An indoor and outdoor integrated real-time combined positioning method

Abstract

The present invention discloses an indoor and outdoor integrated real-time combined positioning method, comprising the following steps: synchronizing a UWB time system of a wearable device with a GNSS time system, and unifying a UWB coordinate system of the wearable device with a GNSS coordinate system; determining an initial position of a monitored target; inputting the initial position of the monitored target or the position information of the last moment and the distance data between the wearable device and a UWB base station or a satellite into a Kalman filter, and a server calculating the real-time position of the detected target at the current moment according to the initial position or the position information of the last moment and the distance data between the wearable device and the UWB base station or the satellite; the present invention combines GNSS and UWB positioning technologies, distinguishes positioning scenes according to different positioning environments in indoor and outdoor environments, and can realize continuous positioning of personnel when switching between indoor and outdoor scenes. Compared with a single positioning system, the application range is wider.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent positioning, and in particular relates to an indoor and outdoor integrated real-time combined positioning method. Background Art

[0002] Currently, there is no single positioning technology that can meet people's requirements for comprehensive services. Although the Global Navigation Satellite System (GNSS) is sufficient to provide mature positioning services in open outdoor areas, there are still many limitations and shortcomings in outdoor obstructed environments when relying solely on GNSS technology.

[0003] In order to solve the problem that outdoor positioning technology cannot achieve accurate indoor positioning, indoor positioning technology has gradually developed. UWB positioning technology uses carrier-free communication technology (UltraWideband) to achieve positioning. UWB positioning technology has strong anti-multipath and penetration capabilities, and can provide high-precision location information indoors. However, indoor positioning is relatively independent from outdoor positioning. For complex indoor and outdoor environments, a single positioning solution is difficult to cover indoor and outdoor spaces at the same time, and cannot well meet the needs of continuous high-precision positioning in various scenarios. Combining multiple positioning technologies based on the needs of actual positioning scenarios and the complementary advantages of various positioning technologies is currently a common high-precision positioning solution.

[0004] Since indoor positioning is developed independently, there is an inherent gap with outdoor GNSS technology. In this case, the problem of achieving positioning in outdoor obstructed environments, positioning in indoor environments, positioning in indoor and outdoor interactive areas, and switching between positioning systems has become the focus of indoor and outdoor seamless positioning technology research. Combining GNSS technology with UWB positioning technology, GNSS technology can be used to achieve positioning in outdoor environments, and UWB positioning technology can be used to achieve location services in indoor environments. At the same time, in order to achieve a smooth transition of positioning services, the problem of free switching between positioning systems of different systems needs to be solved. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes an indoor and outdoor integrated real-time combined positioning method. The present invention uses a wearable device integrated with UWB and GNSS positioning chips to receive ranging information and send the ranging information to a server. The server then receives and parses the received UWB and GNSS ranging information to calculate the real-time position under the two systems. The environment is judged according to the number of received satellites and UWB base stations and the location information is output. The present invention takes into account the indoor and outdoor integrated positioning solution, and designs the judgment strategy of the indoor and outdoor environment positioning solution, providing a solution for seamless indoor and outdoor positioning.

[0006] To achieve the above objectives, the technical solution of the present invention is:

[0007] An indoor and outdoor integrated real-time combined positioning method comprises the following steps:

[0008] S1: System synchronization, wherein the system synchronization includes synchronization of the wearable device UWB time system with the GNSS time system, and unification of the wearable device UWB coordinate system with the GNSS coordinate system;

[0009] S2: Determine the initial position of the monitored target. The S2 step specifically includes:

[0010] S21: Determine whether the monitored target is located in an indoor or outdoor environment

[0011] Set the switching threshold between the wearable device's GNSS positioning system and the UWB positioning system;

[0012] The GNSS positioning module included in the wearable device searches for the number of satellites that can receive satellite signals. When the number of satellites that can receive satellite signals searched by the GNSS positioning module is less than the threshold, the monitored target is indoors, and the wearable device adopts the UWB positioning system;

[0013] When the number of satellites whose satellite signals can be received by the GNSS positioning module is greater than or equal to the threshold, the monitored target is outdoors, and the wearable device uses the GNSS positioning system;

[0014] S22: The wearable device collects distance data and sends it to the server;

[0015] When the wearable device adopts the UWB positioning system, the UWB positioning system included in the wearable device collects the distance data between the monitored target and multiple UWB base stations, and the transmission module included in the wearable device sends the distance data between the monitored target and each UWB base station to the server;

[0016] When the wearable device adopts a GNSS positioning system, the GNSS positioning system included in the wearable device collects distance data between the monitored target and multiple satellites, and the transmission module included in the wearable device sends the distance data between the monitored target and each satellite to the server;

[0017] S23: The server calculates the initial position of the monitored target based on the received distance data;

[0018] When the server receives the distance data between the monitored target and the UWB base station, the server calculates the distance value between the monitored target and each UWB base station based on the distance data between the monitored target and multiple UWB base stations, and the server determines the coordinate value of the initial position of the monitored target in the three-dimensional coordinate system based on the distance value between the monitored target and each UWB base station;

[0019] When the server receives the distance data between the monitored target and the satellite, the server calculates the distance value between the monitored target and each satellite based on the distance data between the monitored target and multiple satellites, and the server determines the coordinate value of the initial position of the monitored target in the three-dimensional coordinate system based on the distance value between the monitored target and each satellite;

[0020] S3: Determine the real-time position of the monitored target at the current moment. The S3 step specifically includes:

[0021] S31: Determine whether the monitored target is currently in an indoor or outdoor environment

[0022] The GNSS positioning module included in the wearable device searches for the number of satellites that can receive satellite signals. When the number of satellites that can receive satellite signals searched by the GNSS positioning module is less than a preset threshold, the monitored target is indoors, and the wearable device adopts the UWB positioning system;

[0023] When the number of satellites whose satellite signals can be received by the GNSS positioning module is greater than or equal to a preset threshold, the monitored target is outdoors, and the wearable device uses the GNSS positioning system;

[0024] S32: The wearable device collects the distance data at the current moment and sends it to the server;

[0025] When the wearable device adopts the UWB positioning system, the UWB positioning system included in the wearable device collects the distance data between the monitored target and the UWB base station, and the transmission module included in the wearable device sends the distance data between the monitored target and the UWB base station to the server;

[0026] When the wearable device adopts a GNSS positioning system, the GNSS positioning system included in the wearable device collects distance data between the monitored target and the satellite, and the transmission module included in the wearable device sends the distance data between the monitored target and the satellite to the server;

[0027] When the wearable device adopts the UWB positioning system, the UWB positioning system included in the wearable device collects the distance data between the monitored target and the UWB base station and sends it to the server;

[0028] When the wearable device adopts a GNSS positioning system, the GNSS positioning system included in the wearable device collects distance data between the monitored target and the satellite and sends it to the server;

[0029] S33: The server calculates the real-time position of the monitored target at the current moment based on the received distance data and the position at the previous moment;

[0030] When the server receives the distance data between the monitored target and multiple UWB base stations, the server calculates the current real-time position state vector x of the monitored target based on the distance data between the monitored target and multiple UWB base stations and the position at the previous moment. k As shown in formula (7):

[0031]

[0032] in It represents the state vector predicted at the current moment of the monitored target when the UWB positioning module is used, which is obtained by the distance data between the monitored target and multiple UWB base stations at the current moment and the position prediction at the previous moment;

[0033] K k represents the gain matrix of Kalman filter;

[0034] is the predicted distance between the monitored target and the UWB base station at the current moment pass The state vector predicted at the current moment and the base station coordinate values ​​are calculated;

[0035] r k It is the actual distance between the monitored target and the UWB base station at the current moment, which is calculated based on the distance data received at the current moment;

[0036] The state vector x of the monitored target at the current moment k Including the current coordinate value of the monitored target, the state vector x of the monitored target at the current moment is calculated k Then the current location of the monitored target is obtained;

[0037] When the server receives the distance data between the monitored target and multiple satellites, the server calculates the real-time position state vector of the monitored target according to the distance data between the monitored target and multiple satellites as shown in formula (11):

[0038]

[0039] Xg i It is the state vector of the monitored target at the current moment when the GNSS positioning module is used;

[0040] State transition matrix It is calculated by the server based on the state vector predicted at the current moment and the state vector at the previous moment;

[0041] Xg i-1 is the initial position or the position state vector of the previous moment;

[0042] k i represents the Kalman filter gain matrix;

[0043] Z(i) is the high-precision distance data after double difference analysis of GNSS-RTK carrier phase difference;

[0044] H i is a coefficient matrix used to represent the conversion relationship between the Kalman filter observation and the state vector, through the Kalman filter derivation method.

[0045] Furthermore, in step S33, the method for calculating the state vector predicted at the current moment of the monitored target is:

[0046]

[0047] in, represents the state vector predicted by the monitored target at the current moment, It includes the predicted coordinate values ​​of the monitored target in the ITRF coordinate frame, the system matrix A k It can be derived from the uniform velocity assumption that A k As shown in formula (4):

[0048]

[0049] x k-1 is the state vector of the monitored target at the last moment. Due to constant speed motion, the control input u k-1 and input gain B k-1 are all zero, Represents the covariance matrix of the current prediction error, P k-1 is the covariance matrix of the previous moment, process noise Q k-1 The acceleration can be preset to be a Gaussian distribution N(0, σ a ) assumption; T is the matrix transpose, and t is the time interval between each Kalman filter iteration.

[0050] Furthermore, in step S33, when the server calculates the current real-time position state of the monitored target based on the distance data between the monitored target and multiple UWB base stations, the gain matrix K of the Kalman filter is k The calculation method is shown in formula (8) and formula (9).

[0051]

[0052] Represents the covariance matrix of the current prediction error, H k is a coefficient matrix, which is used to represent the conversion relationship between the Kalman filter observation and the state vector. Through the Kalman filter derivation method, T is the matrix transpose, R k is the measurement noise covariance, as shown in formula (10), P k Represents the update of the state covariance matrix at the current moment, and I is the unit matrix.

[0053] Furthermore, in step S33, when the server calculates the current real-time position state of the monitored target based on the distance data between the monitored target and multiple satellites, the gain matrix k of the Kalman filter is i It is calculated by the following formula (12), formula (13) and (14):

[0054]

[0055] D ω (i)=(Ik i H i )D x (i,i-1) (14)

[0056] Among them, D x (i,i-1) represents the covariance matrix of the prediction error at the current moment;

[0057] The state transfer matrix is ​​the server-side position information at the last moment and the distance data received; the speed and acceleration information of the monitored target are calculated by the Doppler speed measurement algorithm, and the server constructs the state transfer matrix through the existing method based on the speed and acceleration information

[0058] D x (i-1) is the covariance matrix of the previous moment;

[0059] H i is a coefficient matrix, which is used to represent the conversion relationship between the Kalman filter observation and the state vector, through the Kalman filter derivation method;

[0060] D Δ (i) The observation covariance matrix is ​​calculated by combining the observations of the Kalman filter at the current moment with the observations at the previous moment.

[0061] The beneficial effects of the present invention are:

[0062] 1. The present invention combines GNSS and UWB positioning technologies, distinguishes positioning scenarios for different positioning environments in indoor and outdoor environments, and invents an indoor and outdoor integrated real-time combined positioning method, which can achieve continuous positioning of personnel when switching between indoor and outdoor scenes. Compared with a single positioning system, it has a wider range of applications.

[0063] 2. The present invention designs and implements a seamless switching solution for the system in the connection area of ​​indoor and outdoor scenes, sets judgment thresholds for GNSS and UWB positioning modes, and selects the more appropriate one from the two positioning results for return in the connection area to ensure the continuity of location information.

[0064] 3. For the two positioning schemes in indoor and outdoor scenes, the present invention uses a constant-speed motion model to merge with the position information of the positioning system, which is more in line with the actual use scenarios of wearable devices, helps to improve positioning accuracy, and smoothes the positioning trajectory of personnel.

[0065] 4. The present invention uses a server in the background to calculate the position of the collected data and transmit it back, which helps to reduce the hardware cost of wearable devices and improve the stability of the combined positioning service. The server background distributes the personnel location information, which can achieve effective management of the spatial position and status of construction personnel and equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flow chart of an embodiment of the present invention.

[0067] Figure 2 Schematic diagram of the working principle of wearable devices in the positioning system.

[0068] Figure 3 Schematic diagram of indoor and outdoor positioning mode switching of wearable devices. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the invention clearer, the present invention is further described below with reference to the accompanying drawings.

[0070] In the present invention, the positioning terminal is a wearable device, which collects the location information of the monitored target through the wearable device and sends the collected information to the server terminal; in an embodiment of the present invention, the monitored target is set as a moving person, and the wearable device can be a helmet, bracelet, anklet, etc. worn by the monitored person; the server refers to the server of the monitoring center. For example, when personnel are performing engineering work in other places, the monitoring center needs to grasp the position or movement trajectory of the personnel in real time. The wearable device collects the location information of the monitored person and transmits the location information back to the server of the monitoring center in real time.

[0071] In the present invention, Figure 2As shown, the wearable device includes a UWB positioning module, a GNSS positioning module, a transmission module, and a microprocessor. The transmission module can be a 4G or 5G module. The microprocessor is used to control the UWB positioning module, the GNSS positioning module, and the transmission module to work according to the set program.

[0072] The UWB positioning module communicates wirelessly with the UWB base station via Wi-Fi signals. The UWB positioning module integrated in the wearable device obtains UWB time information, UWB observation data, i.e., the distance data between the person and the UWB base station, and the number of UWB base stations that can receive UWB signals. The information collected by the UWB positioning module is transmitted to the server of the monitoring center via the transmission module of the wearable device.

[0073] The GNSS positioning module receives microwave signals from satellites through antennas, and then obtains GNSS time information, GNSS observation data, i.e., the distance data between personnel and satellites, GNSS navigation information such as satellite orbit information, and the number of visible satellites that the GNSS positioning module can receive signals. The information collected by the GNSS positioning module is transmitted to the server of the monitoring center through the transmission module of the wearable device.

[0074] Under the premise of unified time and space reference of the positioning terminal, the UWB positioning module and the GNSS positioning module respectively receive the distance measurement related information. The UWB positioning module receives the distance data between the person and multiple UWB base stations, and the GNSS positioning module receives the distance data between the person and multiple satellites. The distance data collected by the GNSS positioning module or the UWB positioning module is transmitted to the server of the monitoring center by the transmission module of the wearable device.

[0075] When the server of the monitoring center receives the distance data between a person and multiple UWB base stations collected by the UWB positioning model, the server calculates the distance between the person and each UWB base station based on the distance data. In the three-dimensional coordinate system, the position of each UWB base station is known. The server can calculate the position of the person based on the distance between the person and multiple UWB base stations.

[0076] When the server of the monitoring center receives the distance data between a person and multiple satellites collected by the GNSS positioning model, the server calculates the distance between the person and each satellite based on the distance data. In the three-dimensional coordinate system, the position of each satellite is known. The server can calculate the position of the person based on the distance between the person and multiple satellites.

[0077] If the monitored target is in a moving state, the distance data between the person and multiple UWB base stations collected by the UWB positioning module is transmitted to the server by the transmission module of the wearable device at a set frequency, or the distance data between the person and multiple satellites collected by the GNSS positioning module is transmitted to the server by the transmission module of the wearable device at a set frequency. The server can calculate the person's moving speed and the person's current position based on the person's position at the previous moment and the distance data received at the current moment.

[0078] In the present invention, Figure 1 , Figure 2 and Figure 3 As shown in the figure, during the switching process of the wearable device positioning system, the current indoor or outdoor location is determined according to the switching conditions. At a certain moment, only one positioning algorithm result will be output, and the number of satellites, the number of UWB base stations, and the strength of the positioning signal are used as parameter indicators for switching judgment. The positioning scenarios of the present invention can be mainly divided into the following two types:

[0079] The first one: positioning in indoor environment

[0080] When the scene is completely indoors, the number of available GNSS signals is small, and even satellite signals cannot be captured and tracked. Even if the receiver captures a small number of satellite signals, the signals have undergone a large number of multipath propagations and have large noise, and the signal quality is low. However, at this time, the ranging information of multiple UWB base stations can be received, and real-time positioning can be performed through the UWB system.

[0081] The second type: positioning in open outdoor scenes

[0082] In open outdoor scenes, GNSS observation information is sufficient but ultra-wideband observation information is lacking. Therefore, GNSS pseudorange and pseudorange rate observations can be used to provide an information source for reliable positioning solutions.

[0083] When the wearer of the device enters indoors from outdoors, he will pass through an indoor-outdoor connection area. In this area, the number of satellites that the GNSS positioning module can receive signals from is decreasing, while the number of UWB base stations that the UWB positioning module can receive signals from is increasing.

[0084] To this end, it is necessary to set a threshold for the switching of the positioning system. During the positioning process, satellites and UWB base stations are searched. Specifically, when the number of UWB base stations whose signals can be received by the UWB positioning module is greater than or equal to 4, the microprocessor included in the wearable device sends the distance data collected by the UWB positioning module to the server through the transmission module. The distance data collected by the UWB positioning module is used as the parameter for the final positioning.

[0085] In other cases, the microprocessor included in the wearable device sends the distance data collected by the GNSS positioning module to the server through the transmission module, and the distance data information collected by the GNSS positioning module is used as a parameter for the final positioning. For example, the GNSS positioning module receives satellite signals. When the number of satellites from which the GNSS positioning module can receive signals is greater than or equal to 4, the microprocessor included in the wearable device sends the distance data collected by the GNSS positioning module to the server through the transmission module, and the distance data information collected by the GNSS positioning module is used as a parameter for the final positioning; or when the number of satellites from which the GNSS positioning module can receive signals is decreasing and at the same time the number of UWB base stations from which the UWB positioning module can receive signals is increasing, the microprocessor included in the wearable device sends the distance data collected by the GNSS positioning module to the server through the transmission module, and the distance data information collected by the GNSS positioning module is used as a parameter for the final positioning.

[0086] The present invention provides an indoor and outdoor integrated real-time combined positioning method comprising the following steps:

[0087] Step 1: System synchronization

[0088] Step 1.1: Synchronize the wearable device UWB time system with the GNSS time system

[0089] The synchronization of the wearable device UWB time system and the GNSS time system is a unification of the time system of the GNSS system and the UWB system based on the need to achieve real-time positioning of the movement status of the personnel. The GNSS system and the UWB system have their own specific time systems, so it is necessary to convert the local time of the UWB into the GNSS time system to unify the time scale. Only under the same time scale can seamless real-time positioning be achieved when switching between indoor and outdoor environments. The unification of the time system includes two levels. The first is to unify the time of each GNSS system. The GNSS system includes the European Galileo GALILEO satellite system, the Russian GLONASS satellite system, and the Chinese Beidou BEIDOU satellite system. The second is to unify the local time of the UWB system to the UTC time system.

[0090] The first point is to unify the time of each GNSS system to the UTC time system.

[0091] UTC (Coordinated Universal Time) is also known as World Standard Time.

[0092] Due to different timing bases, there is a time difference Δs between each GNSS system GALILEO, GLONASS, BEIDOU and UTC. Δs represents the time difference between different positioning systems and UTC. Correcting the time difference can convert the time of each system to UTC time.T For: U T =T s -Δs, where T s It is the current time of different GNSS positioning systems.

[0093] The second point is to unify the local time of the UWB system to the UTC time system. UWB usually uses local coordinates. In this case, the time system used by UWB is Beijing time. UTC uses the Greenwich meridian as the starting point. Therefore, the difference between UWB and UTC time is 8 hours. Unifying UWB time to UTC time T for:

[0094] U T =T uwb -8

[0095] Step 1.2, server-side coordinate system synchronization

[0096] The server of the present invention refers to the server of the monitoring center. For example, when personnel are performing engineering work in other places, the monitoring center needs to know the position or movement trajectory of the personnel in real time. The position information of the monitored personnel can be transmitted back to the server of the monitoring center in real time.

[0097] The server side unifies the coordinate systems of UWB and GNSS systems based on the need to realize real-time positioning according to the movement status of personnel. The measurement results of the GNSS system are in the ITRF coordinate frame. ITRF (International Terrestrial Reference Frame) is established by the International Earth Rotation Service Bureau according to certain requirements. The ground observation stations distributed around the world, while UWB uses a local coordinate system. There are large differences between the two in the coordinate origin and the direction of the coordinate axis. The local coordinate system of UWB needs to be transformed into the ITRF coordinate frame. The coordinate reference of UWB ranging information is the independent three-dimensional space rectangular coordinate system where the UWB base station is located. Therefore, the UWB coordinate system can be converted to the ITRF coordinate frame by transforming the UWB base station coordinates. Combined with the prior rotation matrix ω and the origin coordinate difference Δx, the UWB base station coordinates X uwb (x0, y0, z0) is converted to the ITRF coordinate frame, and the UWB base station coordinates X ITRF The calculation formula for (x1,y1,z1) is:

[0098] X ITRF =Δx+ωX uwb

[0099] Δx represents the difference between the origins of the UWB coordinate system and the ITRF coordinate system. uwbis the coordinate of the UWB base station, and ω is the a priori rotation matrix obtained according to the existing method based on the actual situation of the coordinate system.

[0100] Step 2: Determine the initial position of the monitored target

[0101] The initial position determination of the monitored target refers to position initialization, which includes starting up the positioning system, such as starting up the wearable device, and determining the initial position of the monitored target, which specifically includes the following steps:

[0102] S21: Determine whether the monitored target is located in an indoor or outdoor environment

[0103] Set the switching threshold between the wearable device's GNSS positioning system and the UWB positioning system;

[0104] The GNSS positioning module included in the wearable device searches for the number of satellites that can receive satellite signals. When the number of satellites that can receive satellite signals searched by the GNSS positioning module is less than the threshold, the monitored target is indoors, and the wearable device adopts the UWB positioning system;

[0105] When the number of satellites whose satellite signals can be received by the GNSS positioning module is greater than or equal to the threshold, the monitored target is outdoors, and the wearable device uses the GNSS positioning system;

[0106] S22: The wearable device collects distance data and sends it to the server;

[0107] When the wearable device adopts the UWB positioning system, the UWB positioning system included in the wearable device collects the distance data between the monitored target and multiple UWB base stations, and the transmission module included in the wearable device sends the distance data between the monitored target and each UWB base station to the server;

[0108] When the wearable device adopts a GNSS positioning system, the GNSS positioning system included in the wearable device collects distance data between the monitored target and multiple satellites, and the transmission module included in the wearable device sends the distance data between the monitored target and each satellite to the server;

[0109] S23: The server calculates the initial position of the monitored target based on the received distance data;

[0110] When the server receives the distance data between the monitored target and the UWB base station, the server calculates the distance value between the monitored target and each UWB base station based on the distance data between the monitored target and multiple UWB base stations, and the server determines the three-dimensional coordinate value of the initial position of the monitored target based on the distance value between the monitored target and each UWB base station;

[0111] When the server receives the distance data between the monitored target and the satellite, the server calculates the distance value between the monitored target and each satellite based on the distance data between the monitored target and multiple satellites, and determines the three-dimensional coordinate value of the initial position of the monitored target based on the distance value between the monitored target and each satellite.

[0112] S3: Determine the real-time position of the monitored target at the current moment. The S3 step specifically includes:

[0113] S31: Determine whether the monitored target is currently in an indoor or outdoor environment

[0114] The GNSS positioning module included in the wearable device searches for the number of satellites that can receive satellite signals. When the number of satellites that can receive satellite signals searched by the GNSS positioning module is less than a preset threshold, the monitored target is currently indoors, and the wearable device adopts the UWB positioning system;

[0115] When the number of satellites whose satellite signals can be received by the GNSS positioning module is greater than or equal to a preset threshold, the monitored target is currently outdoors, and the wearable device uses the GNSS positioning system;

[0116] S32: The wearable device collects distance data and sends it to the server;

[0117] When the wearable device adopts the UWB positioning system, the UWB positioning system included in the wearable device collects the distance data between the monitored target and multiple UWB base stations, and the transmission module included in the wearable device sends the distance data between the monitored target and each UWB base station to the server;

[0118] When the wearable device adopts a GNSS positioning system, the GNSS positioning system included in the wearable device collects distance data between the monitored target and multiple satellites, and the transmission module included in the wearable device sends the distance data between the monitored target and each satellite to the server;

[0119] S33: The server calculates the real-time position of the monitored target at the current moment based on the received distance data;

[0120] In the present invention, Kalman filtering is used in real-time positioning. The Kalman filtering algorithm is an algorithm that optimally estimates the system state by inputting and outputting observation data of the system. On the server side, the system calculates the predicted position at the current moment through the position recorded at the previous moment, and combines it with the ranging information received at the current moment, and fuses it through the Kalman filter to obtain the optimal estimate of the position of the detected target at the current moment.

[0121] The first case: when the server receives the distance data between the monitored target and multiple UWB base stations, the server calculates the position of the monitored target at the current moment according to the distance data between the monitored target and the multiple UWB base stations.

[0122] The server side calculates the speed and acceleration information of the monitored target through the Doppler speed measurement algorithm based on the position information at the previous moment and the distance data received at the current moment. The server constructs a state transfer matrix through the existing method based on the speed and acceleration information. The state transfer matrix is ​​used for subsequent Kalman filtering.

[0123] Assuming that the movement of personnel wearing devices is a constant-speed motion model, the monitoring center server receives the ranging data collected by the UWB positioning module, and inputs the ranging data and the position of the monitored target at the previous moment into the Kalman filter, and performs information fusion through the extended Kalman filter. Specifically, the trajectory obtained based on the UWB ranging data is fused with the trajectory of the uniform motion model through the extended Kalman filter. The time update process can be expressed as follows: In the ordinary EKF extended Kalman filter, the state vector consists of the defined position and velocity.

[0124] x=[p x ,v x ,p y ,v y ,p z ,v z ] T (1)

[0125] Where x is the current state vector of the monitored target, p x v is the coordinate value of the detected target in the x direction at the current moment in the ITRF coordinate frame; x is the speed of the detected target in the x direction at the current moment, p y v is the coordinate value of the detected target in the y direction at the current moment in the ITRF coordinate frame; y is the speed of the detected target in the y direction at the current moment, p z v is the coordinate value of the detected target in the current z direction in the ITRF coordinate frame; zy is the speed of the detected target in the z direction at the current moment, and T is the matrix transpose.

[0126] Under the constant speed model, the time update process is defined as

[0127]

[0128] in, represents the state vector predicted by the monitored target at the current moment, It includes the predicted coordinate values ​​of the monitored target in the ITRF coordinate frame, the system matrix A k It can be derived from the uniform velocity assumption that x k-1 is the state vector of the monitored target at the last moment. Due to constant speed motion, the control input u k-1 and input gain B k-1 are all zero, Represents the covariance matrix of the current prediction error, P k-1 is the covariance matrix of the previous moment, process noise Q k-1 The acceleration can be preset to be a Gaussian distribution N(0, σ a ) is derived from the assumption that σ a Given according to experience. t is the time interval between each EKF Kalman filter iteration.

[0129] The system matrix A in formula (2) and formula (3) is k Obtained from formula (4).

[0130]

[0131] The process noise Q in formula (3) k-1 Obtained from formula (5).

[0132]

[0133] The predicted distance between the monitored target and the UWB base station at the current moment for:

[0134]

[0135] where p x 、p y 、p z is the state vector predicted by the monitored target at the current moment in formula (2) The predicted coordinates of the monitored target at the current moment, p Ax 、p Ay 、p Az is the coordinate value of the UWB base station.

[0136] The measurement update of the extended Kalman filter is defined as follows, where the measurement value is denoted by r k , r k The UWB positioning module collects the distance data between the monitored target and multiple UWB base stations, and calculates the distance value between the monitored target and the UWB base station through the distance data. k It is the measured value obtained by collecting data.

[0137] The state vector x of the monitored target at the current moment isk As shown in formula (7), the state vector x of the monitored target at the current moment is k Includes the current status value of the monitored target.

[0138]

[0139] in It represents the state vector predicted at the current moment of the monitored target when the UWB positioning module is used, which is obtained by the distance data between the monitored target and multiple UWB base stations at the current moment and the position prediction at the previous moment;

[0140] K k represents the gain matrix of Kalman filter;

[0141] is the predicted distance between the monitored target and the UWB base station at the current moment It is calculated by the state vector predicted at the current moment and the base station coordinate value;

[0142] r k It is the actual distance between the monitored target and the UWB base station at the current moment, which is calculated based on the distance data received at the current moment;

[0143] The gain matrix K of the Kalman filter in formula (7) is k The calculation method is shown in formula (8) and formula (9).

[0144]

[0145] Represents the covariance matrix of the current prediction error, H k is the coefficient matrix, which is used to express the conversion relationship between the observation quantity and the state quantity. It is determined according to the actual situation. T is the matrix transpose, R k is the measurement noise covariance, as shown in formula (10), P k Represents the update of the state covariance matrix at the current moment, and I is the unit matrix.

[0146]

[0147] where σ r It is a set value, which is given according to the distance measurement accuracy of the device.

[0148] The state vector x of the monitored target at the current moment k Including the current coordinate value of the monitored target, the state vector x of the monitored target at the current moment is calculated k The current location of the monitored target is obtained.

[0149] The second situation: when the server receives the distance data between the monitored target and multiple satellites, the server calculates the position of the monitored target at the current moment according to the distance data between the monitored target and the multiple satellites.

[0150] According to the initial position obtained in step S2, the initial position is recorded as Xg i-1 , the covariance matrix D of the initial position or the previous moment x (i-1) and the state transfer matrix And observation information Z(i), coefficient matrix H i , the prior state equation noise covariance D ω (i-1), and the observation covariance matrix D Δ The initial position is filtered and updated by using information such as (i), where the GNSS information in Z(i) is the high-precision observation value after GNSS-RTK double difference. The positioning result Xg at a certain moment based on Kalman filtering i It can be expressed as:

[0151]

[0152] Xg i It is the state vector of the monitored target at the current moment when the GNSS positioning module is used;

[0153] State transition matrix The server calculates the state vector based on the current predicted state vector and the state vector at the previous moment; the speed and acceleration information of the monitored target are calculated by the Doppler speed measurement algorithm, and the server constructs the state transfer matrix by combining the speed and acceleration information through the existing method

[0154] State transition matrix The server side calculates the speed and acceleration information of the monitored target based on the location information of the previous moment and the distance data received; the server constructs the state transfer matrix through the existing method based on the speed and acceleration information

[0155] Xg i-1 is the initial position or the position state vector of the previous moment;

[0156] k i represents the Kalman filter gain matrix, which is calculated by formula (12);

[0157] Z(i) is the high-precision observation value after GNSS-RTK double difference;

[0158] H iIt is a coefficient matrix used to represent the conversion relationship between the Kalman filter observation and the state vector. It is established through the Kalman filter derivation method and determined according to the actual situation.

[0159] The filter gain k in formula (11) i It is calculated by the following formula (12), formula (13) and (14):

[0160]

[0161] D ω (i)=(Ik i H i )D x (i,i-1) (14)

[0162] Among them, D x (i,i-1) represents the covariance matrix of the prediction error at the current moment;

[0163] The state transfer matrix is ​​the server-side position information at the last moment and the distance data received; the speed and acceleration information of the monitored target are calculated by the Doppler speed measurement algorithm, and the server constructs the state transfer matrix through the existing method based on the speed and acceleration information

[0164] H i is a coefficient matrix, which is used to express the conversion relationship between the observed quantity and the state quantity, and is determined according to the actual situation;

[0165] D Δ (i) The observation covariance matrix is ​​calculated by combining the observations at the current moment with the observations at the previous moment.

[0166] While updating the position, the covariance matrix is ​​also updated. The current position and covariance information can be estimated and updated based on the initial position at the previous moment or the covariance information combined with the current moment observation information, and real-time positioning can be achieved by recursive method.

[0167] The server calculates the real-time coordinates of the monitored target and displays the real-time position of the monitored target on the display screen of the server end of the monitoring center. Thus, the process of the indoor and outdoor integrated real-time combined positioning method is completed.

[0168] The present invention elaborates on the indoor and outdoor integrated real-time combined positioning method in detail in combination with specific implementation methods. The method proposed in the present invention can meet the requirements of continuous high-precision positioning in various scenarios and realize an integrated positioning solution covering indoor and outdoor spaces. The present invention combines GNSS and UWB positioning technologies, distinguishes positioning scenarios for different positioning environments in indoor and outdoor environments, and has a wider application range compared to a single positioning system. Secondly, for the connection area of ​​indoor and outdoor scenes, a seamless switching scheme of the system is designed to ensure the continuity of location information. For the two positioning schemes in indoor and outdoor scenes, the constant speed motion model is used to merge with the location information of the positioning system, which is more in line with the actual use scenario of wearable devices, helps to improve positioning accuracy, and smooths the positioning trajectory of personnel. Using the server to solve the position of the collected data in the background and transmit it back helps to reduce the hardware cost of wearable devices and improve the stability of the combined positioning service. The server background distributes the location information of personnel, which can achieve effective management of the spatial position and status of construction personnel and equipment.

[0169] During specific implementation, the above process can be automatically operated using computer software technology.

[0170] Finally, it should be noted that the contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field. The above description is only the preferred embodiment of the present invention and is not used to limit the present invention. Although the present invention is described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An indoor and outdoor integrated real-time combined positioning method, characterized in that: The following steps are involved: S1: System synchronization, wherein the system synchronization includes synchronization of the wearable device UWB time system with the GNSS time system, and unification of the wearable device UWB coordinate system with the GNSS coordinate system; S2: Determine the initial position of the monitored target. The S2 step specifically includes: S21: Determine whether the monitored target is located in an indoor or outdoor environment Set the switching threshold between the wearable device's GNSS positioning system and the UWB positioning system; The GNSS positioning module included in the wearable device searches for the number of satellites that can receive satellite signals. When the number of satellites that can receive satellite signals searched by the GNSS positioning module is less than the threshold, the monitored target is indoors, and the wearable device adopts the UWB positioning system; When the number of satellites whose satellite signals can be received by the GNSS positioning module is greater than or equal to the threshold, the monitored target is outdoors, and the wearable device uses the GNSS positioning system; S22: The wearable device collects distance data and sends it to the server; When the wearable device adopts the UWB positioning system, the UWB positioning system included in the wearable device collects the distance data between the monitored target and multiple UWB base stations, and the transmission module included in the wearable device sends the distance data between the monitored target and each UWB base station to the server; When the wearable device adopts a GNSS positioning system, the GNSS positioning system included in the wearable device collects distance data between the monitored target and multiple satellites, and the transmission module included in the wearable device sends the distance data between the monitored target and each satellite to the server; S23: The server calculates the initial position of the monitored target based on the received distance data; When the server receives the distance data between the monitored target and the UWB base station, the server calculates the distance value between the monitored target and each UWB base station based on the distance data between the monitored target and multiple UWB base stations, and the server determines the initial position of the monitored target based on the distance value between the monitored target and each UWB base station; When the server receives the distance data between the monitored target and the satellite, the server calculates the distance value between the monitored target and each satellite according to the distance data between the monitored target and the multiple satellites, and the server determines the initial position of the monitored target according to the distance value between the monitored target and each satellite; S3: Determine the real-time position of the monitored target at the current moment. The S3 step specifically includes: S31: Determine whether the monitored target is currently in an indoor or outdoor environment The GNSS positioning module included in the wearable device searches for the number of satellites that can receive satellite signals. When the number of satellites that can receive satellite signals searched by the GNSS positioning module is less than a preset threshold, the monitored target is indoors, and the wearable device adopts the UWB positioning system; When the number of satellites whose satellite signals can be received by the GNSS positioning module is greater than or equal to a preset threshold, the monitored target is outdoors, and the wearable device uses the GNSS positioning system; S32: The wearable device collects the distance data at the current moment and sends it to the server; When the wearable device adopts the UWB positioning system, the UWB positioning system included in the wearable device collects the distance data between the monitored target and the UWB base station, and the transmission module included in the wearable device sends the distance data between the monitored target and the UWB base station to the server; When the wearable device adopts a GNSS positioning system, the GNSS positioning system included in the wearable device collects distance data between the monitored target and the satellite, and the transmission module included in the wearable device sends the distance data between the monitored target and the satellite to the server; When the wearable device adopts the UWB positioning system, the UWB positioning system included in the wearable device collects the distance data between the monitored target and the UWB base station and sends it to the server; When the wearable device adopts a GNSS positioning system, the GNSS positioning system included in the wearable device collects distance data between the monitored target and the satellite and sends it to the server; S33: The server calculates the real-time position of the monitored target at the current moment based on the received distance data and the position at the previous moment; When the server receives the distance data between the monitored target and multiple UWB base stations, the server calculates the current real-time position state vector x of the monitored target based on the distance data between the monitored target and multiple UWB base stations and the position at the previous moment. k As shown in formula (7): in It represents the state vector predicted at the current moment of the monitored target when the UWB positioning module is used, which is obtained by the distance data between the monitored target and multiple UWB base stations at the current moment and the position prediction at the previous moment; K k represents the gain matrix of Kalman filter; is the predicted distance between the monitored target and the UWB base station at the current moment It is calculated by the state vector predicted at the current moment and the base station coordinate value; r k It is the actual distance between the monitored target and the UWB base station at the current moment, which is calculated based on the distance data received at the current moment; The calculation method of the state vector predicted by the monitored target at the current moment is: in, represents the state vector predicted by the monitored target at the current moment, It includes the predicted coordinate values ​​of the monitored target in the ITRF coordinate frame, the system matrix A k It can be derived from the uniform velocity assumption that A k As shown in formula (4): x k-1 is the state vector of the monitored target at the last moment. Due to constant speed motion, the control input u k-1 and input gain B k-1 are all zero, Represents the covariance matrix of the current prediction error, P k-1 is the covariance matrix of the previous moment, process noise Q k-1 The acceleration can be preset to be a Gaussian distribution N(0, σ a ) is derived from the assumption that; T is the matrix transpose, and t is the time interval between each Kalman filter iteration; The gain matrix K of the Kalman filter is k The calculation method is shown in formula (8) and formula (9). Represents the covariance matrix of the current prediction error, H k is a coefficient matrix, which is used to represent the conversion relationship between the Kalman filter observation and the state vector. Through the Kalman filter derivation method, T is the matrix transpose, R k is the measurement noise covariance, P k Represents the update of the state covariance matrix at the current moment, and I is the unit matrix; The state vector x of the monitored target at the current moment k Including the current coordinate value of the monitored target, the state vector x of the monitored target at the current moment is calculated k Then the current location of the monitored target is obtained; When the server receives the distance data between the monitored target and multiple satellites, the server calculates the real-time position state vector of the monitored target according to the distance data between the monitored target and multiple satellites as shown in formula (11): Xg i It is the state vector of the monitored target at the current moment when the GNSS positioning module is used; State transition matrix It is calculated by the server based on the state vector predicted at the current moment and the state vector at the previous moment; Xg i-1 is the initial position or the position state vector of the previous moment; k i represents the Kalman filter gain matrix, Z(i) is the high-precision distance data after double difference analysis of GNSS-RTK carrier phase difference; H i is a coefficient matrix used to represent the conversion relationship between the Kalman filter observation and the state vector, through the Kalman filter derivation method.

2. The indoor and outdoor integrated real-time combined positioning method according to claim 1, characterized in that: In the step S33, when the server calculates the current real-time position state of the monitored target based on the distance data between the monitored target and multiple satellites, the gain matrix k of the Kalman filter is i It is calculated by the following formula (12), formula (13) and (14): D ω (i)=(I-k i H i )D x (i,i-1) (14) Among them, D x (i,i-1) represents the covariance matrix of the prediction error at the current moment; The state transfer matrix is ​​the server-side position information at the last moment and the distance data received; the speed and acceleration information of the monitored target are calculated by the Doppler speed measurement algorithm, and the server constructs the state transfer matrix through the existing method based on the speed and acceleration information D x (i-1) is the covariance matrix of the previous moment; H i is a coefficient matrix, which is used to represent the conversion relationship between the Kalman filter observation and the state vector, through the Kalman filter derivation method; D Δ (i) The observation covariance matrix is ​​calculated by combining the observations of the Kalman filter at the current moment with the observations at the previous moment.

Images