Positioning system based on radio orientation

Abstract

The invention discloses a positioning system based on radio orientation. The positioning system comprises an orientation signal transmitting module, a signal receiving module, a signal processing module and a positioning module, the signal transmitting module constructs a directional positioning network comprising at least three base stations, and the directional positioning network generates directional signals through a beam forming technology; the signal receiving module receives directional signals from different base stations, extracts multi-dimensional parameters and uploads the multi-dimensional parameters to the signal processing module; the signal processing module performs multipath interference suppression on the extracted multi-dimensional parameters based on a generalized cyclic cross-correlation entropy function, reversely searches signal propagation paths by adopting a reverse ray tracing algorithm, and eliminates non-line-of-sight propagation paths in the propagation paths to obtain line-of-sight propagation path parameters of each base station; and the positioning module evaluates the signal reliability according to the multi-dimensional parameter quality of each base station, dynamically adjusts the weight coefficient of each base station, carries out weighted fusion on the sight distance path parameter and the geographic position information of each base station, and outputs the three-dimensional coordinate of the target equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a positioning system based on radio orientation. Background Art

[0002] Marine engineering involves many complex links, including the exploration, development, production, and disposal of marine oil and gas, as well as the installation, inspection, repair, and maintenance (IRM) of underwater structures. In these operations, accurately positioning underwater structures to the designed positions and allowing remotely controlled underwater robots (ROVs) to quickly and accurately find underwater targets have become key issues that need to be solved urgently.

[0003] At present, underwater positioning mainly relies on acoustic positioning technology, including long baseline (LBL), short baseline (SBL) and ultra-short baseline (USBL) systems. LBL has the highest accuracy, but it takes a lot of time and cost to set up the array; SBL and USBL are relatively simple, but the working distance and accuracy are limited. In addition, these high-precision positioning devices are usually bulky, expensive, and rely on battery power. The installation and recovery process is complicated, especially in deep-water operations. The risk is high. In order to solve the bottleneck of traditional technologies, new positioning technologies such as radio directional positioning have been introduced in the field of marine engineering. Radio directional positioning uses the propagation characteristics of radio waves for positioning, and has the advantages of wide coverage and strong anti-interference ability. This method combines multiple technologies such as radio direction finding, satellite navigation, inertial navigation and hydroacoustic positioning, and improves positioning accuracy and reliability by fusing data from different sensors.

[0004] However, radio directional positioning still has some limitations. In complex ocean environments, multipath interference can seriously affect positioning accuracy. This interference can cause distortion of positioning signals, causing the received signals to contain false path information, which in turn affects the accuracy of the positioning algorithm and reduces positioning accuracy. Summary of the invention

[0005] The invention proposes a positioning system based on radio orientation, which solves the problem that the accuracy of the existing radio orientation positioning method is reduced due to multipath interference.

[0006] In order to solve the above technical problems, the present invention provides a positioning system based on radio orientation, comprising: a directional signal transmitting module, a signal receiving module, a signal processing module and a positioning module; The signal transmission module: constructs a directional positioning network including at least three base stations, and the directional positioning network generates a directional signal through beamforming technology; The signal receiving module is arranged on the target device, receives directional signals from different base stations, extracts multi-dimensional parameters, and uploads the multi-dimensional parameters to the signal processing module; The signal processing module: performs multipath interference suppression on the extracted multidimensional parameters based on the generalized cyclic cross-correlation entropy function, uses a reverse ray tracing algorithm to reversely search the propagation path of the signal, excludes the non-line-of-sight propagation path in the propagation path according to the multidimensional parameters after multipath interference suppression, and obtains the line-of-sight propagation path parameters of each base station; The positioning module evaluates the signal reliability according to the multi-dimensional parameter quality of each base station, dynamically adjusts the weight coefficient of each base station, weightedly fuses the line-of-sight path parameters and geographic location information of each base station, and outputs the three-dimensional coordinates of the target device.

[0007] Preferably, in the signal transmission module, after the directional signal is generated, the sidelobe interference of each beam is suppressed by a minimum mean square error algorithm, and the weight of each beam is updated. The expression of the minimum mean square error algorithm is: ; In the formula, , Respectively Second and The weight vector for the iteration; is the step size parameter; is the conjugate of the error signal; is the input signal.

[0008] Preferably, after the signal receiving module extracts the signal strength, a sliding window averaging algorithm is used to denoise the signal strength, and a smoothed signal strength is generated by calculating the average value of the signal strength values ​​within the window, wherein the size of the sliding window is adaptively adjusted according to the moving speed of the target device.

[0009] Preferably, an environmental attenuation compensation factor is introduced into the signal receiving module to correct the error of signal strength caused by altitude change and device posture. The expression of the environmental attenuation compensation factor is: ; In the formula, is the environmental attenuation compensation factor; , is the weight coefficient; is the height attenuation term; is the altitude data measured by the barometer of the target device; is the attitude attenuation term; , are the pitch and roll angles output by the target device's gyroscope, respectively.

[0010] Preferably, the target device receives a directional signal from a base station through a regular tetrahedron antenna array, and the signal receiving module extracts the direction of arrival from the directional signal using a quaternion MUSIC algorithm, including the following steps: Step 1: Based on the three-dimensional coordinates of each antenna element in the regular tetrahedron antenna array and the incident direction vector of the signal, convert the position vector of each antenna element and the signal incident direction vector into quaternion form: ; In the formula, For the The quaternion array manifold vector of the antenna elements; is an imaginary unit; is the spacing between antenna units; is the wavelength of the signal; For the The position vector of each antenna element; is the incident direction vector of the signal; Step 2: Calculate the covariance matrix of the quaternion signal to obtain the covariance matrix of the antenna array output; Step 3: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors, and divide the eigenvectors into signal subspace and noise subspace according to the size of the eigenvalues; Step 4: Use the eigenvector of the noise subspace to construct a spatial spectrum function, search the spatial spectrum function, find the direction that makes the spatial spectrum function reach the minimum value, and obtain the arrival direction of the directional signal.

[0011] Preferably, in the signal processing module, after receiving the multi-dimensional parameters uploaded by the target device, the direction of arrival angle is predicted according to the moving trajectory of the target device, and the weight coefficient of each beam is dynamically adjusted by using Kalman filtering: ; In the formula, is the Kalman gain matrix; is the prior estimation error covariance matrix; is the observation matrix; is the observation noise covariance matrix; is the posterior estimation error covariance matrix; is the identity matrix.

[0012] Preferably, the signal processing module performs multipath interference suppression on the extracted multidimensional parameters based on a generalized cyclic cross-correlation entropy function, comprising the following steps: S1: Perform cyclic spectral density analysis on the received multi-dimensional parameters, extract the cyclic stationary characteristics of the received signal, and separate the interference components with different cyclic frequencies; S2: Mapping the multidimensional parameters to the joint feature space, constructing a three-dimensional matrix as an input of a generalized cyclic cross-correlation entropy function, and the generalized cyclic cross-correlation entropy function outputs an entropy matrix of the multidimensional parameters; S3: The kernel width of the kernel function of the generalized cyclic cross-correlation entropy function is adjusted using a reinforcement learning algorithm; S4: Perform singular value decomposition on the entropy matrix and retain the previous K The principal components construct the signal subspace and suppress the components corresponding to the noise subspace; S5: Analyze the eigenvalue distribution of the entropy matrix. When the ratio of the second largest eigenvalue to the largest eigenvalue is greater than the set threshold, it is determined to be multipath interference. The multipath interference in the multidimensional parameters is further reduced through the attenuation compensation mechanism.

[0013] Preferably, the expression of the generalized cyclic cross-correlation entropy function in S2 is: ; In the formula, To characterize the signal and In delay Statistical correlation under is the total number of signal samples; is the Gaussian kernel function; , It is a directional signal from different base stations; is the time variable; For delay.

[0014] Preferably, S3 comprises the following steps: S31: The current environmental noise intensity, historical interference scene data, and signal quality are used as the state space, and the kernel width adjustment rule is used as the action space. The reward value is calculated according to the signal processing performance and adaptability changes of the agent after executing the action. The expression for calculating the reward value is: ; In the formula, Rewards for signal processing performance; Enhance rewards for adaptability; Rewards for signal-to-noise ratio improvement; Reduce rewards for false positive rates; To handle the delay penalty item; Adjust speed bonus for parameters; Reward for parameter stability; Rewards for detecting sudden changes in the environment; S32: Create a Q A value table to store the value of each state-action pair Q Value, where Q The value represents the expected long-term reward of performing an action in a certain state; S33: The agent continuously tries different actions by interacting with the environment and updates the reward based on the reward. Q Value tableQ value, gradually optimize the decision strategy, and obtain the optimal core width adjustment strategy.

[0015] Preferably, the signal processing module excludes non-line-of-sight propagation paths in the propagation path according to the multidimensional parameters after multipath interference suppression, including the following steps: calculating the signal propagation time difference by the arrival time difference after multipath interference suppression, and in combination with the location information of each base station, reversely deriving the difference in signal path length to exclude the non-line-of-sight propagation path; determining the signal arrival direction by using the wave direction after multipath interference suppression, optimizing the initial search direction of ray tracing, predicting the main propagation path in combination with the geographic information system data of the search area, and excluding the non-line-of-sight propagation path; evaluating the path loss based on the signal strength after multipath interference suppression, calculating the energy attenuation of different material surfaces in combination with the Fresnel equation and the material reflection coefficient database, verifying whether the path meets the line-of-sight propagation attenuation characteristics, and excluding the non-line-of-sight propagation path.

[0016] The benefits of the present invention include at least: 1. In complex environments, multipath interference can seriously affect positioning accuracy. The present invention suppresses multipath interference on the extracted multidimensional parameters based on the generalized cyclic cross-correlation entropy function, removes the influence of noise on positioning parameters, improves the quality of positioning data, and ensures positioning accuracy; 2. The reverse ray tracing algorithm is used to reversely search the signal propagation path, and a three-dimensional path map is generated in combination with geographic information. At the same time, the non-line-of-sight propagation path is excluded based on the multi-dimensional parameters after multipath interference suppression, so that the system can more accurately analyze the signal propagation path, reduce the error caused by non-line-of-sight propagation, and provide reliable path information for precise positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a system framework of an embodiment of the present invention; Figure 2 A schematic diagram of a method flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0019] like Figure 1 As shown, an embodiment of the present invention provides a positioning system based on radio orientation, including: a directional signal transmitting module, a signal receiving module, a signal processing module and a positioning module.

[0020] Signal transmission module: Build a directional positioning network including at least three base stations. The directional positioning network generates directional signals through beamforming technology.

[0021] At least three base stations are deployed on the offshore platform or operating area to build a directional positioning network. Each base station is equipped with a multi-band antenna array. The multi-band antenna array should have high gain and low sidelobe level. The antenna spacing adopts an adjustable structure of 0.5-2λ to adapt to the needs of different frequency bands and environments. At the same time, it supports low-frequency (LF) to ultra-high frequency (UHF) signal coverage. The radiation performance and electromagnetic parameters of antennas in each frequency band are independent of each other and do not affect each other. There is sufficient isolation between different frequency bands to avoid signal interference. Low-frequency and high-frequency antenna units can be nested or interlaced to reduce the size of the antenna array and improve the frequency band isolation. The number of antenna units depends on the positioning accuracy and coverage requirements. High-precision positioning systems need to be configured with multiple antenna units to improve signal stability and positioning accuracy. In some applications, the antenna array may contain dozens or even more antenna units. The integrated Beidou / GPS dual-mode timing unit ensures that the time synchronization accuracy between base stations reaches the nanosecond level, providing a basis for the measurement of arrival time difference. The beam weight coefficient is calculated in real time through the field programmable logic gate array FPGA chip, combined with the minimum mean square error LMS algorithm to suppress sidelobe interference and realize dynamic beamforming. Specifically, in low-speed scenarios of 0-10 km / h, a narrow beam is used to improve signal strength; in high-speed scenarios of 30-100 km / h, a wide beam is switched to expand coverage.

[0022] At the same time, the base station is connected to the environmental database, and the marine geological database is connected to the geographic information system (GIS) to obtain the seabed terrain elevation, seawater refractive index, and obstacle material reflection coefficient data to provide environmental parameter support for the improved reverse ray tracing algorithm. A database of urban building material reflection coefficients is established, including Fresnel reflection attenuation models for materials such as glass, concrete, and metal.

[0023] Specifically, directional signals are generated through beamforming technology, which can use digital beamforming (DBF) or analog beamforming technology to achieve flexible beam control. In low-speed scenarios, narrow beam directional scanning is used to improve signal strength and positioning accuracy; in high-speed scenarios, it switches to wide beam fast scanning to expand coverage and track moving targets. The beam weight coefficient is calculated in real time based on the FPGA chip, combined with the minimum mean square error algorithm to suppress sidelobe interference, support multi-user space division multiplexing, and reduce co-frequency interference through orthogonal beam allocation.

[0024] The expression of the minimum mean square error algorithm is: ; In the formula, , Respectively Second and The weight vector for the iteration; is the step size parameter; is the conjugate of the error signal; is the input signal.

[0025] Signal receiving module: It is set on the target device, receives directional signals from different base stations, extracts multi-dimensional parameters including signal strength, arrival time difference and wave direction, and uploads the multi-dimensional parameters to the signal processing module.

[0026] Specifically, a multi-band receiving module is installed on the underwater equipment or remote-controlled underwater robot (ROV), which supports Beidou / GPS signal demodulation and is equipped with a high dynamic range receiver with a dynamic range of ≥80 dB to adapt to base station signals of different strengths. The device has a built-in regular tetrahedron antenna array, which constructs a three-dimensional spatial spectrum based on the quaternion MUSIC algorithm, breaking through the estimation limitations of the traditional two-dimensional wave arrival direction and achieving sub-degree resolution of 0° to 360° in azimuth and -90° to 90° in elevation. The integrated barometer and gyroscope obtain the device altitude and attitude data in real time, where the attitude data includes the pitch angle and roll angle , used to compensate for signal strength environmental attenuation.

[0027] Introduce environmental attenuation compensation factor, combined with target device barometer altitude data With gyroscope attitude data , correct the signal strength deviation. The expression of the compensation model is: ; In the formula, is the environmental attenuation compensation factor; , is the weight coefficient; is the height attenuation term; is the altitude data measured by the barometer of the target device; is the attitude attenuation term; , are the pitch and roll angles output by the target device's gyroscope, respectively.

[0028] The signal strengths of different frequency bands are weighted and integrated, and weights are assigned to the signal strengths of each frequency band according to signal quality and anti-interference capability: Signal quality: Real-time monitoring of the signal quality of each frequency band, such as signal-to-noise ratio (SNR) and signal strength. When the signal quality of a certain frequency band decreases, its weight is reduced, while the weights of other frequency bands are increased to ensure that the frequency band with good signal quality has a higher weight in the fusion and contributes more to the fusion result.

[0029] Anti-interference capability: Low frequency bands (such as 433 MHz) have strong penetration but low resolution, while high frequency bands (such as 5.8 GHz) have high resolution but are easily blocked. Therefore, after evaluating the electromagnetic environment of each frequency band, higher weights are given to frequency bands with less interference to reduce the impact of interference on the fused signal.

[0030] Utilize artificial intelligence and machine learning algorithms, such as neural networks and fuzzy logic, to automatically learn and adjust the weights of each frequency band based on historical data and real-time information to adapt to complex and changing signal environments.

[0031] The target device receives directional signals from different base stations and extracts the following multi-dimensional parameters: Signal strength (RSSI): Sliding window averaging algorithm is used for denoising, and the window size is adaptively adjusted according to the target moving speed.

[0032] First, an initial window size is set according to the initial moving speed of the target device. For example, if the target device is stationary or moving at a low speed, a larger window size can be set. In the embodiment of the present invention, it is set to 100-200 sampling points to obtain a better smoothing effect. If the target device is moving at a high speed, a smaller window size is set. In the embodiment of the present invention, it is set to 20-50 sampling points to quickly respond to signal changes.

[0033] The target device's built-in sensor monitors the target's moving speed in real time, and dynamically adjusts the window size according to the target device's moving speed. When the target's moving speed increases, the window size is reduced to quickly respond to signal changes; when the target's moving speed decreases, the window size is increased to better smooth out noise.

[0034] Time Difference of Arrival (TDOA): The measurement accuracy must reach the nanosecond level, and the generalized cyclic cross-correlation entropy algorithm is used to suppress multipath interference and eliminate the delay error caused by frequency offset.

[0035] Direction of Arrival (DOA): Based on the tetrahedron antenna array receiving signal, the subspace projection technology is used to separate the same-frequency interference and improve the directional resolution in the low signal-to-noise ratio environment. The signal receiving module uses the quaternion MUSIC algorithm to extract the direction of arrival from the directional signal, including the following steps: Step 1: Based on the three-dimensional coordinates of each antenna element in the regular tetrahedron antenna array and the incident direction vector of the signal, convert the position vector of each antenna element and the signal incident direction vector into quaternion form: ; In the formula, For the The quaternion array manifold vector of the antenna elements; is an imaginary unit; is the spacing between antenna units; is the wavelength of the signal; For the The position vector of each antenna element; is the incident direction vector of the signal. Both the position vector and the incident direction vector are represented by quaternions to more comprehensively describe the signal propagation characteristics in three-dimensional space.

[0036] In three-dimensional space, the azimuth The range is usually 0° to 360°, while the elevation The range is generally from −90° to 90°.

[0037] According to the requirements of the actual application for angle resolution, the range of azimuth and elevation is divided into multiple discrete angle points. For example, the azimuth and elevation can be divided into 1° intervals, so there will be 360 ​​points in the azimuth direction and 180 points in the elevation direction.

[0038] By combining the divided azimuth and elevation points, a three-dimensional grid is generated. Specifically, for each azimuth and each elevation angle , calculate the corresponding direction The array manifold matrix under , and use these matrices to construct a gridded search space in three-dimensional space.

[0039] Step 2: Calculate the covariance matrix of the quaternion signal to obtain the covariance matrix of the antenna array output.

[0040] Step 3: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors, and divide the eigenvectors into signal subspace and noise subspace according to the size of the eigenvalues.

[0041] Step 4: Use the eigenvector of the noise subspace to construct a spatial spectrum function, search the spatial spectrum function, find the direction that makes the spatial spectrum function reach the minimum value, and obtain the arrival direction of the directional signal.

[0042] Signal processing module: Multipath interference is suppressed on the extracted multidimensional parameters based on the generalized cyclic cross-correlation entropy function, and the reverse ray tracing algorithm is used to reversely search the signal propagation path to generate a three-dimensional path map. The non-line-of-sight propagation paths in the three-dimensional path map are eliminated based on the multidimensional parameters after multipath interference suppression, and the line-of-sight propagation path parameters of each base station are obtained.

[0043] Specifically, the generalized cyclic cross-correlation entropy function is used to construct a noise suppression model, which is combined with Monte Carlo path screening to eliminate non-line-of-sight signals and reduce the positioning deviation caused by multipath effects.

[0044] The expression of the noise suppression model is: ; In the formula, To characterize the signal and In delay Statistical correlation under is the total number of signal samples; is the Gaussian kernel function; , It is a directional signal from different base stations; is the time variable; For delay.

[0045] The entropy matrix is ​​subjected to singular value decomposition (SVD), and the first K principal components are retained to construct the effective signal subspace and suppress the noise subspace component.

[0046] Through the analysis of the eigenvalue distribution of the entropy matrix, if the ratio of the second largest eigenvalue to the largest eigenvalue exceeds the threshold (such as >0.3), it is determined to be multipath interference and the attenuation compensation mechanism is triggered.

[0047] The reinforcement learning algorithm is used to dynamically adjust the scale (σ) of the kernel function in the generalized cyclic cross-correlation entropy function. The state space includes the environmental noise intensity, historical interference scene data and signal quality; the action space is the kernel width adjustment strategy; the reward function integrates the improvement in signal processing performance and the improvement in adaptability.

[0048] The kernel width adjustment strategy is used to adjust the width parameter of the Gaussian kernel function to adapt to different environmental noise intensities. The width parameter of the Gaussian kernel function ( ) needs to be dynamically adjusted according to the ambient noise intensity to achieve a balance between noise suppression and signal retention. Specifically, when the statistical characteristics of the noise are known, The noise variance Direct derivation: ; In the formula, is the noise variance, reflecting the noise intensity; k It is an empirical coefficient, usually ranging from 1 to 3, and needs to be calibrated with actual measured data.

[0049] If the statistical characteristics of the noise are unknown at this time, the optimal core width adjustment strategy is selected according to the current state. According to the dynamic characteristics of the noise and system requirements, common strategies can be divided into the following four categories: Empirical threshold method: preset noise intensity range and kernel width The mapping relationship of Model adaptation strategy: Establish compensation model by combining sensor data of barometer and gyroscope; Reinforcement learning strategy: Use the signal-to-noise ratio improvement or edge retention as the reward function to train the agent to select the optimal ; Multi-scale fusion strategy: Apply multiple Gaussian kernel of the value, weighted fusion result.

[0050] The reward function combines the improvement in signal processing performance and the improvement in adaptability. Specifically, when the signal processing performance improves, the reward is measured by improving the signal-to-noise ratio or reducing the false detection rate; when the adaptability increases, the reward is given based on the agent's ability to quickly adapt to environmental changes. In order to achieve a balance between performance improvement and adaptability, the expression of the comprehensive reward function is: ; In the formula, Rewards for signal processing performance; Enhance rewards for adaptability; Rewards for signal-to-noise ratio improvement; Reduce rewards for false positive rates; To handle the delay penalty item; Adjust speed bonus for parameters; Reward for parameter stability; Rewards for detecting sudden changes in the environment.

[0051] The environmental adaptability of the intelligent agent needs to be comprehensively evaluated through five dimensional indicators: dynamic response, stability, detection sensitivity, generalization, and resource efficiency, and rely on high-fidelity simulation and real-world scenario iterative verification; in actual deployment, 3-4 core indicators are prioritized to construct the reward function to avoid excessive complexity.

[0052] Create one Q A value table to store the value of each state-action pair Q Value, where Q The value represents the expected long-term reward for performing a certain action in a certain state. The agent continuously tries different actions by interacting with the environment and updates the reward according to the reward obtained. Q Value table Q value, gradually optimize the decision strategy, and obtain the optimal core width adjustment strategy.

[0053] After calculating the real-time position and motion status of the target device based on the TDOA and DOA reported by the target device, the base station predicts the future position of the target device through Kalman filtering and adjusts the beam weight coefficient of the antenna array in real time so that the main lobe of the beam accurately points to the predicted position.

[0054] The state equation of the Kalman filter model is used to predict the target position at the next moment, and the observation equation is combined with the target signal received by the base station to correct the predicted value. The optimal estimated target position and motion trend are output, and the beam weight coefficient is updated through the FPGA chip. The expression of the Kalman filter model is: ; In the formula, is the Kalman gain matrix; is the prior estimation error covariance matrix; is the observation matrix; is the observation noise covariance matrix; is the posterior estimation error covariance matrix; is the identity matrix.

[0055] When the target moves at high speed, signal loss due to beam switching delay can be avoided by predicting and adjusting the beam direction in advance.

[0056] The improved reverse ray tracing algorithm traces the possible paths of the signal from the receiving end, and uses the cross-scanning algorithm to optimize the judgment of the ray propagation direction and reduce the amount of invalid calculations. Combining GIS data with the Fresnel equation, the reflection attenuation of the obstacle surface is calculated, and the diffraction wave field strength model is established. Delay alignment and phase compensation are performed on all valid paths of the receiving point, and a composite channel impulse response model including path loss and Doppler frequency shift is constructed.

[0057] The Monte Carlo method is used to randomly generate path samples, and the line-of-sight (LOS) path is screened through the signal strength threshold and delay consistency to eliminate non-line-of-sight (NLOS) interference.

[0058] The pre-trained CNN network classifies path features such as path length and angle consistency, outputs path credibility weights, and dynamically modifies weights with Kalman filtering to improve screening accuracy in complex environments. The signal reliability is evaluated based on the multi-dimensional parameter quality of each base station, the weight coefficient of each base station is dynamically adjusted, and the line-of-sight path parameters and geographic location information of each base station are weighted and integrated to output the three-dimensional coordinates of the target device.

[0059] Positioning module: Evaluate the signal reliability according to the multi-dimensional parameter quality of each base station, dynamically adjust the weight coefficient of each base station, weightedly fuse the line-of-sight path parameters and geographic location information of each base station, and output the three-dimensional coordinates of the target device.

[0060] Specifically, the local coordinate system of each base station is transformed into the global coordinate system through the rotation and translation matrix. The positioning data of each base station is assigned a weight, and the weight coefficient is dynamically adjusted based on the signal quality (SNR, multipath error, clock synchronization deviation) evaluated by the Kalman filter.

[0061] The weight coefficient is optimized using the improved particle swarm optimization algorithm PSO. The improved PSO algorithm can improve the global search capability and convergence speed by dynamically adjusting the inertia weight. For example, the linear decreasing weight strategy (LDW) or adaptive inertia weight strategy is adopted to enable particles to find the global optimal solution faster.

[0062] In the improved PSO algorithm, the particle weight update formula can be expressed as: ; In the formula, , Respectively i The particle in t +1 and t The weight at the iteration; , are learning factors respectively, and in the embodiment of the present invention, the value is 2; , is a random number between [0,1]; For the i The best historical position of a particle; is the global best position; For the i The particle in t The position in the iteration.

[0063] like Figure 2 As shown, an embodiment of the present invention further provides a positioning method based on radio orientation, which is implemented based on the above-mentioned positioning system based on radio orientation, and includes the following steps: S1. Build a directional positioning network consisting of at least three base stations. Each base station is equipped with a multi-band antenna array to generate directional signals through beamforming technology.

[0064] S2. The target device receives directional signals from different base stations and extracts multi-dimensional parameters such as signal strength, arrival time difference and direction of arrival.

[0065] S3. Based on the generalized cyclic cross-correlation entropy function, a multipath interference suppression model is constructed to perform noise suppression processing on multidimensional parameters.

[0066] S4. Use an improved reverse ray tracing algorithm, combined with geographic information system data, to construct a three-dimensional signal propagation path map and exclude non-line-of-sight propagation paths.

[0067] S5. The positioning data of multiple base stations are integrated through the dynamic weighted particle swarm optimization algorithm to output the three-dimensional coordinates of the target in real time, where the weight coefficient is dynamically adjusted according to the base station signal quality.

[0068] Assume that in a deep-sea oil and gas field development project, key equipment of the subsea production system (Subsea Production System) needs to be accurately installed and regularly maintained. These equipment include underwater wellheads, pipelines, valves, and sensors, which are distributed over a vast seabed area. At the same time, it is necessary to use a remotely controlled underwater robot (ROV) to inspect and repair the equipment.

[0069] System deployment 1. Base station deployment At least three base stations are deployed on the offshore platform. Each base station is equipped with a multi-band antenna array with an adjustable antenna spacing ranging from 0.5 to 2 The base station integrates Beidou / GPS dual-mode timing units to ensure high-precision time synchronization. The base station generates directional signals through beamforming technology to cover the entire operating area.

[0070] 2. Target device The target device is installed on the underwater equipment and ROV, which has multi-band receiving capability, can receive the directional signal from the base station and extract multi-dimensional parameters such as signal strength (RSSI), time difference of arrival (TDOA) and direction of arrival (DOA).

[0071] 3. Signal processing and optimization module Embedded signal processing modules are deployed on the offshore platform, equipped with FPGA chips, to achieve real-time generalized cyclic cross-correlation entropy calculation to suppress multipath interference. At the same time, combined with the dynamic weighted particle swarm optimization algorithm (DPSO) and Kalman filtering, the three-dimensional coordinates of the target device are output in real time.

[0072] 4. 3D path modeling The system is connected to the marine geological database, which contains information on seabed topography, seawater refractive index, and offshore platform structure. Through an improved reverse ray tracing algorithm, combined with GIS data, a three-dimensional signal propagation path map is constructed to exclude non-line-of-sight propagation paths.

[0073] Implementation steps 1. Base station initialization and calibration After deployment, the base stations are initialized and calibrated to ensure the beamforming performance of the antenna array and the synchronization accuracy of the timing unit. The communication link and signal coverage between base stations are verified through test signals.

[0074] 2. Target device location When the ROV or underwater equipment starts working, the target device receives directional signals from multiple base stations and extracts RSSI, TDOA and DOA parameters. The signal processing module suppresses multipath interference through the generalized cyclic cross-correlation entropy function, and fuses multi-base station data through the dynamic weighted particle swarm optimization algorithm to output the three-dimensional coordinates of the target in real time.

[0075] 3. Path optimization and real-time adjustment The system dynamically optimizes signal processing parameters through the adaptive kernel width adjustment unit according to the real-time monitoring of environmental noise intensity. The Kalman filter module dynamically adjusts the weight coefficient according to the signal quality to ensure positioning accuracy. At the same time, through GIS data and reverse ray tracing algorithm, the three-dimensional signal propagation path map is updated in real time to eliminate interference from non-line-of-sight paths.

[0076] 4. Operation monitoring and feedback In the control center of the offshore platform, the three-dimensional coordinates and motion trajectory of the target equipment are monitored in real time. The operator adjusts the operation path of the ROV through the visual interface to ensure that it reaches the target position accurately. At the same time, the system records the signal data and positioning accuracy during the operation for subsequent performance evaluation and optimization.

[0077] A positioning system and method based on radio orientation provided by an embodiment of the present invention can achieve centimeter-level positioning accuracy in a complex marine environment through multi-parameter fusion and dynamic optimization algorithm, and provide reliable technical support for precise operations in marine engineering. By adjusting the beamforming parameters and signal processing kernel function scale in real time, it can adapt to dynamic changes in the marine environment. For example, in response to complex situations such as signal refraction and electromagnetic interference caused by seawater flow, the system can respond quickly, optimize the signal transmission path and processing method, and ensure the stability and accuracy of the positioning signal. By enhancing the quality and reliability of signal transmission, the system enables remote-controlled underwater robots (ROVs) and underwater equipment to reach the target position quickly and accurately. Not only does it reduce the operation time and cost, but it also improves the safety and reliability of the operation, providing a strong guarantee for the efficient execution of marine engineering. The generalized cyclic cross-correlation entropy function and multipath suppression model effectively reduce the influence of complex reflection and refraction signals in the marine environment on the positioning accuracy. The system can identify and suppress multipath interference, remove the influence of noise on positioning parameters, and thus improve the quality of positioning data. The multi-band antenna array can work in multiple frequency bands at the same time to adapt to different signal environments and application requirements. Beamforming technology focuses signal energy in a specific direction, enhancing the signal strength in the target area and expanding the signal coverage range.

[0078] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as within the scope of this specification.

[0079] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A positioning system based on radio orientation, characterized in that: include: Directional signal transmitting module, signal receiving module, signal processing module and positioning module; The signal transmission module is used to construct a directional positioning network including at least three base stations, wherein the directional positioning network generates a directional signal through beamforming technology; The signal receiving module is arranged on the target device, receives directional signals from different base stations, extracts multi-dimensional parameters, and uploads the multi-dimensional parameters to the signal processing module; The signal processing module: performs multipath interference suppression on the extracted multidimensional parameters based on the generalized cyclic cross-correlation entropy function, uses a reverse ray tracing algorithm to reversely search the propagation path of the signal, excludes the non-line-of-sight propagation path in the propagation path according to the multidimensional parameters after multipath interference suppression, and obtains the line-of-sight propagation path parameters of each base station; The positioning module evaluates the signal reliability according to the multi-dimensional parameter quality of each base station, dynamically adjusts the weight coefficient of each base station, weightedly fuses the line-of-sight path parameters and geographic location information of each base station, and outputs the three-dimensional coordinates of the target device.

2. A positioning system based on radio orientation according to claim 1, characterized in that: In the signal transmission module, after the directional signal is generated, the sidelobe interference of each beam is suppressed by the minimum mean square error algorithm, and the weight of each beam is updated. The expression of the minimum mean square error algorithm is: ； In the formula, , Respectively Second and The weight vector for the iteration; is the step size parameter; is the conjugate of the error signal; is the input signal.

3. A positioning system based on radio orientation according to claim 1, characterized in that: After the signal receiving module extracts the signal strength, it uses a sliding window average algorithm to denoise the signal strength and generates a smoothed signal strength by calculating the average value of the signal strength value in the window, wherein the size of the sliding window is adaptively adjusted according to the moving speed of the target device.

4. A positioning system based on radio orientation according to claim 1, characterized in that: The signal receiving module introduces an environmental attenuation compensation factor to correct the error of signal strength caused by altitude change and device posture. The expression of the environmental attenuation compensation factor is: ； In the formula, is the environmental attenuation compensation factor; , is the weight coefficient; is the height attenuation term; is the altitude data measured by the barometer of the target device; is the attitude attenuation term; , are the pitch and roll angles output by the target device's gyroscope, respectively.

5. A positioning system based on radio orientation according to claim 1, characterized in that: The target device receives a directional signal from a base station through a regular tetrahedron antenna array, and the signal receiving module extracts a direction of arrival from the directional signal using a quaternion MUSIC algorithm, including the following steps: Step 1: Based on the three-dimensional coordinates of each antenna element in the regular tetrahedron antenna array and the incident direction vector of the signal, convert the position vector of each antenna element and the signal incident direction vector into quaternion form: ； In the formula, For the The quaternion array manifold vector of antenna elements; is an imaginary unit; is the spacing between antenna units; is the wavelength of the signal; For the The position vector of each antenna element; is the incident direction vector of the signal; Step 2: Calculate the covariance matrix of the quaternion signal to obtain the covariance matrix of the antenna array output; Step 3: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors, and divide the eigenvectors into signal subspace and noise subspace according to the size of the eigenvalues; Step 4: Use the eigenvector of the noise subspace to construct a spatial spectrum function, search the spatial spectrum function, find the direction that makes the spatial spectrum function reach the minimum value, and obtain the arrival direction of the directional signal.

6. A positioning system based on radio orientation according to claim 1, characterized in that: In the signal processing module, after receiving the multi-dimensional parameters uploaded by the target device, the direction of arrival angle is predicted according to the moving trajectory of the target device, and the weight coefficient of each beam is dynamically adjusted using Kalman filtering: ； In the formula, is the Kalman gain matrix; is the prior estimation error covariance matrix; is the observation matrix; is the observation noise covariance matrix; is the posterior estimation error covariance matrix; is the identity matrix.

7. A positioning system based on radio orientation according to claim 1, characterized in that: The signal processing module performs multipath interference suppression on the extracted multidimensional parameters based on the generalized cyclic cross-correlation entropy function, comprising the following steps: S1: Perform cyclic spectral density analysis on the received multi-dimensional parameters, extract the cyclic stationary characteristics of the received signal, and separate the interference components with different cyclic frequencies; S2: Mapping the multidimensional parameters to the joint feature space, constructing a three-dimensional matrix as an input of a generalized cyclic cross-correlation entropy function, and the generalized cyclic cross-correlation entropy function outputs an entropy matrix of the multidimensional parameters; S3: The kernel width of the kernel function of the generalized cyclic cross-correlation entropy function is adjusted using a reinforcement learning algorithm; S4: Perform singular value decomposition on the entropy matrix and retain the previous K The principal components construct the signal subspace and suppress the components corresponding to the noise subspace; S5: Analyze the eigenvalue distribution of the entropy matrix. When the ratio of the second largest eigenvalue to the largest eigenvalue is greater than the set threshold, it is determined to be multipath interference. The multipath interference in the multidimensional parameters is further reduced through the attenuation compensation mechanism.

8. A positioning system based on radio orientation according to claim 7, characterized in that: The expression of the generalized cyclic cross-correlation entropy function described in S2 is: ； In the formula, To characterize the signal and In delay Statistical correlation under is the total number of signal samples; is the Gaussian kernel function; , It is a directional signal from different base stations; is the time variable; For delay.

9. A positioning system based on radio orientation according to claim 7, characterized in that: S3 includes the following steps: S31: The current environmental noise intensity, historical interference scene data, and signal quality are used as the state space, and the kernel width adjustment rule is used as the action space. The reward value is calculated according to the signal processing performance and adaptability changes of the agent after executing the action. The expression for calculating the reward value is: ； In the formula, Rewards for signal processing performance; Enhance rewards for adaptability; Rewards for signal-to-noise ratio improvement; Reduce rewards for false positive rates; To handle the delay penalty item; Adjust speed bonus for parameters; Reward for parameter stability; Rewards for detecting sudden changes in the environment; S32: Create a Q A value table to store the value of each state-action pair Q Value, where Q The value represents the expected long-term reward of performing an action in a certain state; S33: The agent continuously tries different actions by interacting with the environment and updates the reward based on the reward. Q Value table Q value, gradually optimize the decision strategy, and obtain the optimal core width adjustment strategy.

10. A positioning system based on radio orientation according to claim 1, characterized in that: The signal processing module excludes non-line-of-sight propagation paths in the propagation paths according to the multi-dimensional parameters after multipath interference suppression, and includes the following steps: calculating the signal propagation time difference through the arrival time difference after multipath interference suppression, and in combination with the location information of each base station, reversely deriving the signal path length difference, and excluding the non-line-of-sight propagation path; determining the signal arrival direction by using the wave direction after multipath interference suppression, optimizing the initial search direction of ray tracing, and predicting the main propagation path in combination with the geographic information system data of the search area, and excluding the non-line-of-sight propagation path; evaluating the path loss based on the signal strength after multipath interference suppression, calculating the energy attenuation of different material surfaces in combination with the Fresnel equation and the material reflection coefficient database, verifying whether the path meets the line-of-sight propagation attenuation characteristics, and excluding the non-line-of-sight propagation path.

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