Wireless access point positioning method based on received signal strength

Abstract

The invention provides a wireless access point positioning method based on received signal strength. The wireless access point positioning method comprises the steps that S1, RSS original data of a target wireless access point are collected through receivers, and corresponding RSS observation values are obtained through processing according to the RSS original data; s2, constructing a to-be-estimated parameter vector containing a target position coordinate, a path loss index and reference power of the target wireless access point; s3, based on a path loss model, establishing a nonlinear model function between the RSS observed value and the to-be-estimated parameter vector; and S4, constructing a least square problem according to the nonlinear model function, and carrying out iterative solution by adopting a Gaussian-Newton method to obtain a least square estimated value of the to-be-estimated parameter vector as a target position coordinate of the target wireless access point. The method has the advantages that the method can automatically adapt to different indoor positioning scenes, tedious parameter calibration in advance is not needed, and high positioning precision and calculation efficiency can be kept.

Description

A wireless access point localization method based on received signal strength Technical Field

[0001] This invention relates to the technical field of communication positioning, and more specifically, to a method for locating wireless access points based on received signal strength. Background Technology

[0002] With the widespread adoption of wireless communication technology, the number of wireless access points deployed in large indoor venues (such as airports and train stations) has surged. However, due to legacy issues in construction and operation, the location information of a large number of access points (APs) is missing or inaccurate, creating "ghost APs" that are difficult to manage. This not only brings difficulties to network operation and maintenance but also introduces security risks.

[0003] In indoor environments, GPS becomes unusable due to blocked satellite signals. Currently, positioning technology based on received signal strength is one of the mainstream methods to address this issue. These methods typically rely on path loss models of signal propagation, using the received signal strength to infer the distance and location of the signal source.

[0004] However, existing methods face a fundamental challenge in practical applications: key parameters in the path loss model, particularly the path loss exponent and reference transmit power, vary significantly with factors such as environmental layout, building materials, and population density. In most real-world scenarios, these parameters are unknown and difficult to measure in advance. Existing technologies either require tedious manual on-site signal surveys and parameter calibration, greatly increasing deployment costs and time; or they use preset typical parameter values, which leads to a mismatch between the model and the real environment, resulting in significant positioning errors and unreliable positioning results.

[0005] To address the problem of unknown parameters, some studies have attempted to incorporate parameter estimation into positioning algorithms. However, this often results in a complex non-convex optimization problem, which involves a large amount of computation and difficulty in guaranteeing convergence. This makes it unsuitable for practical applications that require real-time performance and cost-effectiveness.

[0006] Therefore, there is an urgent need in this field for a wireless access point positioning method that can automatically adapt to different indoor positioning scenarios, eliminate the need for cumbersome pre-parameter calibration, and maintain high positioning accuracy and computational efficiency. Summary of the Invention

[0007] The technical problem to be solved by this invention is how to achieve automatic adaptation to different indoor positioning scenarios, without the need for cumbersome pre-parameter calibration, and while maintaining high positioning accuracy and computational efficiency. To overcome the shortcomings of the above-mentioned prior art (or related technologies), this invention provides a wireless access point positioning method based on received signal strength.

[0008] This invention provides a wireless access point positioning method based on received signal strength. Multiple receivers at known locations are pre-deployed in an indoor positioning scenario, which also includes at least one target wireless access point. The wireless access point positioning method includes the following steps: Step S1, collecting raw RSS data of the target wireless access point through each receiver and processing the raw RSS data to obtain corresponding RSS observations; Step S2, constructing a parameter vector to be estimated, including the target location coordinates, path loss exponent, and reference power of the target wireless access point; Step S3, establishing a nonlinear model function between the RSS observations and the parameter vector to be estimated based on a path loss model; Step S4, constructing a least squares problem based on the nonlinear model function and iteratively solving it using the Gauss-Newton method to obtain the least squares estimate of the parameter vector to be estimated, which serves as the target location coordinates of the target wireless access point.

[0009] Compared with existing technologies, the wireless access point positioning method based on received signal strength proposed in this invention has the following advantages: This invention provides a complete methodological framework from data acquisition and processing to final positioning calculation, ensuring the feasibility and systematic nature of the positioning task. By using the path loss exponent and reference power—two key and often unknown environmental parameters—along with the target location coordinates as the parameter vector to be estimated, it avoids the reliance on preset or manual calibration of these parameters in traditional methods. This allows the method to automatically adapt to the signal propagation characteristics under different indoor positioning scenarios, significantly improving the universality of target wireless access point positioning and its robustness in complex real-world scenarios. Simultaneously, by employing least squares estimation based on the Gauss-Newton method and iteratively optimizing the nonlinear model function, it can effectively approximate the true value of the target location coordinates, thereby obtaining high positioning accuracy. Therefore, this invention can automatically adapt to different indoor positioning scenarios, eliminate the need for cumbersome pre-parameter calibration, and maintain high positioning accuracy and computational efficiency.

[0010] In one possible implementation, in step S1, for each receiver, RSS raw data is obtained by continuously collecting RSS raw values ​​multiple times through the receiver, and the mean of the RSS raw data is calculated. The RSS raw values ​​within a specific standard deviation range of the mean are selected, and the mean of each RSS raw value is calculated as the RSS observation value corresponding to the receiver.

[0011] Compared with existing technologies, the above technical solution can effectively filter out abnormal signal values ​​and random noise caused by multipath effects, transient interference and other factors by averaging and filtering the raw RSS data, thus ensuring that the RSS observations used for the final calculation are more representative and reliable.

[0012] In one possible implementation, in step S2, the target location coordinates, the path loss index, and the reference power are all used as quantities to be estimated and stacked into a vector form to obtain the parameter vector to be estimated.

[0013] Compared with existing technologies, the above technical solution can process the target location coordinates and environmental parameters in a unified manner in vector form, establish a joint optimization framework from a mathematical model perspective, and improve computational efficiency.

[0014] In one possible implementation, in step S3, the nonlinear model function is constructed using the following expression: , in, Represents the nonlinear model function; Indicates the reference strength; This represents the path loss index; Indicates the target wireless access point to the first The distance between the receivers; The x-axis coordinate represents the target position coordinates; The y-axis coordinate represents the target position coordinates; This represents the vector of parameters to be estimated.

[0015] In one possible implementation, in step S4, the least squares problem is constructed using the following expression: in, This represents the least squares estimate; Represents the vector of parameters to be estimated; This represents the received signal strength vector formed by stacking the RSS observations; Represents the nonlinear model function; Indicates the first The RSS observation value corresponding to each of the receivers; Represents the observation function; Indicates the reference strength; This represents the path loss index; Indicates the target wireless access point to the first The distance of the receivers.

[0016] In one possible implementation, step S4, the iterative solution process using the Gauss-Newton method, includes: step A1, linearizing the nonlinear model function at the current estimated value in each iteration, defining the residual vector and calculating its first-order approximation; step A2, calculating the Jacobian matrix of the nonlinear model function with respect to the parameter vector to be estimated; and step A3, forming a Gauss-Newton update equation based on the residual vector and the Jacobian matrix, and updating the parameters based on the update step size until the iteration converges to obtain the least squares estimate.

[0017] Compared with existing technologies, the above-mentioned technical solution can quickly and stably converge to a local optimum using the classic second-order convergence algorithm Gauss-Newton method. Compared with some simple search methods or other complex global optimization methods, it achieves a good balance between accuracy and computational complexity, making it suitable for application scenarios with certain real-time requirements.

[0018] In one possible implementation, step S1, the process of acquiring the raw RSS data includes: step B1, building at least one data acquisition platform based on an embedded processor and integrating a wireless module on the data acquisition platform to connect each of the receivers; step B2, running an operating system and application on the data acquisition platform, controlling each of the receivers to periodically scan the indoor positioning scene by calling system services, and acquiring wireless signal information including the received signal strength indication, basic service set identifier, and network name of the target wireless access point; step B3, extracting the raw RSS data from the wireless signal information, encapsulating the wireless signal information, and sending it to the host computer for storage in real time through a long connection communication protocol.

[0019] Compared with existing technologies, the above technical solution can clearly locate the required physical carrier and data source. Through embedded processors and automated applications, it realizes the periodic automated collection of wireless signal information, avoiding the time-consuming and labor-intensive traditional manual inspection method. Through long-connection communication protocol, the wireless signal information is sent to the host computer in real time, providing data flow guarantee for real-time or near-real-time positioning calculation and display.

[0020] In one possible implementation, in step B3, the WebSocket protocol is used as the long-connection communication protocol to establish a long-connection communication between the data acquisition platform and the host computer.

[0021] Compared with existing technologies, the above technical solution can establish persistent long-term connection communication through the WebSocket protocol, and realize low-latency real-time push of wireless signal information.

[0022] In one possible implementation, after performing step S4, a result visualization process is further included, which includes the following steps: Step C1, performing scene image modeling based on the indoor positioning scene in the host computer to obtain an indoor scene two-dimensional map; Step C2, establishing a mapping relationship between the pixel coordinate system of the indoor scene two-dimensional map and the actual physical coordinate system; Step C3, converting the target location coordinates of the target wireless access point into pixel coordinates on the indoor scene two-dimensional map based on the mapping relationship and displaying them visually.

[0023] Compared with existing technologies, the above technical solution can convert the calculated abstract target location coordinates into specific icon locations on a two-dimensional map of the indoor scene, making it easier for operators to conduct intuitive investigations. Attached Figure Description

[0024] Figure 1 is a flowchart of the steps of the present invention; Figure 2 is a detailed flowchart of the iterative solution process of the present invention; Figure 3 is a detailed flowchart of the RSS raw data acquisition process of the present invention; Figure 4 is a detailed flowchart of the result visualization process of the present invention; Figure 5 is a structural principle diagram of the receiver of the present invention; Figure 6 is a schematic diagram of the curve of the RMSE value of the present invention changing with the ranging error intensity; Figure 7 is an actual test effect diagram of the indoor AP positioning hardware system of the present invention, wherein Figure 7(a), Figure 7(b) and Figure 7(c) are the actual test effect diagrams of the three tests, respectively. Detailed Implementation

[0025] First, those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention. Those skilled in the art can make adjustments as needed to adapt to specific application scenarios.

[0026] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0027] Referring to Figure 1, this embodiment of the invention discloses a wireless access point positioning method based on received signal strength. Multiple receivers at known locations are pre-deployed within an indoor positioning scenario, which also includes at least one target wireless access point. The wireless access point positioning method includes the following steps: Step S1, collecting raw RSS data of the target wireless access point through each receiver and processing the raw RSS data to obtain corresponding RSS observations; Step S2, constructing a parameter vector to be estimated, including the target location coordinates, path loss index, and reference power of the target wireless access point; Step S3, establishing a nonlinear model function between the RSS observations and the parameter vector to be estimated based on the path loss model; Step S4, constructing a least squares problem based on the nonlinear model function and using the Gauss-Newton method for iterative solution to obtain the least squares estimate of the parameter vector to be estimated, which serves as the target location coordinates of the target wireless access point.

[0028] In this embodiment of the invention, consider an indoor positioning scenario comprising one target wireless access point and M receivers. Assume the coordinates of the M receivers are as follows: ,in , indicating the number of receivers, assuming the coordinates of the target wireless access point are . According to the path loss model Assuming reference distance When the distance is 1 meter, the signal strength received by the m-th receiver is ,in, Let be the distance from the target wireless access point to the m-th receiver, which can be expressed as: , This represents the path loss index. It is a normal shadowing fading, following a mean of 0 and a variance of . The Gaussian distribution.

[0029] In this embodiment of the invention, considering that in a real wireless environment, due to small-scale fading caused by multipath propagation, shadowing effects caused by dynamic environmental obstacles, and co-channel interference in dense heterogeneous networks, the RSS observation value measured by the receiver exhibits significant time-varying characteristics, in order to reduce the impact of this phenomenon on positioning accuracy, in step S1, the raw RSS value is collected multiple times consecutively on the same receiver and the mean is calculated. , and select The raw RSS values ​​within a specific standard deviation range are used to calculate their mean as the RSS observation for that receiver. for The standard deviation of all raw RSS values ​​within a specific standard deviation range.

[0030] In this embodiment of the invention, in step S2, the m RSS observations obtained from m receivers are stacked into a vector form to obtain the received signal strength vector. To improve the adaptability and universality of the positioning, the target location coordinates, path loss index, and reference power are all taken as quantities to be estimated. These quantities are stacked into a vector form, resulting in the parameter vector to be estimated. The corresponding nonlinear model function in step S3 is: , in, Represents a nonlinear model function; Indicates reference strength; Indicates the path loss index; Indicates the distance from the target wireless access point to the number The distance between the receivers; The x-axis coordinate represents the target's position. The y-axis coordinate represents the target's position. This represents the vector of parameters to be estimated.

[0031] In this embodiment of the invention, the least squares problem is constructed in step S4 using the following expression: in, This represents the least squares estimate; Represents the vector of parameters to be estimated; This represents the received signal strength vector formed by stacking the RSS observations; Represents a nonlinear model function; Indicates the first RSS observations corresponding to each receiver; Represents the observation function; Indicates reference strength; Indicates the path loss index; Indicates the distance from the target wireless access point to the number The distance between the receivers.

[0032] In this embodiment of the invention, referring to Figure 2, step S4, the iterative solution process using the Gauss-Newton method includes: step A1, linearizing the nonlinear model function at the current estimated value in each iteration, defining the residual vector and calculating its first-order approximation; step A2, calculating the Jacobian matrix of the nonlinear model function with respect to the parameter vector to be estimated; step A3, forming the Gauss-Newton update equation based on the residual vector and the Jacobian matrix, and updating the parameters based on the update step size until the iteration converges to obtain the least squares estimate.

[0033] In this embodiment of the invention, for the least squares problem, its... Linearization is performed at the point, and the residual vector is defined as follows: Its first-order approximation is ,in For Jacobian matrices, The mth line Specifically defined as: For a single observation function, the partial derivatives of its terms are: Therefore, the Gauss-Newton update equation is: ,in To update the step size, the least squares estimate is obtained when the final iteration converges. .

[0034] In this embodiment of the invention, referring to Figure 3, the process of acquiring RSS raw data in step S1 includes: Step B1, building at least one data acquisition platform based on an embedded processor and integrating a wireless module on the data acquisition platform to connect each receiver; Step B2, running an operating system and application on the data acquisition platform, controlling each receiver to periodically scan the indoor positioning scene by calling system services, and acquiring wireless signal information including the received signal strength indication of the target wireless access point, the basic service set identifier, and the network name; Step B3, extracting the RSS raw data from the wireless signal information, encapsulating the wireless signal information, and sending it to the host computer for storage in real time through a long connection communication protocol.

[0035] In this embodiment of the invention, referring to Figure 4, after executing step S4, a result visualization process is further included. The result visualization process includes the following steps: Step C1, performing scene image modeling based on the indoor positioning scene in the host computer to obtain an indoor scene two-dimensional map; Step C2, establishing a mapping relationship between the pixel coordinate system of the indoor scene two-dimensional map and the actual physical coordinate system; Step C3, converting the target location coordinates of the target wireless access point into pixel coordinates on the indoor scene two-dimensional map based on the mapping relationship and displaying them visually.

[0036] In this embodiment of the invention, in terms of hardware implementation, the receiver is built based on the RK3568 chip, as shown in Figure 5. The RK3568 chip is connected to the RTL8852 Wifi chip through the PCIe interface. It works with a self-developed application based on the Android system to automatically collect relevant information of surrounding access points and send it to the host computer. The host computer processing system is used to receive and process the wireless signal information from the receiver, calculate the target location coordinates through the positioning algorithm, and display the positioning results in real time on the interactive map interface.

[0037] In this embodiment of the invention, to verify the feasibility and effectiveness of the algorithm, the method of the invention is subjected to the following simulation experiment: Consider a 30-meter... Within a 30-meter square area, receivers were evenly distributed around its perimeter. Ten independent location configurations for target wireless access points, transmitters, and receivers were generated within this square area, and 1000 Monte Carlo trials were conducted for each configuration. Figure 6 shows the performance results of the method of this invention under different ranging error intensities when the number of receivers is 6. From Figure 6, it can be seen that the method of this invention exhibits performance under different ranging error variances. Under all conditions, it exhibits good robustness. With increasing ranging noise, the positioning error (RMSE) shows an approximately linear upward trend. When the variance is small, it can maintain a low RMSE value, indicating that it has high positioning accuracy in low-noise environments; while when the ranging error variance is small... When the value is increased to a larger value, although the error increases, the overall convergence characteristics are still good and no obvious divergence occurs. In general, the algorithm proposed in this invention has good effectiveness and stability under different signal-to-noise ratio conditions, and can better adapt to the positioning needs in complex environments, providing a feasible basis for its subsequent application in practical scenarios.

[0038] In this embodiment of the invention, based on the above-mentioned RSS raw data acquisition process and result visualization process, a more detailed method for building an indoor AP positioning hardware system is derived, specifically including the following steps: Step D1, construction and data acquisition of the AP data acquisition platform, further subdivided into: Step D1.1, hardware platform construction, constructing one or more AP data acquisition platforms, each AP data acquisition platform using the Rockchip RK3568 chip as the core processor and integrating the RTL8852be wireless module for Wi-Fi signal sniffing and collection; Step D1.2, operating system deployment, burning the Android operating system onto the constructed AP data acquisition platform, utilizing the underlying architecture of the Android system based on the Linux kernel, and enabling the RTL8852be wireless module to communicate with the operating system kernel through hardware drivers; Step D2, development and implementation of the Wi-Fi signal scanning application, further subdivided into: Step D2.1: Develop a Wi-Fi signal scanning application on the AP data acquisition platform. This application accesses and manages wireless networks by calling the API interfaces of system services such as WifiManager provided by the Android operating system. Step D2.2: Initiate Wi-Fi scanning by programming the `startScan()` method and register a broadcast receiver to listen for system scan results. Use the `getScanResults()` method to parse the scan result list, thereby extracting detailed wireless signal information of other APs in the surrounding environment, including but not limited to Received Signal Strength Indicator (RSSI), Basic Service Set Identifier (BSSID), and Network Name (SSID). Step D2.3: Establish a periodic scanning mechanism. Set a timed task mechanism in the application to achieve periodic and continuous scanning of the surrounding Wi-Fi environment, considering Android... Version 9.0 and above impose frequency restrictions on scanning. To ensure real-time and continuous data acquisition while balancing device power consumption, this task is bound to a foreground service. Step D3, real-time data transmission and stable communication, is further divided into: Step D3.1, establishing a long-connection communication channel: A long-connection communication channel is established between the AP data acquisition platform (as the client) and the host computer data processing platform (as the server) using the WebSocket protocol. The Android client utilizes the WebSocket interface provided by the OkHttp library to establish and maintain the long connection. Step D3.2, data encapsulation and transmission: On the Android client, the wireless signal information (RSSI, BSSID, etc.) obtained through the Wi-Fi scanning module is encapsulated into a JSON (JavaScript Object Notation) format data packet and transmitted via the established WebSocket connection.The `send()` interface periodically sends the encapsulated JSON data packets to a specified port on the host computer server. Step D3.3 ensures communication stability by integrating a heartbeat detection mechanism into the RTL8852be wireless module. The Android client and the host computer server periodically send heartbeat packets to confirm the connection's validity, implementing an automatic reconnection mechanism. When network fluctuations or connection interruptions are detected, the Android client can automatically attempt to re-establish the WebSocket protocol connection, ensuring continuous data upload. Asynchronous message processing is used to avoid network communication blocking the main thread, improving data transmission efficiency and application response speed. Step D4 involves host computer data processing and positioning calculation, subdivided into: Step D4.1, host computer system construction, building a Python-based host computer system including a communication module, a map loading and calibration module, and a positioning algorithm module; Step D4.2, data reception and parsing, the host computer system uses the websocket-client library as a WebSocket server, listening on a fixed port to receive JSON data packets from one or more AP data acquisition platforms. By setting callback functions, the received JSON data packets are parsed in real time and structured for storage, forming a... The data table structure for subsequent positioning algorithms is designed with a message queue caching function to temporarily cache messages during network jitter or interruption to prevent data loss. Step D4.3 involves map loading and coordinate system mapping. The host computer system loads a pre-prepared, calibrated, and constant-scale 2D indoor scene map as a visualization map, providing a user interface. Users can manually select two or more reference points with known physical locations on the visualization map. The user inputs the real physical coordinates of these reference points, and the system calculates and establishes a coordinate system from the pixel coordinates to the actual spatial coordinates based on the user-input image pixel coordinates and the real physical coordinates. A linear mapping model is established to achieve a one-to-one correspondence between the two coordinate systems. Step D4.4 involves location calculation and result visualization. The host computer system integrates fused data from various AP data acquisition platforms (such as BSSID, RSSI, frequency, and timestamps), calls the location algorithm to calculate the target location coordinates of the "ghost AP," and converts the calculated physical coordinates of the target wireless access point into pixel coordinates on a visualized map using the established linear mapping model. Finally, the located AP location is displayed on the visualized map as an icon or other prominent identifier, thus providing an intuitive presentation of the target location coordinates.

[0039] In this embodiment of the invention, to verify the feasibility and effectiveness of the above-mentioned indoor AP positioning hardware system construction method, the following experimental test was conducted: in a space with a size of 10.5 meters... The test was conducted in a 7-meter rectangular indoor room. This environment includes common indoor obstructions and wireless signal interference sources, effectively simulating complex real-world application scenarios. Four receivers based on the RK3568 chip were used as positioning anchor points and were fixedly deployed in the four corners of the room. During the test, the target wireless access point was first placed at a randomly selected test point in the central area of ​​the room, and its true coordinates were obtained using a laser rangefinder. Subsequently, the receivers deployed in the four corners of the room simultaneously collected wireless signal information from the target wireless access point and uploaded it to the host computer in real time. Then, the host computer ran the positioning algorithm of this invention to deduce the estimated position of the target wireless access point. Finally, the positioning accuracy was evaluated by comparing the estimated coordinates with the true coordinates. The test results are shown in Figure 7, which illustrates the positioning results at three different test points in the room, where the green star marks the target wireless access point. The actual location of the access point is shown in Figure 7. The red star marks the estimated location calculated by the algorithm of this invention, while the green dashed circle represents the area with a radius of 1 meter centered on the actual location. As can be seen from Figure 7, in three independent tests, the estimated location (red star) determined by the algorithm fell precisely within the 1-meter error range of the actual location (green star). This result fully verifies that the indoor AP positioning hardware system proposed in this invention has stable and reliable sub-meter positioning accuracy in complex indoor environments, which can meet the needs of practical applications. In addition, Figure 7(c) further shows that the indoor AP positioning hardware system designed in this invention can perform positioning estimation for multiple target wireless access points. In this scenario, the indoor AP positioning hardware system successfully located two independent wireless access points, "Test AP" and "Test AP2", at the same time, which greatly improves the automation and efficiency of positioning.

[0040] In the description of this invention, the references to "one embodiment," "some embodiments," "in this embodiment," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0041] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for locating wireless access points based on received signal strength, characterized in that, Multiple receivers with known locations are pre-deployed in an indoor positioning scenario, which also includes at least one target wireless access point. The wireless access point positioning method includes the following steps: Step S1, collecting raw RSS data of the target wireless access point through each receiver and processing the raw RSS data to obtain corresponding RSS observations; Step S2, constructing a parameter vector to be estimated that includes the target location coordinates, path loss index, and reference power of the target wireless access point; Step S3, establishing a nonlinear model function between the RSS observations and the parameter vector to be estimated based on the path loss model; Step S4, constructing a least squares problem based on the nonlinear model function and using the Gauss-Newton method to iteratively solve the problem to obtain the least squares estimate of the parameter vector to be estimated as the target location coordinates of the target wireless access point.

2. The wireless access point positioning method according to claim 1, characterized in that, In step S1, for each receiver, RSS raw data is obtained by continuously collecting RSS raw values ​​multiple times through the receiver, and the mean of the RSS raw data is calculated. The RSS raw values ​​within a specific standard deviation range of the mean are selected, and the mean of each RSS raw value is calculated as the RSS observation value corresponding to the receiver.

3. The wireless access point positioning method according to claim 1, characterized in that, In step S2, the target location coordinates, the path loss index, and the reference power are all taken as quantities to be estimated and stacked into a vector form to obtain the parameter vector to be estimated.

4. The wireless access point positioning method according to claim 1, characterized in that, In step S3, the nonlinear model function is constructed using the following expression: ， in, Represents the nonlinear model function; Indicates the reference strength; This represents the path loss index; Indicates the target wireless access point to the first The distance between the receivers; The x-axis coordinate represents the target position coordinates; The y-axis coordinate represents the target position coordinates; This represents the vector of parameters to be estimated.

5. The wireless access point positioning method according to claim 1, characterized in that, In step S4, the least squares problem is constructed using the following expression: in, This represents the least squares estimate; Represents the vector of parameters to be estimated; This represents the received signal strength vector formed by stacking the RSS observations; Represents the nonlinear model function; Indicates the first The RSS observation value corresponding to each of the receivers; Represents the observation function; Indicates the reference strength; This represents the path loss index; Indicates the target wireless access point to the first The distance of the receivers.

6. The wireless access point positioning method according to claim 1, characterized in that, In step S4, the iterative solution process using the Gauss-Newton method includes: Step A1, linearizing the nonlinear model function at the current estimated value in each iteration, defining the residual vector and calculating its first-order approximation; Step A2, calculating the Jacobian matrix of the nonlinear model function with respect to the parameter vector to be estimated; Step A3, forming a Gauss-Newton update equation based on the residual vector and the Jacobian matrix, and updating the parameters based on the update step size until the iteration converges to obtain the least squares estimate.

7. The wireless access point positioning method according to claim 1, characterized in that, In step S1, the process of acquiring the raw RSS data includes: Step B1, building at least one data acquisition platform based on an embedded processor and integrating a wireless module on the data acquisition platform to connect each of the receivers; Step B2, running an operating system and application on the data acquisition platform, controlling each of the receivers to periodically scan the indoor positioning scene by calling system services, and acquiring wireless signal information including the received signal strength indication, basic service set identifier, and network name of the target wireless access point; Step B3, extracting the raw RSS data from the wireless signal information, encapsulating the wireless signal information, and sending it to the host computer for storage in real time through a long connection communication protocol.

8. The wireless access point positioning method according to claim 7, characterized in that, In step B3, the WebSocket protocol is used as the long-connection communication protocol to establish a long-connection communication between the data acquisition platform and the host computer.

9. The wireless access point positioning method according to claim 7, characterized in that, After performing step S4, a result visualization process is also included, which includes the following steps: Step C1, performing scene image modeling based on the indoor positioning scene in the host computer to obtain an indoor scene two-dimensional map; Step C2, establishing a mapping relationship between the pixel coordinate system of the indoor scene two-dimensional map and the actual physical coordinate system; Step C3, converting the target location coordinates of the target wireless access point into pixel coordinates on the indoor scene two-dimensional map based on the mapping relationship and displaying them visually.

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