A rotating single-station positioning method and device for a 5G-A industrial internet environment

Abstract

The application provides a rotating single-station positioning method and device for a 5G-A industrial internet environment, obtains a target base station coordinate, a heading angle sequence and a ranging sequence in space-time synchronization in a rotating process of a target object, and fits the heading angle sequence and the ranging sequence to obtain a sine function curve for describing a change of a ranging value with a heading angle; determines a directional moment when the target object directly faces the target base station according to an extreme point of the sine function curve; determines a target heading angle and a target ranging value corresponding to the directional moment according to the heading angle sequence and the ranging sequence respectively; and determines a position coordinate of the target object according to the target heading angle, the target ranging value and the target base station coordinate. The ranging sequence and the heading angle information generated in the rotating process of the target object are combined with a single base station to construct a self-contained positioning model, so that reliable position information is provided for a complex rescue scene in a low-cost and simple operation manner.

Description

Technical Field

[0001] This application relates to the fields of industrial internet and communication technology, and in particular to a rotating monostation positioning method and device for 5G-A industrial internet environments. Background Technology

[0002] 5G-A (5G-Advanced), as an enhanced evolution of 5G, boasts higher bandwidth, lower latency, and stronger connectivity, making it a core communication technology supporting the upgrade of the Industrial Internet towards "ubiquitous connectivity, intelligent sensing, and precise control." In the 5G-A Industrial Internet environment, the interconnected scale of industrial equipment, sensors, and personnel grows exponentially, placing higher demands on the accuracy, real-time performance, and reliability of positioning. The unique characteristics of the 5G-A Industrial Internet environment, such as complex environments with strong interference, limited base station deployment, poor adaptability to dynamic scenarios, and the difficulty in balancing accuracy and robustness, lead to multiple technical challenges in positioning.

[0003] Existing technologies include single-base station positioning methods based on large antenna arrays and reflectors, as well as single-base station positioning methods based on sensors such as inertial navigation and vision. However, the first method relies on large antenna arrays or smart reflectors, which has high hardware costs, low deployment flexibility, and difficulty in adaptive adjustment in dynamic and complex environments. The second method is easily affected by lighting and texture, and the inertial navigation-assisted method cannot handle non-line-of-sight errors, resulting in low orientation accuracy.

[0004] To address the shortcomings of the existing technologies, there is an urgent need for a positioning method that is low-cost, highly flexible in deployment, can adaptively adjust in dynamic and complex environments, and has high orientation accuracy, suitable for the complex environments of the 5G-A Industrial Internet. Summary of the Invention

[0005] In view of the aforementioned problems, this application is proposed to provide a rotating monostation positioning method and apparatus for a 5G-A industrial internet environment that overcomes or at least partially solves the aforementioned problems, comprising:

[0006] A rotating single-base station positioning method for 5G-A industrial internet environments is disclosed, which locates a target object using a single base station. The target object carries an inertial measurement and positioning terminal and an ultra-wideband tag device and performs in-situ rotational motion. The method includes:

[0007] The coordinates of the target base station, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object are obtained, and the heading angle sequence and the ranging sequence are fitted to obtain a sine function curve describing the variation of the ranging value with the heading angle.

[0008] The orientation time of the target object directly facing the target base station is determined based on the extreme points of the sine function curve;

[0009] The target heading angle and target range value corresponding to the orientation time are determined based on the heading angle sequence and the ranging sequence, respectively.

[0010] The position coordinates of the target object are determined based on the target heading angle, the target ranging value, and the target base station coordinates.

[0011] Further, the steps of obtaining the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object include:

[0012] The IMU data sequence collected by the inertial measurement and positioning terminal is acquired, and the IMU data sequence is processed to obtain a continuous heading angle sequence;

[0013] The heading angle sequence and the ranging sequence are spatiotemporally synchronized.

[0014] Further, the steps of obtaining the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object include:

[0015] Acquire two complete round-trip signal data between the ultra-wideband tag device and the target base station, and determine the one-way flight time of the signal between the ultra-wideband tag device and the target base station based on the two complete round-trip signal data;

[0016] The relative distance between the ultra-wideband tag device and the target base station is generated based on the flight time and the speed of light.

[0017] Further, the step of fitting the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle includes:

[0018] An approximate sinusoidal model of the variation of the ranging value with the heading angle is established based on the ranging sequence and the heading angle sequence;

[0019] The parameters of the approximate sine model are globally optimized. When the convergence condition is met, the optimal parameters and the corresponding sine function curve are output. The parameters include the straight-line distance between the rotation center and the target base station, the rotation radius, and the orientation angle of the target object.

[0020] Further, the step of globally optimizing the parameters of the approximate sine model and outputting the optimal parameters and the corresponding sine function curve when the convergence condition is met includes:

[0021] Initialize the parameters of the approximate sinusoidal model;

[0022] Iterative optimization is performed based on dynamically adjusting search weights to determine the fitness value of the current parameter in each iteration;

[0023] When the fitness value reaches the convergence condition, the corresponding optimal parameters and the sine function curve are output.

[0024] Furthermore, the step of determining the fitness value of the current parameter in each iteration based on iterative optimization by dynamically adjusting the search weights includes:

[0025] The weight of the data point is determined based on the heading angle corresponding to the data point in the ranging sequence;

[0026] The predicted distance value of the data point is determined based on the current parameters and the approximate sine model;

[0027] The deviation between the predicted distance value and the measured distance value of the data point is determined, and the deviation is processed by a robust kernel function;

[0028] The fitness value of the current parameter is generated by summing the products of the processed deviations of all data points and their corresponding weights.

[0029] Furthermore, the formula for calculating the fitness is as follows:

[0030]

[0031] In the formula, It is the weight of the i-th data point. It is a robust kernel function;

[0032]

[0033] In the formula, These are the weights of non-line-of-sight data points. It is the heading angle corresponding to the current data point.

[0034] It is the starting angle of the non-line-of-sight interval. It is the end angle of the non-line-of-sight interval;

[0035]

[0036] In the formula, It is the deviation value. It is the error threshold.

[0037] A rotating single-base station positioning device for a 5G-A industrial internet environment is used to locate a target object using a single base station. The target object carries an inertial measurement positioning terminal and an ultra-wideband tag device and performs in-situ rotational motion. The device includes:

[0038] The curve fitting module is used to obtain the target base station coordinates, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object, and to fit the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle.

[0039] The time determination module is used to determine the orientation time of the target object facing the target base station based on the extreme points of the sine function curve;

[0040] The data determination module is used to determine the target heading angle and target range value corresponding to the orientation time based on the heading angle sequence and the ranging sequence, respectively.

[0041] The location calculation module is used to determine the location coordinates of the target object based on the target heading angle, the target ranging value, and the target base station coordinates.

[0042] A computer electronic device includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When executed by the processor, the computer program implements the steps of the rotating single-station positioning method for a 5G-A industrial internet environment as described above.

[0043] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the rotating single-station positioning method for a 5G-A industrial internet environment as described above.

[0044] This application has the following advantages:

[0045] In the embodiments of this application, in contrast to the problems of high hardware cost, low deployment flexibility, difficulty in adaptive adjustment in dynamic and complex environments, and low orientation accuracy in the prior art, this application provides a solution for a rotating single-base station positioning method based on intelligent optimization algorithms that fuse ultra-wideband (UWB) and inertial navigation (IMU). Specifically, it involves: acquiring the coordinates of the target base station, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object, and fitting the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle; determining the orientation time when the target object is facing the target base station based on the extreme points of the sine function curve; determining the target heading angle and target ranging value corresponding to the orientation time based on the heading angle sequence and the ranging sequence, respectively; and determining the position coordinates of the target object based on the target heading angle, the target ranging value, and the coordinates of the target base station. By combining a single base station with the dynamic ranging sequence generated during the target object's rotation and the IMU heading angle information, a self-contained positioning model is constructed. This overcomes the high dependence of traditional multi-base station systems on fixed infrastructure, enabling reliable initial positioning even with a very small number of base stations or single-point deployment. It provides reliable location information for complex rescue scenarios in a low-cost and simple-to-operate manner. Using a fitted sine function curve as an anti-interference positioning strategy, the model solves the interference problems caused by complex metal structures, equipment obstruction, and multipath reflections in the industrial internet environment on rotational positioning accuracy, improving positioning and orientation accuracy and anti-interference capabilities. Attached Figure Description

[0046] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the rotation process of a rotating single-station positioning method for a 5G-A industrial internet environment provided in one embodiment of this application;

[0048] Figure 2 This is a flowchart illustrating the steps of a rotating single-station positioning method for a 5G-A industrial internet environment, as provided in one embodiment of this application.

[0049] Figure 3 This is a flowchart of a rotating single-station positioning method for a 5G-A industrial internet environment provided in one embodiment of this application;

[0050] Figure 4 This is a schematic diagram of a bilateral bidirectional ranging process for a rotating monostation positioning method for a 5G-A industrial internet environment, provided in one embodiment of this application.

[0051] Figure 5 This is a distance measurement dataset image collected in an underground sheltered space, provided in one embodiment of this application;

[0052] Figure 6 This is a sequence of heading angles collected in an underground sheltered space, as provided in one embodiment of this application.

[0053] Figure 7 This is a diagram showing the experimental results of fitting the ranging sequence and heading angle sequence according to an embodiment of this application;

[0054] Figure 8 This is an example of a pilot pattern with 1 or 3 devices provided in one embodiment of this application;

[0055] Figure 9 This is a comparison diagram of positioning results in a mixed line-of-sight environment provided in an embodiment of this application;

[0056] Figure 10 This is a structural block diagram of a rotating single-station positioning device for a 5G-A industrial internet environment provided in one embodiment of this application;

[0057] Figure 11 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, features, and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0059] The inventor discovered through analysis of prior art 1 and prior art 2:

[0060] Existing technology 1: Single base station positioning method based on large antenna array and reflector. This method uses a large antenna array combined with geometric elements such as reflector to perform single base station positioning. Generally, the antenna array is arranged according to certain geometric rules, or the direction of arrival is estimated by using spatial geometric rules, thereby completing the positioning function.

[0061] However, for this type of method, millimeter-wave antennas need to support high gain, narrow beamwidth, and dynamic beamforming capabilities. This places stringent requirements on the phase consistency of antenna elements and the accuracy of array calibration. Especially in the complex environment of 5G-A industrial internet, signal penetration loss and multipath interference can further amplify antenna performance defects, leading to reflector control failure or channel parameter estimation errors. Secondly, intelligent reflectors require a dense arrangement of a large number of tunable electromagnetic elements, which not only results in high hardware costs but also makes it difficult to balance reflector size, element density, and response speed in design. Large-scale deployment faces multiple challenges in terms of power consumption, heat dissipation, and reliability. In addition, the positioning accuracy of the system is highly dependent on the topology of the reflector and the base station. The spatial position and angular orientation of the reflector need to be pre-planned according to the environmental structure. In dynamic scenarios or unknown obstructed areas, it is difficult to achieve real-time adaptive adjustment, which limits the generalization ability of this technology. These factors make this solution face bottlenecks such as poor cost controllability and low deployment flexibility in industrial applications, making it difficult to meet the universal positioning requirements of low cost and rapid deployment.

[0062] Existing technology 2: Single-base station positioning methods based on inertial navigation, vision, and other sensor assistance. The core of this method lies in estimating the angle of arrival and distance through the complementary characteristics of multi-source heterogeneous sensors. This method typically uses the ranging or angular measurement information of a single base station (such as a UWB or millimeter-wave base station) as the global position constraint benchmark, while combining inertial navigation or vision sensors to calculate short-term high-frequency relative displacement and attitude changes or to compensate for multipath and non-line-of-sight errors.

[0063] However, for the existing technology two, the visual positioning module of this method is highly dependent on ambient lighting conditions and texture features. In the complex environment of 5G-A industrial internet, the grayscale images from monocular cameras are prone to exposure anomalies, feature point loss, or matching errors, affecting positioning accuracy. Moreover, it is costly and unsuitable for use in blind environments. Existing technology two cannot handle non-line-of-sight and multipath anomaly observations, and the template matching method is essentially a one-dimensional matching in the time dimension, which does not utilize geometric spatial location information, resulting in poor orientation accuracy.

[0064] To achieve personnel positioning in complex, obstructed spaces with low cost, simple operation, and using only a single base station, the inventors designed a single-base station rotation positioning method that combines inertial navigation and UWB, such as... Figure 1 As shown, the target is the test personnel. The test personnel need to wear a positioning terminal with an integrated inertial measurement unit and hold an ultra-wideband tag device to perform in-situ rotational movements.

[0065] It should be noted that the target object can also be a device capable of rotational motion. The positioning terminal integrated with an inertial measurement unit mainly consists of a three-axis accelerometer, a three-axis gyroscope, and a magnetometer. By measuring the angular velocity and acceleration of the carrier in three-dimensional space, combined with initial position information, it uses a strapdown inertial navigation algorithm to calculate the current position and attitude. The ultra-wideband tag device is a wireless positioning terminal based on pulse radio technology, mainly comprising a UWB radio frequency module, a baseband processing unit, and an antenna system. In this application, it transmits nanosecond-level narrow pulse signals to perform bilateral, bidirectional ranging with the target base station, accurately measuring the signal flight time to calculate the distance.

[0066] The core concept of this invention lies in:

[0067] (1) Unlike existing single-base station positioning which relies on static geometric calculations, this invention constructs a dynamic positioning scenario by actively rotating the device, and innovatively correlates the periodic ranging sequence generated when a person rotates a UWB tag with the attitude heading angle of the IMU in time and space. Based on the sinusoidal variation law of the ranging value induced by rotational motion, the core idea of ​​using dynamic trajectory fitting to replace static point-to-point positioning is proposed. By jointly analyzing the continuous ranging data and heading angle sequence during the rotation process, the dependence of single-base station positioning on geometric constraints is broken, and the positioning reliability in complex occlusion scenarios is significantly improved.

[0068] (2) To address the problem of outliers in ranging and the accumulation of attitude errors caused by environmental interference, a multi-dimensional feature matching framework integrating an improved snake optimization algorithm is proposed. By introducing a dynamic inertial weight adjustment mechanism and an adaptive search strategy, the algorithm's ability to identify and eliminate outliers such as non-line-of-sight (NLOS) and multipath distortion is enhanced. At the same time, the matching process is optimized by combining heading angle confidence weight, achieving robust joint calculation of direction and distance in high-noise environments. This strategy effectively suppresses the negative impact of environmental interference on positioning accuracy through dual optimization of intelligent algorithms and outlier handling. The orientation accuracy is improved by more than 15 degrees compared with traditional methods, showing significant advantages in mixed line-of-sight scenarios.

[0069] Reference Figure 2 and Figure 3 This application illustrates a rotating single-station positioning method for a 5G-A industrial internet environment, provided by an embodiment of this application.

[0070] The method includes:

[0071] S210. Obtain the coordinates of the target base station, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object, and fit the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle.

[0072] S220. Determine the orientation time of the target object towards the target base station based on the extreme points of the sine function curve;

[0073] S230. Determine the target heading angle and target range value corresponding to the orientation time based on the heading angle sequence and the ranging sequence, respectively.

[0074] S240. Determine the position coordinates of the target object based on the target heading angle, the target ranging value, and the target base station coordinates.

[0075] In the embodiments of this application, a self-contained positioning model is constructed by combining a single base station with the dynamic ranging sequence generated during the rotation of the target object and the heading angle information of the IMU. This overcomes the high dependence of traditional multi-base station systems on fixed infrastructure and enables reliable initial positioning even with a very small number of base stations or single-point deployment. It provides reliable location information for complex rescue scenarios in a low-cost and simple-to-operate manner. By using a fitted sine function curve as an anti-interference positioning strategy, the interference problems of complex metal structures, equipment obstruction, and multipath reflections on rotational positioning accuracy in the industrial internet environment are solved, improving positioning and orientation accuracy and anti-interference capabilities.

[0076] The following will further explain a rotating single-station positioning method for a 5G-A industrial internet environment in this exemplary embodiment.

[0077] As described in step S210, the target base station coordinates, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object are obtained, and the heading angle sequence and the ranging sequence are fitted to obtain a sine function curve describing the variation of the ranging value with the heading angle.

[0078] It should be noted that the target base station coordinates are the pre-known positions of the UWB base station in the global coordinate system, which are the reference points for positioning calculations and can be obtained through pre-measurement or system configuration.

[0079] In one embodiment of the present invention, the specific process of "obtaining the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object" in step S210 can be further described in conjunction with the following description.

[0080] As described in the following steps, the IMU data sequence collected by the inertial measurement and positioning terminal is acquired, and the IMU data sequence is processed to obtain a continuous heading angle sequence; the heading angle sequence and the ranging sequence are spatiotemporally synchronized.

[0081] Specifically, testers must wear a positioning terminal with an integrated inertial measurement unit (IMU) and hold an ultra-wideband (UWB) tag while performing a stationary rotation. During the rotation, the system simultaneously performs two core operations: firstly, it acquires raw data such as triaxial acceleration and angular velocity in real time through the IMU, calculates and generates a continuous heading angle sequence based on the strapdown inertial navigation algorithm; secondly, the UWB module continuously receives ranging signals sent by the base station, records the dynamically changing distance measurements during the rotation, and marks timestamps to achieve spatiotemporal synchronization with the IMU data. This step establishes a dynamic relationship between direction and distance through active rotation, providing a foundational dataset for subsequent optimization processing.

[0082] In one embodiment of the present invention, the specific process of obtaining the ranging sequence in step S210 can be further described in conjunction with the following description.

[0083] As described in the following steps, acquire two complete round-trip signal data between the ultra-wideband tag device and the target base station, and determine the one-way flight time of the signal between the ultra-wideband tag device and the target base station based on the two complete round-trip signal data; generate the relative distance between the ultra-wideband tag device and the target base station based on the flight time and the speed of light.

[0084] As an example, the tester holds a UWB tag and continuously performs bilateral two-way ranging with a distant UWB base station (target base station) while rotating. The clock synchronization error is eliminated by two complete signal round trips, achieving high-precision one-way flight time calculation. The signal propagation time and equipment processing time are recorded by timestamps, and the processing time error is eliminated by redundant equations, retaining only the one-way propagation time to ensure that the ranging accuracy is not affected by the equipment clock drift.

[0085] In a specific implementation, the bilateral two-way ranging process is as follows: Figure 4 As shown. During bilateral two-way ranging, the UWB base station and UWB tag record the time for each data transmission and reception, generating a total of 6 timestamps. Based on two complete signal round-trip cycles, redundant equations are constructed using the coordinated timestamp exchange between devices to achieve algebraic elimination of error terms. The signal time-of-flight calculation method is as follows:

[0086]

[0087] In the formula, T prop T is the time of flight of the signal from the UWB tag to the UWB base station. round1 and T round2 T represents the time difference in signal reception by a UWB tag. reply1 and T reply2 This indicates the time difference in signal reception by the UWB base station.

[0088] Based on this, we can obtain the distance measurement data sequence required for positioning and orientation. The method for calculating the relative distance is as follows:

[0089]

[0090] In the formula, It is the speed of light.

[0091] The above steps allow us to collect the ranging sequences during the rotation process, thus completing the construction of the ranging dataset. Actual testing has shown that the ranging dataset collected in underground sheltered spaces yields results such as... Figure 5 As shown in the data, the actual blind environment has a large number of non-line-of-sight effects and multipath effects that cause data distortion, so the facing angle is difficult to determine directly. Therefore, curve fitting is required first to increase the recognition accuracy.

[0092] In one embodiment of the present invention, the specific process of obtaining the heading angle sequence in step S210 can be further described in conjunction with the following description.

[0093] Specifically, to estimate the relative angle, this embodiment uses the heading angle information output by the inertial navigation system (INS) combined with the UWB distance measurement sequence to uniformly determine the relative angle. Specifically, the test personnel carry the INS and hold a UWB tag, rotating in place several times, while the UWB base station continuously performs the ranging process during this period. This allows the collection of UWB data and the heading angle data calculated by the INS over multiple rotation cycles.

[0094] As an example, quaternions are mathematical tools for describing rotations in three-dimensional space. They can be represented as four-dimensional vectors containing one real part and three imaginary parts, reflecting the attitude of a target object. Because they can achieve continuous attitude updates during rotation and have relatively low computational cost, the quaternion method is used to solve for the heading angle.

[0095] The specific process is as follows:

[0096]

[0097] In the formula, This represents the rotation matrix from the body coordinate system to the navigation coordinate system; , , respectively roll Pitch angle and heading angle Then, an attitude information correction operation is performed, which involves a transformation from the original quaternion information to attitude matrix information. This process is as follows:

[0098]

[0099]

[0100]

[0101] In the formula, C32 represents the element in the third row and second column, which represents the projection cosine of the body's y-axis direction onto the z-axis direction of the reference frame;

[0102] C33 represents the element in the third row and third column, which represents the projection cosine of the body's z-axis direction onto the z-axis direction of the reference frame;

[0103] C31 represents the element in the third row and first column, which represents the projection cosine of the body's x-axis direction onto the z-axis direction of the reference frame;

[0104] C21 represents the element in the second row and first column, which represents the projection cosine of the body's x-axis direction onto the y-axis direction of the reference frame;

[0105] C11 represents the element in the first row and first column, which represents the projection cosine of the x-axis direction of the body onto the x-axis direction of the reference frame.

[0106] The above describes the continuous calculation process of attitude information. After field testing, the collected continuous heading angle sequence results are as follows: Figure 6 As shown.

[0107] In one embodiment of the present invention, the specific process of step S210, "fitting the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle," can be further explained in conjunction with the following description.

[0108] As described in the following steps, an approximate sine model of the variation of the ranging value with the heading angle is established based on the ranging sequence and the heading angle sequence; the parameters of the approximate sine model are globally optimized, and when the convergence condition is met, the optimal parameters and the corresponding sine function curve are output; wherein, the parameters include the straight-line distance between the rotation center and the target base station, the rotation radius, and the orientation angle of the target object.

[0109] Specifically, to address the distortion interference in UWB ranging data caused by non-line-of-sight (NLOS) propagation due to human bodies and obstacles, and by multipath effects, an improved snake optimization algorithm is employed to robustly fit the original ranging sequence. This algorithm combines dynamic inertial weight adjustment and an adaptive search strategy to identify and eliminate abnormal ranging points through global optimization, while simultaneously fitting the effective ranging values ​​into a sine function curve that conforms to the laws of rotational motion. This process focuses on solving the signal distortion problem in harsh environments, revealing the periodic characteristics of ranging values ​​changing with the rotation angle through mathematical modeling, and accurately capturing the physical pointing state when the device is directly facing the base station (i.e., the peak / trough points of the sine curve).

[0110] In one embodiment of the present invention, the specific process of "performing global optimization of the parameters of the approximate sine model, and outputting the optimal parameters and the corresponding sine function curve when the convergence condition is met" can be further explained in conjunction with the following description.

[0111] The parameters of the approximate sine model are initialized as described in the following steps; iterative optimization is performed based on dynamically adjusted search weights to determine the fitness value of the current parameter in each iteration; when the fitness value reaches the convergence condition, the corresponding optimal parameters and the sine function curve are output.

[0112] As an example, the initialization method can employ random generation, where parameter combinations are randomly selected within a preset range to ensure diversity in the initial population, laying the foundation for global optimization. This method uses an improved snake optimization algorithm for orientation accuracy tuning. During the iteration process, the algorithm's inertia weight is dynamically changed based on the number of iterations. When the change in fitness value after N consecutive iterations is less than a threshold, the corresponding parameters are considered optimal. Substituting these parameters into an approximate sine model generates a fitted sine function curve. This curve reflects the periodicity of the ranging value changing with the heading angle, with its extreme points corresponding to the times when the target object is facing or away from the base station.

[0113] In one specific implementation, this embodiment is based on anomaly ranging fitting driven by an improved optimization algorithm. During the rotation data collection process, we assume that the initial orientation angle of the person is... The straight-line distance between the rotation center and the base station is If the radius of rotation of the handheld device is r, then the following distance formula applies:

[0114]

[0115] Therefore when >> When the distance is measured, the formula can be approximated as the superposition of sine and cosine values ​​and the bias. The maximum distance is reached when the person is facing away from the base station. When the person turns from facing away to facing forward, the distance value decreases again until it reaches the minimum value when facing forward. Therefore, the obtained distance and angle sequences can be fitted to a sine function curve. If the exhaustive method is used, it will consume a lot of computing resources. Least squares will inevitably get stuck in local optima. Therefore, an improved snake optimization algorithm is used for intelligent parameter finding.

[0116] In one embodiment of the present invention, the specific process of "iterative optimization based on dynamically adjusting search weights to determine the fitness value of the current parameter in each iteration" can be further explained in conjunction with the following description.

[0117] As described in the following steps, the weight of the data point is determined based on the heading angle corresponding to the data point in the ranging sequence; the predicted distance value of the data point is determined based on the current parameter and the approximate sine model; the deviation between the predicted distance value and the measured distance value of the data point is determined, and the deviation is processed by a robust kernel function; the products of the processed deviations of all data points and their corresponding weights are summed to generate the fitness value of the current parameter.

[0118] As an example, when the target object is facing the base station (i.e., line-of-sight propagation), the ranging value is more reliable and has a higher weight; when the target object is facing away from the base station (non-line-of-sight propagation), the ranging value is more susceptible to occlusion interference and has a lower weight. By pre-setting non-line-of-sight intervals, data points falling into these intervals are assigned low weights, while other intervals are assigned high weights, thus suppressing the impact of NLOS error on the fitting.

[0119] The approximate sinusoidal model is constructed based on the geometric characteristics of rotational motion, where the current parameters include the straight-line distance from the rotation center to the base station, the rotation radius, and the initial heading angle. For the heading angle of each data point, the predicted distance value can be calculated by substituting the current parameters into the model. This value reflects the relationship between the distance measurement value and the heading angle under ideal conditions.

[0120] The deviation is the difference between the measured distance and the predicted distance, and its magnitude reflects the degree of deviation between the model and the actual data. Due to multipath reflection and sudden interference in the industrial environment, some data points may exhibit significant deviations. Directly using the squared error would amplify its impact. The robust kernel function processes deviations piecewise: small deviations are squared to retain their corrective effect on the model; large deviations are linearly processed to weaken the interference of outliers and ensure the stability of the fit.

[0121] The quality of the current parameters is evaluated by comprehensively considering the weighted biases of all data points. The fitness value is the core indicator for measuring the model's fit; the smaller the value, the better the sinusoidal model corresponding to the current parameters matches the measured data. During the calculation, the bias in non-line-of-sight intervals is weakened by weights, and the bias of outliers is compressed by a robust kernel function. The final summation reflects both the overall fitting trend and suppresses local interference, providing a reliable objective function for subsequent parameter optimization.

[0122] In a specific implementation, if outlier values ​​and non-line-of-sight distortion values ​​during back-to-back positioning are not differentiated and processed when choosing the fitness function, the fitting accuracy will be unsatisfactory. Therefore, the fitness function selected in this embodiment is:

[0123]

[0124] In the formula, It is the weight of the i-th data point. It is a robust kernel function;

[0125]

[0126] In the formula, These are the weights of non-line-of-sight data points. It is the heading angle corresponding to the current data point.

[0127] It is the starting angle of the non-line-of-sight interval. It is the end angle of the non-line-of-sight interval;

[0128]

[0129] In the formula, It is the deviation value. It is the error threshold.

[0130] By introducing a robust kernel function with varying weight constraints, the impact of relative outliers on orientation accuracy is reduced. Furthermore, the non-line-of-sight effect caused by personnel facing away from the base station also diminishes the impact on orientation accuracy, reflecting the discrepancy between predicted and measured values. Parameter optimization is achieved by fitting ranging and heading angle sequences to find the angle and distance values ​​at the exact moment of facing. The fitting experimental results are as follows: Figure 7 As shown.

[0131] As described in step S220, the orientation time of the target object facing the target base station is determined based on the extreme points of the sine function curve.

[0132] Specifically, when the target object rotates in place, the ranging value changes sinusoidally with the heading angle. When the target object is directly facing the base station, the heading angle points directly to the base station, resulting in the most accurate ranging value and avoiding multipath interference and NLOS errors. In this embodiment, the facing time of the target person is determined by the fitted sine function curve. Since the ranging value changes sinusoidally with the heading angle, the minimum point of the curve corresponds to the time when the target object is directly facing the base station, with minimal occlusion between the UWB tag and the base station; the maximum point corresponds to the time when the target object is facing away from the base station, with maximum occlusion. Therefore, the orientation time can be determined by identifying the minimum point.

[0133] As described in step S230, the target heading angle and target range value corresponding to the orientation time are determined based on the heading angle sequence and the ranging sequence, respectively.

[0134] As an example, the orientation time is correlated with the original data sequence to extract key parameters for positioning. The heading angle sequence is generated by the IMU using quaternion methods and contains angle information corresponding to each timestamp; the ranging sequence is generated by UWB bilateral bidirectional ranging and contains distance information corresponding to each timestamp. Since the two sets of sequences are spatiotemporally synchronized through timestamps, at the orientation time, the corresponding angle value can be directly indexed from the heading angle sequence, and the corresponding distance value can be indexed from the ranging sequence.

[0135] In one specific implementation, after determining the orientation time facing the base station based on the minimum point of the fitted sine curve, the IMU heading angle calculation result corresponding to that time is simultaneously extracted as the final orientation estimate, and the corresponding UWB ranging value is used as the distortion-free distance measurement result. After obtaining the coordinate point corresponding to the minimum value of the fitted curve, it is paired with the collected heading angle sequence according to time to complete the orientation operation. Template matching is a matching algorithm designed by previous researchers. When determining the relevant position of the locator, this method generates a unique template sequence based on the mathematical relationship between the locator and the UWB base station (this relationship is determined by the model itself). Subsequently, the template sequence is compared with the ranging sequence. In this process, the comparison is carried out horizontally in one dimension. During the matching process, the sum of the differences between the two curves at multiple times is continuously calculated until the position with the minimum sum is found. Finally, in the template sequence, the heading angle value corresponding to the minimum value is determined as the target angle value required when the locator faces the base station. This method is essentially a single-dimensional matching process with low accuracy; while the method used in this paper is a matching process based on multi-dimensional features.

[0136] Analysis shows that the improved snake optimization algorithm best matches the actual reference curve. Specifically, the improved snake optimization algorithm has a fitting angle deviation of 5.96 degrees, the genetic algorithm has a fitting angle deviation of 14.32 degrees, and the template matching method has an angle deviation of 21.14 degrees. Compared to the genetic algorithm-optimized fitting method, the proposed algorithm improves the orientation accuracy by 8.36 degrees, and compared to the template matching method, it improves by 15.18 degrees. Furthermore, it improves computational efficiency, avoids the waste of computational resources caused by exhaustive search, and saves time.

[0137] As described in step S240, the position coordinates of the target object are determined based on the target heading angle, the target ranging value, and the target base station coordinates.

[0138] Specifically, the target heading angle and target range are the core inputs for positioning calculation. The target heading angle reflects the target's direction relative to the base station, and the target range reflects the straight-line distance between the target and the base station. Combining these with the base station coordinates allows for the calculation of the target's position through geometric relationships. Timestamp matching achieves spatiotemporal alignment of the heading angle and range data. Combined with the known coordinates of the base station, single-base station positioning is completed, outputting the target's position coordinates. A cross-sensor data fusion strategy overcomes the geometric constraints of single-base station positioning, establishing a deterministic mapping relationship between direction and distance while eliminating environmental interference, ultimately achieving reliable positioning services in complex scenarios. Accurate position estimation is achieved by using the relative direction and relative distance determined through the above steps.

[0139] In one specific implementation, based on the principle of polar coordinate to rectangular coordinate conversion, the relative coordinates of the target object are calculated with the base station coordinates as the origin, the target heading angle as the polar angle, and the target ranging value as the polar radius.

[0140] To verify the superior performance of this invention under conditions of reduced available base stations and severe non-line-of-sight effects, field tests were conducted under both line-of-sight and mixed line-of-sight conditions. In this scenario, the coordinates of base station 1 broadcasting were (5.1m, 0m), and the coordinates of base station 2 were (22m, 7m). The tag then began operation, receiving and storing the information broadcast by the base stations, while simultaneously performing bilateral bidirectional ranging with the other two base stations. After obtaining all the necessary information, the method of this invention was used to perform trilateration on the location information, and finally, the calculated coordinate information was saved. In the rotational positioning process based on the improved snake optimization algorithm designed in this invention, we carried an inertial navigation system and a handheld tag, rotating it several times in place, storing the two collected sequences, and running the snake optimization algorithm for fitting, orientation, and positioning to obtain the required location coordinate information. This is the specific workflow. First, we conducted experiments in a complex 5G-A industrial internet environment without obstructions. The experimental results are as follows... Figure 8 As shown in Table 1, there are examples of pilot diagrams with 1 or 3 devices.

[0141] Table 1 Comparison of RMSE under sight distance conditions

[0142]

[0143] The data above shows that in the relatively harsh environment of 5G-A Industrial Internet, the positioning accuracy of both algorithms is lower than the results of previous simulation analyses. Furthermore, the rotation positioning algorithm based on the improved snake optimization algorithm is only 1.44 cm more accurate than the trilateration method. To better verify the performance and applicable scenarios of the algorithms, this embodiment further conducted experiments under mixed line-of-sight and non-line-of-sight conditions, with the following results: Figure 9 As shown in Table 2.

[0144] Table 2 Comparison of RMSE under mixed sight distance conditions

[0145]

[0146] As can be seen from the above data, because this embodiment adds non-line-of-sight occlusion to the straight-line propagation path between the two base stations and the tag, the result of the trilateration method will shift in one direction. Since the actual position of the person is between the two base stations, some errors are offset, resulting in a more reliable positioning result than single-base station occlusion. Therefore, in this scenario, the trilateration method has relatively low positioning accuracy, only reaching the meter level, due to multiple factors such as limited base station placement and a decrease in the number of available line-of-sight base stations. However, the rotational positioning method based on improved snake optimization proposed in this invention significantly outperforms the former in mixed line-of-sight scenarios, improving accuracy by 80.5% and 88.1% respectively. It can meet relatively accurate emergency positioning needs in such scenarios and provide a more accurate initial position for the fusion positioning system, enhancing system performance.

[0147] The advantages of this invention are:

[0148] (1) In the complex environment of 5G-A industrial internet, the limited deployment and insufficient number of base stations often lead to the failure of the positioning system. To solve this problem, a single base station architecture can be adopted, combined with the active rotation action of personnel, and the dynamic ranging sequence generated during the rotation and the heading angle information of the IMU can be used to construct a self-contained positioning model. This solution breaks through the high dependence of traditional multi-base station systems on fixed infrastructure, and can achieve reliable initial positioning under the condition of very few base stations or single-point deployment, providing reliable location information for complex rescue scenarios in a low-cost and simple operation.

[0149] (2) Considering the interference of complex metal structures, equipment obstruction, and multipath reflection on rotational positioning accuracy in the industrial internet environment, this invention designs an anti-interference positioning strategy based on an improved snake optimization algorithm. This algorithm enhances the sensitivity to abnormal ranging points by dynamically adjusting the search weight and iteration step size, and constructs a multi-dimensional matching constraint by introducing a robust kernel-constrained fitness function and combining the trend of heading angle changes. The ranging sequence is fitted into a periodic azimuth and distance mapping curve. Compared with the existing single-dimensional template matching algorithm, this significantly improves the positioning and orientation accuracy and anti-interference ability. At the same time, it optimizes the spatiotemporal correlation between the heading angle and the ranging value, further improving the orientation accuracy and avoiding getting trapped in local optima.

[0150] As the apparatus embodiment is basically similar to the method embodiment, it is described in a relatively simple manner. For relevant details, please refer to the description of the method embodiment.

[0151] Reference Figure 10This application illustrates a rotating single-base station positioning device for a 5G-A industrial internet environment, provided by an embodiment of the present application. The device is used to locate a target object using a single base station. The target object carries an inertial measurement positioning terminal and an ultra-wideband tag device and performs in-situ rotational motion. The device includes:

[0152] Specifically, it includes:

[0153] The curve fitting module 1010 is used to acquire the target base station coordinates, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object, and to fit the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle.

[0154] The time determination module 1020 is used to determine the orientation time of the target object facing the target base station based on the extreme point of the sine function curve;

[0155] The data determination module 1030 is used to determine the target heading angle and target range value corresponding to the orientation time based on the heading angle sequence and the ranging sequence, respectively.

[0156] The position calculation module 1040 is used to determine the position coordinates of the target object based on the target heading angle, the target ranging value, and the target base station coordinates.

[0157] In one embodiment of the present invention, the curve fitting module 1010 includes:

[0158] The data acquisition submodule is used to acquire the IMU data sequence collected by the inertial measurement and positioning terminal, and to solve the IMU data sequence to obtain a continuous heading angle sequence.

[0159] The data synchronization submodule is used to perform spatiotemporal synchronization processing between the heading angle sequence and the ranging sequence.

[0160] In one embodiment of the present invention, the curve fitting module 1010 includes:

[0161] The UWB information transmission submodule is used to acquire two complete round-trip signal data between the ultra-wideband tag device and the target base station, and to determine the one-way flight time of the signal between the ultra-wideband tag device and the target base station based on the two complete round-trip signal data.

[0162] The UWB ranging submodule is used to generate the relative distance between the ultra-wideband tag device and the target base station based on the flight time and the speed of light.

[0163] In one embodiment of the present invention, the curve fitting module 1010 includes:

[0164] The model building submodule is used to build an approximate sinusoidal model of the variation of the ranging value with the heading angle based on the ranging sequence and the heading angle sequence;

[0165] The parameter optimization submodule is used to perform global optimization of the parameters of the approximate sine model. When the convergence condition is met, it outputs the optimal parameters and the corresponding sine function curve. The parameters include the straight-line distance between the rotation center and the target base station, the rotation radius, and the orientation angle of the target object.

[0166] In one embodiment of the present invention, the parameter optimization submodule includes:

[0167] An initialization unit is used to initialize the parameters of the approximate sinusoidal model;

[0168] The iterative optimization unit is used to perform iterative optimization based on dynamically adjusted search weights and determine the fitness value of the current parameter in each iteration.

[0169] The convergence unit is used to output the corresponding optimal parameters and the sine function curve when the fitness value reaches the convergence condition.

[0170] In one embodiment of the present invention, the iterative optimization unit includes:

[0171] The weight calculation subunit is used to determine the weight of the data points based on the heading angles corresponding to the data points in the ranging sequence.

[0172] The distance prediction subunit is used to determine the predicted distance value of the data point based on the current parameters and the approximate sine model.

[0173] The deviation calculation subunit is used to determine the deviation between the predicted distance value and the measured distance value of the data point, and to process the deviation using a robust kernel function;

[0174] The fitness calculation subunit is used to sum the products of the processed deviations of all data points and their corresponding weights to generate the fitness value of the current parameter.

[0175] In one embodiment of the present invention, the fitness calculation subunit includes:

[0176]

[0177] In the formula, It is the weight of the i-th data point. It is a robust kernel function;

[0178]

[0179] In the formula, These are the weights of non-line-of-sight data points. It is the heading angle corresponding to the current data point.

[0180] It is the starting angle of the non-line-of-sight interval. It is the end angle of the non-line-of-sight interval;

[0181]

[0182] In the formula, It is the deviation value. It is the error threshold.

[0183] Reference Figure 11 The computer device illustrating a rotating single-station positioning method for a 5G-A industrial internet environment according to the present invention may specifically include the following:

[0184] The computer device 12 described above is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0185] Bus 18 refers to one or more of several types of bus 18 architectures, including memory bus 18 or memory controller, peripheral bus 18, graphics acceleration port, processor, or local bus 18 using any of the various bus 18 architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus 18, Micro Channel Architecture (MAC) bus 18, Enhanced ISA bus 18, Audio / Video Electronics Standards Association (VESA) local bus 18, and Peripheral Component Interconnect (PCI) bus 18.

[0186] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0187] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard disk drive"). Although Figure 11Not shown, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42 configured to perform the functions of the embodiments of the present invention.

[0188] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory. Such program modules 42 include—but are not limited to—an operating system, one or more application programs, other program modules 42, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0189] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, camera, etc.), and with one or more devices that enable a user to interact with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN)), wide area network (WAN), and / or public networks (e.g., the Internet) via network adapter 20. Figure 11 As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although... Figure 11 Not shown, it can be combined with computer device 12 to use other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing unit 16, external disk drive array, RAID system, tape drive and data backup storage system 34, etc.

[0190] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing a rotating single-station positioning method for a 5G-A industrial internet environment provided in this embodiment of the invention.

[0191] That is, when the processing unit 16 executes the above program, it performs the following: acquiring the target base station coordinates, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object, and fitting the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle; determining the orientation time of the target object facing the target base station based on the extreme points of the sine function curve; determining the target heading angle and target ranging value corresponding to the orientation time based on the heading angle sequence and the ranging sequence respectively; and determining the position coordinates of the target object based on the target heading angle, the target ranging value, and the target base station coordinates.

[0192] In this embodiment of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements a rotating single-station positioning method for a 5G-A industrial internet environment as provided in all embodiments of this application.

[0193] That is, when the program is executed by the processor, it performs the following: acquiring the target base station coordinates, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object, and fitting the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle; determining the orientation time of the target object facing the target base station based on the extreme points of the sine function curve; determining the target heading angle and target ranging value corresponding to the orientation time based on the heading angle sequence and the ranging sequence respectively; and determining the position coordinates of the target object based on the target heading angle, the target ranging value, and the target base station coordinates.

[0194] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0195] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0196] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider). The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably.

[0197] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0198] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0199] The foregoing has provided a detailed description of a rotating single-station positioning method and apparatus for a 5G-A industrial internet environment. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A rotating single-base station positioning method for a 5G-A industrial internet environment, used to locate a target object using a single base station, wherein the target object carries an inertial measurement positioning terminal and an ultra-wideband tag device and performs in-situ rotational motion; characterized in that, The method includes: The coordinates of the target base station, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object are obtained, and the heading angle sequence and the ranging sequence are fitted to obtain a sine function curve describing the variation of the ranging value with the heading angle. The orientation time of the target object directly facing the target base station is determined based on the extreme points of the sine function curve; The target heading angle and target range value corresponding to the orientation time are determined based on the heading angle sequence and the ranging sequence, respectively. The position coordinates of the target object are determined based on the target heading angle, the target ranging value, and the target base station coordinates.

2. The method according to claim 1, characterized in that, The steps for obtaining the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object include: The IMU data sequence collected by the inertial measurement and positioning terminal is acquired, and the IMU data sequence is processed to obtain a continuous heading angle sequence; The heading angle sequence and the ranging sequence are spatiotemporally synchronized.

3. The method according to claim 1, characterized in that, The steps for obtaining the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object include: Acquire two complete round-trip signal data between the ultra-wideband tag device and the target base station, and determine the one-way flight time of the signal between the ultra-wideband tag device and the target base station based on the two complete round-trip signal data; The relative distance between the ultra-wideband tag device and the target base station is generated based on the flight time and the speed of light.

4. The method according to claim 1, characterized in that, The step of fitting the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle includes: An approximate sinusoidal model of the variation of the ranging value with the heading angle is established based on the ranging sequence and the heading angle sequence; The parameters of the approximate sine model are globally optimized. When the convergence condition is met, the optimal parameters and the corresponding sine function curve are output. The parameters include the straight-line distance between the rotation center and the target base station, the rotation radius, and the orientation angle of the target object.

5. The method according to claim 4, characterized in that, The steps of globally optimizing the parameters of the approximate sine model and outputting the optimal parameters and the corresponding sine function curve when the convergence condition is met include: Initialize the parameters of the approximate sinusoidal model; Iterative optimization is performed based on dynamically adjusting search weights to determine the fitness value of the current parameter in each iteration; When the fitness value reaches the convergence condition, the corresponding optimal parameters and the sine function curve are output.

6. The method according to claim 5, characterized in that, The steps for iterative optimization based on dynamically adjusting search weights, and determining the fitness value of the current parameter in each iteration, include: The weight of the data point is determined based on the heading angle corresponding to the data point in the ranging sequence; The predicted distance value of the data point is determined based on the current parameters and the approximate sine model; The deviation between the predicted distance value and the measured distance value of the data point is determined, and the deviation is processed by a robust kernel function; The fitness value of the current parameter is generated by summing the products of the processed deviations of all data points and their corresponding weights.

7. The method according to claim 6, characterized in that, The formula for calculating fitness is as follows: In the formula, It is the weight of the i-th data point. It is a robust kernel function; In the formula, These are the weights of non-line-of-sight data points. It is the heading angle corresponding to the current data point. It is the starting angle of the non-line-of-sight interval. It is the end angle of the non-line-of-sight interval; In the formula, It is the deviation value. It is the error threshold.

8. A rotating single-base station positioning device for a 5G-A industrial internet environment, used for locating a target object using a single base station, wherein the target object carries an inertial measurement positioning terminal and an ultra-wideband tag device and performs in-situ rotational motion; characterized in that, The device includes: The curve fitting module is used to obtain the target base station coordinates, the spatiotemporally synchronized heading angle sequence and ranging sequence during the rotation of the target object, and to fit the heading angle sequence and the ranging sequence to obtain a sine function curve describing the variation of the ranging value with the heading angle. The time determination module is used to determine the orientation time of the target object facing the target base station based on the extreme points of the sine function curve; The data determination module is used to determine the target heading angle and target range value corresponding to the orientation time based on the heading angle sequence and the ranging sequence, respectively. The location calculation module is used to determine the location coordinates of the target object based on the target heading angle, the target ranging value, and the target base station coordinates.

9. A computer electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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