Radio frequency sensor cooperative positioning method and system based on live-action three-dimensional enabling
By combining dual-channel signal phase difference calculation with real-scene three-dimensional maps, the accuracy bottleneck of traditional positioning technology in complex environments is solved, and high-precision positioning effects are achieved.
Patent Information
- Application Number
- CN202510697361.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-28
- Publication Date
- 2025-09-09
AI Technical Summary
Existing positioning technologies find it difficult to achieve high-precision positioning in complex environments, especially in GNSS-denied environments. Traditional multi-anchor collaborative positioning systems are complex and costly, and real-life three-dimensional maps lack electromagnetic parameters and cannot meet high-precision positioning requirements.
A dual-channel signal phase difference calculation model is combined with a real-scene three-dimensional map. A ranging model is constructed through phase difference and RSSI distance estimation theory. Combined with the phase comparison method and the real-scene three-dimensional map optimization algorithm, high-precision direction and distance measurement is achieved.
It improves positioning accuracy, reduces the impact of the environment on signals, enhances signal reception sensitivity and anti-interference ability, and achieves sub-meter positioning accuracy in complex environments.
Smart Images
Figure CN120610233A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of high-precision positioning, and in particular to a collaborative positioning method and system for radio frequency sensors based on real-scene three-dimensional empowerment. Background Art
[0002] Currently, collaborative positioning technology using multifunctional integrated RF sensor networks has developed a variety of positioning methods, including AOA, RSSI, TOA / TDOA, and RTT, leveraging advantages such as high temporal resolution, multipath robustness, and low power consumption. RSSI, while simple in structure, has limited accuracy, TOA / TDOA relies on strict time synchronization, and RTT combined with AOA enables highly accurate ranging. However, traditional multi-anchor collaborative positioning requires the deployment of a large number of synchronized anchor nodes, resulting in complex and costly systems, and significantly reduced reliability in GNSS-denied environments.
[0003] At the same time, real-world 3D mapping technology is rapidly developing through remote sensing, laser scanning, and drone-assisted oblique photography, enabling high-precision 3D scene modeling. Compared to traditional vehicle-mounted scanning, which is limited by the road's perspective, drone technology can capture building textures from all angles, improving map scalability. While mainstream services domestically and internationally (such as Baidu Street View and Google Maps) offer 3D visualization, they lack depth information, such as electromagnetic parameters, making them difficult to meet high-precision positioning requirements. Summary of the Invention
[0004] The purpose of the present invention is to provide a collaborative positioning method and system for radio frequency sensors based on real-scene three-dimensional empowerment in order to solve the problem that existing positioning technologies are difficult to meet high-precision positioning requirements.
[0005] The above-mentioned purpose of this application is achieved through the following technical solutions: S1: Acquire dual-channel signals and real-scene 3D maps; S2: Construct a phase difference calculation model; S3: The incident angle of the dual-channel signal is obtained by combining the phase difference calculation model with the phase comparison method, and the high-precision direction determination result is obtained; S4: Based on the RRSI distance estimation theory and combined with the real-scene 3D map, a distance measurement model is constructed; S5: Obtain high-precision distance measurement results of dual-channel signals through the ranging model; S6: Determine the final positioning result through the high-precision direction measurement result and the high-precision distance measurement result.
[0006] Optionally, step S1 includes: The phase difference calculation model is as follows: Assume that the receiving distance of the dual-channel signal is d; when the plane wave is at an angle When incident on a dual-antenna system with a spacing of d, the phase difference of the signals received by the two antennas is and the angle of incidence satisfy:
[0007] in Indicates the wavelength of the dual-channel signal; By measuring the phase difference , inversely solve the incident angle as follows:
[0008] in Indicates no ambiguous phase difference; Indicates the baseline length; Indicates the angle with the antenna boresight.
[0009] Optionally, step S5 includes: The ranging model is as follows: Received power of dual-channel signals and distance Relationship:
[0010] By measuring the received power , reverse distance ,as follows:
[0011] in, is the reference distance The received power at is the path loss index, which is determined by the medium type of the object in the real 3D map.
[0012] A radio frequency sensor collaborative positioning system based on real-scene 3D empowerment, the system comprising: a transmitting module, a dual-channel direction-finding module, a real-scene 3D map database, a processing module, and a display module; The transmitting module is used to transmit dual-channel signals; The dual-channel direction finding module is used to receive dual-channel signals; The real-scene 3D map database is used to store the real-scene 3D map; The processing module is used to build a phase difference calculation model; through the phase difference calculation model, combined with the phase comparison method, the incident angle of the dual-channel signal is obtained, and the high-precision direction determination result is obtained; The processing module is also used to build a distance measurement model based on RRSI distance estimation theory and combined with the real-scene 3D map. Through the distance measurement model, high-precision distance measurement results of dual-channel signals are obtained; The processing module is also used to determine the final positioning result through the high-precision direction measurement result and the high-precision distance measurement result; The display module is used to visualize the final positioning results.
[0013] Optionally, the dual-channel direction finding module includes: a first channel module and a second channel module; The first channel module and the second channel module each include: a receiver, a receiving antenna, an amplifier, a mixer, a bandpass filter, an intermediate frequency amplifier, and a differential phase detector; The receiver, the receiving antenna, the amplifier, the mixer, the bandpass filter, the intermediate frequency amplifier and the differential phase detector are connected in sequence.
[0014] Optionally, the transmitting module uses a drone to transmit signals.
[0015] Optionally, the receiving antenna adopts a dual-channel omnidirectional broadband antenna.
[0016] A computer-readable storage medium stores instructions. When the instructions are executed, a radio frequency sensor collaborative positioning method based on real-scene three-dimensional empowerment is executed.
[0017] The beneficial effects of the technical solution provided by this application are: The phase comparison method is used to measure the direction and the received signal strength indicator (RSSI) is used to measure the distance. The two are then combined to calculate the specific direction. Combined with the real-life three-dimensional map optimization algorithm, the positioning accuracy is greatly improved and the impact of the environment on the signal is reduced. Through the dual-channel design, the sensor node can achieve higher signal reception sensitivity and anti-interference ability in complex environments, thereby improving the accuracy and reliability of positioning. This application proposes a collaborative relative positioning method enabled by real-life three-dimensional maps. By integrating three-dimensional geographic data with dielectric parameters, it dynamically corrects the radio frequency signal propagation error and solves the accuracy bottleneck of traditional positioning technology in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The present application will be further described below with reference to the accompanying drawings and embodiments, in which: Figure 1 is a system module diagram in an embodiment of the present application; Figure 2 This is a dual-channel radio frequency direction finding flow chart in an embodiment of the present application; Figure 3 is a ranging flow chart in an embodiment of the present application; Figure 4 It is a display module diagram in an embodiment of the present application. DETAILED DESCRIPTION
[0019] In order to have a clearer understanding of the technical features, purposes and effects of this application, the specific implementation methods of this application are now described in detail with reference to the accompanying drawings.
[0020] The embodiments of the present application provide a method for collaborative positioning of radio frequency sensors based on real-scene three-dimensional empowerment.
[0021] Please refer to Figure 1 , Figure 1 This is a system module diagram of a method for collaborative positioning of radio frequency sensors based on real-scene 3D empowerment in an embodiment of the present application, including: S1: Acquire dual-channel signals and real-scene 3D maps; S2: Construct a phase difference calculation model; S3: The incident angle of the dual-channel signal is obtained by combining the phase difference calculation model with the phase comparison method, and the high-precision direction determination result is obtained; S4: Based on the RRSI distance estimation theory and combined with the real-scene 3D map, a distance measurement model is constructed; S5: Obtain high-precision distance measurement results of dual-channel signals through the ranging model; S6: Determine the final positioning result through the high-precision direction measurement result and the high-precision distance measurement result.
[0022] The present application provides an embodiment as follows: the flow chart of the present invention's real-scene 3D assisted RSSI field strength positioning technology ranging is as follows: Figure 3 This system achieves high-precision positioning by combining real-world 3D maps with RSSI field strength positioning technology. First, through field measurements, a parametric map is established that captures the electromagnetic properties of media such as buildings and vegetation. An improved ray tracing algorithm (integrating geometric optics theory and the method of moments) is then used to accurately simulate signal propagation paths. GPU parallel computing accelerates processing, comprehensively considering factors such as multipath effects and material properties. Ultimately, meter-level positioning is achieved without relying on external infrastructure, making it suitable for a variety of complex indoor and outdoor environments.
[0023] Step S1 includes: The phase difference calculation model is as follows: Assume that the receiving distance of the dual-channel signal is d; when the plane wave is at an angle When incident on a dual-antenna system with a spacing of d, the phase difference of the signals received by the two antennas is and the angle of incidence satisfy:
[0024] in Indicates the wavelength of the dual-channel signal; By measuring the phase difference , inversely solve the incident angle as follows:
[0025] in Indicates no ambiguous phase difference; Indicates the baseline length; Indicates the angle with the antenna boresight.
[0026] The dual-channel radio frequency direction finding flow chart of the present invention is as follows: Figure 2 The dual-channel direction-finding receiver first receives and processes the signal through two independent channels (each channel contains a receiving antenna, amplifier, frequency mixer and bandpass filter). After amplification, frequency reduction and filtering, the output signals of the two channels are input into the differential phase detector for phase difference measurement. The phase difference signal is then sent to the operation unit to calculate the incident angle using the phase comparison method, thereby obtaining a direction determination result with high accuracy.
[0027] Step S5 includes: The ranging model is as follows: Received power of dual-channel signals and distance Relationship:
[0028] By measuring the received power , reverse distance ,as follows:
[0029] in, is the reference distance The received power at is the path loss index, which is determined by the medium type of the object in the real 3D map.
[0030] The present application provides an embodiment as follows. By utilizing the spatial modeling capability of a three-dimensional map, the medium types (such as brick walls, glass, metal, etc.) in different sections of a signal propagation path can be accurately identified, and a characteristic n value verified by actual measurements is assigned to each medium.
[0031] This application provides the following example. Table 1 shows the data processing results. The data in this table shows that, through error analysis of actual, measured, and GPS coordinates, the measured coordinates have a high positioning accuracy (RMSE 2.31 meters), which meets the requirements of most engineering surveying and construction positioning. In contrast, the positioning error of ordinary GPS coordinates is larger (RMSE 9.59 meters), making this accuracy only suitable for scenarios with low positioning accuracy requirements, such as vehicle navigation and outdoor activities. In this experiment, the measurement accuracy in non-line-of-sight conditions was higher than that of GPS, indicating that this method has a certain degree of effectiveness and stability, but there is still room for optimization to address more complex situations.
[0032]
[0033] Table 1 In a specific embodiment of the present application, with the technical support of real-scene three-dimensional maps, the RSSI positioning accuracy can be optimized by establishing a precise correspondence between medium characteristics and signal attenuation.
[0034] A radio frequency sensor collaborative positioning system based on real-scene 3D empowerment, the system comprising: a transmitting module, a dual-channel direction-finding module, a real-scene 3D map database, a processing module, and a display module; The transmitting module is used to transmit dual-channel signals; The dual-channel direction finding module is used to receive dual-channel signals; The real-scene 3D map database is used to store the real-scene 3D map; The processing module is used to build a phase difference calculation model; through the phase difference calculation model, combined with the phase comparison method, the incident angle of the dual-channel signal is obtained, and the high-precision direction determination result is obtained; The processing module is also used to build a distance measurement model based on RRSI distance estimation theory and combined with the real-scene 3D map. Through the distance measurement model, high-precision distance measurement results of dual-channel signals are obtained; The processing module is also used to determine the final positioning result through the high-precision direction measurement result and the high-precision distance measurement result; The display module is used to visualize the final positioning results.
[0035] In a specific embodiment of the present application, Figure 4It is a visual monitoring interface for a collaborative relative positioning system powered by a real-life 3D map. The interface adopts a simple and intuitive design style and is mainly divided into two parts: a real-time data display area and a trajectory display area. In the real-time data display area, the system dynamically updates the azimuth, distance measurement, and plane coordinate position of the current target. The interface uses a striking digital display to present key parameters, including dual-channel received signal strength values. These data provide real-time feedback for the system's direction-finding and ranging functions. The data display area adopts a clearly classified layout, allowing operators to quickly obtain key information. The central area of the interface is the dynamic trajectory display area, where the system draws the real-time motion trajectory of the target. The trajectory line is displayed in high-contrast colors, which contrasts sharply with the background map, making it easy to observe and analyze the target's movement path. The trajectory display supports zooming and panning operations, allowing users to view the target's movement from different angles.
[0036] At the bottom of the interface, a system parameter calibration area displays key calibration parameters and performance indicators for system operation in tabular format. This area uses a columnar design with clearly categorized parameters, making it easier for technicians to monitor system status and evaluate performance.
[0037] The entire interface features a dark background with highlighted elements, ensuring visual comfort for extended use while ensuring the readability of key data. The interface's rational layout and clear information hierarchy fully demonstrate the positioning system's efficient and precise technical features.
[0038] The dual-channel direction finding module includes: a first channel module and a second channel module; The first channel module and the second channel module each include: a receiver, a receiving antenna, an amplifier, a mixer, a bandpass filter, an intermediate frequency amplifier, and a differential phase detector; The receiver, the receiving antenna, the amplifier, the mixer, the bandpass filter, the intermediate frequency amplifier and the differential phase detector are connected in sequence.
[0039] In a specific embodiment of the present application, the dual-channel radio frequency direction finding flow chart of the present invention is as follows: Figure 2 The dual-channel direction-finding receiver first receives and processes the signal through two independent channels (each channel contains a receiving antenna, amplifier, frequency mixer and bandpass filter). After amplification, frequency reduction and filtering, the output signals of the two channels are input into the differential phase detector for phase difference measurement. The phase difference signal is then sent to the operation unit to calculate the incident angle using the phase comparison method, thereby obtaining a direction determination result with high accuracy.
[0040] The transmitting module uses a drone to transmit signals.
[0041] As an example, a non-line-of-sight (NLOS) experimental scenario, consistent with the model's requirements, was selected. The positioning system was deployed in an obstructed area, with the transmitter transmitting a drone signal. The system used a dual-channel RF receiver with an omnidirectional antenna to receive the drone signal. The baseline length, d, was 0.046m (less than half a wavelength, enabling direction finding in an unambiguous environment). The drone's transmitted signal frequency was 2.4GHz.
[0042] The receiving antenna is a dual-channel omnidirectional broadband antenna.
[0043] As an embodiment, the overall system block diagram of the present invention is as follows: Figure 1 The system of the present invention uses a dual-channel omnidirectional broadband antenna (600MHz-6000MHz) to achieve broadband signal reception. It uses the high-performance ADRV9009 RF front-end module to perform signal frequency conversion and conditioning. Based on the ZYNQMP FPGA platform, it implements multi-algorithm fusion processing, including signal processing technologies such as FFT frequency domain analysis, matched filtering, and phase resolution. Combined with dual-channel RF direction finding and RSSI field strength maximum detection algorithms, supplemented by real-scene 3D modeling and dynamic error correction technology, it ultimately achieves sub-meter-level precision real-time positioning and visualization in complex electromagnetic environments. It can be widely used in scenarios such as indoor navigation, industrial automation, and smart warehousing.
[0044] Since co-location RF sensors are often deployed on outdoor mobile platforms such as unmanned vehicles and drones, they place certain demands on sensor size and power consumption. Therefore, this application designs an RF front-end solution based on the ADRV9009 integrated RF transceiver chip. This chip incorporates two independent communication links, enabling ultra-wideband RF signal transmission and reception from 75MHz to 6000MHz. The core control unit utilizes a Xilinx ZYNQMP heterogeneous FPGA, comprised of resource-rich FPGA programmable logic (PL) and four ARM A53 hardcore processing systems (PS). Most digital signal processing (DSP) tasks, such as filtering, sequence acquisition and tracking, direction of arrival angle calculation, and range estimation, are performed in the core control unit.
[0045] The present application also discloses a computer-readable storage medium storing a plurality of instructions suitable for loading by a processor to execute the above-mentioned radio frequency sensor collaborative positioning method based on real-scene three-dimensional empowerment.
[0046] The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. In other words, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure.
[0047] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.
Claims
1. A radio frequency sensor collaborative positioning method based on real-scene three-dimensional empowerment, characterized in that: The method comprises the following steps: S1: Acquire dual-channel signals and real-scene 3D maps; S2: Construct a phase difference calculation model; S3: The incident angle of the dual-channel signal is obtained by combining the phase difference calculation model with the phase comparison method, and the high-precision direction determination result is obtained; S4: Based on the RRSI distance estimation theory and combined with the real-scene 3D map, a distance measurement model is constructed; S5: Obtain high-precision distance measurement results of dual-channel signals through the ranging model; S6: Determine the final positioning result through the high-precision direction measurement result and the high-precision distance measurement result.
2. The method for cooperative positioning of radio frequency sensors based on real-scene 3D empowerment according to claim 1, characterized in that: Step S1 includes: The phase difference calculation model is as follows: Assume that the receiving distance of the dual-channel signal is d; when the plane wave is at an angle When incident on a dual-antenna system with a spacing of d, the phase difference of the signals received by the two antennas is and the angle of incidence satisfy: in Indicates the wavelength of the dual-channel signal; By measuring the phase difference , inversely solve the incident angle as follows: in Indicates no ambiguous phase difference; Indicates the baseline length; Indicates the angle with the antenna boresight.
3. The method for cooperative positioning of radio frequency sensors based on real-scene three-dimensional empowerment according to claim 2, characterized in that: Step S5 includes: The ranging model is as follows: Received power of dual-channel signals and distance Relationship: By measuring the received power , reverse distance ,as follows: in, is the reference distance The received power at is the path loss index, which is determined by the medium type of the object in the real 3D map.
4. A radio frequency sensor collaborative positioning system based on real scene three-dimensional empowerment, used to implement a radio frequency sensor collaborative positioning method based on real scene three-dimensional empowerment according to any one of claims 1 to 3, characterized in that: The system includes: a transmitting module, a dual-channel direction-finding module, a real-scene three-dimensional map database, a processing module, and a display module; The transmitting module is used to transmit dual-channel signals; The dual-channel direction finding module is used to receive dual-channel signals; The real-scene 3D map database is used to store the real-scene 3D map; The processing module is used to build a phase difference calculation model; through the phase difference calculation model, combined with the phase comparison method, the incident angle of the dual-channel signal is obtained, and the high-precision direction determination result is obtained; The processing module is also used to build a distance measurement model based on RRSI distance estimation theory and combined with the real-scene 3D map. Through the distance measurement model, high-precision distance measurement results of dual-channel signals are obtained; The processing module is also used to determine the final positioning result through the high-precision direction measurement result and the high-precision distance measurement result; The display module is used to visualize the final positioning results.
5. The radio frequency sensor collaborative positioning system based on real scene 3D empowerment according to claim 4, characterized in that: The dual-channel direction finding module includes: a first channel module and a second channel module; The first channel module and the second channel module each include: a receiver, a receiving antenna, an amplifier, a mixer, a bandpass filter, an intermediate frequency amplifier, and a differential phase detector; The receiver, the receiving antenna, the amplifier, the mixer, the bandpass filter, the intermediate frequency amplifier and the differential phase detector are connected in sequence.
6. The radio frequency sensor collaborative positioning system based on real scene 3D empowerment according to claim 4, characterized in that: The transmitting module uses a drone to transmit signals.
7. The radio frequency sensor collaborative positioning system based on real scene 3D empowerment according to claim 1, characterized in that: The receiving antenna is a dual-channel omnidirectional broadband antenna.
8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed by a computer, the method according to any one of claims 1 to 3 is executed.