A mine personnel positioning method based on ultra-wideband and millimeter wave radar fusion

Abstract

The present application belongs to the technical field of intelligent mine, and particularly relates to a mine personnel positioning method based on ultra-wideband and millimeter wave radar fusion, aiming to improve the positioning accuracy of mine workers. A portable ultra-wideband positioning terminal is provided for personnel, the personnel is preliminarily positioned through the high time resolution characteristics of the ultra-wideband signal, a millimeter wave radar device is used to synchronously detect the three-dimensional space of the surrounding environment of the personnel, and dynamic environment information including obstacles, terrain changes and personnel movement is obtained, a center processing unit adopts a multi-source data fusion algorithm based on Kalman filtering and deep neural network combination, the obtained preliminary positioning data and dynamic environment information are jointly modeled, the errors in the preliminary positioning data are dynamically corrected by analyzing the complementarity and environment correlation of the two types of data, and high-precision positioning results are obtained, the obtained high-precision positioning results are output to a monitoring platform, and accurate tracking and dynamic management of the personnel are realized.

Description

Technical Field

[0001] This invention relates to the field of smart mining technology, and in particular to a method for locating personnel in mines based on the fusion of ultra-wideband and millimeter-wave radar. Background Technology

[0002] The mining environment is characterized by its confined space, complex structure, dense obstacles, and strong enclosure, posing a significant challenge to personnel positioning. Traditional satellite positioning technology is completely ineffective in underground spaces, while positioning methods based on wireless communication, such as Wi-Fi and Bluetooth, are limited by signal coverage and stability, and are easily affected by multipath effects and obstructions caused by walls, metal equipment, etc., making it difficult to meet the positioning accuracy requirements for safe production and emergency rescue.

[0003] While ultra-wideband positioning (UWB) is widely used in indoor positioning due to its high temporal resolution and anti-interference capabilities, it remains susceptible to factors such as non-line-of-sight, multipath reflection, and obstacle obstruction in the complex environment of mines, resulting in large accuracy fluctuations and insufficient robustness. Although millimeter-wave radar possesses three-dimensional spatial perception and a certain penetration capability, potentially providing a means of positioning error compensation, the technology for deep integration of both technologies for high-precision positioning in mines without infrastructure is still immature. Summary of the Invention

[0004] The purpose of this invention is to provide a mine personnel positioning method based on the fusion of ultra-wideband and millimeter-wave radar, which aims to improve the positioning accuracy of mine workers.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The present invention provides a mine personnel positioning method based on the fusion of ultra-wideband and millimeter-wave radar. The method includes S1: equipping personnel with portable ultra-wideband positioning terminals, using the high temporal resolution characteristics of ultra-wideband signals to perform preliminary positioning of personnel, acquiring preliminary positioning data and transmitting it to a central processing unit as the basic data source for fusion positioning; S2: using millimeter-wave radar equipment to simultaneously perform three-dimensional spatial detection of the personnel's surrounding environment, acquiring dynamic environmental information including obstacles, terrain changes, and personnel movement, and transmitting it to the central processing unit to provide environmental feature information for data fusion; S3: the central processing unit adopts a multi-source data fusion algorithm based on the combination of Kalman filtering and deep neural networks to jointly model the preliminary positioning data acquired in step S1 and the dynamic environmental information acquired in step S2. By analyzing the complementarity and environmental correlation of the two types of data, the errors in the preliminary positioning data are dynamically corrected to obtain a high-precision positioning result; S4: outputting the high-precision positioning result obtained in step S3 to a monitoring platform to achieve accurate tracking and dynamic management of personnel.

[0006] In step S1, preliminary positioning of personnel using the high time resolution characteristics of ultra-wideband signals includes: communicating with multiple preset ultra-wideband anchor points via ultra-wideband wireless signals using a portable ultra-wideband positioning terminal equipped with the personnel, and obtaining the signal arrival time difference. The distance from the ultra-wideband positioning terminal to each ultra-wideband anchor point is estimated based on the time difference of arrival. A system of positioning equations was established and solved to obtain a preliminary estimate of the personnel's location. The system of positioning equations is as follows:

[0007] Among them, ultra-wideband anchor points refer to devices that are fixedly installed in the mine to receive signals transmitted by ultra-wideband positioning terminals and assist in positioning. ; This refers to the arrival time of the signal received at the ultra-wideband anchor point; c is the speed of light. Errors introduced by noise interference; ; ( ); and These are the three-dimensional coordinates of the portable ultra-wideband positioning terminal and the positions of multiple preset ultra-wideband anchor points, respectively. This refers to the initial time when a portable ultra-wideband positioning terminal transmits an ultra-wideband signal.

[0008] The least squares method was used to solve the positioning equations to obtain a preliminary estimate of the personnel's location. ;in, .

[0009] In step S2, the three-dimensional spatial detection of the surrounding environment of personnel using millimeter-wave radar equipment includes: the millimeter-wave radar equipment transmits frequency-modulated continuous waves and receives the reflected signals of target points in space, calculates the distance of the target points based on the round-trip time delay of the echo signals; obtains the horizontal angle and elevation angle of the target points by combining the radar's angle resolution capability, calculates the three-dimensional coordinates of the target points, and forms a point cloud set to describe the structure and dynamic changes of the surrounding environment of personnel.

[0010] In step S3, a state vector of a person at a certain moment is defined. This state vector includes the person's three-dimensional position coordinates and velocity components. The state vector is predicted and updated using Kalman filtering to obtain the state value after Kalman filtering. A deep neural network is introduced to perform residual learning correction on the state value after Kalman filtering to obtain the high-precision positioning result.

[0011] When using Kalman filtering for state updates, the observation vectors used include the distance from the ultra-wideband positioning terminal to each ultra-wideband anchor point and the three-dimensional point cloud coordinates obtained by the millimeter-wave radar equipment; where the observation vector refers to the set of multi-source observation data used to correct the Kalman filter predicted state.

[0012] The observation vector is jointly constructed using communication data between the ultra-wideband positioning terminal and the ultra-wideband anchor point, and three-dimensional spatial detection data from the millimeter-wave radar equipment.

[0013] Deep neural networks are gated recurrent unit neural networks.

[0014] In step S4, the movement trajectory of the personnel is generated based on the high-precision positioning results at continuous times; combined with the preset danger zone information, it is determined whether the personnel have entered the high-risk zone, and an alarm is issued when the personnel enter the high-risk zone.

[0015] Step S4 also includes: generating the optimal emergency rescue route based on the high-precision positioning results of personnel and the location of the accident point.

[0016] Compared with the prior art, the beneficial effects of this application are as follows:

[0017] 1. The embodiments of this application provide a mine personnel positioning method based on the fusion of ultra-wideband and millimeter-wave radar. By fusing the preliminary location data of ultra-wideband positioning with the three-dimensional environmental information of millimeter-wave radar, the algorithm combining Kalman filtering and gated recurrent unit neural network is used to dynamically correct the error, effectively overcoming the interference of multipath, obstruction, and non-line-of-sight factors in the complex environment of the mine on single ultra-wideband positioning, and achieving high-precision, stable and reliable personnel positioning.

[0018] 2. Positioning can be completed using only portable ultra-wideband positioning terminals and millimeter-wave radar equipment worn by personnel, without the need for additional ground base stations, beacons and other fixed facilities, which greatly reduces deployment and maintenance costs and enhances adaptability and scalability in scenarios with variable mine structures and limited space.

[0019] 3. The high-precision positioning results output can generate personnel movement trajectories, combine with the judgment of dangerous areas to realize risk warning, and also provide optimal path planning for emergency rescue. It can deeply link mine safety monitoring, dynamic management and emergency response and other businesses, significantly improving the safety assurance level and management efficiency of mine operations. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a mine personnel positioning method based on the fusion of ultra-wideband and millimeter-wave radar provided in an embodiment of this application;

[0021] Figure 2This is a framework diagram of a mine personnel positioning method based on the fusion of ultra-wideband and millimeter-wave radar provided in an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] In embodiments of the invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus 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, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0024] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0025] This application provides a method for locating personnel in mines based on the fusion of ultra-wideband and millimeter-wave radar. For example, it combines... Figure 1 and Figure 2 The methods include:

[0026] S1: Equip personnel with portable ultra-wideband positioning terminals to perform preliminary positioning of personnel using the high time resolution characteristics of ultra-wideband signals, acquire preliminary positioning data and transmit it to the central processing unit as the basic data source for fusion positioning.

[0027] Ultra-wideband (UWB) positioning terminals are portable devices that can transmit and receive ultra-wideband wireless signals to collect personnel location information.

[0028] In step S1, preliminary positioning of personnel using the high time resolution characteristics of ultra-wideband signals includes: communicating with multiple preset ultra-wideband anchor points via ultra-wideband wireless signals using a portable ultra-wideband positioning terminal equipped with the personnel, and obtaining the signal arrival time difference. The distance from the ultra-wideband positioning terminal to each ultra-wideband anchor point is estimated based on the time difference of arrival. A system of positioning equations was established and solved to obtain a preliminary estimate of the personnel's location. The system of positioning equations is as follows:

[0029] Among them, ultra-wideband anchor points refer to devices that are fixedly installed in the mine to receive signals transmitted by ultra-wideband positioning terminals and assist in positioning. ; This refers to the arrival time of the signal received at the ultra-wideband anchor point; c is the speed of light. Errors introduced by noise interference; ; ( ); and These are the three-dimensional coordinates of the portable ultra-wideband positioning terminal and the positions of multiple preset ultra-wideband anchor points. This refers to the initial time when a portable ultra-wideband positioning terminal transmits an ultra-wideband signal.

[0030] It should be understood that in actual mine environment deployment, the number N of ultra-wideband anchor points needs to be adapted to the length, width and complexity of the roadway. Usually, there should be no less than 3 to meet the basic requirements of three-dimensional positioning. For complex areas with many bends and branches, the anchor point density needs to be appropriately increased to improve the reliability of the preliminary positioning data.

[0031] As one possible approach, the least squares method is used to solve the positioning equations to obtain a preliminary estimate of the personnel's location. ;in, When the initial location estimate of the personnel is obtained At the same time, the distance measurement data of each anchor point and the original observation information with timestamps are transmitted to the central processing unit to provide data support for environmental perception and multi-source fusion correction.

[0032] For example, during data transmission, an anti-interference wireless transmission protocol is adopted to ensure that preliminary positioning data can be transmitted completely and in real time to the central processing unit in the strong electromagnetic interference environment of the mine. The transmission frequency is synchronized with the detection frequency of the millimeter-wave radar, usually set to 10-20Hz, to ensure the time consistency of the two types of data.

[0033] S2: Simultaneously conduct three-dimensional spatial detection of the surrounding environment of personnel using millimeter-wave radar equipment, acquire dynamic environmental information including obstacles, terrain changes and personnel movement, and transmit it to the central processing unit to provide environmental feature information for data fusion.

[0034] Among them, millimeter-wave radar equipment refers to equipment that can transmit and receive millimeter-wave signals to detect the surrounding three-dimensional spatial environment and dynamic targets in the mine.

[0035] In step S2, the three-dimensional spatial detection of the surrounding environment of personnel using millimeter-wave radar equipment includes: the millimeter-wave radar equipment transmits frequency-modulated continuous waves and receives the reflected signals of target points in space, calculates the distance of the target points based on the round-trip time delay of the echo signals; obtains the horizontal angle and elevation angle of the target points by combining the radar's angle resolution capability, calculates the three-dimensional coordinates of the target points, and forms a point cloud set to describe the structure and dynamic changes of the surrounding environment of personnel.

[0036] More specifically, in obtaining the initial location estimate of personnel Based on this, millimeter-wave radar equipment is used to perform three-dimensional spatial detection and dynamic perception of the surrounding environment of personnel. The millimeter-wave radar is installed at key nodes in the equipment mine or carried by personnel, and its coordinates are... Millimeter-wave radar transmits at a frequency of A frequency-modulated continuous wave (FM wave) scans different directions in space. For a target point within space, its reflected signal is received by the millimeter-wave radar. Based on the measurement principle of millimeter-wave radar, the distance... Round-trip delay of the echo signal calculate:

[0037]

[0038] in, At the speed of light, This represents all echo points detected by the millimeter-wave radar in a single scan.

[0039] Based on the angular resolution capability of millimeter-wave radar, let the horizontal angle of a certain echo point be... and pitch angle Then the first in space The three-dimensional coordinates of the point cloud are:

[0040] The point cloud set obtained by each scan of the millimeter-wave radar It records the three-dimensional environmental structure and dynamic changes around the personnel. By comparing the point cloud at adjacent time points, it is possible to analyze personnel movement, obstacle dynamics, and spatial changes.

[0041] It should be understood that the detection range of millimeter-wave radar needs to cover a spatial area of ​​5-10 meters around personnel to ensure complete capture of obstacle information that may affect ultra-wideband positioning. As one possible approach, the collected point cloud data is preprocessed, including noise removal and stitching together consecutive frame point clouds. Through algorithms such as ground extraction and obstacle clustering, static obstacles such as walls, equipment, and temporary debris, as well as dynamic targets such as personnel and mobile devices, are separated from the point cloud set, providing targeted environmental features for subsequent error correction.

[0042] S3: The central processing unit adopts a multi-source data fusion algorithm based on Kalman filtering and deep neural networks to jointly model the preliminary positioning data obtained in step S1 and the dynamic environmental information obtained in step S2. By analyzing the complementarity of the two types of data and the correlation with the environment, the error in the preliminary positioning data is dynamically corrected to obtain a high-precision positioning result.

[0043] In step S3, a state vector of a person at a certain moment is defined. This state vector includes the person's three-dimensional position coordinates and velocity components. The state vector is predicted and updated using Kalman filtering to obtain the state value after Kalman filtering. A deep neural network is introduced to perform residual learning correction on the state value after Kalman filtering to obtain the high-precision positioning result.

[0044] For example, let the state vector of a person at time k be... ;in, For three-dimensional position coordinates, For the velocity component, the state of the person is predicted using a classical Kalman filter. The state transition equation is: ;in, Let k be the predicted state value at time k. Here is the state transition matrix. For process noise, Here is the process noise covariance matrix; the state covariance prediction is:

[0045]

[0046] in, To predict the error covariance, This is the error covariance after the previous update.

[0047] When using Kalman filtering for state updates, the observation vectors employed include the distances from the ultra-wideband positioning terminal to each ultra-wideband anchor point and the 3D point cloud coordinates acquired by the millimeter-wave radar equipment. The observation vectors refer to the multi-source observation data set used to correct the Kalman filter-predicted state. These observation vectors are jointly constructed using communication data between the ultra-wideband positioning terminal and the ultra-wideband anchor points, as well as the 3D spatial detection data from the millimeter-wave radar equipment.

[0048] For example, multi-source observation and measurement updates are performed, and the fused observation vector is defined.

[0049]

[0050] in, Let k be the distance from the person to the i-th anchor point measured from UWB. The coordinates of the j-th 3D point cloud obtained by the millimeter-wave radar; the measurement model is...

[0051]

[0052] in, The measurement matrix is ​​derived from the spatial geometric relationships of UWB range observations and millimeter-wave radar point cloud observations. For observation noise; the Kalman gain matrix is ​​calculated as follows:

[0053]

[0054] Where R is the measurement noise covariance matrix; the state is updated using observations:

[0055]

[0056] And update the state covariance

[0057] In the formula, Represents the identity matrix, where the dimension of the identity matrix I is the same as the personnel state vector. The dimensions are consistent.

[0058] As one possible implementation, the deep neural network is a gated recurrent unit neural network. A gated recurrent unit neural network is introduced to perform residual learning correction on the state prediction value, and a correction network is defined. The input is the current Kalman filter prediction state. and observation vector The output is a nonlinear residual correction. :

[0059]

[0060] The final fusion localization result is

[0061]

[0062] in This outputs a high-precision positioning result after Kalman filtering and neural network correction.

[0063] Gated loop unit parameters End-to-end training was performed by collecting historical data, and high-precision locations were manually labeled. Minimize positioning error loss: The training dataset for the gated recurrent unit neural network covers ultra-wideband positioning data, millimeter-wave radar point cloud data, and corresponding high-precision ground truth locations under different scenarios in the mine (such as straight tunnels, curves, and areas with dense equipment) and different personnel movement states (stationary, walking, and walking with tools). The validation set is used to monitor the model's generalization ability in real time to avoid overfitting.

[0064] S4: Output the high-precision positioning results obtained in step S3 to the monitoring platform to achieve accurate tracking and dynamic management of personnel.

[0065] In step S4, the movement trajectory of the personnel is generated based on the high-precision positioning results at continuous times; combined with the preset danger zone information, it is determined whether the personnel have entered the high-risk zone, and an alarm is issued when the personnel enter the high-risk zone.

[0066] For example, the final fused positioning output is This indicates that personnel are at all times Three-dimensional spatial coordinates; positioning results at consecutive time points. The movement trajectory of the workers:

[0067]

[0068] The precise tracking and monitoring system can display the movement trajectory of personnel in real time. Combining mine map with digital twin mine model The system dynamically maps the position, movement path, and dwell area of ​​personnel in the underground space; based on the trajectory differential, the personnel's velocity and acceleration can be calculated in real time:

[0069]

[0070]

[0071] in, This represents the sampling time interval.

[0072] Using trajectory points and danger zones Based on spatial relationships, the formula for determining whether personnel have entered a high-risk area is as follows:

[0073]

[0074] when , When the preset safe distance threshold is not met, the system will automatically issue an alarm and record the event.

[0075] Step S4 also includes: generating the optimal emergency rescue route based on the high-precision positioning results of personnel and the location of the accident point.

[0076] As one possible approach, linking real-time location results with the mine management platform can enable personnel distribution statistics: counting the number of personnel in each work area at any given time.

[0077]

[0078] in, As an index function, determine the first Is the person in the first partition Inside;

[0079] Location results It can provide real-time optimal route recommendations for emergency rescue, assuming the accident point is... Shortest rescue route Dijkstra's algorithm can be used to... The format is automatically generated.

[0080] This application innovatively integrates ultra-wideband positioning (UWB) and millimeter-wave radar environmental perception technologies to construct a multi-source data dynamic error compensation and adaptive correction mechanism. While UWB positioning is susceptible to multipath interference, non-line-of-sight interference, and occlusion in complex environments, leading to larger positioning errors, this invention leverages the high-resolution perception capabilities of millimeter-wave radar for three-dimensional obstacles and dynamic environments. Combined with intelligent fusion algorithms such as Kalman filtering and deep neural networks, it achieves dynamic correction and highly robust, accurate positioning of personnel. Compared to existing single UWB, Wi-Fi, or Bluetooth positioning technologies, this invention effectively resists multipath and occlusion interference in extreme environments such as mines and tunnels, achieving centimeter-level continuous, high-precision, and low-latency personnel positioning, significantly improving the system's accuracy, stability, and applicability.

[0081] The positioning system of this invention does not rely on traditional ground base stations, beacons, or other fixed infrastructure. It only requires personnel to wear portable ultra-wideband positioning terminals and millimeter-wave radar equipment to achieve high-precision personnel positioning and real-time environmental awareness. Compared to existing technologies that require extensive deployment and maintenance of base stations, beacons, reference points, and other infrastructure, this invention significantly reduces deployment and maintenance costs. The system can be flexibly deployed according to actual site needs, greatly enhancing its adaptability and scalability in scenarios such as mine structure changes, space constraints, and complex environments. It is particularly suitable for mines and underground spaces with high mobility, temporary operations, or limited infrastructure deployment.

[0082] The high-precision, continuous 3D personnel motion trajectory output by this invention can not only be directly integrated into mine safety monitoring and intelligent management platforms, but also deeply linked with various business systems such as work processes, emergency rescue, and risk control. The system can determine personnel distribution and dynamic status in real time, automatically identify abnormal behavior and intrusion into dangerous areas, and provide accurate, real-time location information support for emergency rescue route planning and personnel evacuation. Compared with existing passive monitoring and low-resolution personnel positioning methods, this invention significantly improves the safety assurance capabilities, emergency response efficiency, and management intelligence level of mine operations, laying a solid technical foundation for the construction of smart mines and the digital transformation of mines, and has broad application prospects.

[0083] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0084] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for locating personnel in mines based on the fusion of ultra-wideband and millimeter-wave radar, characterized in that, include: S1: Equip personnel with portable ultra-wideband positioning terminals to perform preliminary positioning of personnel using the high temporal resolution characteristics of ultra-wideband signals, acquire preliminary positioning data and transmit it to the central processing unit as the basic data source for fusion positioning; S2: Simultaneously use millimeter-wave radar equipment to perform three-dimensional spatial detection of the surrounding environment of personnel, obtain dynamic environmental information including obstacles, terrain changes and personnel movement, and transmit it to the central processing unit to provide environmental feature information for data fusion. S3: The central processing unit adopts a multi-source data fusion algorithm based on Kalman filtering and deep neural network to jointly model the preliminary positioning data obtained in step S1 and the dynamic environmental information obtained in step S2. By analyzing the complementarity and environmental correlation of the two types of data, the error in the preliminary positioning data is dynamically corrected to obtain a high-precision positioning result. In step S3, a state vector of a person at a certain moment is defined. This state vector includes the person's three-dimensional position coordinates and velocity components. Kalman filtering is used to predict and update this state vector to obtain the state value after Kalman filtering. A deep neural network is introduced to perform residual learning correction on the state value after Kalman filtering to obtain the high-precision positioning result. When updating the state using Kalman filtering, the observation vector used includes the distance from the ultra-wideband positioning terminal to each ultra-wideband anchor point and the three-dimensional point cloud coordinates obtained by the millimeter-wave radar equipment. The observation vector refers to the multi-source observation data set used to correct the Kalman filter predicted state. The deep neural network is a gated recurrent unit neural network. A gated recurrent unit neural network is introduced to learn and correct the residual error of the state prediction value, and a correction network is defined , the input is the current Kalman filter prediction state and the observation vector , and the output is the nonlinear residual correction value : The final fusion localization result is wherein is a high-precision positioning output corrected by Kalman filtering and neural network S4: Output the high-precision positioning results obtained in step S3 to the monitoring platform to achieve accurate tracking and dynamic management of personnel.

2. The mine personnel positioning method based on the fusion of ultra-wideband and millimeter wave radars according to claim 1, characterized in that, In step S1, preliminary positioning of personnel using the high time resolution characteristics of ultra-wideband signals includes: communicating with multiple preset ultra-wideband anchor points via ultra-wideband wireless signals using a portable ultra-wideband positioning terminal equipped with the personnel, and obtaining the signal arrival time difference. The distance from the ultra-wideband positioning terminal to each ultra-wideband anchor point is estimated based on the time difference of arrival. A system of positioning equations was established and solved to obtain a preliminary estimate of the personnel's location. The system of positioning equations is as follows: Among them, ultra-wideband anchor points refer to devices that are fixedly installed in the mine to receive signals transmitted by ultra-wideband positioning terminals and assist in positioning. ; This refers to the arrival time of the signal received at the ultra-wideband anchor point; c is the speed of light. Errors introduced by noise interference; ; ( ); and These are the three-dimensional coordinates of the portable ultra-wideband positioning terminal and the positions of multiple preset ultra-wideband anchor points, respectively. This refers to the initial time when a portable ultra-wideband positioning terminal transmits an ultra-wideband signal.

3. The method for locating personnel in mines based on the fusion of ultra-wideband and millimeter-wave radar according to claim 1, characterized in that, In step S2, the three-dimensional spatial detection of the surrounding environment of personnel using millimeter-wave radar equipment includes: the millimeter-wave radar equipment transmits frequency-modulated continuous waves and receives the reflected signals of target points in space, calculates the distance of the target points based on the round-trip time delay of the echo signals; obtains the horizontal angle and elevation angle of the target points by combining the radar's angle resolution capability, calculates the three-dimensional coordinates of the target points, and forms a point cloud set to describe the structure and dynamic changes of the surrounding environment of personnel.

4. The mine personnel positioning method based on the fusion of ultra-wideband and millimeter wave radars according to claim 1, characterized in that, The observation vector is jointly constructed using communication data between the ultra-wideband positioning terminal and the ultra-wideband anchor point, and three-dimensional spatial detection data from the millimeter-wave radar equipment.

5. The mine personnel positioning method based on the fusion of ultra-wideband and millimeter wave radars according to claim 1, characterized in that, In step S4, the movement trajectory of the personnel is generated based on the high-precision positioning results at continuous times; combined with the preset danger zone information, it is determined whether the personnel have entered the high-risk zone, and an alarm is issued when the personnel enter the high-risk zone.

6. The mine personnel positioning method based on the fusion of ultra-wideband and millimeter wave radars according to claim 5, characterized in that, Step S4 also includes: generating the optimal emergency rescue route based on the high-precision positioning results of personnel and the location of the accident point.

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