Single base station UWB / GNSS-RTK-based seamless positioning method suitable for large storage environment

Abstract

The invention discloses a seamless positioning method based on a single base station UWB / GNSS-RTK, which is suitable for a large storage environment. According to the method, UWB and GNSS-RTK data of a single base station are collected and combined, a fusion positioning model is established, and a weighted filtering extended Kalman filter (WEKF) is adopted to realize high-precision positioning and seamless switching inside and outside a warehouse. According to the characteristics of the internal and external environments of the warehouse, fusion algorithm parameters and a switching strategy are optimized: high-precision positioning which is wide in coverage and simple and convenient to deploy is realized in the warehouse by utilizing a single base station UWB fusion residual GNSS-RTK signal, and centimeter-level precision is provided outside the warehouse by depending on the GNSS-RTK technology. By monitoring and correcting positioning errors in real time, the stability and reliability of the system are ensured, and finally a high-precision positioning result is output. The method effectively fuses the positioning information inside and outside the warehouse, reduces the complexity of deployment of multiple base stations, has the characteristics of high precision, low cost and high stability, and can be widely applied to the scenes of material management, vehicle scheduling, automatic equipment navigation and the like.

Description

Technical Field

[0001] The present invention relates to the field of indoor and outdoor positioning technology, and specifically to a seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environments. Background Art

[0002] With the continuous development of society, mobile Internet and Internet of Things technologies have been deeply integrated into modern society and have become an indispensable and important part. In this context, location information, as a basic and key core element, has become increasingly important. Especially in the face of increasingly diversified and sophisticated industry needs, the role of high-precision location information is particularly critical. It can not only meet the industry's requirements for accuracy, but also significantly improve business value and operating efficiency, injecting new impetus into the development of various fields.

[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] Outdoor positioning systems (such as GPS and GNSS) have been studied since the 1960s and have gradually been widely used. These technologies have greatly increased the popularity of navigation and location services, making them an indispensable part of people's daily lives. However, since satellite navigation signals are severely attenuated when penetrating buildings, it is difficult for them to provide stable and accurate positioning information in indoor environments. In addition, during the transition from outdoor to indoor environments, these systems usually have difficulty achieving continuous high-precision positioning, and most people spend a lot of time indoors, further highlighting the challenges brought about by this technical limitation.

[0005] With the continuous improvement of living standards and the rapid development of construction technology, the demand for indoor positioning technology in various industries continues to grow. Indoor positioning can not only significantly improve the experience of consumers in the service industry, but also optimize the logistics efficiency for large warehousing companies. In emerging economic fields, such as industrial AGV robots and special robots, high-precision indoor positioning systems are an important support for promoting the development of automation and intelligence. More importantly, the positioning problem in the transition area between indoor and outdoor involves smooth switching in a complex signal environment, which is crucial to ensuring the continuity and stability of location information. Research on how to achieve seamless connection between different signal environments is not only an urgent need for current technological development, but also an important direction for improving positioning performance. Indoor positioning technology has shown a wide range of application prospects in different fields, and it has far-reaching significance for improving work efficiency and quality of life. Therefore, focusing on solving the technical difficulties of positioning in the transition area between indoor and outdoor will lay the foundation for achieving seamless switching between indoor and outdoor positioning, and create more convenience and development opportunities for society.

[0006] Location-based services have been widely popularized, promoting the continuous development and improvement of indoor and outdoor positioning technologies. Among them, the Global Navigation Satellite System (GNSS), as the core technology for outdoor positioning, can provide accurate geographic location and speed information, bringing great convenience to people's daily lives. However, when the satellite geometry layout is limited or the signal path is blocked, the signal strength of GNSS will be significantly reduced, and it will be seriously affected by the multipath effect, resulting in a decrease in positioning performance and difficulty in achieving high-precision positioning effects. In the field of indoor positioning, wireless local area network (WLAN), ultra-wideband (UWB), Bluetooth and infrared technologies have shown good positioning potential. Among them, UWB technology has become a research hotspot because of its advantages such as strong signal penetration, wide bandwidth and strong anti-interference ability. However, UWB is susceptible to multipath effects in complex environments, resulting in signal attenuation or even interruption, which in turn leads to problems such as reduced positioning accuracy and unstable position estimation. In addition, indoor and outdoor positioning technologies usually operate independently, and high-precision positioning faces severe challenges in seamless switching between indoor and outdoor transition areas. Especially in complex seamless scenes, the ping-pong effect will further affect the continuity and stability of positioning, resulting in the failure to effectively solve the positioning problem in the transition area between indoor and outdoor areas. Therefore, developing technologies that can achieve high-precision seamless positioning has become an important direction of current research.

[0007] Therefore, it is necessary to propose a seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environments 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 present invention proposes a seamless positioning method based on single-base station UWB / GNSS-RTK suitable for large storage environments. This method collects single-base station UWB and GNSS-RTK data, establishes a fusion positioning model, and uses a weighted extended Kalman filter (WEKF) to achieve high-precision positioning and seamless switching inside and outside the warehouse. According to the characteristics of the environment inside and outside the warehouse, the fusion algorithm parameters and switching strategies are optimized: the warehouse uses a single-base station UWB fusion residual GNSS-RTK signal to achieve high-precision positioning with wide coverage and simple deployment, while the warehouse relies on GNSS-RTK technology to provide centimeter-level accuracy. By real-time monitoring and correction of positioning errors, the stability and reliability of the system are guaranteed, and finally a high-precision positioning result is output. This method effectively integrates the positioning information inside and outside the warehouse, reduces the complexity of multi-base station deployment, and has the characteristics of high precision, low cost and high stability. It can be widely used in scenarios such as material management, vehicle scheduling and automated equipment navigation.

[0009] 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 study uses the fusion of GNSS and UWB using the WEKF algorithm to realize the position estimation of the current transition area, realize the positioning of the indoor and outdoor transition area, and improve the positioning accuracy.

[0010] In order to solve the above technical problems, the technical solution of the present invention is: a seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environment, the specific steps are:

[0011] S1: Collect UWB and GNSS-RTK data, where UWB is used for indoor positioning and GNSS-RTK is used for outdoor positioning and auxiliary correction of indoor positioning;

[0012] S2: Establish a fusion positioning model to fuse UWB and GNSS data, and use the weighted extended Kalman filter (WEKF) to achieve seamless switching between indoor and outdoor positioning and high-precision positioning in indoor and outdoor areas;

[0013] S3: Optimize fusion algorithm parameters and switching strategies according to the different characteristics of indoor and outdoor environments to improve positioning accuracy and switching efficiency;

[0014] S4: Real-time monitoring and correction of positioning errors to ensure the stability and reliability of the positioning system;

[0015] S5: Output the final positioning result.

[0016] The seamless indoor and outdoor positioning in this technical solution refers to the continuity and high accuracy of positioning when a moving object switches between indoors and outdoors. To address the challenges of positioning in transition areas, this method uses a weighted extended Kalman filter (WEKF) to fusion estimate the location information of the current area. By optimizing the positioning algorithm in the transition area, this technology achieves seamless connection between indoor and outdoor environments, effectively improving the accuracy and stability of positioning in the transition area.

[0017] Preferably, the specific steps of step S1 are:

[0018] S11: In a single-base station UWB system, the UWB tag node sends signals, and the base station receives and processes these signals. By calculating the signal's propagation time (TOF) and its changing characteristics, and combining parameters such as signal strength, the base station can determine the tag node's relative position to itself, thereby achieving high-precision positioning;

[0019] S12: The GNSS-RTK receiver device synchronously receives signals from at least four GNSS satellites, and combines the differential correction information sent by the base station, using carrier phase measurement and differential algorithms to calculate the receiver's high-precision longitude, latitude, and altitude information;

[0020] Preferably, the specific steps of step S11 are:

[0021] S111: Base station TOF positioning uses a one-way flight time measurement method. The UWB tag actively sends a signal, and the UWB base station receives the signal and records the receiving time t receive , by sending a known signal at time t send , calculate the signal's flight time Δt; Δt = t receive -t send In order to ensure high-precision time measurement, single-base station TOF positioning has high requirements for clock synchronization between the base station and the tag, which can be achieved through a high-precision synchronization module (such as GPS or indoor high-precision clock).

[0022] S112: The flight time and the propagation speed of electromagnetic waves in the air C (the speed of light, about 3×10 8 m / s), the distance S from the tag to the base station can be calculated: S = C Δt in: Δt is the signal flight time; C is the propagation speed of electromagnetic waves. Since only one base station is used here, the distance S obtained is the spherical distance from the tag to the base station. The specific coordinates of the tag cannot be directly calculated. We can only know that it is located on a sphere with the base station as the center and the distance S as the radius.

[0023] S113: If the coordinates of the tag position relative to the base station are (x, y, z), and the known coordinates of the base station are (x0, y0, z0), then the distance between the tag and the base station satisfies the following formula: This represents a sphere with the base station as the center and S as the radius.

[0024] S114: Single-base station TOF positioning is usually used in combination with other positioning methods (such as IMU inertial navigation or map constraints), and the specific coordinates of the tag can be determined by supplementing with additional information. For example: Fixed plane assumption: If the tag is located on a known plane (such as the ground, z = z 0 ), the dimension of the solution can be reduced by constraints and its two-dimensional coordinates can be calculated. Dynamic trajectory tracking: Combined with the tag's motion trajectory prediction, the continuously measured distance information is used to estimate its position change on the spherical surface, thereby inferring the actual coordinates.

[0025] Preferably, the specific steps of step S2 are:

[0026] S121: GNSS-RTK positioning uses the carrier phase observation method and relies on the differential technology between the base station and the mobile station to achieve high-precision positioning. The GNSS base station is set at a known fixed position, continuously receives satellite signals and calculates pseudorange and carrier phase information. The mobile station receives the same satellite signal and records its own observation data, and receives differential correction information sent by the base station through a wireless link. The base station and the mobile station respectively record the pseudorange ρ and carrier phase observation value L of the satellite. The pseudorange formula is: ρ = r + c (δt-δT) + ∈ ρ in: r: the actual geometric distance between the receiver and the satellite; c: speed of light; δt, δT: clock deviations of receiver and satellite; ∈ ρ : Pseudorange measurement error. The carrier phase formula is: in: λ: carrier wavelength; N: integer ambiguity; ∈ L : Carrier phase measurement error.

[0027] S122: GNSS-RTK transmits the pseudo-range and carrier phase information of the base station in real time, and the mobile station uses the correction data of the base station to eliminate most common errors, including satellite orbit error, satellite clock error and ionosphere error. The pseudo-range differential equation between the base station and the mobile station is: Δρ=ρ mobile -ρ base The carrier phase difference equation is: ΔL=L mobile -L base After eliminating the common error, the remaining error mainly comes from the baseline error between the base station and the mobile station.

[0028] S123: The key to RTK positioning is to resolve the integer ambiguity N. After resolving N, the baseline vector between the base station and the mobile station can be accurately calculated by the differential carrier phase observation value: B = (x m -x b ,y m -y b ,z m -z b ) in: (x m ,y m ,z m ): mobile station coordinates; (x b ,y b ,z b ): base station coordinates; B: Baseline vector between the base station and the mobile station. By knowing the precise location of the base station (x b ,y b ,z b ), the three-dimensional coordinates of the mobile station can be solved: (x m ,y m ,z m )=(x b ,y b ,z b )+B

[0029] S124: RTK positioning relies on observation data from multiple satellites and uses the least squares method to solve the position of the mobile station: The high-precision three-dimensional coordinates of the mobile station are solved through optimization.

[0030] Preferably, the specific steps of step S2 are:

[0031] S21: Model the single-base station UWB ranging noise and GNSS-RTK observation noise, and define the corresponding noise covariance matrix. Single-base station UWB ranging noise modeling: Assuming that the ranging error conforms to the normal distribution, its covariance matrix is ​​defined as: in, is the standard deviation of UWB ranging of a single base station. GNSS-RTK observation noise modeling: GNSS-RTK observation noise includes pseudorange and carrier phase observation noise, and its covariance matrix is: in: is the standard deviation of the GNSS pseudorange measurements; is the standard deviation of the GNSS carrier phase measurement.

[0032] S22: Build a fusion positioning model and process the single-base station UWB and GNSS-RTK observation data using weighted extended Kalman filtering (WEKF) to achieve seamless switching and high-precision positioning. State vector definition: Construct a spatial state vector containing position, velocity, and time error states: X k =[x k ,y k ,z k ,v x ,v y ,v z ,b k ] T . in: x k ,y k ,z k : Current location coordinates; v x ,v y ,v z : current speed; b k : Receiver clock error. State transfer equation: describes the dynamic change of state, defined as: X k+1 =FX k +w k . in: F: state transfer matrix; wk: system noise, satisfying w k ~N(0,Q).,Q is the system noise covariance matrix. Observation model: Fusion of single-base station UWB and GNSS-RTK observation data to establish the observation equation: Z k =HX k +v k . in: H: observation matrix; v k : Observation noise, satisfying v k ~N(0,R),R=R UWB +R GNSS . . Weighted filtering: According to the current environment (indoor or outdoor), the UWB and GNSS observations are weighted separately: W = diag (w UwB ,w GNSS ). Indoor:w UWB >w GNSS , strengthen the impact of UWB; Outdoor: w GNSS >w UWB , strengthening the impact of GNSS. Filter update: Use weighted extended Kalman filter (WEKF) for state estimation and calculate Kalman Gain: K k =P k|k-1 H T (HP k|k-1 H T +WRW) -1 . Update the state vector: Update the covariance matrix: P k =(IK k H)P k|k-1 .

[0033] Preferably, the specific steps of step S3 are:

[0034] According to the different characteristics of indoor and outdoor environments, the fusion algorithm parameters and switching strategies are optimized to improve positioning accuracy and switching efficiency; In indoor environments, single-base station UWB is used as the main positioning means to optimize the observation noise covariance matrix parameters to ensure ranging accuracy in complex reflection environments. At the same time, GNSS-RTK data is used as a reference to monitor positioning errors and dynamically adjust the filter weights according to the environment. In outdoor environments, GNSS-RTK is used as the main positioning means to optimize the pseudorange and carrier phase observation noise weights. In view of the occlusion in the storage environment, UWB data is used as an auxiliary observation to compensate for the accuracy loss caused by GNSS signal occlusion. In the indoor and outdoor transition area, a dynamic switching strategy is adopted. Single-base station UWB and GNSS-RTK data are used as low-frequency update inputs, and a weighted filtering algorithm is used to achieve smooth switching to ensure the continuity and stability of positioning. According to the real-time needs of the storage environment, such as vehicle navigation or cargo tracking, the parameters in the fusion algorithm, such as switching delay and measurement weight, are dynamically adjusted to improve the adaptability and efficiency of the algorithm. Through optimized algorithm parameters and switching strategies, high-precision positioning and seamless switching capabilities are guaranteed in large storage environments to meet actual application needs.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) Seamless indoor and outdoor positioning capability Compared with a single positioning technology (such as UWB or GNSS), the present invention achieves high-precision seamless positioning inside and outside the warehouse by integrating single-base station UWB and GNSS-RTK technology. Single-base station UWB has good coverage and positioning accuracy indoors, but is greatly affected by multipath effects and signal attenuation outdoors; while GNSS-RTK can provide centimeter-level positioning accuracy outdoors, but is limited by signal penetration indoors. Combining the two and integrating the high-precision differential information of RTK, this method can maintain high-precision positioning during indoor and outdoor switching, meeting the application requirements of complex warehousing environments.

[0037] (2) The present invention uses fusion positioning technology to improve stability and accuracy. The present invention uses a weighted extended Kalman filter (WEKF) to fuse the positioning data of a single base station UWB and GNSS-RTK, significantly improving positioning stability and accuracy. The fusion positioning method can effectively overcome the limitations of a single technology in a complex environment, and ensure the reliability of the positioning results by real-time monitoring and correction of positioning errors.

[0038] (3) Intelligent seamless switching capability. The present invention has the ability to seamlessly switch between different environments. For example, when the target enters the warehouse from outside, the system can automatically switch to a single-base station UWB-based positioning mode to achieve rapid adaptation; conversely, when entering the outside of the warehouse, it switches to a GNSS-RTK mode to continuously provide high-precision positioning. This seamless switching mechanism not only improves the adaptability of the positioning system, but also optimizes the user experience and significantly enhances the practicality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings are only for the purpose of illustrating the preferred embodiments and are not to be construed as limiting the invention.

[0040] Figure 1 It is a system block diagram of a seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environments of the present invention.

[0041] Figure 2 It is a system block diagram of the conversion of a seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environments to indoor positioning in the present invention.

[0042] Figure 3 It is a system block diagram of the conversion of a seamless positioning method based on single-base UWB / GNSS-RTK suitable for large storage environments to outdoor positioning in the present invention. DETAILED DESCRIPTION

[0043] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the indoor and outdoor seamless positioning method based on UWB / GNSS / IMU proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and embodiments.

[0044] Example: Figure 1 As shown, this is a seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environments. The specific steps are:

[0045] Collect UWB and GNSS-RTK data, where UWB is used for indoor positioning and GNSS-RTK is used for outdoor positioning and auxiliary correction of indoor positioning;

[0045] The specific steps of step S1 are:

[0046] S11: In a single-base station UWB system, the UWB tag node sends signals, and the base station receives and processes these signals. By calculating the signal's propagation time (TOF) and its changing characteristics, and combining parameters such as signal strength, the base station can determine the tag node's relative position to itself, thereby achieving high-precision positioning;

[0047] S12: The GNSS-RTK receiver device synchronously receives signals from at least four GNSS satellites, and combines the differential correction information sent by the base station, using carrier phase measurement and differential algorithms to calculate the receiver's high-precision longitude, latitude, and altitude information;

[0048] The specific steps of step S11 are:

[0049] S111: Single base station TOF positioning uses a one-way flight time measurement method. The UWB tag actively sends a signal, and the UWB base station receives the signal and records the receiving time t receive , by sending a known signal at time t send , calculate the signal's flight time Δt; Δt = t receive -t send In order to ensure high-precision time measurement, single-base station TOF positioning has high requirements for clock synchronization between the base station and the tag, which can be achieved through a high-precision synchronization module (such as GPS or indoor high-precision clock).

[0050] S111: Single base station TOF positioning uses a one-way flight time measurement method. The UWB tag actively sends a signal, and the UWB base station receives the signal and records the receiving time t receive , by sending a known signal at time t send , calculate the signal's flight time Δt; Δt = t receive -t send In order to ensure high-precision time measurement, single-base station TOF positioning has high requirements for clock synchronization between the base station and the tag, which can be achieved through a high-precision synchronization module (such as GPS or indoor high-precision clock).

[0051] S113: If the coordinates of the tag position relative to the base station are (x, y, z), and the known coordinates of the base station are (x 0 ,y 0 , z 0 ), then the distance between the tag and the base station satisfies the following formula: This represents a sphere with the base station as the center and S as the radius.

[0052] S114: Single-base station TOF positioning is usually used in combination with other positioning methods (such as IMU inertial navigation or map constraints), and the specific coordinates of the tag can be determined by supplementing with additional information. For example: Fixed plane assumption: If the tag is located on a known plane (such as the ground, z = z 0 ), the dimension of the solution can be reduced by constraints and its two-dimensional coordinates can be calculated. Dynamic trajectory tracking: Combined with the tag's motion trajectory prediction, the continuously measured distance information is used to estimate its position change on the spherical surface, thereby inferring the actual coordinates.

[0053] The specific steps of step S12 are:

[0054] S121: GNSS-RTK positioning uses the carrier phase observation method and relies on the differential technology between the base station and the mobile station to achieve high-precision positioning. The GNSS base station is set at a known fixed position, continuously receives satellite signals and calculates pseudorange and carrier phase information. The mobile station receives the same satellite signal and records its own observation data, and receives differential correction information sent by the base station through a wireless link. The base station and the mobile station respectively record the pseudorange ρ and carrier phase observation value L of the satellite. The pseudorange formula is: ρ = r + c (δt-δT) + ∈ ρ in: r: the actual geometric distance between the receiver and the satellite; c: speed of light; δt, δT: clock deviations of receiver and satellite; ∈ ρ : Pseudorange measurement error. The carrier phase formula is: in: λ: carrier wavelength; N: integer ambiguity; ∈ L : Carrier phase measurement error.

[0055] S122: GNSS-RTK transmits the pseudo-range and carrier phase information of the base station in real time, and the mobile station uses the correction data of the base station to eliminate most common errors, including satellite orbit error, satellite clock error and ionosphere error. The pseudo-range differential equation between the base station and the mobile station is: Δρ=ρ mobile -ρ base The carrier phase difference equation is: ΔL=L mobile -L base After eliminating the common error, the remaining error mainly comes from the baseline error between the base station and the mobile station.

[0056] S123: The key to RTK positioning is to resolve the integer ambiguity N. After resolving N, the baseline vector between the base station and the mobile station can be accurately calculated by the differential carrier phase observation value: B = (x m -x b ,y m -y b ,z m -z b ) in: (x m ,y m ,z m ): mobile station coordinates; (xb ,y b ,z b ): base station coordinates; B: Baseline vector between the base station and the mobile station. By knowing the precise location of the base station (x b ,y b ,z b ), the three-dimensional coordinates of the mobile station can be solved: (x m ,y m ,z m )=(x b ,y b ,z b )+B

[0057] S124: RTK positioning relies on observation data from multiple satellites and uses the least squares method to solve the position of the mobile station: The high-precision three-dimensional coordinates of the mobile station are solved through optimization.

[0058] S2: Establish a fusion positioning model to fuse UWB and GNSS data, and use the weighted extended Kalman filter (WEKF) to achieve seamless switching between indoor and outdoor positioning and high-precision positioning in indoor and outdoor areas;

[0059] The specific steps of step S2 are:

[0060] S21: Model the single-base station UWB ranging noise and GNSS-RTK observation noise, and define the corresponding noise covariance matrix. Single-base station UWB ranging noise modeling: Assuming that the ranging error conforms to the normal distribution, its covariance matrix is ​​defined as: in, is the standard deviation of UWB ranging of a single base station. GNSS-RTK observation noise modeling: GNSS-RTK observation noise includes pseudorange and carrier phase observation noise, and its covariance matrix is: in: is the standard deviation of the GNSS pseudorange measurements; is the standard deviation of the GNSS carrier phase measurement.

[0061] S22: Build a fusion positioning model and process the single-base station UWB and GNSS-RTK observation data using weighted extended Kalman filtering (WEKF) to achieve seamless switching and high-precision positioning. State vector definition: Construct a spatial state vector containing position, velocity, and time error states: X k =[x k ,y k ,z k ,v x ,v y ,v z ,b k ] T . in: x k ,y k ,z k : Current location coordinates; v x ,v y ,v z : current speed; b k : Receiver clock error. State transfer equation: describes the dynamic change of state, defined as: X k+1 =FX k +w k . in: F: state transfer matrix; wk: system noise, satisfying w k ~N(0,Q).,Q is the system noise covariance matrix. Observation model: Fusion of single-base station UWB and GNSS-RTK observation data to establish the observation equation: Z k =HX k +v k . in: H: observation matrix; v k : Observation noise, satisfying v k ~N(0,R),R=R UWB +R GNSS . . Weighted filtering: According to the current environment (indoor or outdoor), UWB and GNSS observations are weighted separately: W = diag(w UwB ,w GNSS ). Indoor:w UWB >w GNSS , strengthen the impact of UWB; Outdoor: w GNSS >w UWB , strengthening the impact of GNSS. Filter update: Use weighted extended Kalman filter (WEKF) for state estimation and calculate Kalman Gain: K k =P k|k-1 H T (HP k|k-1 H T +WRW) -1 . Update the state vector: Update the covariance matrix: P k =(IK k H)P k|k-1 .

[0062] S3: Optimize fusion algorithm parameters and switching strategies according to the different characteristics of indoor and outdoor environments to improve positioning accuracy and switching efficiency;

[0063] The specific steps of step S3 are:

[0064] According to the different characteristics of indoor and outdoor environments, the fusion algorithm parameters and switching strategies are optimized to improve positioning accuracy and switching efficiency; In indoor environments, single-base station UWB is used as the main positioning method to optimize the observation noise covariance matrix parameters to ensure ranging accuracy in complex reflection environments. At the same time, GNSS-RTK data is used as a reference to monitor positioning errors and dynamically adjust filter weights according to the environment. In outdoor environments, GNSS-RTK is used as the main positioning method to optimize the noise weights of pseudorange and carrier phase observations. In view of the occlusion in the storage environment, UWB data is used as auxiliary observation to compensate for the accuracy loss caused by GNSS signal occlusion. In the transition area between indoor and outdoor, a dynamic switching strategy is adopted. Single base station UWB and GNSS-RTK data are used as low-frequency update inputs, and a weighted filtering algorithm is used to achieve smooth switching to ensure the continuity and stability of positioning. According to the real-time needs of the warehousing environment, such as vehicle navigation or cargo tracking, the parameters in the fusion algorithm, such as switching delay and measurement weight, are dynamically adjusted to improve the adaptability and efficiency of the algorithm. Through optimized algorithm parameters and switching strategies, high-precision positioning and seamless switching capabilities are guaranteed in large storage environments to meet actual application needs.

[0065] S4: Real-time monitoring and correction of positioning errors to ensure the stability and reliability of the positioning system;

[0066] S5: Output the final positioning result.

[0067] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A seamless positioning method based on single-base station UWB / GNSS-RTK suitable for large storage environments, characterized in that: The specific steps are: S1: Collect UWB and GNSS-RTK data, where UWB is used for indoor positioning and GNSS-RTK is used for outdoor positioning and auxiliary correction of indoor positioning; S2: Establish a fusion positioning model to fuse UWB and GNSS data, and use the weighted extended Kalman filter (WEKF) to achieve seamless switching between indoor and outdoor positioning and high-precision positioning in indoor and outdoor areas; S3: Optimize fusion algorithm parameters and switching strategies according to the different characteristics of indoor and outdoor environments to improve positioning accuracy and switching efficiency; S4: Real-time monitoring and correction of positioning errors to ensure the stability and reliability of the positioning system; S5: Output the final positioning result.

2. According to claim 1, a seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environment, characterized in that: The specific steps of step S1 are: S11: In a single-base station UWB system, the UWB tag node sends signals, and the base station receives and processes these signals. By calculating the signal propagation time (TOF) and its changing characteristics, and combining parameters such as signal strength, the base station determines the position of the tag node relative to itself, thereby achieving high-precision positioning; S12: The GNSS-RTK receiver device synchronously receives signals from at least four GNSS satellites, and combines the differential correction information sent by the base station, using carrier phase measurement and differential algorithms to calculate the receiver's high-precision longitude, latitude and altitude information.

3. According to claim 2, a seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environment, characterized in that: The specific steps of step S11 are: S111: Single-base station TOF positioning uses a one-way flight time measurement method. The UWB tag actively sends a signal, and the UWB base station receives the signal and records the receiving time t receive When sending via a known signal, Room send , calculate the signal's flight time Δt; Δt = t receive -t send , In order to ensure high-precision time measurement, single-base station TOF positioning uses a high-precision synchronization module to synchronize the clocks of the base station and the tag; S112: Calculate the distance S from the tag to the base station by using the flight time and the propagation speed C of electromagnetic waves in the air: S = C·Δt. in: Δt is the signal flight time, C is the propagation speed of electromagnetic waves; The distance S obtained is the spherical distance from the tag to the base station. The tag is located on a sphere with the base station as the center and the distance S as the radius. S113: If the coordinates of the tag position relative to the base station are (x, y, z), and the known coordinates of the base station are (x0, y0, z0), then the distance between the tag and the base station satisfies the following formula: This represents a sphere with the base station as the center and S as the radius; S114: Fixed plane assumption: Assume that the label is located in a known plane, reduce the dimension of the solution through constraints, and calculate its two-dimensional coordinates; Dynamic trajectory tracking: Combined with the tag's motion trajectory prediction, the continuously measured distance information is used to estimate its position change on the spherical surface, thereby inferring the actual coordinates.

4. According to claim 2, a seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environment is characterized in that: The specific steps of step S12 are: S121: GNSS-RTK positioning uses the carrier phase observation method and relies on the differential technology between the base station and the mobile station to achieve high-precision positioning. The GNSS base station is set at a known fixed position, continuously receives satellite signals and calculates pseudorange and carrier phase information. The mobile station receives the same satellite signals and records its own observation data, and receives the differential correction information sent by the base station through the wireless link. The base station and the mobile station respectively record the pseudorange ρ and carrier phase observation value L of the satellite. The pseudorange formula is: ρ = r + c (δt-δT) + ∈ ρ , in: r: the actual geometric distance between the receiver and the satellite; c: speed of light; δt, δT: clock deviations of receiver and satellite; ∈ ρ : Pseudorange measurement error; The carrier phase formula is: in: λ: carrier wavelength; N: integer ambiguity; ∈ L : Carrier phase measurement error; S122: GNSS-RTK transmits the pseudo-range and carrier phase information of the base station in real time. The mobile station uses the correction data of the base station to eliminate most of the common errors, including satellite orbit error, satellite clock error and ionosphere error. The pseudo-range differential equation between the base station and the mobile station is: Dr=r mobile -r base , The carrier phase difference equation is: ΔL=L mobile -L base ; S123: RTK positioning solves the integer ambiguity N. After solving N, the baseline vector between the base station and the mobile station can be accurately calculated through the differential carrier phase observation value: in: (x m ,y m ,z m ): mobile station coordinates; (x b ,y b ,z b ): base station coordinates; B: baseline vector between the base station and the mobile station; By knowing the precise location of the base station (x b ,y b ,z b ), solve the three-dimensional coordinates of the mobile station: (x m ,y m ,z m )=(x b ,y b ,z b )+B S124: RTK positioning relies on observation data from multiple satellites and uses the least squares method to solve the position of the mobile station: The high-precision three-dimensional coordinates of the mobile station are solved through optimization.

5. The seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environment according to claim 1, characterized in that: The specific steps of step S2 are: S21: Model the single-base station UWB ranging noise and GNSS-RTK observation noise, and define the corresponding noise covariance matrix; Single-base station UWB ranging noise modeling: Assuming that the ranging error conforms to the normal distribution, its covariance matrix is ​​defined as: in, is the standard deviation of UWB ranging of a single base station; GNSS-RTK observation noise modeling: GNSS-RTK observation noise includes pseudorange and carrier phase observation noise, and its covariance matrix is: in: is the standard deviation of the GNSS pseudorange measurements; is the standard deviation of the GNSS carrier phase measurement; S22: Build a fusion positioning model to process the single-base station UWB and GNSS-RTK observation data using weighted extended Kalman filtering (WEKF) to achieve seamless switching and high-precision positioning; State vector definition: Construct a spatial state vector containing position, velocity, and time error states: X k =[x k ,y k ,z k ,v x ,v y ,v z ,b k ] T , in: x k ,y k ,z k : Current location coordinates; v x ,v y ,v z : current speed; b k : Receiver clock error; State transfer equation: describes the dynamic change of state, defined as: X k+1 =FX k +w k ,in: F: state transfer matrix; wk: system noise, satisfying w k ~N(0,Q), Q is the system noise covariance matrix; Observation model: Fusion of single-base station UWB and GNSS-RTK observation data to establish the observation equation: Z k =HX k +v k , in: H: observation matrix; v k : Observation noise, satisfying v k ~N(0,R),R=R UWB +R GNSS , Weighted filtering: According to the current environment (indoor or outdoor), UWB and GNSS observations are weighted separately: In=diag(in UwB ,In GNSS ), Indoor: w UWB >w GNSS , strengthen the impact of UWB; Outdoor: w GNSS >w UWB , strengthen the impact of GNSS; Filter update: Use weighted extended Kalman filter (WEKF) for state estimation and calculate Kalman Gain: K k =P k|k-1 H T (HP k|k-1 H T +WRW) -1 ; Update the state vector: Update the covariance matrix: P k =(IK k H)P k|k-1 .

6. The seamless positioning method based on single base station UWB / GNSS-RTK suitable for large storage environment according to claim 1, characterized in that: The specific steps of step S3 are: According to the different characteristics of indoor and outdoor environments, the fusion algorithm parameters and switching strategies are optimized to improve positioning accuracy and switching efficiency; In indoor environments, single-base station UWB is used as a positioning method to optimize the observation noise covariance matrix parameters to ensure ranging accuracy in complex reflection environments. At the same time, GNSS-RTK data is used as a reference to monitor positioning errors and dynamically adjust filter weights according to the environment. In outdoor environments, GNSS-RTK is used as a positioning method to optimize the noise weights of pseudorange and carrier phase observations. In view of the occlusion in the storage environment, UWB data is used as auxiliary observation to compensate for the accuracy loss caused by GNSS signal occlusion. In the transition area between indoor and outdoor, a dynamic switching strategy is adopted, with single-base station UWB and GNSS-RTK data as low-frequency update input, and a weighted filtering algorithm is used to achieve smooth switching to ensure the continuity and stability of positioning; Dynamically adjust the parameters in the fusion algorithm according to the real-time needs of the storage environment to improve the adaptability and efficiency of the algorithm; Through optimized algorithm parameters and switching strategies, high-precision positioning and seamless switching capabilities are guaranteed in large storage environments to meet actual application needs.

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