with the goal of maximizing the power of the transmitted signal received by the second receiver Rx2, a greedy algorithm is used to traverse the states of the reflection units of the backward RIS, and a corresponding code book composed of the states of the reflection units of the backward RIS is obtained to determine the radiation mode of the backward RIS.

5. The imaging system according to claim 2, characterized in that: The length of each block divided within the target sensing area SoI is consistent with the main lobe width of the forward RIS.

6. The imaging system according to claim 1, characterized in that: The deep learning-based imaging network includes: An encoder, used to encode the reflection signal and the transmission signal, and to generate features representing the reflection coefficient and the transmission coefficient respectively; The fusion module is used to fuse the features generated by the encoder using an external attention mechanism to obtain enhanced features that focus on the target contour; The decoder is used to decode the enhanced features to obtain a contour map of the target.

7. The imaging system according to claim 6, characterized in that: The encoder and the decoder are Res-CNN.

8. The imaging system according to claim 1, characterized in that: The central operating frequency band of the transmitter Tx is 5.8 GHz.

9. A dual RIS-assisted radio complementary mode imaging method, characterized in that: include: The imaging system according to any one of claims 1 to 8 is used to perform contour imaging on a target in a target perception area SoI to obtain a contour map of the target.

Images