A method and system for positioning a wireless terminal

Abstract

The application discloses a wireless terminal positioning method and system, and the method comprises data acquisition, feature extraction and error correction, environment sensing knowledge recognition model establishment, positioning coordinate registration, multi-factor fusion and position positioning presentation. The application belongs to the technical field of positioning, and specifically relates to a wireless terminal positioning method and system. The scheme is characterized in that color feature analysis, neighborhood average method smoothing and feature extraction based on a color space model are adopted, pulse spectrum features are used as measurements of a tracking algorithm, an environment sensing knowledge recognition model is established, adaptive virtual positioning is generated by changing the position, radius, curvature and other parameters of a node, different factors in the environment are sensed and recognized by constructing a position code and designing a similarity measurement function, interference in a complex environment can be coped with, and the stability, robustness, adaptability and real-time performance of a positioning result are improved, and the application is suitable for positioning requirements in various different scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of positioning, in particular to a wireless terminal positioning method and system. BACKGROUND

[0002] Current wireless terminal positioning technologies mainly include methods based on global positioning system (GPS), WiFi positioning, Bluetooth positioning, UWB positioning, etc. Among them, GPS positioning has high accuracy, but it is easy to fail in environments with poor signal coverage such as indoors or urban canyons; Wi-Fi and Bluetooth positioning are limited by signal coverage range and area density, and have low positioning accuracy; UWB positioning has high accuracy, but has high cost and is easily affected by multipath effects in complex environments, resulting in a decrease in positioning accuracy. Therefore, in current wireless terminal positioning technologies, the application of a single sensor often cannot meet the demand for accurate positioning, and there are problems such as low positioning accuracy, poor robustness, limited applicability, and inability to cope with positioning in complex environments. SUMMARY

[0003] In view of the above, in order to overcome the defects of the prior art, the present application provides a wireless terminal positioning method and system. In order to solve the problem of low positioning accuracy, the present application uses a feature extraction and error correction method, uses a visual feature extraction algorithm to analyze color features, smoothes using a neighborhood average method, extracts features based on a color space model, and performs feature extraction and error correction processing on visual features to improve positioning accuracy. In order to solve the problem of poor robustness, the present application establishes an environmental sensing knowledge recognition model and a positioning coordinate registration, changes the parameters of the position, radius, curvature, and rotation angle of the node, generates an adaptive virtual positioning, and then constructs a position code and designs a similarity measurement function to quantify the transmission frequency and realize the perception and recognition of different factors in the environment. The data is subjected to coordinate conversion, data space-time correlation and synchronization, and data fusion, which can better cope with interference in complex environments, improve the stability, robustness, adaptability, and real-time performance of the positioning results, and make the present application suitable for positioning needs in various different scenarios, and have wider applicability.

[0004] The technical scheme adopted by the present application is as follows: the present application provides a wireless terminal positioning method, which comprises the following steps:

[0005] Step S1: data acquisition;

[0006] Step S2: feature extraction and error correction, extract visual features from environmental data, use a visual feature extraction algorithm to analyze color features, smooth using a neighborhood average method, extract features based on a color space model, and perform feature extraction and error correction processing on visual features to obtain preliminary positioning information; use pulse spectrum features as measurements for tracking algorithms to extract accurate distance information between terminals and base stations;

[0007] Visual features are usually the most intuitive and accurate reflection of environmental information. Planning is performed based on the acquired environmental image information, and the surrounding local environmental information is continuously perceived during movement to make preliminary positioning decisions. Using pulse spectrum features as a measurement for tracking algorithms can obtain precise distance information, which can improve data accuracy and reliability.

[0008] Step S3: Establishing an environmental perception and recognition model. Based on the visual feature data and precise distance information obtained through feature extraction and error correction, adaptive virtual positioning is generated by changing the node's position, radius, curvature, and rotation angle parameters to meet different environmental positioning requirements and generate the node's position coordinates. Furthermore, by constructing a position code and designing a similarity measurement function to quantify the emission frequency, the model can perceive and recognize different factors in the environment.

[0009] Step S4: Positioning coordinate registration, performing coordinate conversion, data spatiotemporal correlation and synchronization, and data fusion on the data of feature extraction and error correction and the data of the environment perception recognition model to obtain accurate positioning coordinates;

[0010] Step S5: Multi-factor fusion and position positioning presentation, combining the positioning coordinates output by the positioning coordinate registration module, performing multi-factor fusion, and finally achieving real-time positioning of the wireless terminal;

[0011] The multi-factor fusion and location positioning presentation stage transmits the terminal's final location information to the user device, providing users with accurate and real-time location positioning services.

[0012] Furthermore, in step S1, the data collection specifically involves acquiring environmental data through a data collection device; the data collection device includes a camera, a UWB radar, and a global navigation satellite system, and the environmental data includes image data, UWB radar data, map data, and positioning data.

[0013] Furthermore, in step S2, the visual feature extraction algorithm performs recognition based on the acquired image information and continuously senses the surrounding local environment information to make preliminary visual feature positioning, which specifically includes the following steps:

[0014] Step S21: Color feature analysis: using a CCD visual sensor to collect the original image, graying the color image collected by the CCD to obtain grayscale image information. The specific algorithm formula is as follows:

[0015] ;

[0016] Among them, R, G, and B represent the data of the three color channels of red, green, and blue respectively;

[0017] The purpose of the grayscale is to make the values of three color components R, G, B equal, and the grayscale image information only changed by the average value algorithm of three components R, G, B still contains various noises and distortions, so the grayscale image information must be smoothed and filtered before the image is analyzed;

[0018] Step S22: The grayscale image information is smoothed by using the neighborhood average method, first convolves the grayscale image information with a one-dimensional Gaussian function, and then convolves the result of the convolution with a one-dimensional Gaussian function in the same vertical direction, and the specific algorithm formula is as follows:

[0019] ;

[0020] Wherein, represents the pixel value located at the coordinate , i and j are the horizontal and vertical coordinates of the pixel respectively, represents the smoothing parameter;

[0021] ;

[0022] Wherein, represents the filtered pixel value located at the coordinate ; is a weight coefficient, which represents the relationship between the pixel at and the surrounding pixels; m and n are the position offsets relative to ; represents the pixel value located at the coordinate in the original image; and represent the coordinates of the pixel in the original image respectively; K represents the distance of the window from the center pixel to the left or right in the horizontal direction; L represents the distance of the window from the center pixel to the top or bottom in the vertical direction;

[0023] Different from simple image smoothing, in the neighborhood average method smoothing of the image in the present scheme, different pixels at different positions are given different weights when the image neighborhood average is performed, and the noise obeying the normal distribution has a better removal effect; After smoothing and filtering, there are still types that cannot be determined, including light intensity, shadow, obstacle or background, so the original image needs to be classified according to the difference of color;

[0024] Step S23: The image is segmented by using color features; the present scheme adopts a binary method which is simple in algorithm, easy to understand and implement, fast in calculation, small in memory consumption, and low in requirement for calculation device, and the specific content is as follows:

[0025] ,

[0026] ,

[0027] ;

[0028] wherein, represents the brightness of the color, ranging from 0 to 1; the brightness refers to the intensity of the light stimulus generated by the colored light to the human eye, and the brightness is related to the energy of the light and the color of the light; is a proportional value, ranging from 0 to 1, representing the ratio between the selected color and the maximum purity of the color; represents the color information, represented by 0° to 360°, and the red, green and blue three colors are separated by 120°; the hue is mainly determined by the wavelength of each component of the visible light, which is the basic feature of the colored light; and is basically not affected by light, and is good for image processing;

[0029] wherein, The specific algorithm formula of is as follows:

[0030] ;

[0031] wherein, is a spatial transformation equation, and R, G and B represent the data of the red, green and blue three color channels respectively.

[0032] For some specific colors, it is difficult to extract the features of RGB, and due to the influence of light intensity and surrounding environment, the extraction effect is not very good, which brings problems to the acquisition of visual feature information in the later stage, so the color space model in the scheme is used for feature extraction, which can effectively solve the problem of poor extraction effect.

[0033] Further, in step S2, by using the pulse spectrum feature as the measurement of the tracking algorithm, the specific algorithm formula of the tracking algorithm is:

[0034] ,

[0035] ;

[0036] wherein, represents the position of the target in the horizontal direction; represents the speed of the target in the horizontal direction; represents the error standard deviation of the position of the target in the horizontal direction; represents the angle of the target in the horizontal direction; represents the position of the target in the vertical direction; represents the speed of the target in the vertical direction; represents the error standard deviation of the position of the target in the vertical direction; Indicates the target's vertical angle;

[0037] In two-dimensional space, the UWB radar system is orthogonally set up for two-dimensional tracking, where, and The specific algorithm formula is as follows:

[0038] ,

[0039] ;

[0040] in, Indicates the angle of the target in the horizontal direction; Indicates the distance from the target to the sensor; Indicates the standard deviation of the error of the target's horizontal position; Indicates the standard deviation of the error of the target's vertical position; Indicates the target's horizontal speed; Indicates the target's vertical angle; Indicates the standard deviation of the error of the target's vertical position; Indicates the target's vertical speed.

[0041] Furthermore, in step S3, the environment perception recognition model is established, which specifically includes the following contents:

[0042] Step S31: Obtain landmarks. The specific algorithm formula is as follows:

[0043] ;

[0044] in, Indicates at time The above number is The position coordinates of the node, Indicates at time The above number is The radius of the node; Indicates at time The above number is The curvature of the nodes; Indicates at time The above number is The rotation angle of the node; Indicates at time The above number is The tangent direction of the node;

[0045] By changing the parameters of the node's position, radius, curvature, and rotation angle, adaptive virtual positioning can be generated to meet different environmental positioning requirements and generate the node's position coordinates;

[0046] Step S32, constructing position code, the specific algorithm formula as follows:

[0047] ;

[0048] Wherein, represents the position code of the node at time ; represents the path length from the starting point to the node at time ; represents the path length from the starting point to the node 2 at time ; represents the path length from the starting point to the node N at time ; represents the path length from the starting point to the node N at time ;

[0049] By calculating the position code and path length of the node at different times, the position and path length of the positioning node on the path can be determined;

[0050] Step S33, designing a similarity measure function to quantify the emission frequency;

[0051] ;

[0052] Wherein, represents the emission frequency of the node numbered at time ; represents the number related to the node numbered ; represents the weight related to the node numbered ; represents the weight related to the distance , the value range is 0 to 1; represents the distance of the node numbered at time ; represents the distance between the nodes numbered and ; represents the variance of the distance; represents the weight related to the angle , the value range is 0 to 1; represents the angle of the node numbered at time ; represents the angle of the node numbered ; variance of angle;

[0053] wherein, The specific algorithm formula is as follows:

[0054] ;

[0055] wherein, represents the weight related to the node number ; represents the similarity measure of the current node, which is used to measure the similarity between nodes; represents the number of nodes at time ;

[0056] The environmental perception recognition model calculates the emission frequency of each node according to the similarity measure function between nodes. The emission frequency can be used to represent the importance or weight of the node in positioning, which can be used to determine the position of the node and evaluate and compare the characteristics of the node, providing a more comprehensive environmental perception for the model. Combined with the importance and characteristics of the node, the perception and recognition of different factors in the environment are realized. The emission frequency plays an important role in positioning and environmental perception recognition, and can be used for accurate positioning to improve the accuracy of positioning and the reliability of environmental recognition.

[0057] Further, in step S4, the positioning coordinate registration integrates and calibrates the data of feature extraction and error correction and the data of the environmental perception recognition model, to obtain accurate positioning coordinates, and the specific content is as follows:

[0058] Coordinate conversion: The received positioning data comes from different sensors or different coordinate systems, and needs to be converted to the same coordinate system for subsequent data processing and analysis;

[0059] Data space-time association and synchronization: The data collected and processed at different times or by different sensors are associated and synchronized in space-time, and the time stamp or feature matching method is used to determine the space-time relationship between the data, and the space-time is calibrated for real-time positioning;

[0060] Data fusion: The data associated and synchronized in space-time are fused to take advantage of the advantages of each data source, including accuracy and coverage range, to improve the overall performance of wireless terminal positioning;

[0061] Quality evaluation and feedback: The quality of the registered data is evaluated to check the accuracy and stability of the positioning result, and the evaluation result is fed back to the system to optimize the algorithm and parameter setting;

[0062] The positioning coordinate registration can significantly improve the performance and reliability of the positioning system, is suitable for various complex environments and application scenarios, and provides more reliable positioning services for the wireless terminal.

[0063] Further, in step S5, the multi-factor fusion and position presentation is combined with the positioning coordinate registration output positioning coordinate, and specifically includes the following steps:

[0064] Position information encoding: the final position information after data fusion is encoded to effectively compress and transmit data during transmission, and a standard encoding algorithm or a specific position information encoding algorithm is used for encoding;

[0065] Select a communication mode to transmit the position information; the communication mode includes a wireless network, Bluetooth, infrared, or point-to-point communication or broadcast communication according to a specific scene;

[0066] Establish a communication connection between the wireless terminal and the user equipment according to the selected communication mode; including the pairing between the devices, the establishment of the network connection, and ensuring that the data can be effectively transmitted;

[0067] Data transmission and reception: the encoded position information is transmitted from the wireless terminal to the user equipment through the selected communication mode; after the user equipment receives the position information, the position information is decoded and processed to obtain the final position information;

[0068] Position information display: the received final position information is displayed on the user equipment; the final position information includes a marked position on a map and a detailed position information text description, so that the user can intuitively understand and use the information;

[0069] Error handling and feedback: if there is an error or lost data during transmission, corresponding error handling and retransmission mechanism are performed to ensure the integrity and accuracy of the position information; at the same time, the transmission state and result are fed back to the user equipment, so that the user can understand the transmission situation in time.

[0070] Through the above steps, the multi-factor fusion and position presentation step can effectively transmit the final position information of the wireless terminal to the user equipment, and provide accurate and real-time position positioning services for the user.

[0071] The wireless terminal positioning system provided by the application comprises a data acquisition module, a feature extraction and error correction module, an environment sensing knowledge recognition model establishment module, a positioning coordinate registration module, and a multi-factor fusion and position presentation module.

[0072] The data acquisition module acquires environment data through a data acquisition device and sends the environment data to the feature extraction and error correction module.

[0073] The feature extraction and error correction module is used to extract visual features in environmental data, and the visual features are extracted and error corrected by using a visual feature extraction algorithm to perform color feature analysis, neighborhood average method smoothing, and feature extraction based on a color space model, to obtain preliminary positioning information; and the accurate distance information between the terminal and the base station is extracted by using pulse spectrum features as measurements for the tracking algorithm.

[0074] The environmental sensing recognition model modeling module generates adaptive virtual positioning by changing the parameters of the position, radius, curvature, and rotation angle of the nodes based on the visual feature data and the accurate distance information extracted and error corrected by the feature extraction and error correction module, to cope with different environmental positioning requirements and generate the position coordinates of the nodes; and the sensing and recognition of different factors in the environment are realized by constructing a position code and designing a similarity measurement function to quantify the transmission frequency.

[0075] The positioning coordinate registration module performs coordinate conversion, data space-time association and synchronization, and data fusion on the data extracted and error corrected by the feature extraction and error correction module and the data of the environmental sensing recognition model, to obtain accurate positioning coordinates.

[0076] The multi-factor fusion and position positioning presentation module is used to combine the positioning coordinates output by the positioning coordinate registration module, perform multi-factor fusion, and finally realize the real-time positioning of the wireless terminal.

[0077] The above scheme has the following beneficial effects:

[0078] (1) To solve the problem of low positioning accuracy, the feature extraction and error correction method is used in the scheme, the visual features are extracted and error corrected by using a visual feature extraction algorithm to perform color feature analysis, neighborhood average method smoothing, and feature extraction based on a color space model, and pulse spectrum features are used as measurements for the tracking algorithm, to improve the positioning accuracy.

[0079] (2) To solve the problem of poor robustness, the environmental sensing recognition model and the positioning coordinate registration are established in the scheme, adaptive virtual positioning is generated by changing the parameters of the position, radius, curvature, and rotation angle of the nodes, the sensing and recognition of different factors in the environment are realized by constructing a position code and designing a similarity measurement function to quantify the transmission frequency, and the data are subjected to coordinate conversion, data space-time association and synchronization, and data fusion, which can better cope with interference in complex environments, improve the stability, robustness, adaptability, and real-time performance of the positioning results, and make the scheme applicable to positioning requirements in various different scenarios, with wider applicability.

[0080] (3) For some specific colors, RGB is difficult to extract its features, due to the influence of light intensity and surrounding environment, the extraction effect is not very good, which brings problems to the acquisition of visual feature information in the later stage, therefore, the color space model is used for feature extraction, which can effectively solve the problem of poor extraction effect.

[0081] (4) The environmental perception recognition model calculates the emission frequency of each node according to the similarity measurement function between nodes. The emission frequency is used to represent the importance or weight of the node in positioning, to determine the position of the node, and to evaluate and compare the characteristics of the node, to provide a more comprehensive environmental perception for the model. Combined with the importance and characteristics of the node, the perception and recognition of different factors in the environment can be realized. The emission frequency plays an important role in positioning and environmental perception recognition, and can be used for accurate positioning to improve the accuracy of positioning and the reliability of environmental recognition.

[0082] (5) The positioning coordinate registration can significantly improve the performance and reliability of the positioning system, and is suitable for various complex environments and application scenarios, to provide more reliable positioning services for wireless terminals.

[0083] (6) The multi-factor fusion and position positioning presentation module can effectively transmit the final position information of the wireless terminal to the user equipment, to provide accurate and real-time position positioning services for users. BRIEF DESCRIPTION OF DRAWINGS

[0084] Figure 1 A flowchart of a wireless terminal positioning method provided by the present application is shown in the figure.

[0085] Figure 2 A schematic diagram of a wireless terminal positioning system provided by the present application is shown in the figure.

[0086] Figure 3 A flowchart of step S3 is shown in the figure.

[0087] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation of the present application. DETAILED DESCRIPTION

[0088] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0089] In the description of the present invention, it should be understood that terms such as "up", "down", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the system or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0090] Example 1, see Figure 1 The present invention provides a wireless terminal positioning method, which includes the following steps:

[0091] Step S1: data collection;

[0092] Step S2: Feature extraction and error correction: extract visual features from the environmental data, and use a visual feature extraction algorithm to perform feature extraction and error correction on the visual features through color feature analysis, neighborhood average smoothing, and feature extraction based on a color space model to obtain preliminary positioning information. By using pulse spectrum characteristics as a measurement for the tracking algorithm, the precise distance information between the terminal and the base station is extracted.

[0093] Visual features are usually the most intuitive and accurate reflection of environmental information. Planning is performed based on the acquired environmental image information, and the surrounding local environmental information is continuously perceived during movement to make preliminary positioning decisions. Using pulse spectrum features as a measurement for tracking algorithms can obtain precise distance information, which can improve data accuracy and reliability.

[0094] Step S3: Establishing an environmental perception and recognition model. Based on the visual feature data and precise distance information obtained through feature extraction and error correction, adaptive virtual positioning is generated by changing the node's position, radius, curvature, and rotation angle parameters to meet different environmental positioning requirements and generate the node's position coordinates. Furthermore, by constructing a position code and designing a similarity measurement function to quantify the emission frequency, the model can perceive and recognize different factors in the environment.

[0095] Step S4: Positioning coordinate registration, performing coordinate conversion, data spatiotemporal correlation and synchronization, and data fusion on the data of feature extraction and error correction and the data of the environment perception recognition model to obtain accurate positioning coordinates;

[0096] Step S5: Multi-factor fusion and position positioning presentation, combining the positioning coordinates output by the positioning coordinate registration module, performing multi-factor fusion, and finally achieving real-time positioning of the wireless terminal;

[0097] The multi-factor fusion and location positioning presentation stage transmits the terminal's final location information to the user device, providing users with accurate and real-time location positioning services.

[0098] Embodiment two, refer to Figure 1 , this embodiment is based on the above embodiment, in step S1, the data collection is specifically acquiring environmental data through a data collection device; the data collection device includes a camera, a UWB radar and a global navigation satellite system, and the environmental data includes image data, UWB radar data, map data and positioning data.

[0099] Embodiment three, refer to Figure 1 , this embodiment is based on the above embodiment, in step S2, the visual feature extraction algorithm identifies according to the acquired image information, and constantly perceives the surrounding local environment information, makes a preliminary visual feature positioning, which includes the following steps:

[0100] Step S21: color feature analysis, using a CCD vision sensor to collect an original image, carrying out gray scale on the color image collected by the CCD to obtain gray scale image information, and the specific algorithm formula is as follows:

[0101] ;

[0102] Among them, R, G and B represent the data of red, green and blue three color channels respectively;

[0103] The purpose of gray scale is to make the values of three color components R, G and B equal, and the gray scale image information that is only gray still contains various noises and distortions by using the average value algorithm of three components R, G and B, so the gray scale image information must be smoothed and filtered before the image is analyzed;

[0104] Step S22: smoothing the gray scale image information using neighborhood average method, first convolving the gray scale image information with a one-dimensional Gaussian function, and then convolving the convolution result with a one-dimensional Gaussian function in the same vertical direction, and the specific algorithm formula is as follows:

[0105] ;

[0106] Among them, represents the pixel value located at the coordinate , i and j are the horizontal and vertical coordinates of the pixel respectively, represents the smoothing parameter;

[0107] ;

[0108] Among them, represents the filtered pixel value located at the coordinate ; is a weight coefficient, which represents the relationship between the pixel at and the surrounding pixels; m and n are the position offsets relative to ; represents the pixel value in the original image at coordinates ; and respectively represent the coordinates of the pixel in the original image; K represents the distance of the window from the center pixel to the left or right in the horizontal direction; L represents the distance of the window from the center pixel to the top or bottom in the vertical direction;

[0109] As an embodiment of the present scheme, unlike simple image smoothing, in the image neighborhood averaging smoothing of the present embodiment, different positions of pixels are assigned different weights in image neighborhood averaging, which has better removal effect on noise subject to normal distribution.

[0110] After smoothing and filtering, there are still types that cannot be determined, including light intensity, shadow, obstacle or background, so it is necessary to classify the original image according to the difference in color;

[0111] Step S23: The present embodiment adopts a binary method with simple algorithm, easy to understand and implement, fast calculation speed, small memory consumption, low requirement for computing device, and uses color features to segment the image; Specifically, feature extraction based on color space model, the specific content is as follows:

[0112] ,

[0113] ,

[0114] ;

[0115] Among them, represents the brightness of the color, ranging from 0 to 1; brightness refers to the intensity of light stimulus produced by colored light to the human eye, and brightness is related to the energy of light and the color of light; is a proportional value ranging from 0 to 1, representing the ratio between the selected color and the maximum purity of the color; represents color information, represented by 0° to 360°, with red, green and blue colors separated by 120°; hue is mainly determined by the wavelength of each component of visible light, which is the basic feature of colored light; and are basically not affected by light and are used for image processing with good effect;

[0116] The specific algorithm formula of in the above formula is as follows:

[0117] ;

[0118] Among them, is a space transformation equation, and R, G and B represent the data of red, green and blue color channels respectively.

[0119] For some specific colors, RGB is difficult to extract its features, due to the influence of light intensity and the surrounding environment, the extraction effect is not very good, the acquisition of visual feature information in the later stage brings problems, therefore, the color space model is used in the scheme to extract features, which can effectively solve the problem of poor extraction effect.

[0120] Embodiment four, refer to Figure 1 This embodiment is based on the above embodiment, in step S2, by using pulse spectrum feature as the measurement of tracking algorithm, the specific algorithm formula of tracking algorithm is:

[0121] ,

[0122] ;

[0123] Among them, Indicates the position of the target in the horizontal direction; Indicates the speed of the target in the horizontal direction; Indicates the error standard deviation of the position of the target in the horizontal direction; Indicates the angle of the target in the horizontal direction; Indicates the position of the target in the vertical direction; Indicates the speed of the target in the vertical direction; Indicates the error standard deviation of the position of the target in the vertical direction; Indicates the angle of the target in the vertical direction;

[0124] In two-dimensional space, the UWB radar system is arranged orthogonally for two-dimensional tracking, wherein, And The specific algorithm formula is as follows:

[0125] ,

[0126] ;

[0127] Among them, Indicates the angle of the target in the horizontal direction; Indicates the distance from the target to the sensor; Indicates the error standard deviation of the position of the target in the horizontal direction; Indicates the error standard deviation of the position of the target in the vertical direction; Indicates the speed of the target in the horizontal direction; Indicates the angle of the target in the vertical direction; Indicates the error standard deviation of the position of the target in the vertical direction; Indicates the speed of the target in the vertical direction.

[0128] Example 5, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In step S3, an environment perception recognition model is established, which specifically includes the following contents:

[0129] Step S31: Obtain landmarks. The specific algorithm formula is as follows:

[0130] ;

[0131] in, Indicates at time The above number is The position coordinates of the node, Indicates at time The above number is The radius of the node; Indicates at time The above number is The curvature of the nodes; Indicates at time The above number is The rotation angle of the node; Indicates at time The above number is The tangent direction of the node;

[0132] By changing the parameters of the node's position, radius, curvature, and rotation angle, adaptive virtual positioning can be generated to meet different environmental positioning requirements and generate the node's position coordinates;

[0133] Step S32: construct a location code. The specific algorithm formula is as follows:

[0134] ;

[0135] in, Indicates at time The location code of the previous node; Indicates at time From the starting point to the node Path length; Indicates at time The length of the path from the starting point to node 2; Indicates at time The length of the path from the starting point to node N; Indicates at time The length of the path from the starting point to the node N is multiplied by the time;

[0136] By calculating the location code and path length of the node at different times, the location and path length of the positioning node on the path can be determined;

[0137] Step S33, design a similarity measure function to quantify the emission frequency;

[0138]

[0139] wherein, denotes the emission frequency of the node numbered at time ; denotes the quantity related to the node numbered ; denotes the weight related to the node numbered ; denotes the weight related to the distance , with a value ranging from 0 to 1; denotes the distance of the node numbered at time ; denotes the distance between the nodes numbered and ; denotes the variance of the distance; denotes the weight related to the angle , with a value ranging from 0 to 1; denotes the angle of the node numbered at time ; denotes the angle of the node numbered ; denotes the variance of the angle;

[0140] wherein, the specific algorithm formula is as follows:

[0141]

[0142] wherein, denotes the weight related to the node numbered ; denotes the similarity measure of the current node, used to measure the similarity between nodes; denotes the quantity of nodes at time ;

[0143] ​​The environmental perception recognition model calculates the emission frequency of each node according to the similarity measurement function between nodes. The emission frequency can be used to represent the importance or weight of the node in positioning, which can be used to determine the position of the node and evaluate and compare the characteristics of the node, providing a more comprehensive environmental perception for the model. By combining the importance and characteristics of the node, the perception and recognition of different factors in the environment are realized. The emission frequency plays an important role in positioning and environmental perception recognition, and can be used for accurate positioning to improve the accuracy of positioning and the reliability of environmental recognition.

[0144] Embodiment six, refer to Figure 1 This embodiment is based on the above embodiment, in step S4, the positioning coordinate registration integrates and calibrates the data of feature extraction and error correction and the data of environmental perception recognition model, to obtain accurate positioning coordinates, the specific content is as follows:

[0145] Coordinate conversion: The received positioning data comes from different sensors or different coordinate systems, and needs to be converted to the same coordinate system for subsequent data processing and analysis;

[0146] Data space correlation and synchronization: The data collected and processed at different times or by different sensors are correlated and synchronized in space and time, the time stamp or feature matching method is used to determine the space-time relationship between the data, and the space-time calibration is performed for real-time positioning;

[0147] Data fusion: The data correlated and synchronized in space and time are fused to take advantage of each data source, including accuracy and coverage range, to improve the overall performance of wireless terminal positioning;

[0148] Quality evaluation and feedback: The quality of the registered data is evaluated to check the accuracy and stability of the positioning result, and the evaluation result is fed back to the system to optimize the algorithm and parameter setting;

[0149] Positioning coordinate registration can significantly improve the performance and reliability of the positioning system, and is suitable for various complex environments and application scenarios, providing more reliable positioning services for wireless terminals.

[0150] Embodiment seven, refer to Figure 1 This embodiment is based on the above embodiment, in step S5, multi-factor fusion and position positioning presentation, combining the positioning coordinates output by the positioning coordinate registration, including the following steps:

[0151] Position information coding: The final position information after data fusion is coded for effective data compression and transmission optimization during transmission, and the coding algorithm or specific position information coding algorithm is used;

[0152] Select a communication method to transmit location information; communication methods include wireless network, Bluetooth, infrared, or point-to-point communication or broadcast communication according to the specific scenario;

[0153] Establishing a communication connection: Establishing a communication connection between the wireless terminal and the user equipment based on the selected communication mode; including the steps involved in pairing the devices and establishing a network connection to ensure effective data transmission;

[0154] Data transmission and reception: The encoded location information is transmitted from the wireless terminal to the user equipment through the selected communication method; after the user equipment receives the location information, it decodes and processes it to obtain the final location information;

[0155] Location information display: Display the final location information received on the user's device. The final location information includes marking the location on a map and displaying a detailed text description of the location information so that users can intuitively understand and use this information;

[0156] Error handling and feedback: If errors or data loss occur during transmission, corresponding error handling and retransmission mechanisms are implemented to ensure the integrity and accuracy of location information; at the same time, the transmission status and results are fed back to the user device so that the user can understand the transmission status in a timely manner.

[0157] Through the above steps, the multi-factor fusion and position positioning presentation steps can effectively transmit the final position information of the wireless terminal to the user equipment, providing the user with accurate and real-time position positioning services.

[0158] Example 8, see Figure 2 This embodiment is based on the above embodiment. The present invention provides a wireless terminal positioning system, including a data acquisition module, a feature extraction and error correction module, an environment perception and recognition model establishment module, a positioning coordinate registration module, and a multi-factor fusion and position positioning presentation module.

[0159] The data acquisition module obtains environmental data through data acquisition equipment and sends the environmental data to the feature extraction and error correction module;

[0160] The feature extraction and error correction module is used to extract visual features from environmental data. It uses a visual feature extraction algorithm to perform feature extraction and error correction on visual features through color feature analysis, neighborhood averaging smoothing, and feature extraction based on a color space model to obtain preliminary positioning information. It uses pulse spectrum characteristics as a measurement for the tracking algorithm to extract the precise distance information between the terminal and the base station.

[0161] The environmental sensing and recognition model module generates self-adaptive virtual positioning to cope with different environmental positioning requirements and generates node position coordinates by changing the parameters of node position, radius, curvature and rotation angle based on the visual feature data and accurate distance information extracted and corrected.

[0162] The positioning coordinate registration module obtains accurate positioning coordinates through coordinate conversion, data space-time correlation and synchronization and data fusion of the data extracted and corrected and the data of the environmental sensing and recognition model.

[0163] The multi-factor fusion and position positioning presentation module is used for multi-factor fusion in combination with the positioning coordinates output by the positioning coordinate registration module to finally realize real-time positioning of the wireless terminal.

[0164] It should be noted that the relational terms herein such as first and second and the like are used only to differentiate one entity or action from another, and do not necessarily require or imply that any such actual relationship or order exists between or among the entities or actions. Also, the terms "comprising", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article or apparatus.

[0165] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application.

[0166] The above describes the present application and its embodiments, which are not restrictive, and the embodiments shown in the drawings are only one of the embodiments of the present application, and the actual structure is not limited thereto. In general, if a person skilled in the art is inspired by it, without departing from the principles and spirit of the present application, without creative design, similar structure and embodiments of the technical solution can be designed, which should belong to the protection scope of the present application.

Claims

1. A wireless terminal positioning method, characterized in that: The method comprises the following steps: Step S1: Data acquisition; the data acquisition equipment includes a camera, a UWB radar, and a global navigation satellite system; Step S2: Feature extraction and error correction: extract visual features from the environmental data, and use a visual feature extraction algorithm to perform feature extraction and error correction on the visual features through color feature analysis, neighborhood average smoothing, and feature extraction based on a color space model to obtain preliminary positioning information. By using pulse spectrum characteristics as a measurement for the tracking algorithm, the precise distance information between the terminal and the base station is extracted. Step S3: Establishing an environmental perception and recognition model. Based on the visual feature data and precise distance information obtained through feature extraction and error correction, adaptive virtual positioning is generated by changing the parameters of the node's position, radius, curvature, and rotation angle to meet different environmental positioning requirements and generate the node's position coordinates. Furthermore, by constructing a position code and designing a similarity measurement function to quantify the emission frequency, the model can realize the perception and recognition of different factors in the environment. In step S3, the environment perception recognition model is established, and the design of the similarity measurement function specifically includes the following contents: Step S33, designing a similarity measurement function to quantify the emission frequency; Among them, f k (t) represents the transmission frequency of the node numbered k at time t; N k Indicates the number of nodes related to the number k; ω a represents the weight associated with node number a; ω d Represents the weight related to the distance d, ranging from 0 to 1; d a (t) represents the distance to the node numbered a at time t; represents the distance between nodes numbered a and k; represents the variance of the distance; ω A Indicates the weight related to angle A, ranging from 0 to 1; A a (t) represents the angle of the node numbered a at time t; represents the angle between nodes numbered a and k; represents the variance of the angle; Where, ω a The specific algorithm formula is as follows: Among them, ω a represents the weight associated with node number a; s a Represents the similarity metric of the current node, which is used to measure the similarity between nodes; N(t) represents the number of nodes at time t; The environmental perception and recognition model calculates the transmission frequency of each node based on the similarity measurement function between nodes, thereby realizing the perception and recognition of different factors in the environment; Step S4: Positioning coordinate registration, performing coordinate conversion, data spatiotemporal correlation and synchronization, and data fusion on the data of feature extraction and error correction and the data of the environment perception recognition model to obtain accurate positioning coordinates; Step S5: Multi-factor fusion and position positioning presentation, combining the positioning coordinates output by the positioning coordinate registration module, performing multi-factor fusion, and finally achieving real-time positioning of the wireless terminal.

2. A wireless terminal positioning method according to claim 1, characterized in that: In step S2, the pulse spectrum characteristics are used as the measurement of the tracking algorithm. The specific algorithm formula of the tracking algorithm is: x=d x -r x cos(β x ), y=d y -r y cos(β y ); Where x represents the horizontal position of the target; d x Indicates the target's horizontal speed; r x Indicates the standard deviation of the error of the target position in the horizontal direction; β x represents the horizontal angle of the target; y represents the vertical position of the target; d y Indicates the speed of the target in the vertical direction; r y represents the standard deviation of the error of the target's vertical position; β y Indicates the target's vertical angle; In two-dimensional space, the UWB radar system is orthogonally set for two-dimensional tracking, where β x and β y The specific algorithm formula is as follows: Among them, β x represents the angle of the target in the horizontal direction; d represents the distance from the target to the sensor; r x Indicates the standard deviation of the error of the target's horizontal position; r y Indicates the standard deviation of the error of the target's vertical position; d x Indicates the target's horizontal speed; β y Indicates the target's vertical angle; d y Indicates the target's vertical speed.

3. The wireless terminal positioning method according to claim 1, wherein: In step S3, the environment perception recognition model is established, which specifically includes the following contents: Step S31: Obtain landmarks. The specific algorithm formula is as follows: PC a (t)=(r a (t),s a (t),A a (t),d a (t),a=1,2,...,N(t)); Among them, PC a (t) represents the position coordinates of the node numbered a at time t, r a (t) represents the radius of the node numbered a at time t; s a (t) represents the curvature of the node numbered a at time t; A a (t) represents the rotation angle of the node numbered a at time t; d a (t) represents the tangent direction of the node numbered a at time t; N(t) represents the number of nodes at time t; By changing the parameters of the node's position, radius, curvature, and rotation angle, adaptive virtual positioning can be generated to meet different environmental positioning requirements and generate the node's position coordinates; Step S32: constructing a location code. The location code and path length of the node at different times can be used to determine the location of the node on the path and the path length.

4. A wireless terminal positioning method according to claim 1, characterized in that: In step S4, the positioning coordinate registration integrates and calibrates the data of feature extraction and error correction with the data of the environment perception recognition model to obtain accurate positioning coordinates. The specific contents are as follows: Coordinate conversion: The received positioning data comes from different sensors or different coordinate systems, and coordinate conversion is required to convert all data into the same coordinate system for subsequent data processing and analysis; Data spatiotemporal correlation and synchronization: Data collected and processed at different times or by different sensors are temporally correlated and synchronized. Timestamps or feature matching methods are used to determine the spatiotemporal relationship between the data, perform spatiotemporal calibration, and perform real-time positioning. Data fusion combines data that has been temporally correlated and synchronized, leveraging the strengths of each data source, including accuracy and coverage, to improve the overall performance of wireless terminal positioning. Quality assessment and feedback: perform quality assessment on the registered data, check the accuracy and stability of the positioning results, and feed the assessment results back to the system to optimize the algorithm and parameter settings.

5. A wireless terminal positioning method according to claim 1, characterized in that: In step S1, the data collection specifically involves acquiring environmental data through a data collection device; the data collection device includes a camera, a UWB radar, and a global navigation satellite system, and the environmental data includes image data, UWB radar data, map data, and positioning data.

6. A wireless terminal positioning method according to claim 1, characterized in that: In step S5, the multi-factor fusion and position positioning presentation are combined with the positioning coordinates to output the positioning coordinates, specifically including the following steps: Position information encoding: encoding the final position information after data fusion; Select the communication method according to the specific scenario and transmit the location information; Establishing a communication connection: establishing a communication connection between the wireless terminal and the user equipment according to the selected communication mode; Data transmission and reception, transmitting the encoded location information from the wireless terminal to the user equipment via the selected communication method; After receiving the location information, the user device decodes and processes it to obtain the final location information; Position information display, displaying the final received position information on the user's device; Error handling and feedback: If errors or data loss occur during transmission, corresponding error handling and retransmission mechanisms are implemented to ensure the integrity and accuracy of location information; at the same time, the transmission status and results are fed back to the user device so that the user can understand the transmission status in a timely manner.

7. A wireless terminal positioning system, configured to implement a wireless terminal positioning method according to any one of claims 1 to 6, characterized in that: It includes data acquisition module, feature extraction and error correction module, environment perception and recognition model building module, positioning coordinate registration module, multi-factor fusion and position positioning presentation module; The data acquisition module acquires environmental data through a data acquisition device and sends the environmental data to the feature extraction and error correction module; The feature extraction and error correction module is used to extract visual features from environmental data. It uses a visual feature extraction algorithm to perform feature extraction and error correction on the visual features through color feature analysis, neighborhood average smoothing, and feature extraction based on a color space model to obtain preliminary positioning information. By using pulse spectrum characteristics as a measurement for the tracking algorithm, it extracts the precise distance information between the terminal and the base station. The environment perception and recognition model building module is based on feature extraction and error correction of visual feature data and precise distance information. By changing the parameters of the node's position, radius, curvature, and rotation angle, it generates adaptive virtual positioning to meet different environmental positioning requirements and generates the node's position coordinates. It then constructs a position code and designs a similarity measurement function to quantify the emission frequency to achieve perception and recognition of different factors in the environment. The positioning coordinate registration module performs coordinate conversion, data spatiotemporal correlation and synchronization, and data fusion on the data of feature extraction and error correction and the data of the environment perception recognition model to obtain accurate positioning coordinates; The multi-factor fusion and position positioning presentation module is used to combine the positioning coordinates output by the positioning coordinate registration module to perform multi-factor fusion, and finally realize the real-time positioning of the wireless terminal.

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