Underground positioning method based on UWB and IMU

Abstract

The invention discloses an underground positioning method based on UWB (Ultra Wideband) and IMU (Inertial Measurement Unit). The UWB and the IMU are integrated on the positioning card, and the plurality of underground signal transmitters communicate with the positioning card and the positioning base station. The positioning base station processing module executes the following method: receiving first positioning data which is sent by a signal transmitter and contains initial positioning data determined by UWB, IMU data and data packet association information of at least one path; selecting a target path according to the IMU data, and correcting the positioning distance in the initial positioning data in combination with the data packet association information of the target path; determining fusion ranging information and fusion speed information based on the corrected positioning distance, the first positioning data, the signal receiving signal-to-noise ratio and the bit error rate; determining a first positioning direction based on a target axial acceleration in the IMU data, obtaining a second positioning direction by combining the initial positioning direction, and then determining a target positioning direction by fusing a historical positioning direction and confidence in a sliding window; and determining target positioning information based on the target positioning direction and the fused ranging information. And the accuracy of underground positioning is improved.

Description

Technical Field

[0001] This invention relates to the field of downhole positioning technology, and in particular to a downhole positioning method based on UWB and IMU. Background Technology

[0002] Current precision positioning systems for mines primarily employ ultra-wideband (UWB) technology. However, the metallic structures within mine tunnels generate numerous reflection paths for UWB signals, making it difficult for the system to reliably identify the direct signal used for precise distance measurement. This results in fluctuating distance values, and direction determination based on the time difference between dual antennas is highly prone to error. Inadequate sensor placement leads to physical isolation between the motion information collected by the IMU and the moving target itself, failing to accurately and in real-time reflect the microscopic movements of the positioning card. This introduces delays and errors in data synchronization and fusion. Furthermore, direction determination relying solely on the UWB time difference is extremely vulnerable in multipath environments, lacking effective correction mechanisms. This results in frequent jumps or misjudgments of direction information, ultimately leading to inaccurate positioning results. Summary of the Invention

[0003] This invention provides a downhole positioning method based on UWB and IMU to solve the problem of inaccurate downhole positioning.

[0004] According to one aspect of the present invention, an underground positioning method based on UWB and IMU is provided, wherein UWB and IMU are integrated in a positioning card, the positioning card is a positioning device configured for at least one work object in an underground working area, multiple signal transmitters are deployed in the underground working area, the signal transmitters communicate with the positioning device and the positioning base station respectively, and the processing module in the positioning base station executes the following method:

[0005] The receiver receives first positioning data sent by a signal transmitter, wherein the first positioning data is data determined by the signal transmitter based on the received original positioning data, and the first positioning data includes at least initial positioning data determined based on UWB data, IMU data, and data packet association information corresponding to at least one path;

[0006] The target path is determined from at least one path based on IMU data, and the positioning distance in the initial positioning data is corrected based on the data packet association information of the target path.

[0007] The extended Kalman filter algorithm is used to process the corrected positioning distance, the first positioning data, the signal-to-noise ratio, and the bit error rate to obtain fused ranging information and fused speed information; among them, the fused speed information is used to characterize the signal transmission rate between the signal transmitter and the positioning device.

[0008] Based on the target axial acceleration in the IMU data, the first positioning direction is determined, and based on the first positioning direction and the initial positioning direction in the initial positioning data, the second positioning direction is determined.

[0009] The target positioning direction is determined based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data within the sliding window, and the corresponding confidence level.

[0010] Based on the target positioning direction and fused ranging information, the target positioning information of the work object at the current moment is determined.

[0011] Optionally, the received first positioning data is determined based on the following method: raw data sent by the positioning card and associated packet data determined based on the raw data, wherein the raw data includes P packet data sent by the positioning card, and the associated packet data includes R packet data generated based on the received P packet data and the finally generated F packet data; the initial positioning data in the first positioning data is determined according to the timestamps in the P packet data, R packet data and F packet data, wherein the initial positioning data includes at least the initial positioning direction and initial ranging information; the initial positioning data, IMU data, signal reception signal-to-noise ratio, bit error rate and data packet association information corresponding to at least one path are used as the first positioning data.

[0012] Optionally, a target path is determined from at least one path based on IMU data, including: when the acceleration in all axes of the IMU data is less than a first acceleration threshold, the path with the largest signal amplitude is selected as the target path; when the acceleration in any axis of the IMU data is less than the first acceleration threshold, the path with a rising edge slope greater than the first threshold and a target amplitude not less than a preset proportion of the maximum amplitude is selected as the target path.

[0013] Optionally, the positioning distance in the initial positioning data is corrected based on the data packet association information of the target path, including: correcting the positioning time in the initial positioning data based on the timestamp information and the initial timestamp information in the data packet association information to obtain the corrected time information; acquiring the temperature and humidity data of the environment to which the signal transmitter belongs, and determining the corrected positioning distance based on the temperature data, humidity data, corrected time information and the target signal propagation speed determination function, and using the corrected positioning distance as the positioning distance in the initial positioning data.

[0014] Optionally, the corrected positioning distance, the first positioning data, the signal-to-noise ratio (SNR) of the received signal, and the bit error rate (BER) are processed using the extended Kalman filter algorithm to obtain fused ranging information and fused speed information. This includes: using the positioning distance and IMU data in the first positioning data as state vectors, and determining the fused ranging information and fused speed information based on the state equation, the observation equation, and the dynamic observation noise; wherein, the dynamic observation noise is related to the BER and the SNR of the received signal.

[0015] Optionally, a first positioning direction is determined based on the target axial acceleration in the IMU data, and a second positioning direction is determined based on the first positioning direction and the initial positioning direction in the initial positioning data, including: when the angular velocity of the target axial direction in the IMU data is greater than a preset angular velocity threshold, it is determined that the work object is in a turning state, and the first positioning direction is determined based on the angular velocity; when the initial positioning direction in the initial positioning data is the same as the first positioning direction, the initial positioning direction or the first positioning direction is used as the second positioning direction.

[0016] Optionally, the method further includes: if the initial positioning direction is different from the first positioning direction, using the first positioning direction as the second positioning direction.

[0017] Optionally, the target positioning direction is determined based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data within the sliding window, and the corresponding confidence level. This includes: obtaining the confidence level of the second positioning direction; wherein the confidence level is an evaluation attribute of the reliability of the positioning direction obtained by the target model after processing the IMU data; adjusting the second positioning direction based on the second positioning direction, the confidence level of the second positioning direction, the historical positioning directions and corresponding confidence levels of all historical positioning data within the sliding window, and the weight data of each historical positioning data relative to the first positioning data to obtain the target positioning direction; wherein the weight data is inversely proportional to the interval duration, and the interval duration corresponds to the collection interval duration between the historical positioning data and the first positioning data.

[0018] Optionally, after determining the target positioning information, the method further includes: determining the ranging variance based on the historical fused ranging information of the historical positioning data in the sliding window and the fused ranging information in the first positioning data; when the ranging variance is less than a preset variance threshold, updating the target positioning information as historical positioning data to the sliding window, so that when the target positioning information is determined again, the target positioning direction can be determined based on the data in the sliding window.

[0019] Optionally, the method also includes: updating the display information of the job object on the target display device based on the target positioning information.

[0020] According to another aspect of the present invention, a downhole positioning device based on UWB and IMU is provided, comprising:

[0021] The first positioning data determination module is used to receive the first positioning data sent by the signal transmitter. The first positioning data is the data determined by the signal transmitter based on the received original positioning data. The first positioning data includes at least the initial positioning data determined based on UWB data, IMU data, and data packet association information corresponding to at least one path.

[0022] The positioning distance correction module is used to determine the target path from at least one path based on IMU data, and to correct the positioning distance in the initial positioning data based on the data packet association information of the target path.

[0023] The fusion information determination module is used to process the corrected positioning distance, the first positioning data, the signal-to-noise ratio, and the bit error rate using the extended Kalman filter algorithm to obtain fused ranging information and fused speed information; wherein, the fused speed information is used to characterize the signal transmission rate between the signal transmitter and the positioning device.

[0024] The positioning direction determination module is used to determine a first positioning direction based on the target axial acceleration in the IMU data, and to determine a second positioning direction based on the first positioning direction and the initial positioning direction in the initial positioning data;

[0025] The target positioning direction determination module is used to determine the target positioning direction based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data in the sliding window, and the corresponding confidence level.

[0026] The target positioning information determination module is used to determine the target positioning information of the work object at the current moment based on the target positioning direction and fused ranging information.

[0027] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0028] At least one processor; and

[0029] A memory that is communicatively connected to at least one processor; wherein,

[0030] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to execute the UWB and IMU-based downhole positioning method according to any embodiment of the present invention.

[0031] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the UWB and IMU-based downhole positioning method of any embodiment of the present invention.

[0032] The technical solution of this invention integrates UWB and IMU into a positioning card. The positioning card is a positioning device configured for at least one work object within a mine working area. Multiple signal transmitters are deployed in the mine working area, communicating with both the positioning device and a positioning base station. The processing module in the positioning base station executes the following method: receiving first positioning data sent by the signal transmitters. The first positioning data is data determined by the signal transmitter based on received original positioning data. The first positioning data includes at least initial positioning data determined based on UWB data, IMU data, and data packet association information corresponding to at least one path. This achieves the acquisition of multi-dimensional core positioning data for subsequent... This system provides data support for subsequent path selection and distance correction. Based on IMU data, a target path is determined from at least one path. The positioning distance in the initial positioning data is corrected based on the data packet association information of the target path. This achieves target path selection based on real-time motion state captured by IMU data and positioning distance correction combined with data packet association information, effectively offsetting the effects of path transmission deviation and environmental interference. The corrected positioning distance, first positioning data, signal-to-noise ratio, and bit error rate are processed using an extended Kalman filter algorithm to obtain fused ranging information and fused speed information. The fused speed information is used to characterize the signal transmission rate between the signal transmitter and the positioning device, enabling the measurement of distances through the IMU data. Kalman filters handle multi-source data fusion in nonlinear systems, and also incorporate signal-to-noise ratio and bit error rate to assess data reliability, improving the anti-interference capability of the fusion results. This enables the output of accurate fused ranging and signal transmission rate information, providing more comprehensive and high-quality data support for subsequent positioning direction calculations and target positioning information derivation. Based on the target axial acceleration in the IMU data, a first positioning direction is determined. Then, based on the first positioning direction and the initial positioning direction in the initial positioning data, a second positioning direction is determined. The second positioning direction is determined by fusing the initial and first positioning directions, preserving the basic accuracy of the initial positioning while compensating for the limitations of single data. This gives the second positioning direction both dynamic tracking and stability, providing a foundation for subsequent... The system provides high-quality foundational data for optimizing the target positioning direction. Based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data point within the sliding window, and the corresponding confidence level, the target positioning direction is determined. This achieves the advantages of combining the real-time performance of the second positioning direction with the confidence level of the historical positioning direction, avoiding the random deviation of single-direction data. By optimizing the fusion logic through confidence level weighting, the reliability and stability of the target positioning direction are improved, providing key directional support for the final accurate positioning. Based on the target positioning direction and fused ranging information, the target positioning information of the work object at the current moment is determined. This achieves the derivation of the target positioning direction based on high reliability and high precision fused ranging information, ensuring the accuracy of the positioning information.This solution achieves multi-source data collaboration, relying on UWB data to ensure initial accuracy while capturing real-time dynamics through IMU data to improve the comprehensiveness of positioning. Target path filtering and distance correction, along with extended Kalman filtering, optimize the data layer by layer, effectively offsetting interference and deviations. The positioning direction undergoes multiple rounds of verification and fusion, ensuring both real-time performance and stability. The entire process is closed-loop and logically coherent, ultimately outputting high-precision and high-reliability target positioning information. This solves the problem of inaccurate underground positioning caused by the failure to fully utilize motion states, achieving high-precision, high-robustness, and high-directional accuracy positioning in complex mining environments, adapting to the precise positioning needs in both dynamic and static scenarios.

[0033] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of a downhole positioning method based on UWB and IMU provided in Embodiment 1 of the present invention;

[0036] Figure 2 This is a flowchart of a downhole positioning method based on UWB and IMU provided in Embodiment 2 of the present invention;

[0037] Figure 3 This is a schematic diagram of a downhole positioning device based on UWB and IMU provided in Embodiment 3 of the present invention;

[0038] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the downhole positioning method based on UWB and IMU according to an embodiment of the present invention. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0041] Example 1

[0042] Figure 1 This is a flowchart of an underground positioning method based on UWB and IMU provided in Embodiment 1 of the present invention. This embodiment is applicable to underground positioning. The method can be executed by an underground positioning device based on UWB and IMU, which can be implemented in hardware and / or software. This UWB and IMU-based underground positioning device can be configured in electronic devices such as positioning cards and controllers. Specifically, UWB and IMU are integrated into the positioning card, which is a positioning device configured for at least one work object in the underground working area. Multiple signal transmitters are deployed in the underground working area, and the signal transmitters communicate with the positioning device and the positioning base station respectively. The processing module in the positioning base station executes the following... Figure 1 The method shown includes:

[0043] S110. Receive first positioning data sent by the signal transmitter, wherein the first positioning data is data determined by the signal transmitter based on the received original positioning data, and the first positioning data includes at least initial positioning data determined based on UWB data, IMU data, and data packet association information corresponding to at least one path.

[0044] Specifically, raw positioning data can be understood as the basic positioning-related data directly received by the signal transmitter in the positioning system, without any preprocessing such as filtering, calibration, or integration. It is the original source of subsequent positioning data (such as UWB data, IMU data, and primary positioning data). For example, in a tunnel scenario, this includes raw P-packet data sent by the positioning card, uncalibrated voltage signals directly output by the IMU sensor, and raw pulse signals transmitted by the UWB base station. The data format is not standardized and requires further processing before it can be used for positioning calculations. UWB data can be specifically understood as distance or position-related data obtained through ultra-wideband technology. It is one of the core data for achieving high-precision positioning, such as the raw distance data between two points calculated based on the UWB signal propagation time and the UWB direction. IMU data can be specifically understood as the motion state data of the work object captured by the inertial measurement unit, used to reflect the real-time dynamics of the work object, such as the X / Y / Z axis acceleration data when the work object moves and the angular velocity data during the turning process. The first positioning data can be understood as structured data formed after the signal transmitter integrates, filters and preprocesses the original positioning data. It includes at least the initial positioning data determined based on UWB data, IMU data, and data packet association information associated with different transmission paths, which are used for subsequent efficient and accurate positioning calculations.

[0045] Specifically, the positioning device receives the first positioning data sent by the signal transmitter mounted on the workers or equipment through a stable communication link adapted to the complex environment of the mine roadway. This data is generated by the signal transmitter after filtering, integrating and preprocessing the raw positioning data received in the roadway. The core of this data includes the initial positioning information calculated based on UWB data, the real-time motion data of the work object captured by the IMU, and the data packet association information corresponding to at least one underground data transmission path. The data packet association information includes at least timestamp information. After receiving the data, the device completes data parsing, classification and storage, providing a basic data source for subsequent roadway positioning calculations. For example, the signal transmitter first performs a preliminary screening of the received raw positioning data in the tunnel (such as raw UWB pulse signals and raw IMU sensor voltage signals) to remove abnormal data caused by underground obstruction or interference, such as invalid data with low signal strength or mismatched data format. Then, the filtered valid data is classified and organized. UWB-related data is extracted separately and used to generate initial positioning data, such as initial coordinates, initial distance, and positioning direction, through ranging algorithms. The raw IMU signals are calibrated and converted into standardized motion data, such as axial acceleration and angular velocity. Next, the data packet association information corresponding to each data transmission path is extracted, such as timestamp information, and matched with the above-mentioned initial positioning data and IMU standardized data for field matching and format unification. Finally, the data is integrated and packaged into a first positioning data with a standardized structure and complete information, ensuring that the positioning device can directly use it for subsequent positioning calculations after receiving it.

[0046] In this embodiment, by acquiring multi-dimensional core positioning data, the need for multiple additional data collections is eliminated, thus improving data acquisition efficiency. The high-precision advantages of UWB data and the dynamic capture capabilities of IMU data are integrated with data packet association information to provide comprehensive support for subsequent path selection, distance correction, and other processes. The data is preprocessed by the signal transmitter to reduce the computational burden on the positioning device while ensuring the validity of the input data, thus laying a solid data foundation for accurate positioning.

[0047] Optionally, the received first positioning data is determined based on the following method: raw data sent by the positioning card and associated packet data determined based on the raw data, wherein the raw data includes P packet data sent by the positioning card, and the associated packet data includes R packet data generated based on the received P packet data and the finally generated F packet data; the initial positioning data in the first positioning data is determined according to the timestamps in the P packet data, R packet data and F packet data, wherein the initial positioning data includes at least the initial positioning direction and initial ranging information; the initial positioning data, IMU data, signal reception signal-to-noise ratio, bit error rate and data packet association information corresponding to at least one path are used as the first positioning data.

[0048] Specifically, the signal transmitter first receives the raw data including P-packet data sent by the positioning card, then generates associated packet data including R-packet data and F-packet data based on the P-packet data. Subsequently, it completes time synchronization and calculation by extracting the timestamps from the three types of data packets, determines the initial positioning data including the initial positioning direction and initial ranging information, and finally integrates the initial positioning data, IMU data, signal reception noise ratio, bit error rate, and data packet association information corresponding to at least one path to form the first positioning data and send it. The receiving end completes data reception.

[0049] In this embodiment, the time synchronization accuracy of the initial positioning data is ensured by linking P / R / F packet timestamps, thereby improving the basic reliability of positioning. The integration of core positioning data, motion dynamic data, and communication quality data provides a comprehensive and multi-dimensional data source for subsequent path selection, distance correction, and filtering. The signal transmitter completes data generation and integration in advance, reducing the computational burden on the positioning device. At the same time, the standardized data format can reduce the adaptation cost of subsequent processing and meet the needs of efficient data application in complex positioning scenarios.

[0050] S120. Determine the target path from at least one path based on IMU data, and correct the positioning distance in the initial positioning data based on the data packet association information of the target path.

[0051] The target path specifically represents the optimal transmission path with strong signal stability and high adaptability to the motion state. By combining the target motion state reflected in IMU data with characteristics such as transmission stability and data integrity of each path, the optimal suitable path can be selected from at least one path corresponding to the positioning card. The data packet association information transmitted through this optimal suitable path can be used to correct the positioning distance, ensuring the reliability and accuracy of the positioning data. It should be noted that "at least one path" refers to multiple selectable data transmission links in the positioning system from the positioning card to the signal transmitter. In tunnel scenarios, this is usually related to the distribution of UWB base stations deployed underground and the signal propagation path, such as direct transmission paths between different base stations and the signal transmitter, and indirect transmission paths reflected by the tunnel walls.

[0052] Specifically, the IMU data in the first positioning data is first analyzed to extract motion state information such as axial acceleration and turning angular velocity of the working object. Combined with the signal transmission stability and delay fluctuation of each candidate path, the target path with the highest adaptability to the current motion state and the least signal interference is selected. Then, the data packet association information of the target path is extracted. Through time synchronization compensation, path loss correction and other algorithms, the influence of environmental interference and transmission error on the initial positioning distance is offset to obtain the accurately corrected positioning distance.

[0053] In this embodiment, the target path is selected based on the real-time motion state captured by IMU data to ensure the adaptability and reliability of the data source; the positioning distance is corrected by combining data packet association information to effectively offset the effects of path transmission deviation and environmental interference; the correction process is highly targeted, which not only improves the accuracy of the positioning distance, but also ensures the dynamic adaptability of the positioning results, and helps to achieve highly stable positioning output.

[0054] Optionally, a target path is determined from at least one path based on IMU data, including: when the acceleration in all axes of the IMU data is less than a first acceleration threshold, the path with the largest signal amplitude is selected as the target path; when the acceleration in any axis of the IMU data is less than the first acceleration threshold, the path with a rising edge slope greater than the first threshold and a target amplitude not less than a preset proportion of the maximum amplitude is selected as the target path.

[0055] Specifically, the acceleration values ​​for each axis are first extracted from the IMU data and compared one by one with a first acceleration threshold. When the acceleration of all axes is less than the threshold, the target is determined to be stationary or in a low-dynamic state, and the path with the largest signal amplitude among all paths is directly selected as the target path. When the acceleration of any axis is less than the threshold, the signal characteristics of each path are further analyzed, and paths with a rising edge slope greater than the first threshold and a target amplitude not less than a preset proportion of the maximum amplitude of all paths are selected as the target path. For example, when the IMU acceleration is ≤0.1m / s² (stationary), the path with the largest signal amplitude is selected. When the IMU acceleration is >0.1m / s² (moving), the path with a rising edge slope >5V / μs and an amplitude not less than 80% of the maximum amplitude is selected.

[0056] In this embodiment, the target dynamic state reflected by IMU data is used as the core classification criterion to achieve scenario-based adaptation of path selection; in static / low dynamic scenarios, the path with the largest signal amplitude is given priority to ensure the stability and strength of data transmission; in low-to-medium dynamic scenarios, the slope and amplitude are superimposed as dual screening conditions to take into account both signal response speed and effective strength, reduce the impact of environmental interference and invalid paths, improve the accuracy and reliability of target path selection, and lay a solid data foundation for subsequent positioning correction.

[0057] Optionally, the positioning distance in the initial positioning data is corrected based on the data packet association information of the target path, including: correcting the positioning time in the initial positioning data based on the timestamp information and the initial timestamp information in the data packet association information to obtain the corrected time information; acquiring the temperature and humidity data of the environment to which the signal transmitter belongs, and determining the corrected positioning distance based on the temperature data, humidity data, corrected time information and the target signal propagation speed determination function, and using the corrected positioning distance as the positioning distance in the initial positioning data.

[0058] Specifically, firstly, timestamp information is extracted from the data packet association information of the target path and compared and calibrated with the initial timestamp information to correct the positioning time in the initial positioning data to obtain accurate corrected time information. Next, temperature and humidity data of the environment where the signal transmitter is located are collected. The temperature data, humidity data, and corrected time information are substituted into a preset target signal propagation speed determination function to calculate the signal propagation speed adapted to the current environment. Then, the corrected positioning distance is derived by combining this speed with the corrected time information, and finally, this corrected positioning distance replaces the original positioning distance in the initial positioning data. The expression of the preset target signal propagation speed determination function is as follows:

[0059] ;

[0060] Where h represents humidity data and t represents temperature data, the signal propagation speed is compensated in real time by a preset target signal propagation speed determination function.

[0061] In this embodiment, the positioning time is corrected by timestamp calibration to eliminate time deviation in data transmission and ensure the accuracy of the time dimension. The signal propagation speed is dynamically adjusted by combining ambient temperature and humidity to adapt to changes in signal propagation characteristics under different environments, which greatly improves the accuracy of positioning distance calculation and avoids interference from environmental factors on ranging. Multi-dimensional parameters are used to collaboratively correct the positioning distance, effectively reducing the impact of time deviation and environmental interference on ranging. This not only improves the environmental adaptability and accuracy of ranging results, but also provides reliable distance data support for subsequent high-precision positioning.

[0062] S130. The corrected positioning distance, first positioning data, signal reception signal-to-noise ratio, and bit error rate are processed using the extended Kalman filter algorithm to obtain fused ranging information and fused speed information; wherein, the fused speed information is used to characterize the signal transmission rate between the signal transmitter and the positioning device.

[0063] Specifically, a pre-built extended Kalman filter model adapted to the alleyway positioning scenario is loaded. The corrected positioning distance, IMU motion data in the first positioning data, initial positioning information, and signal-to-noise ratio and bit error rate during signal reception are used as model inputs. Through the model's prediction step, the prior estimates of the current positioning and velocity are estimated by combining the system state equation. Then, the observation equation is used to compare the input multi-dimensional data with the prior estimates. The observation accuracy is adjusted by using signal-to-noise ratio and bit error rate as data reliability weights. Finally, the prior error is corrected through the update step, and the accurate fused ranging information of multi-source data and the fused speed information representing the signal transmission rate between the signal transmitter and the positioning device are output.

[0064] In this embodiment, the extended Kalman filter algorithm excels at handling multi-source data fusion in nonlinear systems, fully leveraging the complementary value of each data source. It combines signal-to-noise ratio and bit error rate to assess data reliability and enhance the anti-interference capability of the fusion results. Simultaneously, it outputs accurate fused ranging information and signal transmission rate information, ensuring the accuracy of core positioning data and providing a crucial rate reference for subsequent positioning optimization. This provides more comprehensive, high-quality data support for subsequent positioning direction calculations and target positioning information derivation, adapting to the precise positioning needs in complex roadway environments.

[0065] Optionally, the corrected positioning distance, the first positioning data, the signal-to-noise ratio (SNR) of the received signal, and the bit error rate (BER) are processed using the extended Kalman filter algorithm to obtain fused ranging information and fused speed information. This includes: using the positioning distance and IMU data in the first positioning data as state vectors, and determining the fused ranging information and fused speed information based on the state equation, the observation equation, and the dynamic observation noise; wherein, the dynamic observation noise is related to the BER and the SNR of the received signal.

[0066] Specifically, an extended Kalman filter model is constructed to integrate the corrected positioning distance and IMU data from the first positioning data into a system state vector, explicitly describing the state equation and the observation equation associated with the observation data. Then, the observation noise parameters are dynamically adjusted based on the signal-to-noise ratio (SNR) and bit error rate (BER) (higher SNR, lower BER, smaller observation noise, and higher data weights). Subsequently, through a model prediction step, prior values ​​of positioning distance and velocity are estimated based on the state equation, and observation updates are completed by combining dynamic observation noise to correct prior errors. Finally, accurate fused ranging information from multiple sources is output, along with fused speed information characterizing the signal transmission rate between the signal transmitter and the positioning device. For example, the expressions for the state vector, state equation, and observation method are shown below:

[0067] State vector: ;

[0068] Equations of state: ,in , ;

[0069] Observation method: ,in, Observations ;

[0070] Dynamic observation noise: R=0.02 when bit error rate ≤1%; R=0.1 when bit error rate >5%;

[0071] Iterative output: Outputs the optimal fusion distance through a prediction-update loop. With fusion speed .

[0072] In this embodiment, core positioning data and motion state data are used as state vectors to ensure the core targeting of filtering processing; dynamic observation noise is linked to signal-to-noise ratio and bit error rate to make the noise model more consistent with actual data quality and improve filtering accuracy; by fusing multi-dimensional data and dynamic noise adjustment through algorithms, interference signals and data deviations are effectively filtered out, while accurate ranging and velocity information are output simultaneously, providing comprehensive and reliable fused data support for subsequent positioning optimization.

[0073] S140. Based on the target axial acceleration in the IMU data, determine the first positioning direction, and based on the first positioning direction and the initial positioning direction in the initial positioning data, determine the second positioning direction.

[0074] The initial positioning direction can be understood as the basic positioning direction calculated based on UWB data. It serves as the initial reference direction for the positioning system, reflecting the spatial orientation of the work object at the beginning of the positioning process, such as directly in front at a distance of 120m along a tunnel. The first positioning direction can be understood as the real-time dynamic positioning direction obtained by analyzing the target axial acceleration in IMU data, through attitude calculation and integration. It can accurately capture changes in the work object's motion attitude, such as its real-time orientation during turning or speed changes. The second positioning direction can be understood as the final positioning direction obtained after calibrating and correcting the first and initial positioning directions. For example, weighted fusion and trend comparison can be used for calibration. This direction calibration process retains the basic accuracy of the initial positioning direction while incorporating the dynamic tracking characteristics of the first positioning direction, balancing stability and real-time performance. It serves as the core direction reference for subsequent positioning calculations, eliminating biases from a single data source through weighted calculation or trend verification.

[0075] Specifically, by analyzing the target's axial acceleration (such as the X-axis acceleration along the forward direction in a tunnel scenario, and the radial acceleration during turning) in the IMU data, real-time motion attitude changes of the work object are obtained through integration and attitude calculation, thereby determining the first positioning direction reflecting the current dynamic trend. This first positioning direction is then weighted and fused with the initial positioning direction from the initial positioning data for data calibration and deviation correction. For example, weights are assigned based on the reliability of both data sources: the IMU dynamic data weights adapt to the real-time motion state, while the initial positioning direction weights ensure basic accuracy. Through data calibration and deviation correction, a second positioning direction that balances dynamic response and basic stability is finally determined.

[0076] In this embodiment, the first positioning direction is determined based on the IMU axial acceleration, which can quickly capture the real-time motion posture changes of the work object and adapt to dynamic positioning requirements. The initial positioning direction and the first positioning direction are fused, which not only retains the basic accuracy of the initial positioning, but also makes up for the limitations of single data. The two-step derivation makes the second positioning direction have both dynamic tracking and stability, providing high-quality basic data for the subsequent optimization of the target positioning direction, adapting to the positioning requirements in complex scenarios such as alleys, reducing the impact of environmental interference or single data deviation, improving the stability and reliability of the positioning direction, and providing key directional support for accurate positioning.

[0077] Optionally, a first positioning direction is determined based on the target axial acceleration in the IMU data, and a second positioning direction is determined based on the first positioning direction and the initial positioning direction in the initial positioning data, including: when the angular velocity of the target axial direction in the IMU data is greater than a preset angular velocity threshold, it is determined that the work object is in a turning state, and the first positioning direction is determined based on the angular velocity; when the initial positioning direction in the initial positioning data is the same as the first positioning direction, the initial positioning direction or the first positioning direction is used as the second positioning direction.

[0078] Specifically, the angular velocity and acceleration of the target axis are extracted from the IMU data. The angular velocity is compared with a preset threshold. If the angular velocity of the target axis is greater than the preset angular velocity threshold, it is determined that the working object is in a turning state. Based on this angular velocity and attitude calculation, a first positioning direction reflecting the real-time turning trend is determined. Then, the first positioning direction is compared with the initial positioning direction in the initial positioning data. If the two directions are consistent, the initial positioning direction or the first positioning direction is directly used as the second positioning direction. If they are inconsistent, the first positioning direction needs to be suppressed and the IMU trend direction is adopted first.

[0079] For example, a preliminary judgment is made based on the time difference of the PRF packets received by the dual antennas of the UWB reader. The basic UWB positioning direction, i.e., the first positioning direction is left / center / right, and the IMU trend arbitration process is as follows.

[0080] (1) Specifically extract the Z-axis angular velocity of the IMU .when When this occurs, it is determined to be a turning state, and the directional trend is output;

[0081] (2) Integration decision-making includes:

[0082] i. If the first positioning direction is consistent with the IMU trend direction, that is, the UWB trend is consistent with the IMU trend, the UWB judgment is adopted, that is, the first positioning direction can be used as the second positioning direction.

[0083] ii. Ambiguity / Conflict: When UWB is judged as "neutral" or conflicts with the IMU trend, the IMU trend is adopted first, and UWB outliers are suppressed;

[0084] (3) Steering smoothing: In the steering state, the final output direction is smoothed by weighted average to avoid jitter.

[0085] In this embodiment, the turning state is determined by an angular velocity threshold, making the determination of the first positioning direction more consistent with the actual movement scenario of the work object and improving the accuracy of dynamic response. When the directions are consistent, the first positioning direction is directly reused, simplifying the calculation process and preserving real-time performance. Consistency verification is performed with the high precision of the initial positioning direction as a reference to ensure the accuracy of the positioning direction. In turning scenarios, the first positioning direction with stronger real-time performance is given priority, taking into account both dynamic adaptability and result reliability, reducing the lag and deviation of the positioning direction during the turning process, and providing stable directional support for accurate positioning.

[0086] Optionally, the method further includes: if the initial positioning direction is different from the first positioning direction, using the first positioning direction as the second positioning direction.

[0087] Specifically, the consistency check between the first positioning direction and the initial positioning direction is first completed. When the two directions are detected to be different, it indicates that the motion state of the work object has changed, such as the turning adjustment. At this time, the initial positioning direction that does not match the real-time motion is directly discarded, and the first positioning direction that is determined based on IMU data and can accurately reflect the current motion trend is directly assigned as the second positioning direction for final positioning calculation.

[0088] In this embodiment, when the motion state changes, the first positioning direction with stronger real-time performance is preferred to ensure that the second positioning direction matches the target's current actual motion direction; complex fusion calculations are eliminated, the direction determination process is simplified, and the efficiency of positioning direction updates is improved; the impact of the lag of the initial positioning direction on the positioning result is avoided, the direction deviation in dynamic scenarios such as turning is reduced, and the dynamic adaptability and reliability of the positioning direction are guaranteed.

[0089] S150. Determine the target positioning direction based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data in the sliding window, and the corresponding confidence level.

[0090] Confidence level can be understood as a quantitative indicator measuring the reliability of the positioning direction. Its value is positively correlated with the quality of the positioning data (such as signal-to-noise ratio and bit error rate) and data accuracy (such as ranging error and attitude calculation deviation). The higher the value, the more reliable the positioning direction is. It is the core reference for selecting effective positioning directions and optimizing the fusion logic. Target positioning direction can be understood as the core direction reference representing the final output of the positioning system. It is the final positioning direction obtained by combining the real-time dynamic characteristics of the second positioning direction, the stability of at least one historical positioning direction within the sliding window, and the confidence level corresponding to each historical positioning direction through weighted fusion or trend verification. It combines real-time adaptability, historical consistency, and high reliability, and is the core direction basis for accurate positioning.

[0091] Specifically, the determined second positioning direction is first extracted, and at the same time, the historical positioning direction corresponding to at least one historical positioning data stored in the sliding window is retrieved, as well as the confidence level associated with each historical positioning direction (the confidence level reflects the reliability and adaptability of the historical direction). Then, by combining the real-time characteristics of the second positioning direction with the confidence weight of the historical positioning direction, the target positioning direction that has both real-time dynamic adaptability and historical stability is comprehensively determined through weighted fusion or trend consistency verification.

[0092] In this embodiment, the real-time performance of the second positioning direction is combined with the confidence level of historical positioning directions to avoid accidental deviations in single-direction data; a sliding window mechanism is used to filter valid historical data to ensure the continuity of direction fusion; and the fusion logic is optimized by confidence weighting to improve the reliability and stability of the target positioning direction, providing key directional support for final accurate positioning.

[0093] S160. Based on the target positioning direction and fused ranging information, determine the target positioning information of the work object at the current moment.

[0094] Among them, target positioning information refers to the specific spatial location information of the work object at the current moment (such as three-dimensional coordinates, planar coordinates, etc.), which is the core result of the final output of the positioning system and is used to accurately represent the real-time spatial distribution of the work object. The determination technology is based on multi-source data fusion: high-precision initial positioning data can be obtained through UWB technology, combined with IMU data to capture the target's dynamic movement, and reliable fused ranging information is obtained through path filtering and distance correction; then, the second positioning direction is determined by verifying and fusing the IMU data with the initial positioning direction, and the target positioning direction is obtained by combining the historical positioning direction and confidence level within the sliding window; finally, using the target positioning direction as the spatial direction and the fused ranging information as the distance basis, the target positioning information is finally output through spatial coordinate conversion and positioning model calculation, and algorithms such as extended Kalman filtering are incorporated throughout the process to ensure data accuracy and anti-interference capability.

[0095] Specifically, given a clear target positioning direction and fused ranging information, the historical positioning information of the work object is used as a benchmark. The positioning vector direction in the spatial coordinate system is determined by combining the target positioning direction. Then, the specific distance in that direction is determined based on the fused ranging information. Through coordinate conversion and spatial positioning algorithms, the direction and distance parameters are transformed into the specific position coordinates of the work object at the current moment, such as the mileage and lateral deviation distance in the tunnel. Finally, complete target positioning information, such as three-dimensional coordinates or planar position information, is formed.

[0096] In this embodiment, the accuracy of the positioning information is ensured by deriving the target positioning direction based on high reliability and the fusion ranging information based on high precision. By combining real-time direction and distance, the positioning results are both continuous and real-time. The whole process is logically simple, with high data utilization, and can effectively adapt to complex environments such as tunnels. It provides accurate and reliable core data support for the location monitoring of the work object and meets the real-time positioning needs of the work object.

[0097] Optionally, after determining the target positioning information, the method further includes: determining the ranging variance based on the historical fused ranging information of the historical positioning data in the sliding window and the fused ranging information in the first positioning data; when the ranging variance is less than a preset variance threshold, updating the target positioning information as historical positioning data to the sliding window, so that when the target positioning information is determined again, the target positioning direction can be determined based on the data in the sliding window.

[0098] Specifically, after determining the target positioning information of the task object, the historical fusion ranging information corresponding to the historical positioning data in the sliding window is first extracted. Then, combined with the fusion ranging information in the current first positioning data, the ranging variance reflecting the dispersion of the ranging data is calculated through the variance calculation model. The ranging variance is compared with the preset variance threshold. If it is less than the threshold, it means that the stability of the current fusion ranging information meets the standard. The target positioning information (including target positioning direction, fusion ranging information, etc.) is used as the new historical positioning data and updated in the sliding window to provide more comprehensive historical data support for the subsequent recalculation of the target positioning direction.

[0099] In this embodiment, the stability of the current positioning data is verified by measuring the range variance to ensure that the historical data stored in the sliding window is reliable and effective; the historical positioning data in the sliding window is dynamically updated to ensure the timeliness and accuracy of subsequent target positioning direction calculations; stable data is selected and stored in the window to avoid interference from abnormal data, while maintaining the continuity of historical data, which helps to continuously output high-precision and high-stability positioning results.

[0100] Optionally, the method also includes: updating the display information of the job object on the target display device based on the target positioning information.

[0101] Specifically, after determining the target location information of the work object, the location information is synchronized to the target display device through the data transmission link. After receiving the data, the device parses and processes it, transforming the abstract location data into a visual presentation form, such as icon position, coordinate value, movement trajectory, etc., and updates the corresponding display information of the work object on the display interface in real time to ensure that the displayed content is consistent with the actual location status.

[0102] In this embodiment, the target positioning information and display content are synchronized in real time, allowing users to intuitively grasp the dynamic location of the work object and improve the practicality of the positioning results; the visual presentation lowers the threshold for data understanding and facilitates quick decision-making and operation; the displayed information is dynamically updated with the positioning data, ensuring the timeliness of the information, which is suitable for scenarios that require real-time monitoring of the location of the work object and improves the overall operational efficiency and control accuracy.

[0103] The technical solution of this embodiment involves receiving first positioning data sent by a signal transmitter. The first positioning data includes initial positioning data based on UWB data, IMU data, and data packet association information of at least one path. A suitable target path is selected based on the IMU data, and the initial positioning distance is corrected by combining the data packet association information of that path. Subsequently, the corrected positioning distance, the first positioning data, the signal-to-noise ratio, and the bit error rate are fused using an extended Kalman filter algorithm to obtain fused speed information related to the fused ranging information and the signal transmission rate. Next, a first positioning direction is determined based on the target axial acceleration in the IMU data, and a second positioning direction is obtained by combining it with the initial positioning direction. Then, the second positioning direction, historical positioning directions within a sliding window, and corresponding confidence levels are fused to determine the target positioning direction. Finally, the target positioning direction is used as the spatial pointing direction, and the fused ranging information is used as the distance basis to calculate the target positioning information of the work object at the current moment. It achieves multi-source data collaboration, relying on UWB data to ensure initial accuracy and using IMU data to capture real-time dynamics, improving the comprehensiveness of positioning. Target path filtering and distance correction, as well as extended Kalman filtering, optimize the data layer by layer to effectively offset interference and deviation. The positioning direction is verified and fused through multiple rounds, combining real-time performance and stability. The entire process is closed-loop and logically coherent, ultimately outputting high-precision and high-reliability target positioning information, adapting to the precise positioning needs in both dynamic and static scenarios.

[0104] Example 2

[0105] Figure 2 This is a flowchart of a downhole positioning method based on UWB and IMU provided in Embodiment 2 of the present invention. The method in this embodiment is a further optimization of the method in the above embodiments. Optionally, the confidence level of a second positioning direction is obtained; wherein, the confidence level is an evaluation attribute of the reliability of the positioning direction obtained after the target model processes the IMU data; based on the second positioning direction, the confidence level of the second positioning direction, the historical positioning directions and corresponding confidence levels of all historical positioning data in the sliding window, and the weight data of each historical positioning data relative to the first positioning data, the second positioning direction is adjusted to obtain the target positioning direction; wherein, the weight data is inversely proportional to the interval duration, and the interval duration corresponds to the acquisition interval duration between the historical positioning data and the first positioning data. Figure 2 As shown, the method includes:

[0106] S210. Receive first positioning data sent by the signal transmitter, wherein the first positioning data is data determined by the signal transmitter based on the received original positioning data, and the first positioning data includes at least initial positioning data determined based on UWB data, IMU data, and data packet association information corresponding to at least one path.

[0107] S220. Determine the target path from at least one path based on IMU data, and correct the positioning distance in the initial positioning data based on the data packet association information of the target path.

[0108] S230. The corrected positioning distance, first positioning data, signal reception signal-to-noise ratio, and bit error rate are processed using the extended Kalman filter algorithm to obtain fused ranging information and fused speed information; wherein, the fused speed information is used to characterize the signal transmission rate between the signal transmitter and the positioning device.

[0109] S240. Based on the target axial acceleration in the IMU data, determine the first positioning direction, and based on the first positioning direction and the initial positioning direction in the initial positioning data, determine the second positioning direction.

[0110] S250. Obtain the confidence level of the second positioning direction; where confidence level is the evaluation attribute of the reliability of the positioning direction obtained by the target model after processing the IMU data.

[0111] Specifically, the preset target model is first invoked, and the relevant IMU data (such as target axial acceleration, angular velocity, etc.) used to determine the second positioning direction are input into the model. The model analyzes and calculates the completeness, validity and motion state matching degree of the IMU data, and outputs the evaluation result that characterizes the reliability of the second positioning direction. This result is the confidence level of the second positioning direction.

[0112] In this embodiment, the reliability of the second positioning direction is quantitatively evaluated using a target model, providing clear data support for subsequent positioning direction fusion. The confidence level can intuitively reflect the compatibility between IMU data quality and positioning direction, making it easier to select highly reliable positioning directions for subsequent calculations. This reduces subjective judgment bias, improves the scientific nature of the positioning direction fusion logic, and further ensures the accuracy and stability of the final target positioning information.

[0113] S260. Based on the second positioning direction, the confidence level of the second positioning direction, the historical positioning directions and corresponding confidence levels of all historical positioning data in the sliding window, and the weight data of each historical positioning data relative to the first positioning data, the second positioning direction is adjusted to obtain the target positioning direction.

[0114] Among them, the weighted data and the interval duration are inversely proportional, and the interval duration corresponds to the collection interval between historical positioning data and the first positioning data.

[0115] Specifically, the process first collects the second positioning direction and its confidence level, then retrieves the historical positioning direction, corresponding confidence level, and weight data of each historical positioning data relative to the current first positioning data from the sliding window (the shorter the interval, the higher the weight; the longer the interval, the lower the weight). Subsequently, the confidence level of the second positioning direction, the confidence level of the historical positioning direction, and the corresponding weight data are combined, and the second positioning direction is dynamically adjusted through a weighted fusion algorithm to finally obtain a target positioning direction that combines real-time performance, stability, and high reliability.

[0116] In this embodiment, multi-dimensional evaluation indicators such as confidence level and time weight are integrated to make the positioning direction adjustment more in line with data quality and time correlation; the weight of historical data is linked to the interval length, and recent highly relevant data is given priority to ensure the timeliness of the integration; by quantitatively weighting the correction of the second positioning direction, the impact of single data deviation and historical redundant information is effectively reduced, the accuracy and adaptability of the target positioning direction are improved, and a solid foundation is laid for the final positioning result.

[0117] S270. Based on the target positioning direction and fused ranging information, determine the target positioning information of the work object at the current moment.

[0118] The technical solution of this embodiment involves receiving first positioning data sent by a signal transmitter. This first positioning data includes initial positioning data derived from UWB, IMU data, and data packet association information for at least one path. The target path is selected based on the IMU data, and the initial positioning distance is corrected by combining it with the data packet association information. Then, the corrected positioning distance, the first positioning data, the signal-to-noise ratio, and the bit error rate are fused using an extended Kalman filter algorithm to obtain fused ranging information and fused speed information representing the signal transmission rate. Subsequently, a first positioning direction is determined based on the target axial acceleration in the IMU data, and combined with the initial positioning direction to obtain a second positioning direction. The confidence level of the second positioning direction is obtained by processing the IMU data through a target model. Next, the historical positioning directions, corresponding confidence levels, and weighted data (the shorter the interval, the higher the weight) of all historical positioning data within the sliding window are retrieved. The second positioning direction is then adjusted through weighted fusion to obtain the target positioning direction. Finally, using the target positioning direction as the spatial direction and the fused ranging information as the distance basis, the target positioning information of the working object at the current moment is calculated. This solution utilizes deep collaboration of multi-source data, with UWB ensuring initial accuracy and IMU capturing dynamics. Layered optimization of data quality is achieved through path filtering and distance correction. An extended Kalman filter algorithm effectively filters out interference, improving the reliability of fused ranging and velocity information. The positioning direction undergoes multiple rounds of verification, confidence quantification, and time-weighted fusion, balancing real-time performance and stability. The entire process is centered on data quantization, with a closed-loop logic adapted to dynamic scenarios, ultimately outputting high-precision, high-reliability target positioning information to meet the precise positioning needs in complex scenarios.

[0119] Example 3

[0120] Figure 3 This is a schematic diagram of a downhole positioning device based on UWB and IMU provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0121] The first positioning data determination module 310 is used to receive the first positioning data sent by the signal transmitter. The first positioning data is the data determined by the signal transmitter based on the received original positioning data. The first positioning data includes at least the initial positioning data determined based on UWB data, IMU data, and data packet association information corresponding to at least one path.

[0122] The positioning distance correction module 320 is used to determine the target path from at least one path based on IMU data, and to correct the positioning distance in the initial positioning data based on the data packet association information of the target path.

[0123] The fusion information determination module 330 is used to process the corrected positioning distance, the first positioning data, the signal receiving signal-to-noise ratio, and the bit error rate using the extended Kalman filter algorithm to obtain fused ranging information and fused speed information; wherein, the fused speed information is used to characterize the signal transmission rate between the signal transmitter and the positioning device.

[0124] The positioning direction determination module 340 is used to determine a first positioning direction based on the target axial acceleration in the IMU data, and to determine a second positioning direction based on the first positioning direction and the initial positioning direction in the initial positioning data;

[0125] The target positioning direction determination module 350 is used to determine the target positioning direction based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data in the sliding window, and the corresponding confidence level.

[0126] The target positioning information determination module 360 ​​is used to determine the target positioning information of the work object at the current moment based on the target positioning direction and fused ranging information.

[0127] The technical solution of this embodiment involves a first positioning data determination module receiving first positioning data sent by a signal transmitter. This first positioning data is determined by the signal transmitter based on received raw positioning data and includes at least initial positioning data determined based on UWB data, IMU data, and data packet association information corresponding to at least one path. A positioning distance correction module determines a target path from at least one path based on the IMU data and corrects the positioning distance in the initial positioning data based on the data packet association information of the target path. A fusion information determination module uses an extended Kalman filter algorithm to determine the corrected positioning distance, the first positioning data, and the signal reception signal-to-noise ratio. The system processes data including the bit error rate to obtain fused ranging and fused velocity information. The fused velocity information characterizes the signal transmission rate between the signal transmitter and the positioning device. The positioning direction determination module determines the first positioning direction based on the target axial acceleration in the IMU data, and then determines the second positioning direction based on the first positioning direction and the initial positioning direction in the initial positioning data. The target positioning direction determination module determines the target positioning direction based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data point within the sliding window, and the corresponding confidence level. The target positioning information determination module determines the target positioning information of the work object at the current moment based on the target positioning direction and the fused ranging information. This system achieves multi-source data collaborative linkage, relying on UWB data to ensure initial accuracy while capturing real-time dynamics through IMU data to improve the comprehensiveness of positioning. Target path filtering and distance correction, along with extended Kalman filtering, optimize the data layer by layer, effectively offsetting interference and deviations. The positioning direction undergoes multiple rounds of verification and fusion, combining real-time performance with stability. The entire process is closed-loop and logically coherent, ultimately outputting high-precision, high-reliability target positioning information, adapting to the precise positioning needs in both dynamic and static scenarios.

[0128] Based on the above embodiments, optionally, the first positioning data determination module 310 is specifically used to receive raw data sent by the positioning card and associated packet data determined based on the raw data from the signal transmitter. The raw data includes P packet data sent by the positioning card, and the associated packet data includes R packet data generated based on the received P packet data and F packet data generated at the end. Based on the timestamps in the P packet data, R packet data, and F packet data, the module determines the initial positioning data in the first positioning data. The initial positioning data includes at least the initial positioning direction and initial ranging information. The module uses the initial positioning data, IMU data, signal reception noise ratio, bit error rate, and data packet association information corresponding to at least one path as the first positioning data.

[0129] Optionally, the positioning distance correction module 320 includes a target path determination unit and a positioning distance correction unit. The target path determination unit is used to select the path with the largest signal amplitude as the target path when the acceleration in all axes of the IMU data is less than a first acceleration threshold; and to select the path with a rising edge slope greater than the first acceleration threshold and a target amplitude not less than a preset proportion of the maximum amplitude when the acceleration in any axis of the IMU data is less than the first acceleration threshold. The positioning distance correction unit is used to correct the positioning time in the initial positioning data based on the timestamp information and initial timestamp information in the data packet association information to obtain corrected time information; to acquire the temperature and humidity data of the environment to which the signal transmitter belongs, and to determine the corrected positioning distance based on the temperature data, humidity data, corrected time information, and a target signal propagation speed determination function, and to use the corrected positioning distance as the positioning distance in the initial positioning data.

[0130] Optionally, the fusion information determination module 330 is specifically used to take the positioning distance and IMU data in the first positioning data as state vectors, and determine the fused ranging information and fused speed information based on the state equation, observation equation and dynamic observation noise; wherein, the dynamic observation noise is related to the bit error rate and the signal-to-noise ratio of the received signal.

[0131] Optionally, the positioning direction determination module 340 is specifically used to determine that the work object is in a turning state when the angular velocity of the target axis in the IMU data is greater than a preset angular velocity threshold, and to determine the first positioning direction based on the angular velocity; when the initial positioning direction in the initial positioning data is the same as the first positioning direction, the initial positioning direction or the first positioning direction is used as the second positioning direction.

[0132] Optionally, the positioning direction determination module 340 is further used to take the first positioning direction as the second positioning direction when the initial positioning direction is different from the first positioning direction.

[0133] Optionally, the target positioning direction determination module 350 is specifically used to obtain the confidence level of the second positioning direction; wherein, the confidence level is an evaluation attribute of the reliability of the positioning direction obtained by the target model after processing the IMU data; based on the second positioning direction, the confidence level of the second positioning direction, the historical positioning directions and corresponding confidence levels of all historical positioning data in the sliding window, and the weight data of each historical positioning data relative to the first positioning data, the second positioning direction is adjusted to obtain the target positioning direction; wherein, the weight data is inversely proportional to the interval duration, and the interval duration corresponds to the collection interval duration between the historical positioning data and the first positioning data.

[0134] Optionally, after determining the target positioning information, the device is further configured to determine the ranging variance based on the historical fused ranging information of the historical positioning data in the sliding window and the fused ranging information in the first positioning data; when the ranging variance is less than a preset variance threshold, the target positioning information is updated to the sliding window as historical positioning data, so that when the target positioning information is determined again, the target positioning direction is determined based on the data in the sliding window.

[0135] Optionally, the device is also used to update the display information of the work object on the target display device based on the target positioning information.

[0136] The downhole positioning device based on UWB and IMU provided in the embodiments of the present invention can execute the downhole positioning method based on UWB and IMU provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0137] Example 4

[0138] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0139] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0140] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0141] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as downhole positioning methods based on UWB and IMU.

[0142] In some embodiments, the UWB and IMU-based downhole positioning method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the UWB and IMU-based downhole positioning method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the UWB and IMU-based downhole positioning method by any other suitable means (e.g., by means of firmware).

[0143] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] Computer programs for implementing the UWB and IMU-based downhole positioning method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0145] Example 5

[0146] Embodiment 5 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a downhole positioning method based on UWB and IMU, the method comprising:

[0147] The receiver receives first positioning data sent by a signal transmitter, wherein the first positioning data is data determined by the signal transmitter based on the received original positioning data, and the first positioning data includes at least initial positioning data determined based on UWB data, IMU data, and data packet association information corresponding to at least one path;

[0148] The target path is determined from at least one path based on IMU data, and the positioning distance in the initial positioning data is corrected based on the data packet association information of the target path.

[0149] The extended Kalman filter algorithm is used to process the corrected positioning distance, the first positioning data, the signal-to-noise ratio, and the bit error rate to obtain fused ranging information and fused speed information; among them, the fused speed information is used to characterize the signal transmission rate between the signal transmitter and the positioning device.

[0150] Based on the target axial acceleration in the IMU data, the first positioning direction is determined, and based on the first positioning direction and the initial positioning direction in the initial positioning data, the second positioning direction is determined.

[0151] The target positioning direction is determined based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data within the sliding window, and the corresponding confidence level.

[0152] Based on the target positioning direction and fused ranging information, the target positioning information of the work object at the current moment is determined.

[0153] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0154] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0155] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0156] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0157] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0158] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A downhole positioning method based on UWB and IMU, characterized in that, UWB and IMU are integrated into a positioning card, which is a positioning device configured for at least one work object in the underground working area. Multiple signal transmitters are deployed in the underground working area, and these transmitters communicate with both the positioning device and the positioning base station. The processing module in the positioning base station executes the following method: The system receives first positioning data sent by the signal transmitter, wherein the first positioning data is data determined by the signal transmitter based on the received original positioning data, and the first positioning data includes at least initial positioning data determined based on UWB data, IMU data, and data packet association information corresponding to at least one path; Based on the IMU data, a target path is determined from the at least one path, and the positioning distance in the initial positioning data is corrected based on the data packet association information of the target path; The corrected positioning distance, the first positioning data, the signal-to-noise ratio, and the bit error rate are processed using the extended Kalman filter algorithm to obtain fused ranging information and fused speed information; wherein, the fused speed information is used to characterize the signal transmission rate between the signal transmitter and the positioning device; Based on the target axial acceleration in the IMU data, a first positioning direction is determined, and a second positioning direction is determined based on the first positioning direction and the initial positioning direction in the initial positioning data. The target positioning direction is determined based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data within the sliding window, and the corresponding confidence level. Based on the target positioning direction and the fused ranging information, the target positioning information of the work object at the current moment is determined.

2. The method according to claim 1, characterized in that, The first location data received was determined based on the following method: The signal transmitter receives raw data sent by the positioning card and associated packet data determined based on the raw data. The raw data includes P packet data sent by the positioning card, and the associated packet data includes R packet data generated based on the received P packet data and F packet data generated at the end. Based on the timestamps in the P-packet data, R-packet data, and F-packet data, the initial positioning data in the first positioning data is determined, wherein the initial positioning data includes at least the initial positioning direction and initial ranging information; The initial positioning data, the IMU data, the signal-to-noise ratio, the bit error rate, and the data packet association information corresponding to at least one path are used as the first positioning data.

3. The method according to claim 1, characterized in that, Determining the target path from the at least one path based on the IMU data includes: When the acceleration in all axes of the IMU data is less than the first acceleration threshold, the path with the largest signal amplitude is selected as the target path. When the acceleration in any axis of the IMU data is less than the first acceleration threshold, the path with a rising edge slope greater than the first threshold and a target amplitude not less than a preset proportion of the maximum amplitude is determined as the target path.

4. The method according to claim 1, characterized in that, The step of correcting the positioning distance in the initial positioning data based on the data packet association information of the target path includes: Based on the timestamp information and initial timestamp information in the data packet association information, the positioning time in the initial positioning data is corrected to obtain the corrected time information; The temperature and humidity data of the environment to which the signal transmitter belongs are obtained, and the corrected positioning distance is determined based on the temperature data, the humidity data, the corrected time information and the target signal propagation speed determination function. The corrected positioning distance is then used as the positioning distance in the initial positioning data.

5. The method according to claim 1, characterized in that, The process of using the extended Kalman filter algorithm to process the corrected positioning distance, the first positioning data, the signal-to-noise ratio, and the bit error rate yields fused ranging information and fused speed information, including: Using the positioning distance and IMU data in the first positioning data as state vectors, the fused ranging information and fused speed information are determined based on the state equation, observation equation and dynamic observation noise. The dynamic observation noise is related to the bit error rate and the signal-to-noise ratio of the received signal.

6. The method according to claim 1, characterized in that, The step of determining a first positioning direction based on the target axial acceleration in the IMU data, and determining a second positioning direction based on the first positioning direction and the initial positioning direction in the initial positioning data, includes: When the angular velocity of the target axis in the IMU data is greater than a preset angular velocity threshold, it is determined that the work object is in a turning state, and a first positioning direction is determined based on the angular velocity; When the initial positioning direction in the initial positioning data is the same as the first positioning direction, the initial positioning direction or the first positioning direction shall be used as the second positioning direction.

7. The method according to claim 6, characterized in that, The method further includes: If the initial positioning direction is different from the first positioning direction, the first positioning direction shall be used as the second positioning direction.

8. The method according to claim 1, characterized in that, The step of determining the target positioning direction based on the second positioning direction, the historical positioning direction corresponding to at least one historical positioning data within the sliding window, and the corresponding confidence level includes: Obtain the confidence level of the second positioning direction; wherein, the confidence level is an evaluation attribute of the reliability of the positioning direction obtained by the target model after processing the IMU data; The second positioning direction is adjusted based on the second positioning direction, the confidence level of the second positioning direction, the historical positioning directions and corresponding confidence levels of all historical positioning data in the sliding window, and the weight data of each historical positioning data relative to the first positioning data, to obtain the target positioning direction. The weighted data is inversely proportional to the interval duration, which corresponds to the collection interval between historical positioning data and the first positioning data.

9. The method according to claim 1, characterized in that, After determining the target location information, the method further includes: Based on the historical fused ranging information of the historical positioning data in the sliding window and the fused ranging information in the first positioning data, the ranging variance is determined. When the ranging variance is less than a preset variance threshold, the target positioning information is updated to the sliding window as historical positioning data, so that when the target positioning information is determined again, the target positioning direction can be determined based on the data in the sliding window.

10. The method according to claim 1, characterized in that, The method further includes: The display information of the work object on the target display device is updated based on the target positioning information.

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