Passive Internet of Things terminal positioning method and system

Abstract

The invention discloses a passive Internet of Things terminal positioning method and system, belongs to the technical field of wireless communication and positioning, and solves the problems of high power consumption caused by continuous monitoring / interaction of a terminal, low precision caused by NLOS and multipath, high uplink report quantity, easy congestion, blind area caused by weak coverage, high cost of fingerprint database and sensor remediation and error accumulation in traditional positioning. Comprising a regulation and control subsystem, a sensing subsystem and a calculation subsystem, the communication connection calculation subsystem is in communication connection with the regulation and control subsystem and the sensing subsystem, and the regulation and control subsystem forms a wireless environment by dynamically controlling a reflection mode of the RIS. And the sensing subsystem synchronously receives the reflected signals and uploads the received data to the computing subsystem. According to the invention, a 5G / 5G-A (5G-Advanced) heterogeneous network, a reconfigurable intelligent surface and an environment backscatter communication principle are utilized, so that low-power-consumption and high-precision indoor and outdoor seamless positioning is realized.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and positioning technology, and in particular to a passive Internet of Things (IoT) terminal positioning method and system. Background Technology

[0002] With the rapid development of IoT technology, billions of IoT devices are connected to the network, leading to a surge in demand for location services. Traditional outdoor positioning technologies based on the Global Navigation Satellite System (GNSS) cannot meet the coverage requirements of indoor and complex urban scenarios. Existing 4G / 5G cellular network positioning technologies, such as uplink time difference of arrival (TDOA), downlink TDOA, and observation TDOA, primarily rely on active signal measurement and interaction between the terminal and multiple base stations. These technologies have the following inherent drawbacks: 1. High terminal power consumption: The terminal needs to continuously listen to and measure positioning reference signals and perform multiple signaling interactions with the network. For large-scale IoT terminals that are powered by batteries and require a lifespan of several years, the power consumption is unacceptable.

[0003] 2. Limited positioning accuracy: In non-line-of-sight, multipath-effect-prone indoor and urban canyon environments, the propagation path of wireless signals is complex, resulting in large errors in time- or angle-based measurements and a sharp decline in positioning accuracy.

[0004] 3. Heavy network burden: The uplink transmission of a large number of positioning measurement reports consumes valuable wireless air interface resources, increases network signaling overhead, and is prone to causing network congestion when large-scale terminals are simultaneously positioning.

[0005] 4. Coverage blind spot problem: In areas with weak base station signal coverage (such as basements or edge areas), the terminal cannot complete effective signal measurement, resulting in positioning failure.

[0006] While some studies have proposed using technologies such as signal fingerprinting or sensor fusion, the cost of building and maintaining fingerprint databases is high, and sensors suffer from cumulative errors. Therefore, there is an urgent need for a new solution that can fundamentally reduce terminal power consumption and achieve stable and reliable positioning in complex environments.

[0007] Therefore, a passive IoT terminal positioning method and system are proposed to solve or alleviate the above problems. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of existing technologies by proposing a passive IoT terminal positioning method and system.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: A passive IoT terminal positioning system includes a control subsystem, a sensing subsystem, and a computing subsystem. The computing subsystem is communicatively connected to both the control subsystem and the sensing subsystem. The control subsystem shapes the wireless environment by dynamically controlling the reflection mode of a RIS (Radio Reflection System). The sensing subsystem synchronously receives reflected signals and uploads the received data to the computing subsystem. The computing subsystem performs signal separation and feature extraction on the received data based on the reflection mode sequence, constructs a spatiotemporal observation matrix, and calculates the final location coordinates of the IoT terminal by solving an optimization problem of a forward channel model that integrates a direct channel, a reconfigurable smart surface reflection channel, terminal reflection characteristics, and spatial location.

[0010] Preferably, the control subsystem includes a central controller and several reconfigurable smart surface units deployed in the positioning area. The central controller and the reconfigurable smart surface units are connected via a control link. The central controller is used to receive instructions from an external computing subsystem and generate control signals based on the instructions. The reconfigurable smart surface units are used to receive control signals from the central controller and dynamically adjust the reflection coefficient of their surface electromagnetic units to controllably reflect and beamform incident wireless signals.

[0011] Preferably, the sensing subsystem includes multiple receiving nodes, each of which is connected to the computing subsystem via a communication link. The receiving nodes synchronously receive passive reflected signals from IoT terminals within the positioning area and upload the data of the reflected signals to the computing subsystem.

[0012] Preferably, the computing subsystem includes an edge cloud server, which establishes communication connections with the control subsystem and the sensing subsystem respectively through a high-speed backhaul network. The edge cloud server receives data from the sensing subsystem to execute signal processing and positioning algorithms and sends control commands to the control subsystem.

[0013] This invention provides a passive IoT terminal positioning method, which, in conjunction with the passive IoT terminal positioning system described above, performs positioning and includes the following steps: S1 Environmental Detection and RIS Beamforming Planning; S2 dynamically switches to RIS mode, and the base station transmits reference signals; The S3 terminal passively reflects signals, and multiple receiving nodes receive them synchronously. S4 edge cloud performs joint signal processing and location calculation; The S5 outputs the positioning results and can optionally perform feedback optimization.

[0014] Preferably, the S1 environment detection and RIS beamforming planning specifically includes the following steps: The computing subsystem collects channel state information of the current environment through the sensing subsystem; The computational subsystem plans a series of different RIS reflection beam patterns; Control commands are sent to the reconfigurable smart surface to dynamically switch its reflection state according to a preset sequence.

[0015] Preferably, the S2 dynamic switching of RIS mode and the base station transmitting a reference signal specifically includes the following steps: The base station continuously transmits downlink positioning reference signals; The downlink positioning reference signal reaches the IoT terminal via a direct path and a path through dynamic reflection from a reconfigurable smart surface.

[0016] Preferably, the S3 terminal passively reflects the signal, and multiple receiving nodes receive it synchronously, specifically including the following steps: The IoT terminal passively reflects the received mixed signal to form a reflected signal carrying the terminal's reflection characteristics, which is then received synchronously by multiple receiving nodes.

[0017] Preferably, the S4 edge cloud undergoes joint signal processing and location calculation, specifically including the following steps: The signal data received by each receiving node is uploaded to the computing subsystem; the computing subsystem uses the known reconfigurable smart surface reflection mode sequence to separate the signal components corresponding to each reflection mode and each receiving node from the received signal, and extracts the observation values ​​to construct a spatiotemporal observation matrix. Based on a spatiotemporal observation matrix and a forward channel model, an optimization problem is constructed. By solving the optimization problem, the location estimate and reflection characteristic estimate of the IoT terminal are obtained simultaneously.

[0018] Preferably, step S5 outputs the positioning result and optionally performs feedback optimization, specifically including the following steps: Send the calculated final location coordinates to the application server; Based on the results of this positioning, the system can adaptively adjust the RIS mode sequence for the next round to achieve more refined scanning of specific areas or tracking of mobile terminals, thus forming a closed-loop optimization.

[0019] The present invention has the following beneficial effects: 1. The terminal has extremely low power consumption. The terminal is completely passive throughout the positioning process, acting only as a reflector. It does not need to perform any active signal measurement, calculation or uplink reporting, thus achieving true "passive" or "near-passive" positioning, which is particularly suitable for passive IoT and ultra-low power IoT scenarios.

[0020] 2. High positioning accuracy and strong resistance to NLOS: By introducing spatiotemporally controllable artificial multipath through actively controlled RIS (Radio Reflection System), the signal features available for positioning are greatly enriched. Even if the direct path is blocked, the dynamic RIS reflection path can provide sufficient geometric information. By processing long-term observation data, the algorithm can effectively distinguish different paths, thereby significantly suppressing NLOS errors and improving positioning accuracy, theoretically reaching sub-meter level or even higher.

[0021] 3. The system has a large capacity and low network overhead. Since the terminal does not need to report measurement information uplink, the air interface signaling overhead is significantly reduced. The network side can complete the positioning by processing the reflected signal, and a single downlink broadcast signal can provide positioning services to a large number of terminals in the area simultaneously, resulting in high system capacity.

[0022] 4. Strong environmental adaptability: The reflection mode of RIS can be optimized in real time according to the environment. The system has learning ability and can actively adapt to environmental changes, which has significant advantages over static fingerprinting. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a structural block diagram of the present invention; Figure 2 This is a flowchart of the present invention.

[0025] 1. Control subsystem; 2. Sensing subsystem; 3. Computation subsystem. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0028] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0029] In the description of this invention, it should be understood that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0030] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0031] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0032] A passive IoT terminal positioning system, such as Figure 1 As shown, it includes a control subsystem 1, a sensing subsystem 2, and a computing subsystem 3. The computing subsystem 3 is connected to the control subsystem 1 and the sensing subsystem 2. The control subsystem 1 shapes the wireless environment by dynamically controlling the reflection mode of the RIS. The sensing subsystem 2 synchronously receives the reflected signals and uploads the received data to the computing subsystem 3. The computing subsystem 3 performs signal separation and feature extraction on the received data based on the reflection mode sequence, constructs a spatiotemporal observation matrix, and solves an optimization problem of a forward channel model that integrates the direct channel, the reconfigurable smart surface reflection channel, the terminal reflection characteristics, and the spatial position, and calculates the final location coordinates of the IoT terminal.

[0033] The control subsystem 1 includes a central controller and several reconfigurable smart surface units deployed in the positioning area. The central controller and the reconfigurable smart surface units are connected through a control link. The central controller is used to receive instructions from the external computing subsystem 3 and generate control signals based on the instructions. The reconfigurable smart surface units are used to receive control signals from the central controller and dynamically adjust the reflection coefficient of their surface electromagnetic units to controllably reflect and beamform the incident wireless signal. The RIS is composed of a programmable metasurface and can dynamically adjust the reflection coefficient (including amplitude and phase) of its surface electromagnetic units under the instructions of the controller, thereby controlling the reflection and modulation of the incident base station downlink reference signal and generating an artificial multipath signal with a specific spatiotemporal coding pattern.

[0034] The sensing subsystem 2 includes multiple receiving nodes, each of which is connected to the computing subsystem 3 via a communication link. The receiving nodes synchronously receive passive reflected signals from IoT terminals within the positioning area and upload the reflected signal data to the computing subsystem 3. The receiving nodes can be 5G macro base stations, micro base stations, or dedicated receiving anchors. These receiving nodes are responsible for synchronously receiving signals from IoT terminals. The signals are not actively transmitted by the terminals, but rather are mixed reflected signals formed by the terminals acting as passive devices, performing nonlinear reflection or simple modulation on the direct signals from the base station and the RIS reflected signals.

[0035] The computing subsystem 3 includes an edge cloud server. The edge cloud server establishes communication connections with the control subsystem 1 and the sensing subsystem 2 through a high-speed backhaul network. The edge cloud server receives data from the sensing subsystem 2 to execute signal processing and positioning algorithms and sends control commands to the control subsystem 1. The edge cloud server has built-in high-complexity signal processing and positioning algorithms.

[0036] This invention provides a passive IoT terminal positioning method, such as... Figure 2 As shown, positioning using the passive IoT terminal positioning system described above includes the following steps: S1 Environment Detection and RIS Beamforming Planning: Specifically, the computing subsystem 3 collects the channel state information of the current environment through the sensing subsystem 2, and the computing subsystem 3 plans a series of different RIS reflection beam patterns and sends control commands to the reconfigurable smart surface so that the reconfigurable smart surface dynamically switches its reflection state according to a preset sequence. More specifically, the computing subsystem 3 collects channel state information of the current environment through the sensing subsystem 2 (receiving node). Based on this information, the computing subsystem 3 plans a series (e.g., K) different RIS reflection beam patterns. These patterns spatially cover the entire positioning area and switch sequentially in time. According to the plan, the central controller issues control commands to each RIS unit, causing it to dynamically switch its reflection state according to the preset sequence P1(t), P2(t), ..., Pk(t).

[0037] S2 dynamically switches to RIS mode, and the base station transmits reference signals. Specifically, the base station continuously transmits downlink positioning reference signals, which reach the IoT terminal via a direct path and a path dynamically reflected by a reconfigurable smart surface. More specifically, the base station continuously transmits downlink positioning reference signals. These signals reach the terminal via two main paths: Direct path: Sdirect(t); RIS reflection path: SRIS(t) = Γ(t) * Sin(t), where Γ(t) is the time-varying RIS reflection coefficient matrix; The terminal antenna receives the superposition of these signals, Srx(t) = Sdirect(t) + SRIS(t). Due to the mismatch between the antenna impedance and the circuit load of the IoT terminal, a nonlinear effect or weak load modulation will occur on the carrier signal, which is equivalent to applying a weak "marker" to the incident signal that is related to the physical characteristics of the terminal.

[0038] This process is similar to backscatter communication, where the terminal inadvertently leaves its unique "fingerprint" on the reflected signal. The reflected signal Sback(t) can be modeled as: Sback(t) = η * Srx(t) + n(t), where η is a coefficient related to the terminal's reflection characteristics (which can be regarded as the terminal's "reflection fingerprint"), and n(t) is the ambient noise. The S3 terminal passively reflects the signal, and multiple receiving nodes receive it synchronously. Specifically, the IoT terminal reflects the received mixed signal in a passive manner to form a reflected signal carrying the terminal's reflection characteristics. The reflected signal is received synchronously by multiple receiving nodes. As a passive reflector, the IoT terminal's reflection process is achieved by nonlinear effects caused by the mismatch between the terminal antenna and the load impedance or by load modulation. More specifically, multiple receiving nodes synchronously receive the reflected signal Ym(t) from the terminal, where m=1,2,...,M are the receiving node indices. Each receiving node uploads the received raw data along with the timestamp to the computing subsystem 3. S4 edge cloud performs joint signal processing and location calculation. Specifically, it uploads the signal data received by each receiving node to computing subsystem 3. Computing subsystem 3 uses the known reconfigurable smart surface reflection mode sequence to separate the signal components corresponding to each reflection mode and each receiving node from the received signal, and extracts the observations to construct a spatiotemporal observation matrix. Based on the spatiotemporal observation matrix and a forward channel model, it constructs an optimization problem. By solving the optimization problem, it obtains the location estimate and reflection characteristic estimate of the IoT terminal. The forward channel model is used to predict the location of a given terminal, the reflection characteristics of a given terminal, and the reconfigurable smart surface. The mathematical model of the theoretical observations of each receiving node under reflection mode, the forward channel model comprehensively considers the following factors: the direct channel from the base station to the IoT terminal, the reflection channel from the base station through the reconfigurable smart surface to the IoT terminal, the reflection channel from the IoT terminal to each receiving node, the dynamic reflection coefficient of the reconfigurable smart surface, and the reflection characteristics of the IoT terminal. The objective of the optimization problem is to find a set of parameters for the terminal location and terminal reflection characteristics such that the overall difference between the theoretical observations calculated based on the forward channel model and the actual observations obtained from the spatiotemporal observation matrix is ​​minimized. The mathematical form of the optimization problem is a nonlinear least squares estimation problem. More specifically, computational subsystem 3 is the core of the localization process, executing the following algorithms: 1. Signal separation and feature extraction: Using the known RIS switching sequence Pk(t) as prior information, the terminal reflection signal component Ym,k corresponding to each RIS mode Pk is separated from the long-term received signal Ym(t) through algorithms such as correlation detection or compressed sensing.

[0039] 2. Construct a spatiotemporal observation matrix. For each receiving node m and each RIS mode k, an observation value Om,k can be extracted. This observation value can be the strength, phase, and channel impulse response of the received signal. All these observation values ​​constitute a high-dimensional spatiotemporal observation matrix O.

[0040] 3. Position Calculation Model (Core Formula): An optimization problem based on maximum likelihood estimation is established to solve for the terminal position (x, y, z). By solving this nonlinear least squares problem, the terminal position (x, y, z) and its reflection characteristics η can be estimated simultaneously. ,in, It is the optimal combination of parameters that minimizes the objective function, which is the final estimate of the terminal position. The parameter point that minimizes the objective function. M is the total number of receiving nodes, and K is the total number of reconfigurable smart surface reflection modes. It is the actual observation value measured by the m-th receiving node under the k-th RIS reflection mode. It is a forward channel model, which predicts the direction of travel when the terminal is located in the forward channel. The theoretical value that the m-th receiving node should observe when the RIS has a reflection coefficient η and is in the k-th reflection mode.

[0041] The S5 outputs the positioning results and can optionally perform feedback optimization. Specifically, the calculated final location coordinates are sent to the application server. The system can adaptively adjust the RIS mode sequence for the next round based on the positioning results to achieve more refined scanning of specific areas or tracking of mobile terminals, forming a closed-loop optimization. Based on the positioning results, the system can adaptively adjust the reflection mode sequence of the reconfigurable smart surface in the next round of positioning. More specifically, the calculated final location coordinates are sent to the application server. Simultaneously, the system can adaptively adjust the next round of the RIS mode sequence based on the positioning results, enabling more precise scanning of specific areas or tracking of mobile terminals, thus forming a closed-loop optimization.

[0042] In practice 1. Deployment: Two 5G small base stations are deployed on the warehouse ceiling, and four dedicated receiving anchor points are deployed on the pillars around the warehouse (Sensing Subsystem 2). Three reconfigurable smart surfaces (RIS, Control Subsystem 1) are evenly deployed above the central area of ​​the warehouse. All devices are connected to the edge server (Computing Subsystem 3) deployed in the warehouse server room via Ethernet.

[0043] 2. Initialization: After the system starts up, a channel calibration is first performed in an empty environment to measure the static channel information between the base station, RIS, and receiving anchor point.

[0044] 3. Positioning process: The edge server is designed with a set of RIS pattern sequences containing 64 different beam directions.

[0045] The controller controls the three RIS to switch synchronously and rapidly in this sequence (e.g., switching modes every 10ms).

[0046] 5G small base stations continuously transmit 5G downlink positioning reference signals.

[0047] A passive IoT tag attached to a cargo box receives dynamic signals from a base station and a RIS (Reference Signal Processor) and generates a weak reflection.

[0048] The four receiving anchor points synchronously collect these reflected signals and upload the raw data stream to the edge server.

[0049] The edge server runs the core algorithm of this invention: Using 64 known RIS modes, the received signal was separated, resulting in 64 × 4 = 256 observations.

[0050] Substitute these observations into the preset forward channel model F m,k middle.

[0051] The least squares optimization problem is solved using gradient descent or particle swarm optimization, and the final three-dimensional coordinates (x, y, z) of the label and its reflection coefficient η are calculated iteratively.

[0052] As a result, the entire positioning process took approximately 700ms (including data transmission and calculation), and the measured positioning accuracy reached 0.8 meters, fully meeting the requirements for warehouse goods positioning. Furthermore, the tag consumed no energy throughout the entire process.

[0053] The terminal of this positioning technology has extremely low power consumption characteristics. It is always in a completely passive working state throughout the entire positioning process, only playing the role of a signal reflector. It does not need to actively carry out any power-consuming operations such as signal measurement, data calculation or uplink reporting, and truly achieves the positioning effect of "passive" or "near-passive".

[0054] This feature makes it irreplaceable in passive IoT and ultra-low power IoT scenarios with extremely stringent power consumption requirements. It eliminates the need to configure large-capacity power supply modules for terminals or frequently replace power supplies, significantly reducing the cost and difficulty of IoT deployment and maintenance.

[0055] Moreover, this technology also boasts excellent positioning accuracy and strong resistance to non-line-of-sight (NLOS) interference. By introducing actively controllable intelligent metasurfaces (RIS), it can generate artificial multipath signals with controllable spatiotemporal dimensions, greatly enriching the signal feature dimensions required for positioning.

[0056] Even in NLOS scenarios where the direct path is blocked by obstacles, the dynamically adjusted RIS reflection path can still provide sufficient geometric information for positioning. Combined with in-depth processing of long-term series observation data, the positioning algorithm can accurately distinguish the signal differences of different paths, thereby effectively suppressing the impact of NLOS errors and significantly improving positioning accuracy. Theoretically, it can achieve sub-meter level or even higher accuracy, meeting the needs of industrial monitoring, indoor navigation and other scenarios with stringent positioning accuracy requirements.

[0057] More noteworthy is that this technology boasts extremely high system capacity and very low network overhead. Since the terminal does not need to report any measurement information to the network side through the uplink, the redundancy overhead of air interface signaling is greatly reduced, and network transmission efficiency is significantly optimized.

[0058] Meanwhile, the network side only needs to analyze and process the signal reflected by the terminal to complete the positioning process. A single downlink broadcast signal can simultaneously cover a large number of terminals in the area and provide them with positioning services, breaking the limitation of the number of terminals on system capacity in traditional positioning technology, and can efficiently support the synchronous positioning needs of large-scale IoT terminals.

[0059] In addition, the technology also exhibits excellent environmental adaptability. The RIS signal reflection mode can be dynamically optimized and adjusted according to real-time environmental conditions. The entire system has autonomous learning capabilities, enabling it to actively sense environmental changes and make adaptive adjustments. Compared with the traditional static fingerprint positioning method that relies on fixed environmental features, it has more advantages in dealing with complex situations such as environmental occlusion changes and signal interference fluctuations. It can continuously and stably output high-precision positioning results in different scenarios, providing reliable positioning guarantees for various IoT applications.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A passive Internet of Things terminal positioning system, characterized in that, The system comprises a regulation subsystem (1), a perception subsystem (2), and a computing subsystem (3). The computing subsystem (3) is communicatively connected to the regulation subsystem (1) and the perception subsystem (2). The regulation subsystem (1) shapes the wireless environment by dynamically controlling the reflection mode of the RIS. The perception subsystem (2) synchronously receives the reflected signals and uploads the received data to the computing subsystem (3). The computing subsystem (3) performs signal separation and feature extraction based on the reflection mode sequence, constructs a space-time observation matrix, and solves an optimization problem that integrates the direct channel, the reconfigurable intelligent surface reflection channel, the terminal reflection characteristics, and the spatial position to obtain the final position coordinates of the Internet of Things terminal.

2. A passive Internet of Things terminal positioning system according to claim 1, characterized in that, The regulation subsystem (1) comprises a central controller and a plurality of reconfigurable intelligent surface units deployed in the positioning area. The central controller is connected to the reconfigurable intelligent surface units through a control link. The central controller receives instructions from the external computing subsystem (3) and generates control signals based on the instructions. The reconfigurable intelligent surface units receive control signals from the central controller and dynamically adjust the reflection coefficients of their surface electromagnetic units to controllably reflect and beamform the incident wireless signals.

3. The passive Internet of Things terminal positioning system according to claim 1, wherein, The perception subsystem (2) comprises a plurality of receiving nodes, each of which is connected to the computing subsystem (3) through a communication link. The receiving nodes synchronously receive passive reflected signals from the Internet of Things terminals in the positioning area and upload the data of the reflected signals to the computing subsystem (3).

4. The passive Internet of Things terminal positioning system of claim 1, wherein, The computing subsystem (3) comprises an edge cloud server that is communicatively connected to the regulation subsystem (1) and the perception subsystem (2) through a high-speed backhaul network. The edge cloud server receives data from the perception subsystem (2) to perform signal processing and positioning algorithm and issues control instructions to the regulation subsystem (1).

5. A passive IoT terminal positioning method, cooperating with the passive IoT terminal positioning system according to any one of claims 1-4, characterized in that, The method comprises the following steps: S1 environment detection and RIS beamforming planning; S2 dynamically switching RIS mode, base station transmitting reference signal; S3 terminal passive reflection signal, multiple receiving nodes synchronously receiving; S4 edge cloud performing joint signal processing and position solving; S5 outputting positioning results and optionally performing feedback optimization.

6. The method of claim 5, wherein, The S1 environment detection and RIS beamforming planning specifically comprises the following steps: The computing subsystem (3) collects the channel state information of the current environment through the perception subsystem (2); The computing subsystem (3) plans a series of different RIS reflection beam modes; The control instructions are issued to the reconfigurable intelligent surface to dynamically switch its reflection state according to the preset sequence.

7. The method of claim 5, wherein, The S2 dynamically switching RIS mode, base station transmitting reference signal specifically comprises the following steps: The base station continuously transmits downlink positioning reference signals; The downlink positioning reference signals reach the Internet of Things terminal via direct path and dynamically reflected path through the reconfigurable intelligent surface.

8. The method of claim 5, wherein, The S3 terminal passive reflection signal, multiple receiving nodes synchronously receiving specifically comprises the following steps: The Internet of Things terminal reflects the received mixed signal in a passive manner to form a reflected signal carrying the reflection characteristics of the terminal, and the reflected signal is synchronously received by multiple receiving nodes.

9. The method of claim 5, wherein, The S4 edge cloud performs joint signal processing and position calculation, specifically including the following steps: Signal data received by each receiving node is uploaded to a computing subsystem (3); the computing subsystem (3) separates the signal components corresponding to each reflection mode and each receiving node from the received signal using a known reconfigurable intelligent surface reflection mode sequence, and extracts the observation values to construct a space-time observation matrix; Based on the space-time observation matrix and a forward channel model, an optimization problem is constructed, and the position estimate of the Internet of Things terminal and its reflection characteristic estimate are obtained by solving the optimization problem.

10. The method of claim 5, wherein, The S5 outputs the positioning result and can optionally perform feedback optimization, specifically including the following steps: The final position coordinates calculated are sent to an application server; The system can adaptively adjust the RIS mode sequence for the next round according to the result of this positioning to achieve more fine scanning of a specific area or tracking of a mobile terminal, forming a closed-loop optimization.

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