An integrated communication and positioning system that combines BeiDou base stations and 5G base stations

Abstract

This invention relates to the field of MOS semiconductor technology and discloses an integrated communication and positioning system that combines BeiDou base stations and 5G base stations. The system includes: an antenna unit comprising an antenna array consisting of a multi-band BeiDou antenna, a 5G antenna, and a millimeter-wave radar, wherein the millimeter-wave radar is used for environmental perception; and an edge computing platform equipped with a scene classification model, a dynamic fusion processor, and a collaborative control center. The scene classification model is used to determine whether the environment is indoor motion or indoor stationary based on feedback from the millimeter-wave radar. This invention addresses the pain point of sharp accuracy drops in traditional integrated positioning systems under dynamic indoor scenarios by using a scene adaptation mechanism driven by millimeter-wave radar and a 5G base station error collaborative compensation algorithm. Simultaneously, it achieves low-latency processing through an edge computing platform, providing reliable technical support for high-precision positioning requirements in smart cities, industrial IoT, and other scenarios.

Description

Technical Field

[0001] This invention relates to the field of MOS semiconductor technology, and in particular to an integrated communication and positioning system that combines Beidou base stations and 5G base stations. Background Technology

[0002] While current positioning systems integrating BeiDou and 5G perform well in outdoor scenarios, they exhibit significant shortcomings in dynamic indoor environments (such as factories and shopping malls with dense crowds). Traditional solutions rely on static environmental assumptions and cannot perceive scene changes in real time, leading to problems such as multipath effects and signal obstruction that cause a sharp increase in positioning errors. At the same time, the local positioning deviations of 5G base stations (such as clock drift) lack a collaborative compensation mechanism, further amplifying errors when tracking moving targets.

[0003] An existing patent discloses an integrated communication and positioning system combining BeiDou base stations and 5G base stations (publication number CN118363052A), comprising: an antenna unit for receiving or transmitting signal data; wherein the signal data includes satellite signals and 5G signals; and a remote radio frequency processing unit connected to the antenna unit for converting the signal data into digital baseband signals or performing 5G data transmission tasks. While this existing technology attempts to introduce sensor assistance, it is limited by insufficient environmental classification accuracy (such as misjudging open spaces as indoors) and weak real-time processing capabilities at the edge, making it difficult to meet the high-precision, low-latency positioning requirements of smart cities and the Industrial Internet of Things. Summary of the Invention

[0004] This invention provides an integrated communication and positioning system that combines BeiDou base stations and 5G base stations to solve the existing technical problems, thus resolving the issues mentioned in the background.

[0005] To solve the above-mentioned technical problems, according to one aspect of the present invention, more specifically, an integrated communication and positioning system that combines BeiDou base stations and 5G base stations, comprising:

[0006] The antenna unit is an antenna array consisting of a multi-band BeiDou antenna, a 5G antenna, and a millimeter-wave radar, wherein the millimeter-wave radar is used for environmental perception.

[0007] The edge computing platform is equipped with a scene classification model, a dynamic fusion processor, and a collaborative control center;

[0008] The scene classification model is used to determine whether the environment is an indoor moving state or an indoor stationary state based on the feedback from the millimeter-wave radar.

[0009] When the environment is indoors and in motion, the dynamic fusion processor will generate a global compensation amount by summing and averaging the local positioning deviation of each 5G base station.

[0010] The intelligent radio frequency processing unit is used to receive feedback from the edge computing platform and antenna unit, and acts as a relay between digital signals and modal signals.

[0011] Furthermore, the specific steps by which the scene classification model determines whether the environment is an indoor moving state or an indoor stationary state based on feedback from millimeter-wave radar are as follows:

[0012] 1) Millimeter-wave radar data acquisition;

[0013] 2) Feature extraction and preprocessing for both time and space;

[0014] 3) Input the pre-processed features into the pre-trained model to output whether the model is in motion or stationary indoors.

[0015] Furthermore, the millimeter-wave radar data acquisition involves transmitting a 77-81 GHz frequency-modulated continuous wave using a millimeter-wave radar and receiving reflected signals to generate a point cloud matrix, wherein the sampling rate for receiving reflected signals is 20 frames per second.

[0016] Furthermore, the specific formula for the preprocessing is as follows:

[0017] ,

[0018] In the formula, This represents the feature data after preprocessing; This represents the feature data collected in real time, including the multipath reflection intensity ratio of the reflected signal, the distribution entropy of the reflection point, the point cloud velocity, and the change entropy of the point cloud structure; This represents the historical mean of the feature data; The standard deviation of the feature data.

[0019] Furthermore, the specific steps for the pre-trained model to determine whether the location is indoors include:

[0020] 1) Determine if the wall reflects the signal multiple times:

[0021] When the multipath reflection intensity ratio When the reflection is significant, it indicates that the wall reflects light multiple times.

[0022] When the multipath reflection intensity ratio When this occurs, it indicates that the space is open and has low reflection.

[0023] 2) Determine if the reflection points are highly concentrated:

[0024] When the distribution entropy of the reflection point When this occurs, it indicates that the reflection points are highly concentrated;

[0025] When the distribution entropy of the reflection point When this occurs, it indicates that the reflection points are highly dispersed;

[0026] 3) When multiple reflections are significant and the reflection points are highly concentrated, it indicates that the person is indoors.

[0027] Furthermore, the specific steps for the pre-trained model to determine whether the user is in an indoor motion state or an indoor stationary state include:

[0028] 1) Determine if there is a significant difference in the target speed:

[0029] When point cloud velocity variance When this occurs, it indicates a large difference in target speed;

[0030] When point cloud velocity variance When this occurs, it indicates that the target velocity distribution is concentrated;

[0031] 2) Determine if the point cloud structure is changing rapidly:

[0032] When the entropy of point cloud structure changes When this occurs, it indicates that the point cloud structure is changing rapidly;

[0033] When the entropy of point cloud structure changes When this time, it indicates that the point cloud structure is stable;

[0034] 3) When the speed difference is large and the point cloud structure changes rapidly, it indicates that the object is in an indoor motion state.

[0035] Furthermore, the dynamic fusion processor generates a global compensation amount based on the local positioning deviation of each 5G base station by summing and averaging. The formula for calculating the global average positioning error compensation amount is as follows:

[0036] ,

[0037] In the above formula, The mean compensation vector for all 5G base station positioning errors is represented by m; N represents the total number of nodes in the cooperative positioning network, i.e., the number of 5G base stations participating in the compensation. The local positioning result of the i-th 5G base station, in meters; The true location value of the i-th 5G base station, in meters; i represents the index number of the 5G base station.

[0038] Furthermore, the collaborative control center is oriented towards the user terminal and is used to display the final positioning results through the user terminal.

[0039] This invention provides an integrated communication and positioning system that combines BeiDou base stations and 5G base stations. Compared with existing technologies, the advantages achieved by this method are as follows:

[0040] 1. This invention uses millimeter-wave radar combined with multi-dimensional dynamic feature quantification analysis, namely, analyzing multipath reflection intensity ratio, reflection point distribution entropy, point cloud velocity variance and structural change entropy, to achieve accurate identification of indoor / outdoor scenes and motion states.

[0041] 2. This invention solves the problem of sharp drop in accuracy of traditional fusion positioning systems in indoor dynamic scenes by using a scene adaptation mechanism driven by millimeter-wave radar and a 5G base station error collaborative compensation algorithm. At the same time, it relies on the edge computing platform to achieve low-latency processing, providing reliable technical support for high-precision positioning scenarios such as smart cities and industrial Internet of Things.

[0042] 3. This invention calculates the global compensation amount through a dynamic fusion processor, aggregates the local positioning deviations of multiple base stations, and performs real-time correction on the original positioning results of user terminals under indoor motion conditions, significantly improving the accuracy of network collaborative positioning, with an average error reduction rate of over 40%. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the structure of the present invention;

[0044] Figure 2 This is a flowchart of the present invention;

[0045] Figure 3 This is a schematic diagram illustrating global average positioning error compensation for two 5G base stations in this invention. Detailed Implementation

[0046] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Example

[0047] like Figure 1 , 2 As shown, according to one aspect of the present invention, an integrated communication and positioning system integrating BeiDou base stations and 5G base stations is provided, comprising: an antenna unit, an antenna array composed of a multi-band BeiDou antenna, a 5G antenna, and a millimeter-wave radar, wherein the millimeter-wave radar is used for environmental perception; an edge computing platform, equipped with a scene classification model, a dynamic fusion processor, and a collaborative control center; the scene classification model is used to determine whether the environment is an indoor moving state or an indoor stationary state based on the feedback from the millimeter-wave radar; the specific steps of the scene classification model in determining whether the environment is an indoor moving state or an indoor stationary state based on the feedback from the millimeter-wave radar are as follows:

[0048] 1) Millimeter-wave radar data acquisition: Millimeter-wave radar data acquisition involves transmitting 77-81 GHz frequency-modulated continuous waves and receiving reflected signals to generate a point cloud matrix. The sampling rate for receiving reflected signals is 20 frames / second.

[0049] 2) Feature extraction and preprocessing for both time and space;

[0050] 3) Input the preprocessed features into the pre-trained model to output the indoor motion state or indoor stationary state. The specific formula for preprocessing is:

[0051] ,

[0052] In the formula, This represents the feature data after preprocessing; This represents the feature data collected in real time, including the multipath reflection intensity ratio of the reflected signal, the distribution entropy of the reflection point, the point cloud velocity, and the change entropy of the point cloud structure; This represents the historical mean of the feature data; The standard deviation of the feature data is represented. A high-precision point cloud matrix is ​​generated using millimeter-wave radar with a high-frequency continuous wave of 77-81 GHz (sampling rate of 20 frames / second). Combined with the feature standardization preprocessing formula, the dynamic features such as multipath reflection intensity ratio and distribution entropy are efficiently extracted and normalized, which significantly improves the anti-interference ability of environmental perception data and the stability of model input, providing a reliable data foundation for scene classification. Example

[0053] like Figure 1 , 2 As shown, the specific steps for the pre-trained model to determine whether the user is indoors include:

[0054] 1) Determine if the wall reflects the signal multiple times:

[0055] When the multipath reflection intensity ratio When the reflection is significant, it indicates that the wall reflects light multiple times.

[0056] When the multipath reflection intensity ratio When this occurs, it indicates that the space is open and has low reflection.

[0057] 2) Determine if the reflection points are highly concentrated:

[0058] When the distribution entropy of the reflection point When this occurs, it indicates that the reflection points are highly concentrated;

[0059] When the distribution entropy of the reflection point If so, it indicates that the reflection points are highly dispersed;

[0060] 3) When multiple reflections are significant and the reflection points are highly concentrated, it indicates that the person is indoors.

[0061] Based on the dual threshold logic of multipath reflection intensity ratio (>35% indicates significant multiple reflections) and reflection point distribution entropy (<1.2 bits indicates high concentration), it accurately identifies enclosed indoor environments, effectively avoids misjudgment of open spaces (reflection ratio <10% or entropy >2.0 bits), significantly improves the accuracy of indoor and outdoor scene classification, and provides key decision-making basis for subsequent positioning mode switching. Example

[0062] like Figure 1 , 2 As shown, the specific steps for the pre-trained model to determine whether the user is in an indoor motion state or an indoor stationary state are as follows:

[0063] 1) Determine if there is a significant difference in the target speed:

[0064] When point cloud velocity variance When this occurs, it indicates a large difference in target speed;

[0065] When point cloud velocity variance When this occurs, it indicates that the target velocity distribution is concentrated;

[0066] 2) Determine if the point cloud structure is changing rapidly:

[0067] When the entropy of point cloud structure changes When this occurs, it indicates that the point cloud structure is changing rapidly;

[0068] When the entropy of point cloud structure changes When this time, it indicates that the point cloud structure is stable;

[0069] 3) When the speed difference is large and the point cloud structure changes rapidly, it indicates that the object is in an indoor motion state.

[0070] Using the point cloud velocity variance (>0.4m² / s) 4 A collaborative judgment mechanism based on the differences in judgment speed (large differences in judgment speed) and the entropy of point cloud structure change (>3.0 bits determines drastic changes in structure) dynamically distinguishes between indoor motion states and static states (variance <0.05m² / s). 4 Furthermore, entropy < 0.8 bits indicates a stationary target, significantly enhancing the robustness of moving target recognition in complex indoor scenes and supporting the adaptation of precise positioning strategies. Example

[0071] like Figure 1 , 2 As shown in Figure 3, when the environment is indoors and in motion, the dynamic fusion processor will generate a global compensation amount by summing and averaging the local positioning deviations of each 5G base station. The formula for calculating the global average positioning error compensation amount is as follows:

[0072] ,

[0073] In the above formula, The mean compensation vector for all 5G base station positioning errors is represented by m; N represents the total number of nodes in the cooperative positioning network, i.e., the number of 5G base stations participating in the compensation. The local positioning result of the i-th 5G base station, in meters; The true location value of the i-th 5G base station, in meters; i represents the index number of the 5G base station.

[0074] like Figure 3 The diagram illustrates the process of obtaining the global average positioning error compensation for two 5G base stations. By calculating the global compensation amount through a dynamic fusion processor and aggregating local positioning deviations from multiple base stations (such as multipath effects and system errors caused by clock drift), the original positioning results of the user terminal are corrected in real time under indoor motion conditions, significantly improving the accuracy of network collaborative positioning, with an average error reduction rate exceeding 40% (e.g., ...). Figure 3 verify).

[0075] The final location result for the user terminal is corrected as follows:

[0076] ,

[0077] In the formula, This indicates the final location result of the user terminal; This represents the original positioning solution value of the terminal; This represents the mean compensation vector for all 5G base station positioning errors. Furthermore, the local positioning deviation for each base station is calculated as follows:

[0078] ,

[0079] This formula is used to reflect the systematic errors caused by multipath effects, clock drift, etc., in a single base station.

[0080] Furthermore, the collaborative control center faces the user terminal, where it displays the final positioning results. The intelligent radio frequency processing unit receives feedback from the edge computing platform and antenna unit, and acts as a relay between digital and modal signals.

[0081] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. An integrated communication and positioning system that combines BeiDou base stations and 5G base stations, characterized in that, include: The antenna unit is an antenna array consisting of a 5G antenna and a millimeter-wave radar, wherein the millimeter-wave radar is used for environmental perception. The edge computing platform is equipped with a scene classification model, a dynamic fusion processor, and a collaborative control center; The scene classification model is used to determine whether the environment is an indoor moving state or an indoor stationary state based on the feedback from the millimeter-wave radar. When the environment is indoors and in motion, the dynamic fusion processor will generate a global compensation amount by summing and averaging the local positioning deviation of each 5G base station. The dynamic fusion processor generates a global compensation amount based on the local positioning deviation of each 5G base station by summing and averaging. The formula for calculating the global average positioning error compensation amount is as follows: ； In the above formula, The mean compensation vector for all 5G base station positioning errors is represented by m; N represents the total number of nodes in the cooperative positioning network, i.e., the number of 5G base stations participating in the compensation. No. Local location results of 5G base stations, in meters; No. The true location value of a 5G base station, in meters; i represents the index number of the 5G base station; The intelligent radio frequency processing unit is used to receive feedback from the edge computing platform and antenna unit, and acts as a relay between digital signals and modal signals; Furthermore, the collaborative control center is used to coordinate the data flow between the antenna unit, the edge computing platform, and the intelligent radio frequency processing unit, process the positioning results, and send the final positioning results to the user terminal for display. Specifically, the system determines whether the location is indoors based on a pre-trained model; if indoors, it determines whether the location is in motion or stationary; if in motion, it triggers a dynamic fusion processor to calculate global compensation, and the final localization result is corrected as follows: ； In the formula, This indicates the final location result of the user terminal; This represents the original positioning solution value of the terminal.

2. The integrated communication and positioning system combining BeiDou base stations and 5G base stations according to claim 1, characterized in that: The specific steps by which the scene classification model determines whether the environment is an indoor moving state or an indoor stationary state based on the feedback from millimeter-wave radar are as follows: 1) Millimeter-wave radar data acquisition; 2) Feature extraction and preprocessing for both time and space; 3) Input the pre-processed features into the pre-trained model to output whether the model is in motion or stationary indoors.

3. The integrated communication and positioning system combining BeiDou base stations and 5G base stations according to claim 2, characterized in that: The millimeter-wave radar data acquisition is achieved by transmitting a 77-81 GHz frequency-modulated continuous wave from the millimeter-wave radar and receiving the reflected signal to generate a point cloud matrix, wherein the sampling rate of the received reflected signal is 20 frames / second.

4. The integrated communication and positioning system combining BeiDou base stations and 5G base stations according to claim 2, characterized in that: The specific formula for the preprocessing is as follows: ； In the formula, This represents the feature data after preprocessing; This represents the feature data collected in real time, including the multipath reflection intensity ratio of the reflected signal, the distribution entropy of the reflection point, the point cloud velocity, and the change entropy of the point cloud structure; This represents the historical mean of the feature data; The standard deviation of the feature data.

5. The integrated communication and positioning system combining BeiDou base stations and 5G base stations according to claim 4, characterized in that: The specific steps for the pre-trained model to determine whether the location is indoors include: 1) Determine if the wall reflects the signal multiple times: When the multipath reflection intensity ratio When the reflection is significant, it indicates that the wall reflects light multiple times. When the multipath reflection intensity ratio When this occurs, it indicates that the space is open and has low reflection. 2) Determine if the reflection points are highly concentrated: When the distribution entropy of the reflection point When this occurs, it indicates that the reflection points are highly concentrated; When the distribution entropy of the reflection point When this occurs, it indicates that the reflection points are highly dispersed; 3) When multiple reflections are significant and the reflection points are highly concentrated, it indicates that the person is indoors.

6. The integrated communication and positioning system combining BeiDou base stations and 5G base stations according to claim 4, characterized in that: The specific steps for the pre-trained model to determine whether the person is in an indoor motion state or an indoor stationary state are as follows: 1) Determine if there is a significant difference in the target speed: When point cloud velocity variance When this occurs, it indicates a large difference in target speed; When point cloud velocity variance When this occurs, it indicates that the target velocity distribution is concentrated; 2) Determine if the point cloud structure is changing rapidly: When the entropy of point cloud structure changes When this occurs, it indicates that the point cloud structure is changing rapidly; When the entropy of point cloud structure changes When this time, it indicates that the point cloud structure is stable; 3) When the speed difference is large and the point cloud structure changes rapidly, it indicates that the object is in an indoor motion state.

7. The integrated communication and positioning system combining BeiDou base stations and 5G base stations according to claim 1, characterized in that: The collaborative control center faces the user terminal and is used to display the final location results through the user terminal.

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