Indoor and outdoor seamless positioning method based on UWB / GNSS / IMU

Abstract

The invention discloses a method for seamless indoor and outdoor positioning based on UWB / GNSS / IMU, and the steps are as follows: S1: deploy multiple UWB base stations to collect UWB data, collect IMU data through an IMU system, and collect outdoor GNSS data; S2: establish a fusion positioning model, fuse UWB data and IMU data, or fuse with GNSS data; then use the fusion filter algorithm to extend the Kalman filter EKF to update data, so as to achieve seamless switching and high-precision positioning of indoor and outdoor positioning; S3: optimize the parameters and switching strategy of the fusion filter algorithm according to the different characteristics of indoor and outdoor environments to improve positioning accuracy and switching efficiency; S4: monitor and correct positioning errors in real time, if the data is wrong positioning, input the data into the inertial navigation error model; if the data is correct positioning, output the positioning result. The positioning method realizes seamless indoor and outdoor positioning with high positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of indoor and outdoor positioning technology, and in particular to a UWB / GNSS / IMU-based indoor and outdoor seamless positioning method. Background Art

[0002] With the continuous development of society, mobile Internet and the Internet of Things have become an indispensable part of modern society. In this process, location information has become one of the most basic and key elements. Especially for more and more sophisticated industry needs, high-precision location information has become crucial because it can bring higher use value and efficiency improvement to all walks of life.

[0003] Outdoor positioning systems, such as GPS and GNSS, have been studied and widely used since the 1960s. These systems have greatly improved people's travel experience, making navigation and location services a part of daily life. However, people spend most of their time indoors, and satellite navigation system signals are difficult to penetrate buildings, resulting in the inability to obtain stable and accurate positioning information indoors and the inability to achieve a precise positioning effect when entering indoors.

[0004] At present, indoor positioning technology still faces many problems that need to be solved and needs further in-depth research. Compared with the open outdoor environment, the indoor environment has obstacles such as walls and furniture, and the space is more narrow, which causes the positioning signal to penetrate the obstacles during the indoor propagation process, resulting in signal attenuation or signal reflection, resulting in non-line-of-sight propagation errors and indoor multipath fading, which leads to inaccurate positioning information. At the same time, in indoor positioning, the positioning technology's positioning accuracy, anti-interference, penetration, layout complexity, equipment cost and other factors also need to be considered.

[0005] Therefore, the study of indoor positioning technology is not only an urgent need, but also the positioning problem in the transition area between indoor and outdoor is particularly important. With the improvement of living standards and the advancement of construction technology, the demand for indoor positioning in all walks of life is increasing. Indoor positioning can improve the experience of consumers in the service industry, improve logistics efficiency for large warehousing companies, and in emerging economic fields such as industrial AGV robots and special robots, high-precision indoor positioning systems can bring more opportunities for automation and intelligence. It is particularly worth emphasizing that the positioning problem in the transition area between indoor and outdoor involves a smooth transition under different signal environments, which is crucial to ensure continuous location information. Indoor positioning technology has a wide range of application prospects in different fields and has far-reaching significance for improving work efficiency and quality of life. Therefore, studying the positioning problem in the transition area between indoor and outdoor to achieve seamless connection between indoor and outdoor positioning will bring greater convenience and opportunities to society.

[0006] Location-based services have gained enormous popularity, which has led to the continuous development and enhancement of indoor and outdoor positioning technologies. The global navigation satellite system (GNSS) is the main technology for outdoor positioning services. It can provide accurate information such as geographic location and driving speed, which has brought great convenience to people's daily lives. However, the signal strength of GNSS will be significantly reduced when the satellite geometry is blocked or poorly arranged, and it will be seriously affected by the multipath effect. The performance of GNSS will be greatly reduced, and it is difficult to obtain satisfactory positioning accuracy. In terms of indoor positioning, technologies such as wireless local area network (WLAN), ultra-wideband (UWB), Bluetooth and infrared have shown good positioning effects. Among them, UWB positioning technology is favored by researchers because of its strong signal penetration, wide bandwidth and strong anti-interference ability. However, due to the influence of multipath effect, the positioning signal is severely attenuated or even interrupted, resulting in a significant reduction in positioning accuracy or even failure and instability of position estimation. Since indoor and outdoor positioning technologies are independent of each other, seamless positioning technology is required. However, in complex seamless scenarios, due to the influence of ping-pong effect, the problem of high-precision positioning in the indoor and outdoor transition zone has not been fully solved.

[0007] Therefore, it is necessary to propose a seamless indoor and outdoor positioning method based on UWB / GNSS / IMU to solve the above problems that the GNSS positioning accuracy in the indoor and outdoor transition area is affected by the signal strength and the indoor UWB is affected by the multipath effect, so as to improve the positioning accuracy of the positioning system. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a UWB / GNSS / IMU-based indoor and outdoor seamless positioning method, which can seamlessly switch between indoor and outdoor positioning and improve the positioning accuracy of the positioning system.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is: the indoor and outdoor seamless positioning method based on UWB / GNSS / IMU specifically includes the following steps:

[0010] S1: Deploy multiple UWB base stations to collect UWB data, collect IMU data through the IMU system, and collect outdoor GNSS data;

[0011] S2: Establishing a fusion positioning model, fusing the UWB data collected in step S1 with the IMU data, or fusing them with the GNSS data; and then using the fusion filter algorithm to extend the Kalman filter EKF to update the data, thereby achieving seamless switching and high-precision positioning between indoor and outdoor positioning;

[0012] S3: According to the different characteristics of indoor and outdoor environments, optimize the parameters and switching strategy of the fusion filter algorithm to improve positioning accuracy and switching efficiency;

[0013] S4: Real-time monitoring and correction of positioning errors. If the data is an incorrect positioning, the data is input into the inertial navigation error model; if the data is a correct positioning, the positioning result is output.

[0014] By adopting the above technical scheme and utilizing the traditional UWB positioning system, multiple UWB base stations and IMU systems are integrated to solve the positioning problems in UWB blind areas and areas blocked by obstacles. At the same time, the solution values ​​of UWB tags by multiple UWB base stations are used as observation values, and the Kalman filter algorithm is improved to optimize the calculated values ​​of the IMU system in real time to eliminate the accumulated errors of the IMU system. The positioning blind area range of the multi-base station positioning system is tested experimentally. When the UWB tag enters the blind area range, only the IMU is used for positioning, and UWB optimization is no longer used. Since the blind area range of multiple UWB base stations is relatively small, IMU can be used for short-term positioning. Compared with the positioning system of multiple UWB base stations, which is easily affected by non-line-of-sight interference, the use of IMU data fusion positioning can improve its positioning stability. The positioning error is corrected to ensure the stability and reliability of the positioning system. At the same time, in the indoor and outdoor transition area, since the amount of GNSS satellite signals that can be received at this time is small and UWB positioning is interfered by multipath paths, the GNSS data, UWB data and IMU data are fused using the Kalman filter EKFEKF algorithm to estimate the position of the current transition area, achieve positioning in the indoor and outdoor transition area, and improve positioning accuracy. Indoor and outdoor seamless positioning in this technical solution means that the current moving object moves from indoors to outdoors or from outdoors to indoors. This method uses the fusion filter algorithm extended Kalman filter (EKF) to achieve fusion and estimate the position of the current transition area, achieve positioning in the indoor and outdoor transition area, and improve positioning accuracy.

[0015] Preferably, in step S1, the UWB data is used for indoor positioning, the GNSS data is used for outdoor positioning, and the IMU data is used for attitude estimation and motion tracking.

[0016] Preferably, the specific steps of step S1 are:

[0017] S11: sending a UWB signal through a UWB tag node, and receiving the UWB signal by a UWB base station; and determining the position of the UWB tag relative to the UWB base station by calculating parameters such as the propagation time and signal strength of the UWB signal;

[0018] S12: After collecting GNSS signals from at least three satellites using a GNSS receiver device, calculate the longitude, latitude and altitude information of the location of the GNSS receiver through triangulation or Doppler effect technology;

[0019] S13: The IMU sensor is used to continuously measure the acceleration and angular velocity of the target object, and the acceleration data and the angular velocity data are recorded.

[0020] Preferably, in step S11, bilateral bidirectional ranging is adopted, which is divided into two ranging times; the specific steps are:

[0021] S111: First, the UWB tag actively initiates the first ranging message, and the UWB base station responds; after the UWB tag receives the data, it returns the data, and finally records the timestamps of data sending and data receiving, thereby obtaining four time differences: T round1 ,T round2 ,T reply1 ,T reply2 ;

[0022] S112: The formula for the distance between the UWB tag and the UWB base station is:

[0023] S=C×T prop ;

[0024] Among them, C represents the speed of electromagnetic waves in the air. The method of unilateral distance measurement can be known from the formula That is, to obtain a prop The system of equations is expressed as:

[0025]

[0026] After further simplification and merging, the formula is:

[0027]

[0028] S113: Calculate the distance S between a single UWB tag and a single UWB base station; that is, indoors, use the TDOA algorithm with four UWB base stations and one UWB tag to find the left side information of the UWB tag, and then obtain the positioning information of the UWB tag on the car by intersecting the four circles formed by the four UWB base stations and one UWB tag, thereby obtaining the corresponding coordinate information of the UWB tag.

[0029] Preferably, the formula for obtaining the coordinate information corresponding to the UWB tag in step S113 is:

[0030]

[0031] Among them, the initial position information of the UWB tag in the ultra-wideband coordinate system P (X0, Y0, Z0), C×T pop is the distance between the UWB tag and the UWB base station; thereby obtaining the location information of the UWB tag at time i.

[0032] Preferably, the acceleration data and angular velocity data in step S13 are both recorded in the form of time series, including accelerometer measurement values ​​and gyroscope measurement values.

[0033] Preferably, the specific steps of step S2 are:

[0034] S21: First, the UWB ranging noise, the accumulated error of the IMU system and the GNSS observation noise are modeled respectively, and the corresponding noise covariance matrix is ​​defined;

[0035] S22: Construct a spatial state vector containing the error states of UWB data, GNSS data and IMU data, and a state transfer equation describing the dynamic changes of UWB data, GNSS data and IMU data; use the fusion filter algorithm to extend the Kalman filter EKF for iterative filtering, use the position, speed and attitude state predicted by the IMU data at the current moment as a priori estimation, and then update the observation quantity according to the newly received UWB ranging and GNSS carrier phase data. The state transfer equation is a priori technology.

[0036] Preferably, in step S21, the acceleration data and angular velocity data of the IMU data are error corrected by an inertial navigation error model, and the accumulated error of the IMU system includes a gyroscope drift deviation and an accelerometer deviation.

[0037] Preferably, different modes for different scenarios are designed in step S3, including the use of a UWB / IMU loosely coupled mode in an indoor environment; in the outdoor-indoor transition area, the IMU system first outputs high-frequency position, velocity and attitude information, and then adds low-frequency GNSS data and UWB data as observation inputs of the EKF to achieve continuity and stability of positioning performance.

[0038] Preferably, the step S4 includes respectively correcting the indoor environment and the indoor-outdoor transition area, and the specific steps are:

[0039] S41: In indoor environments, UWB data is used to provide accurate distance measurements and periodically correct drift errors in IMU data;

[0040] S42: In the transition area between indoors and outdoors, positioning jumps are suppressed by combining the remaining valid observations in the GNSS data, the ranging provided by the UWB data, and the attitude and speed information continuously output by the IMU system.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) Compared with single positioning technologies, such as UWB or GNSS, the present invention adopts a method of integrating UWB, GNSS and IMU, thereby achieving seamless positioning indoors and outdoors. Single UWB technology has high accuracy indoors, but is easily affected by multipath effects and signal attenuation outdoors, while single GNSS technology is accurate in outdoor positioning, but is easily affected by poor signal penetration indoors; the integration of IMU inertial information can improve the continuous tracking of user positions and reduce positioning errors. Therefore, the present invention combines the advantages of various technologies and can achieve more accurate positioning in indoor and outdoor environments.

[0043] (2) The present invention adopts a fusion positioning method and uses a filter (such as an extended Kalman filter or a particle filter) to fuse the information of UWB, GNSS and IMU, thereby improving the stability and accuracy of positioning; compared with a single positioning technology, fusion positioning can better overcome the limitations of each technology and provide more reliable positioning results.

[0044] (3) The fusion positioning method of the present invention can achieve seamless switching in different environments and has stronger adaptability. For example, when a user enters indoors from outdoors, the system can automatically switch to positioning based on UWB and IMU to ensure the continuity and accuracy of positioning; and vice versa; this seamless switching capability improves the user experience and makes the positioning system more intelligent and practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the process framework of the UWB / GNSS / IMU-based indoor and outdoor seamless positioning method of the present invention;

[0046] Figure 2 A schematic diagram of a process framework for converting the indoor and outdoor seamless positioning method based on UWB / GNSS / IMU of the present invention to indoor positioning;

[0047] Figure 3 It is a positioning flow chart of multiple UWB base stations in the indoor and outdoor seamless positioning method based on UWB / GNSS / IMU of the present invention;

[0048] FIG4( a ) is a diagram showing the indoor fusion positioning trajectory of the UWB / GNSS / IMU-based indoor and outdoor seamless positioning method of the present invention and other positioning methods;

[0049] FIG4( b ) is a diagram of outdoor fusion positioning trajectory of the indoor and outdoor seamless positioning method based on UWB / GNSS / IMU of the present invention. DETAILED DESCRIPTION

[0050] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the protection scope of the present invention.

[0051] Example: Figures 1-2 As shown, the indoor and outdoor seamless positioning method based on UWB / GNSS / IMU specifically includes the following steps:

[0052] S1: deploy multiple UWB base stations to collect UWB data, collect IMU data through an IMU system, and collect outdoor GNSS data; in step S1, the UWB data is used for indoor positioning, the GNSS data is used for outdoor positioning, and the IMU data is used for attitude estimation and motion tracking;

[0053] The specific steps of step S1 are:

[0054] S11: sending a UWB signal through a UWB tag node, and receiving the UWB signal by a UWB base station; and determining the position of the UWB tag relative to the UWB base station by calculating parameters such as the propagation time and signal strength of the UWB signal;

[0055] In step S11, bilateral two-way ranging is adopted, which is divided into two ranging times; the specific steps are as follows:

[0056] S111: First, the UWB tag actively initiates the first ranging message, and the UWB base station responds; after the UWB tag receives the data, it returns the data, and finally records the timestamps of data sending and data receiving, thereby obtaining four time differences: T round1 ,T round2 ,T reply1 ,T reply2 ;

[0057] S112: The formula for the distance between the UWB tag and the UWB base station is:

[0058] S=C×T prop ;

[0059] Among them, C represents the speed of electromagnetic waves in the air. The method of unilateral distance measurement can be known from the formula That is, to obtain a prop The system of equations is expressed as:

[0060]

[0061] After further simplification and merging, the formula is:

[0062]

[0063] S113: Calculate the distance S between a single UWB tag and a single UWB base station; that is, indoors, use the TDOA algorithm with four UWB base stations and one UWB tag to obtain the left information of the UWB tag, and then obtain the positioning information of the UWB tag on the car by intersecting the four circles formed by the four UWB base stations and one UWB tag, so as to obtain the corresponding coordinate information of the UWB tag;

[0064] The formula for obtaining the coordinate information corresponding to the UWB tag in step S113 is:

[0065]

[0066] Among them, the initial position information of the UWB tag in the ultra-wideband coordinate system P (X0, Y0, Z0), C×T pop is the distance between the UWB tag and the UWB base station, so as to obtain the location information of the UWB tag at time i;

[0067] S12: After collecting GNSS signals from at least three satellites using a GNSS receiver device, calculate the longitude, latitude and altitude information of the location of the GNSS receiver through triangulation or Doppler effect technology;

[0068] S13: using an IMU sensor to continuously measure the acceleration and angular velocity of the target object, and recording the acceleration data and the angular velocity data; the acceleration data and the angular velocity data in step S13 are recorded in the form of a time series, including accelerometer measurement values ​​and gyroscope measurement values;

[0069] S2: Establishing a fusion positioning model, fusing the UWB data collected in step S1 with the IMU data, or fusing them with the GNSS data; and then using the fusion filter algorithm to extend the Kalman filter EKF to update the data, thereby achieving seamless switching and high-precision positioning between indoor and outdoor positioning;

[0070] The specific steps of step S2 are:

[0071] S21: First, UWB ranging noise, cumulative error of the IMU system and GNSS observation noise are modeled respectively, and corresponding noise covariance matrices are defined; in the step S21, the acceleration data and angular velocity data of the IMU data are error corrected by the inertial navigation error model, and the cumulative error of the IMU system includes gyroscope drift deviation and accelerometer deviation;

[0072] S22: Construct a spatial state vector containing the error states of UWB data, GNSS data and IMU data, as well as a state transfer equation that describes the dynamic changes of UWB data, GNSS data and IMU data; use the fusion filter algorithm to extend the Kalman filter EKF for iterative filtering, take the position, velocity and attitude state predicted by the IMU data at the current moment as a priori estimation, and then update the observation value according to the newly received UWB ranging and GNSS carrier phase data;

[0073] S3: Optimize the parameters and switching strategy of the fusion filter algorithm according to the different characteristics of the indoor and outdoor environments to improve the positioning accuracy and switching efficiency; in the step S3, design different modes for different scenarios, including using the UWB / IMU loose coupling mode in the indoor environment; in the outdoor-indoor transition area, first output the position, velocity and attitude information at high frequency through the IMU system, and then add the low-frequency GNSS data and UWB data as the observation input of the EKF to achieve the continuity and stability of the positioning performance;

[0074] S4: real-time monitoring and correction of positioning errors. If the data is an erroneous positioning, the data is input into the inertial navigation error model; if the data is a correct positioning, the positioning result is output; in step S4, corrections are respectively made to the indoor environment and the indoor-outdoor transition area, and the specific steps are as follows:

[0075] S41: In indoor environments, UWB data is used to provide accurate distance measurements and periodically correct drift errors in IMU data;

[0076] S42: In the transition area between indoors and outdoors, positioning jumps are suppressed by combining the remaining valid observations in the GNSS data, the short-distance high-precision ranging provided by the UWB data, and the attitude and speed information continuously output by the IMU system.

[0077] In order to better illustrate the technical effect of the present invention, other positioning methods are used to conduct experimental verification with the positioning method of the present invention. The verification results are as follows: Figure 4(a) to Figure 4(b) As shown, FIG4(a) is a fusion positioning trajectory diagram of the positioning method of the present invention and other positioning methods indoors; FIG4(b) is a fusion positioning trajectory diagram of the positioning method of the present invention outdoors; during the verification process, some positioning methods performed very poorly in fusion positioning, so the fusion positioning method was compared.

[0078] For ordinary technicians in this field, the specific embodiments are only illustrative descriptions of the present invention. It is obvious that the specific implementation of the present invention is not limited to the above-mentioned methods. As long as various non-substantial improvements are made using the method concepts and technical solutions of the present invention, or the concepts and technical solutions of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A seamless indoor and outdoor positioning method based on UWB / GNSS / IMU, characterized in that: The specific steps include: S1: Deploy multiple UWB base stations to collect UWB data, collect IMU data through the IMU system, and collect outdoor GNSS data; S2: Establishing a fusion positioning model, fusing the UWB data collected in step S1 with the IMU data, or fusing them with the GNSS data; and then using the fusion filter algorithm to extend the Kalman filter EKF to update the data, thereby achieving seamless switching and high-precision positioning between indoor and outdoor positioning; S3: According to the different characteristics of indoor and outdoor environments, optimize the parameters and switching strategy of the fusion filter algorithm to improve positioning accuracy and switching efficiency; S4: Real-time monitoring and correction of positioning errors. If the data is an incorrect positioning, the data is input into the inertial navigation error model; if the data is a correct positioning, the positioning result is output; In step S1, the UWB data is used for indoor positioning, the GNSS data is used for outdoor positioning, and the IMU data is used for attitude estimation and motion tracking; The specific steps of step S1 are: S11: sending a UWB signal through a UWB tag node, and receiving the UWB signal by a UWB base station; and determining the position of the UWB tag relative to the UWB base station by calculating parameters of the UWB signal; S12: After collecting GNSS signals from at least three satellites using a GNSS receiver device, calculate the longitude, latitude and altitude information of the location of the GNSS receiver through triangulation or Doppler effect technology; S13: using an IMU sensor to continuously measure the acceleration and angular velocity of the target object, and recording the acceleration data and the angular velocity data; In step S11, bilateral two-way ranging is adopted, which is divided into two ranging times; The specific steps are: S111: First, the UWB tag actively initiates the first ranging message, and the UWB base station responds; after the UWB tag receives the data, it returns the data, and finally records the timestamps of data sending and data receiving, thereby obtaining four time differences: T round1 ,T round2 ,T reply1 ,T reply2 ; S112: The formula for the distance between the UWB tag and the UWB base station is: S=C×T prop ; Among them, C represents the speed of electromagnetic waves in the air. The method of unilateral distance measurement can be known from the formula That is, to obtain a prop The system of equations is expressed as: After further simplification and merging, the formula is: S113: Calculate the distance S between a single UWB tag and a single UWB base station; that is, indoors, use the TDOA algorithm with four UWB base stations and one UWB tag to obtain the left information of the UWB tag, and then obtain the positioning information of the UWB tag on the car by intersecting the four circles formed by the four UWB base stations and one UWB tag, so as to obtain the corresponding coordinate information of the UWB tag; The formula for obtaining the coordinate information corresponding to the UWB tag in step S113 is: Among them, the initial position information of the UWB tag in the ultra-wideband coordinate system P (X0, Y0, Z0), C×T pop is the distance between the UWB tag and the UWB base station, so as to obtain the location information of the UWB tag at time i.

2. The UWB / GNSS / IMU-based indoor and outdoor seamless positioning method according to claim 1, characterized in that: The acceleration data and angular velocity data in step S13 are both recorded in the form of time series, including accelerometer measurement values ​​and gyroscope measurement values.

3. The UWB / GNSS / IMU-based indoor and outdoor seamless positioning method according to claim 1, characterized in that: The specific steps of step S2 are: S21: First, the UWB ranging noise, the accumulated error of the IMU system and the GNSS observation noise are modeled respectively, and the corresponding noise covariance matrix is ​​defined; S22: construct a spatial state vector including the error states of UWB data, GNSS data and IMU data, and a state transfer equation describing the dynamic changes of UWB data, GNSS data and IMU data; The extended Kalman filter (EKF) with fusion filtering algorithm is used for iterative filtering. The position, velocity and attitude state predicted by the IMU data at the current moment are used as prior estimates, and then the observation value is updated according to the newly received UWB ranging and GNSS carrier phase data.

4. The UWB / GNSS / IMU-based indoor and outdoor seamless positioning method according to claim 3, characterized in that: In the step S21, the acceleration data and angular velocity data of the IMU data are error corrected by using an inertial navigation error model, and the accumulated error of the IMU system includes a gyroscope drift deviation and an accelerometer deviation.

5. The UWB / GNSS / IMU-based indoor and outdoor seamless positioning method according to claim 4, characterized in that: In step S3, different modes for different scenarios are designed, including a loosely coupled UWB / IMU mode in an indoor environment; in the outdoor-indoor transition area, the IMU system first outputs high-frequency position, velocity and attitude information, and then adds low-frequency GNSS data and UWB data as observation inputs of the EKF to achieve continuity and stability of positioning performance.

6. The UWB / GNSS / IMU-based indoor and outdoor seamless positioning method according to claim 5, characterized in that: The step S4 includes respectively correcting the indoor environment and the indoor-outdoor transition area, and the specific steps are: S41: In indoor environments, the drift error of the IMU data is periodically corrected using the distance measurement provided by the UWB data; S42: In the transition area between indoors and outdoors, positioning jumps are suppressed by combining the remaining valid observations in the GNSS data, the ranging provided by the UWB data, and the attitude and speed information continuously output by the IMU system.

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