Mine tight combination positioning method based on environment self-adaption

Abstract

The invention relates to the technical field of high-precision positioning, in particular to a mine tight combination positioning method based on environment self-adaption, and aims to solve the problem of efficient positioning of personnel and equipment in a complex and limited space under a mine. The method comprises the step of acquiring a time difference of arrival measurement value through the ultra-wideband positioning tag and the positioning base station. Chi-square testing is carried out based on the innovation statistics of the extended Kalman filtering, and whether the environment is a sight distance environment is judged according to comparison between the innovation statistics and a threshold value. If the innovation statistic is lower than a threshold value, determining that the environment is a sight distance environment, and if the innovation statistic is higher than or equal to the threshold value, determining that the environment is a non-sight-distance environment. For a sight distance environment, high-precision coordinate estimation is obtained by adopting a time difference of arrival positioning algorithm, and a positioning result is optimized through extended Kalman filtering fusion. And activating the inertial measurement unit, constructing an ultra wide band / inertial measurement unit tight combination extended Kalman filtering model for a non-line-of-sight environment, and outputting a positioning result.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of high-precision positioning technology, and particularly relates to a mine tight integrated positioning method based on environment adaptation. BACKGROUND

[0002] In the complex and limited space of a coal mine, high-precision and low-power positioning of personnel and equipment is crucial to safety production. In the mine environment, the ultra-wideband signal is often in a dynamic switching state of line-of-sight and non-line-of-sight mixed: the ultra-wideband technology has high positioning accuracy in the line-of-sight environment, but in the non-line-of-sight environment, the signal shielding or reflection will cause the positioning accuracy to decrease seriously. The inertial measurement unit can independently calculate the pose change, but there is a problem of error accumulation with time, which needs external information correction.

[0003] The existing combination positioning technology of ultra-wideband and inertial measurement unit mostly adopts a continuous fusion strategy. In the mine line-of-sight and non-line-of-sight mixed scene, there is a prominent contradiction between energy consumption and accuracy. In the line-of-sight environment, the ultra-wideband can provide high-precision positioning, and the inertial measurement unit with continuous work has limited improvement on positioning accuracy, but it becomes the main source of the overall energy consumption of the tag device due to high power consumption, which seriously restricts the endurance time of the mine positioning device, increases the maintenance cost and safety hazards. SUMMARY

[0004] The present application relates to the field of high-precision positioning technology, and particularly relates to a mine tight integrated positioning method based on environment adaptation.

[0005] To achieve the above object, the present application adopts the following technical scheme: The present application provides a mine tight combination positioning method based on environment self-adaption, comprising S1: obtaining the time difference of arrival measurement value through the ultra-wideband positioning tag and the positioning base station. The time difference of arrival measurement value is the time difference of the same ultra-wideband positioning tag signal received by different base stations, and the time difference indicates the distance difference between the ultra-wideband positioning tag and different base stations. S2: performing chi-square test on the innovation statistics of the extended Kalman filter, and determining whether it is a line-of-sight environment according to the size comparison of the innovation statistics and the threshold. If the innovation statistics is lower than the threshold, it is determined as a line-of-sight environment, and if the innovation statistics is higher than or equal to the threshold, it is determined as a non-line-of-sight environment. The innovation statistics is a statistical quantity for reflecting the difference between the observation value and the predicted value, which is calculated based on the innovation vector and the covariance matrix of the extended Kalman filter. S3: for the line-of-sight environment, the time difference of arrival positioning algorithm is used to obtain high-precision coordinate estimation, and the positioning result is optimized by fusing the extended Kalman filter. The data acquisition module of the inertial measurement unit for collecting motion data such as acceleration and angular velocity enters the sleep state. S4: activating the inertial measurement unit, for the non-line-of-sight environment, constructing an ultra-wideband / inertial measurement unit tight combination extended Kalman filter model, and outputting the positioning result. The ultra-wideband / inertial measurement unit tight combination extended Kalman filter model is a model that directly inputs the original measurement value of the ultra-wideband and the pre-integration data of the inertial measurement unit into the extended Kalman filter for fusion.

[0006] The mine tight combination positioning method based on environment self-adaption provided by the embodiments of the present application can dynamically distinguish between line-of-sight and non-line-of-sight environments and switch the positioning mode through chi-square test based on the innovation statistics of the extended Kalman filter. Different line-of-sight environments are processed accordingly. In the line-of-sight environment, high-precision coordinates can be obtained by using the time difference of arrival positioning algorithm combined with the extended Kalman filter optimization. In the non-line-of-sight environment, the inertial measurement unit is activated to construct a tight combination model, and the original measurement value of the ultra-wideband and the pre-integration data of the inertial measurement unit are fused, which makes up for the insufficient positioning accuracy of the ultra-wideband, solves the positioning limitations of a single technology in complex environments, and improves the overall positioning reliability. In the line-of-sight environment, the ultra-wideband itself can provide high-precision positioning, so the inertial measurement unit data acquisition module is put to sleep at this time to avoid the high power consumption caused by its continuous work, reduce the contribution of the overall energy consumption of the tag device, effectively solve the contradiction between energy consumption and precision in the traditional continuous fusion strategy in the line-of-sight environment, prolong the endurance time of the battery-powered mine positioning device, and reduce the maintenance cost and safety hazards.

[0007] In some embodiments, obtaining the time difference of arrival measurement value comprises: recording the coordinates of the base station, the coordinates of the ultra-wideband positioning tag, and the time of receiving the signal by the base station, and obtaining the time difference of arrival measurement value by combining the distance formula between the ultra-wideband positioning tag and the base station.

[0008] In some embodiments, the innovation vector is equal to the difference between the actual observation vector at the current time and the observation prediction vector based on the state prediction at the previous time. The actual observation vector is a vector composed of the time difference of arrival measurements obtained in step S1.

[0009] In some embodiments, the covariance matrix of the innovation vector is calculated as follows: k = H k P k|k-1 + R k . Wherein H k is the observation Jacobian matrix, P k|k-1 is the prediction covariance matrix, and R k is the observation noise covariance matrix. is the transpose matrix of H k .

[0010] In some embodiments, the innovation statistics are calculated as follows: . Wherein, is the innovation vector, is the transpose vector of H , is the inverse matrix of H .

[0011] In some embodiments, the time difference of arrival positioning algorithm is used to obtain high-precision coordinate estimation, including: obtaining an initial coordinate estimation by the Chan algorithm, and then iteratively optimizing the initial coordinate estimation based on Taylor series expansion to obtain a high-precision coordinate.

[0012] In some embodiments, the extended Kalman filter fuses and optimizes the positioning result in the following manner: the optimized high-precision coordinate estimation is input as an observation value into the state update module of the extended Kalman filter, and the state prediction value generated by the extended Kalman filter based on the state prediction model is weighted and fused.

[0013] In some embodiments, after the inertial measurement unit is activated, the accelerometer data and the gyroscope data of the inertial measurement unit are obtained, and pre-integration processing is performed to obtain a relative pose change. The relative pose change includes a position increment, a velocity increment, and an attitude increment.

[0014] In some embodiments, an ultra-wideband / inertial measurement unit tightly coupled extended Kalman filter model is constructed to output a positioning result, including: obtaining a state vector of the ultra-wideband / inertial measurement unit tightly coupled extended Kalman filter model. The state vector of the ultra-wideband / inertial measurement unit tightly coupled extended Kalman filter model includes: an ultra-wideband positioning tag position, a velocity, an attitude, and an inertial measurement unit bias. The inertial measurement unit bias is a bias in the measurement value of the inertial measurement unit.

[0015] In some embodiments, the observation input of the tightly combined extended Kalman filter model of ultra-wideband and inertial measurement unit is the original arrival time difference measurement value and the inertial measurement unit pre-integrated component, and the positioning result and the updated inertial measurement unit zero bias estimate are output through fusion processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of a mine tight combination positioning method based on environmental adaptation provided by an embodiment of the present application; Figure 2 This is a flow chart of an environmental chi-square detection provided by an embodiment of the present application; Figure 3 This is a positioning flow chart in a line-of-sight environment provided by an embodiment of the present application; Figure 4 This is a flowchart of positioning in a non-line-of-sight environment provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] In the description of the invention, it should be understood that the terms "upper," "lower," "left," "right," "front," "back," "inner," "outer," and the like, indicating directions or positional relationships, are based on the directions or relative positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of the invention and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific direction, be constructed, or operate in a specific direction. Therefore, they should not be construed as limitations on the invention. Unless otherwise specified, the above-mentioned directions may be flexibly set in actual application, provided that the relative positional relationships shown in the accompanying drawings are met.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0020] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connected," and "communicated" should be understood broadly. For example, they may refer to fixed connections, detachable connections, or integral connections. They may be directly connected, indirectly connected through an intermediary, or internally connected between two components. Those skilled in the art will understand the specific meanings of these terms in the present invention based on specific circumstances.

[0021] In embodiments of the present invention, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of other identical elements in the process, article, or apparatus comprising the element.

[0022] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0023] As mentioned in the background technology, in order to address the deficiencies of the existing technology, the embodiment of the present application provides a mine tight combination positioning method based on environmental adaptation, for example, Figure 1 The method includes: S1: Obtain the arrival time difference measurement value through the ultra-wideband positioning tag and the positioning base station.

[0024] The arrival time difference measurement value is the time difference between different base stations receiving the signal sent by the same ultra-wideband positioning tag, and the time difference indicates the distance difference between the ultra-wideband positioning tag and different base stations.

[0025] As a possible implementation, multiple ultra-wideband (UWB) base stations are deployed within a pre-defined area within a coal mine. UWB base stations are signal receiving devices deployed within a pre-defined area within the coal mine. Their coordinates are known, so they are also called positioning base stations. Their primary function is to receive signals from ultra-wideband positioning tags and determine the distance difference between the tags and the base station, providing data for subsequent TDOA positioning and ranging algorithm measurements.

[0026] In some embodiments, the TDOA measurement is obtained by recording coordinates of the base station, coordinates of the UWB positioning tag, and time of signal received by the base station, and combining a distance formula between the UWB positioning tag and the base station.

[0027] For example, the coordinates of the base station are recorded , the coordinates of the tag are , and the time of signal received by the base station is , the distance formula between the UWB tag and the base station is , the measured distance between the UWB base station and the tag is , and the TDOA measurement is solved as , wherein .

[0028] The obtained distance difference equation set is as follows, , wherein c is the speed of light.

[0029] S2: Perform chi-square test on the innovation statistics based on the extended Kalman filter, and determine whether it is a line-of-sight environment according to the size comparison between the innovation statistics and the threshold. If the innovation statistics is lower than the threshold, it is determined to be a line-of-sight environment, and if the innovation statistics is higher than or equal to the threshold, it is determined to be a non-line-of-sight environment.

[0030] The innovation statistics is a statistical quantity calculated based on the innovation vector and the covariance matrix of the extended Kalman filter, which is used to reflect the difference between the actual observation value (TDOA measurement) and the predicted value.

[0031] The innovation statistics can effectively reflect the difference between the actual observation value (TDOA measurement) and the predicted value. Through chi-square test and threshold comparison, the change in the propagation characteristics of the ultra-wideband signal in the line-of-sight and non-line-of-sight environments can be captured sensitively, so as to accurately distinguish between the two environments and perform targeted optimization, providing a reliable basis for subsequent adaptive switching of the positioning mode.

[0032] When the environment is line-of-sight, the efficient positioning mode relying only on ultra-wideband is determined and enabled in time, and its high-precision advantage is fully utilized. When the environment changes to non-line-of-sight, the tight combination mode of ultra-wideband and inertial measurement unit is quickly identified and switched to, and the inertial measurement unit is used to compensate for the defects of the degraded performance of ultra-wideband, thereby avoiding the problem of sudden drop in positioning accuracy of a single mode in complex environment and improving the adaptability and stability of the overall positioning system.

[0033] In some embodiments, the innovation vector is equal to the difference between the actual observation vector at the current time and the observation prediction vector predicted based on the state at the previous time. The actual observation vector is a vector composed of the TDOA measurements obtained in step S1.

[0034] In the update stage of the extended Kalman filter (EKF), the innovation vector at the current time is calculated , whose expression is: , wherein: represents the actual observation vector at the current moment (the time difference of arrival measurement value, i.e., TDOA measurement value obtained in step S1 constitutes), represents the observation prediction vector obtained based on the state prediction at the k-1 moment.

[0035] In some embodiments, the covariance matrix of the innovation vector is calculated in the following manner: k = H k P k|k-1 + R k . Wherein, H k is the observation Jacobian matrix, P k|k-1 is the prediction covariance matrix, and R k is the observation noise covariance matrix. is the transpose matrix of H k .

[0036] In some embodiments, the innovation statistic is calculated in the following manner: . Wherein, is the innovation vector, is the transpose vector of , and is the inverse matrix of .

[0037] As a possible implementation, the threshold for comparison is a preset chi-square test threshold. The calculated innovation statistic is compared with a preset chi-square test threshold . If , it is determined that the current propagation environment is a line-of-sight environment, and if > , it is determined that the current propagation environment is a non-line-of-sight environment. Wherein: is a preset significance level, representing the probability of misjudging as a non-line-of-sight environment in a line-of-sight environment, is the dimension of the innovation vector , which is equal to the number of observation quantities, and the process is as shown in Figure 2 .

[0038] S3: for the line-of-sight environment, a time difference of arrival positioning algorithm is used to obtain high-precision coordinate estimation, and the positioning result is fused and optimized by an extended Kalman filter.

[0039] Refer to Figure 3In some embodiments, a time difference of arrival positioning algorithm is used to obtain a high-precision coordinate estimate, including: obtaining an initial coordinate estimate through a Chan algorithm, and then iteratively optimizing the initial coordinate estimate based on a Taylor series expansion to obtain a high-precision coordinate.

[0040] The method provided in this embodiment first analyzes the time difference of arrival (TDOA) measurements using the Chan algorithm to obtain an initial estimate of the tag's location. Based on this initial value, a Taylor series expansion is performed on the TDOA observation equation, retaining the first-order terms. This linearized error equation is then constructed, and the coordinate correction is solved using the least squares method. It is then iterated and optimized until the correction is less than a convergence threshold, ultimately achieving a high-precision coordinate estimate.

[0041] For example, the Chan algorithm is used to analyze the TDOA measurement value: the obtained TDOA measurement value Input Chan algorithm to solve the initial coordinate estimate of the tag position Based on Taylor series expansion iterative optimization, the initial coordinates are estimated using the Chan algorithm. As the initial value of the iteration. Perform Taylor series expansion on the TDOA observation equation at the initial value of the iteration, retain the first-order term, and establish the linearized error equation. Use the least squares method to solve the linearized error equation and obtain the coordinate correction value , update the coordinate estimate: Repeat the Taylor series expansion iterative optimization steps until the coordinate correction amount is less than the preset convergence threshold, and obtain the optimized high-precision coordinate estimate .

[0042] In a line-of-sight environment, the data acquisition module of the inertial measurement unit used to collect motion data such as acceleration and angular velocity enters a sleep state and stops collecting accelerometer data and gyroscope data to reduce power consumption.

[0043] In some embodiments, the extended Kalman filter fuses the optimized positioning results in the following manner: inputting the optimized high-precision coordinate estimate as an observation value into the state update module of the extended Kalman filter, and performing weighted fusion with the state prediction value generated by the extended Kalman filter based on the state prediction model.

[0044] As a possible implementation method, the optimized high-precision coordinate estimate is input as the observation value into the state update module of the extended Kalman filter (EKF). The EKF combines the predicted value generated by the state prediction model for weighted fusion to further suppress instantaneous noise and improve the stability of the positioning results.

[0045] S4: Activate the inertial measurement unit, build an ultra-wideband / inertial measurement unit tightly combined extended Kalman filter model for non-line-of-sight environments, and output the positioning results.

[0046] The ultra-wideband / inertial measurement unit tightly coupled extended Kalman filter model is a model that directly inputs the original measurement value of the ultra-wideband and the pre-integration data of the inertial measurement unit into the extended Kalman filter for fusion. The original measurement value of the ultra-wideband refers to the time difference of arrival measurement value obtained by the ultra-wideband positioning tag and the positioning base station.

[0047] Referring to Figure 4 In some embodiments, in the case of judging a non-line-of-sight environment, the inertial measurement unit is activated dynamically, pre-integration data is generated, and zero bias initialization is performed. After activating the inertial measurement unit, the accelerometer data and gyroscope data of the inertial measurement unit are obtained, and pre-integration processing is performed to obtain the relative pose change amount. The relative pose change amount includes position increment, velocity increment and attitude increment.

[0048] For example, the accelerometer data and gyroscope data collected by the inertial measurement unit are combined with the zero bias data of the inertial measurement unit, that is, the accelerometer zero bias and the gyroscope zero bias . In a continuous time interval , pre-integration processing is performed to calculate the relative pose change amount in this time period, including position increment , velocity increment and attitude increment .

[0049] In some embodiments, the ultra-wideband / inertial measurement unit tightly coupled extended Kalman filter model is constructed, and the positioning result is output, including: obtaining the state vector of the ultra-wideband / inertial measurement unit tightly coupled extended Kalman filter model. The state vector of the ultra-wideband / inertial measurement unit tightly coupled extended Kalman filter model includes: the position, velocity and attitude of the ultra-wideband positioning tag and the zero bias of the inertial measurement unit. The zero bias of the inertial measurement unit is the bias in the measurement value of the inertial measurement unit.

[0050] For example, the ultra-wideband / inertial measurement unit tightly coupled extended Kalman filter model is constructed, and the state vector of the extended Kalman filter is defined first: . Wherein: , v, is the position / velocity / attitude of the tag, , is the zero bias of the inertial measurement unit.

[0051] In some embodiments, the observation input of the ultra-wideband / inertial measurement unit tightly coupled extended Kalman filter model is the original time difference of arrival measurement value and the pre-integration amount of the inertial measurement unit, and the positioning result and the updated zero bias estimation value of the inertial measurement unit are output through fusion processing.

[0052] The acquired raw TDOA measurements and the obtained inertial measurement unit pre-integration quantities , , are taken as observation inputs of the extended Kalman filter; in the state update stage of the extended Kalman filter, the raw TDOA observations and the pre-integration data of the inertial measurement unit are fused simultaneously, the position error and the inertial measurement unit bias error are jointly estimated by the ultra-wideband observations, and the inertial measurement unit bias drift is dynamically compensated. The updated bias estimation value , is updated to the state vector of the extended Kalman filter for pre-integration calculation in the next period.

[0053] The mine tight combination positioning method based on environment self-adaptation provided by the embodiments of the present application can dynamically distinguish the line-of-sight and non-line-of-sight environments and switch the positioning modes through the chi-square test based on the innovation statistics of the extended Kalman filter. Different line-of-sight environments are processed accordingly. In the line-of-sight environment, high-precision coordinates can be obtained by using the time difference of arrival positioning algorithm combined with the optimization of the extended Kalman filter. In the non-line-of-sight environment, the inertial measurement unit is activated to construct a tight combination model, and the ultra-wideband raw measurement values and the inertial measurement unit pre-integration data are fused to make up for the insufficient positioning accuracy of the ultra-wideband, solve the positioning limitations of a single technology in a complex environment, and improve the overall positioning reliability. In the line-of-sight environment, the ultra-wideband itself can provide high-precision positioning, so the inertial measurement unit data acquisition module is put into hibernation to avoid high power consumption caused by continuous work, reduce the contribution of the overall energy consumption of the tag device, effectively solve the contradiction between energy consumption and accuracy in the traditional continuous fusion strategy in the line-of-sight environment, prolong the endurance time of the battery-powered mine positioning device, and reduce the maintenance cost and safety hazards.

[0054] In the description of the present specification, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0055] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A mine tight combination positioning method based on environmental adaptation, characterized in that: include: S1: Obtain the time difference of arrival measurement value through the ultra-wideband positioning tag and the positioning base station; The arrival time difference measurement value is the time difference between different base stations receiving the signal sent by the same ultra-wideband positioning tag, and the time difference indicates the distance difference between the ultra-wideband positioning tag and different base stations; S2: Perform a chi-square test based on the innovation statistic of the extended Kalman filter, and determine whether it is a line-of-sight environment based on the comparison of the innovation statistic with the threshold; if the innovation statistic is lower than the threshold, it is determined to be a line-of-sight environment; if the innovation statistic is higher than or equal to the threshold, it is determined to be a non-line-of-sight environment; wherein the innovation statistic is a statistic calculated based on the innovation vector and covariance matrix of the extended Kalman filter to reflect the difference between the observed value and the predicted value; S3: In the line-of-sight environment, a time difference of arrival positioning algorithm is used to obtain high-precision coordinate estimates, and the positioning results are optimized through extended Kalman filtering fusion. In particular, the data acquisition module of the inertial measurement unit for collecting motion data such as acceleration and angular velocity enters a sleep state; S4: Activate the inertial measurement unit, construct an ultra-wideband / inertial measurement unit tightly combined extended Kalman filter model for the non-line-of-sight environment, and output a positioning result; wherein the ultra-wideband / inertial measurement unit tightly combined extended Kalman filter model is a model that directly inputs the original measurement value of the ultra-wideband and the pre-integrated data of the inertial measurement unit into the extended Kalman filter for fusion.

2. The mine tight combination positioning method based on environmental adaptation according to claim 1 is characterized in that: The obtaining of the arrival time difference measurement value includes: The coordinates of the base station, the coordinates of the ultra-wideband positioning tag, and the time when the base station receives the signal are recorded, and the arrival time difference measurement value is obtained by combining the distance formula between the ultra-wideband positioning tag and the base station.

3. The mine tight combination positioning method based on environmental adaptation according to claim 1 is characterized in that: The innovation vector is equal to the difference between the actual observation vector at the current moment and the observation prediction vector obtained based on the state prediction at the previous moment; wherein the actual observation vector is a vector composed of the arrival time difference measurement values ​​obtained in step S1.

4. The mine tight combination positioning method based on environmental adaptation according to claim 3 is characterized in that: The covariance matrix of the innovation vector is calculated as follows: k =H k P k|k-1 +R k ; Among them, H k is the observation Jacobian matrix, P k|k-1 is the prediction covariance matrix, R k is the observation noise covariance matrix; H k The transposed matrix of .

5. The mine tight combination positioning method based on environmental adaptation according to claim 4 is characterized in that: The calculation method of the innovation statistic is: ; in, is the innovation vector, for The transposed vector of for The inverse matrix of .

6. The mine tight combination positioning method based on environmental adaptation according to claim 1 is characterized in that: The method of obtaining a high-precision coordinate estimate by using a time difference of arrival positioning algorithm includes obtaining an initial coordinate estimate by using a Chan algorithm, and then iteratively optimizing the initial coordinate estimate based on a Taylor series expansion to obtain a high-precision coordinate.

7. The mine tight combination positioning method based on environmental adaptation according to claim 6 is characterized in that: The method of fusing the optimized positioning result of the extended Kalman filter includes: inputting the optimized high-precision coordinate estimate as an observation value into the state update module of the extended Kalman filter, and performing weighted fusion with the state prediction value generated by the extended Kalman filter based on the state prediction model.

8. The mine tight combination positioning method based on environmental adaptation according to claim 1 is characterized in that: After the inertial measurement unit is activated, accelerometer data and gyroscope data of the inertial measurement unit are acquired, and pre-integration processing is performed to obtain a relative posture change; wherein the relative posture change includes a position increment, a velocity increment, and an attitude increment.

9. The mine tight combination positioning method based on environmental adaptation according to claim 8 is characterized in that: The method of constructing an ultra-wideband / inertial measurement unit tightly combined extended Kalman filter model and outputting a positioning result includes: obtaining a state vector of the ultra-wideband / inertial measurement unit tightly combined extended Kalman filter model; the state vector of the ultra-wideband / inertial measurement unit tightly combined extended Kalman filter model includes: an ultra-wideband positioning tag position, speed, attitude, and an inertial measurement unit zero bias; wherein the inertial measurement unit zero bias is a deviation in an inertial measurement unit measurement value.

10. The mine tight combination positioning method based on environmental adaptation according to claim 9 is characterized in that: The observation inputs of the tightly combined extended Kalman filter model of ultra-wideband and inertial measurement unit are the original arrival time difference measurement value and the inertial measurement unit pre-integrated component, and the positioning result and the updated inertial measurement unit zero bias estimation value are output through fusion processing.

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