CN-121977586-A - Indoor and outdoor integrated positioning navigation method, device, equipment and storage medium

CN121977586ACN 121977586 ACN121977586 ACN 121977586ACN-121977586-A

Abstract

The application relates to a positioning navigation method, a device, equipment and a storage medium for indoor and outdoor integration, which comprises the steps of dynamically identifying indoor and outdoor scenes through a plurality of sensors, dynamically adjusting weights according to the real-time credibility of each sensor and satellite signals, integrating and determining the outdoor scene, the indoor scene or the indoor and outdoor juncture scene where a user is currently located, adaptively activating a corresponding integration positioning mode according to the identified scene category, constructing a federal Kalman filtering model which takes an INS error equation as a state variable and RTK and UWB positioning information as observables, calculating the spatial precision factor of the RTK and the horizontal precision factor of the UWB in real time as dynamic indexes for measuring the current positioning precision of each sub-positioning system, dynamically calculating the information distribution coefficient of each sub-filter according to the dynamic indexes, integrating the local state estimation of each sub-filter to obtain a global optimal state estimation value, and feeding back the global optimal state estimation value to the INS to correct accumulated errors.

Inventors

TONG KAI
XU JIANPING
YAO LINGFENG
ZHANG WEI

Assignees

华信正能集团有限公司

Dates

Publication Date: 20260505
Application Date: 20260409

Claims (10)

1. The indoor and outdoor integrated positioning navigation method is characterized by comprising the following steps of: acquiring light sensor data, temperature sensor data, satellite signal data, UWB ranging data and inertial measurement unit data in real time, acquiring multi-source data, and performing time alignment and coordinate system integrated processing on the multi-source data; According to the real-time credibility of the sensor data, the voting weight of each sensor in the scene judgment is dynamically adjusted, the preliminary judgment result of each sensor is fused through a weighted voting mechanism, and the scene category of the user is determined, wherein the scene category comprises an outdoor scene, an indoor scene and an indoor and outdoor juncture scene; According to scene categories, the corresponding fusion positioning mode is selected in a self-adaptive mode, and if the scene is an outdoor scene, the RTK and INS tightly combined positioning mode is activated, if the scene is an indoor scene, the UWB and INS tightly combined positioning mode is activated, and if the scene is an indoor and outdoor boundary scene, the RTK, UWB and INS fully-source fusion positioning mode is activated; The method comprises the steps of establishing a system state model taking an INS error equation as a state variable, respectively establishing a corresponding system observation model according to a selected fusion positioning mode, calculating a space precision factor of RTK positioning and a horizontal precision factor of UWB positioning in real time, and taking the space precision factor and the horizontal precision factor as dynamic indexes for measuring the current positioning precision of each sub-positioning system; According to the space precision factor and the horizontal precision factor, dynamically calculating information distribution coefficients of all sub-filters in the Federal Kalman filter, wherein the information distribution coefficients are positively correlated with the positioning precision of a corresponding sub-positioning system, injecting the information distribution coefficients into the Federal Kalman filter, carrying out self-adaptive fusion on local state estimation of all the sub-filters to obtain a global optimal state estimation value, carrying out real-time correction on accumulated errors of INS by using the global optimal state estimation value, and outputting a fusion positioning result after correction.
2. The positioning and navigation method of claim 1, wherein the dynamically adjusting the voting weight of each sensor in the scene decision according to the real-time reliability of each sensor data, and fusing the preliminary decision result of each sensor through a weighted voting mechanism, determining the scene category of the user at present comprises: The day and night is distinguished according to the sunrise and sunset time, a dynamic threshold method is adopted in the daytime, the threshold value is linearly adjusted according to the time period after sunrise and before sunset and the air humidity, and the illumination intensity is higher than the threshold value and is judged to be outdoor or indoor; Judging generalized summer or generalized winter according to the current date, constructing a short-time and long-time temperature average value, calculating a difference absolute value and comparing the difference absolute value with a preset threshold value, wherein in summer, the difference value is judged to be switched from indoor to outdoor and the negative value is opposite; judging that the satellite is outdoor when the number of the visible satellites is not smaller than a first satellite number threshold value and the signal to noise ratio is not smaller than a first signal to noise ratio threshold value; judging that the number of the visible satellites is not more than a second satellite number threshold value and the signal to noise ratio is not more than a second signal to noise ratio threshold value as indoor if the number of the visible satellites and the signal to noise ratio are in an intermediate state, taking the signal to noise ratio as a main judgment basis, judging that the number of the visible satellites is outdoor if the signal to noise ratio is more than the second signal to noise ratio threshold value, and judging that the number of the visible satellites and the signal to noise ratio are indoor if the number of the visible satellites and the signal to noise ratio are not more than the second signal to noise ratio threshold value; and multiplying the preliminary judgment result of each sensor by the dynamic weight, and respectively accumulating the total number of votes obtained from the indoor and the outdoor to obtain the scene of the user.
3. The indoor and outdoor integrated positioning navigation method according to claim 2, wherein the calculating the spatial precision factor of the RTK positioning and the horizontal precision factor of the UWB positioning in real time and using them as dynamic indexes for measuring the current positioning precision of each sub-positioning system comprises: Based on an error equation of an inertial navigation system, establishing fifteen-dimensional system state variables including a three-dimensional platform misalignment angle, a three-dimensional speed error, a three-dimensional position error, a three-dimensional gyroscope zero bias and a three-dimensional accelerometer zero bias; Based on the current activated fusion positioning mode, respectively constructing a corresponding observation equation, namely constructing an observation model of a first sub-filter if the RTK and INS tightly combined positioning mode are activated, and constructing an observation relation between the RTK and the INS by taking the difference between the position and the speed calculated by the RTK and the position and the speed output by the INS as observation vectors; The method comprises the steps of acquiring satellite geometric distribution information in an RTK positioning system in real time, calculating a spatial precision factor for quantifying the current error amplification degree of RTK positioning, simultaneously extending the concept of the precision factor to UWB indoor positioning, acquiring geometric distribution information of each base station in the UWB positioning system in real time, calculating a horizontal precision factor for quantifying the current error amplification degree of UWB positioning, and taking the spatial precision factor and the horizontal precision factor as dynamic indexes for measuring the current positioning precision of each sub-positioning system.
4. A positioning navigation method of indoor and outdoor integration according to claim 3, wherein the calculating the horizontal precision factor comprises: acquiring satellite signal data which currently participates in resolving in an RTK positioning system in real time, wherein the satellite signal data comprises azimuth angles, pitch angles and pseudo-range observation values of all satellites relative to a receiver; According to azimuth angles and pitch angles of all satellites, directional cosine of all satellites from the positions of the receiver is calculated, a geometric observation matrix of the RTK positioning system is constructed, the number of rows of the geometric observation matrix corresponds to the number of the current visible satellites, and the number of columns corresponds to space three-dimensional coordinates and receiver clock error parameters; extracting diagonal line elements corresponding to the space three-dimensional coordinates in the co-factor matrix, and calculating square sum roots to obtain a space precision factor at the current moment; collecting data of each base station which currently participates in resolving in a UWB positioning system in real time, wherein the data comprise azimuth angles, pitch angles and ranging values of each base station relative to a positioning tag; Extending the concept of precision factors to indoor UWB positioning, calculating the directional cosine pointing to each base station from the position of a positioning tag according to the azimuth angle and the pitch angle of each base station, and constructing a geometric observation matrix of the UWB positioning system, wherein the number of lines of the geometric observation matrix corresponds to the number of currently available base stations, and the number of columns corresponds to horizontal two-dimensional coordinates and distance measurement error parameters; And extracting diagonal line elements corresponding to horizontal two-dimensional coordinates in the co-factor matrix, and calculating square sum roots to obtain a horizontal precision factor at the current moment.
5. The indoor and outdoor converged positioning and navigation method of claim 3, wherein the global optimum state estimation comprises: Comparing the space precision factor and the horizontal precision factor with a preset first precision threshold and a preset second precision threshold respectively, and determining whether the RTK positioning system and the UWB positioning system are currently available or not based on a preset judging rule; dynamically determining information distribution coefficients of all sub-filters in the Federal Kalman filter according to availability status and precision factor values of all sub-positioning systems; Injecting the information distribution coefficients into a first sub-filter and a second sub-filter respectively, and reassigning a system noise covariance matrix and a state error covariance matrix of the sub-filters according to the information distribution coefficients to finish initialization of the sub-filters; controlling each sub-filter to independently execute time update and observation update based on a system state equation and a corresponding observation equation to respectively obtain a local state estimated value and a local state error covariance matrix; based on the local state estimation value and the local state error covariance matrix which are output by each sub-filter and received by the main filter, combining the information distribution coefficients, and carrying out weighted fusion on the local estimation of each sub-filter to obtain the global optimal state estimation value and the global state error covariance matrix; and simultaneously, the global optimal state estimation value and the global state error covariance matrix are redistributed to each sub-filter according to the information distribution coefficient to serve as initial values of the next filtering period.
6. The indoor and outdoor integrated positioning navigation method according to claim 5, wherein the dynamically determining the information distribution coefficient of each sub-filter in the federal kalman filter according to the availability status of each sub-positioning system and the precision factor value thereof comprises: When the RTK positioning system and the UWB positioning system are judged to be available, adopting a dynamic allocation strategy based on the relative size of the precision factors; When only the RTK positioning system is available and the UWB positioning system is not available, setting the information distribution coefficient of the RTK corresponding sub-filter to be a maximum value and setting the information distribution coefficient of the UWB corresponding sub-filter to be zero; setting the information allocation coefficient of the UWB corresponding sub-filter to a maximum value and the information allocation coefficient of the RTK corresponding sub-filter to zero when only the UWB positioning system is available and the RTK positioning system is not available; When the RTK positioning system and the UWB positioning system are not available, the information distribution coefficients of the two sub-filters are set to zero, and independent positioning is performed only by means of INS.
7. The indoor and outdoor converged positioning and navigation method of claim 6, wherein when both the RTK positioning system and the UWB positioning system are determined to be available, a dynamic allocation strategy based on relative sizes of the precision factors is adopted, comprising: The spatial precision factor output by the RTK positioning system at the current moment and the horizontal precision factor output by the UWB positioning system are read, so that the two values are ensured to be effective positive numbers; accumulating the values of the spatial precision factors and the horizontal precision factors to obtain a precision factor accumulation sum which is used as a reference denominator for subsequent normalization processing; calculating an initial information distribution coefficient of the first sub-filter based on the ratio of the spatial precision factor to the precision factor accumulation sum, wherein the initial information distribution coefficient and the size of the spatial precision factor are in a negative correlation, calculating the proportion of the spatial precision factor to the precision factor accumulation sum, and subtracting the proportion from the maximum value to obtain the initial information distribution coefficient of the first sub-filter; Calculating an initial information distribution coefficient of the second sub-filter based on the ratio of the horizontal precision factor to the precision factor accumulation sum, wherein the initial information distribution coefficient and the size of the horizontal precision factor are in a negative correlation, calculating the proportion of the horizontal precision factor to the precision factor accumulation sum, and subtracting the corresponding proportion from the maximum value to obtain the initial information distribution coefficient of the second sub-filter; Carrying out summation verification on initial information distribution coefficients of the first sub-filter and the second sub-filter, and ensuring that the sum of the initial information distribution coefficients is equal to a preset maximum distribution coefficient value; And taking the initial information distribution coefficients of the verified first sub-filter and the verified second sub-filter as the final information distribution coefficients.
8. An indoor and outdoor integrated positioning navigation device, the device comprising: The data acquisition module is used for acquiring light sensor data, temperature sensor data, satellite signal data, UWB ranging data and inertial measurement unit data in real time, acquiring multi-source data, and carrying out time alignment and coordinate system integrated processing on the multi-source data; The data judging module is used for independently carrying out preliminary judgment on indoor and outdoor scenes based on the light sensor data, the temperature sensor data and the satellite signal data respectively, dynamically adjusting voting weights of the sensors in scene judgment according to real-time credibility of the sensor data, and fusing preliminary judgment results of the sensors through a weighted voting mechanism to determine scene categories of the user, wherein the scene categories comprise an outdoor scene, an indoor scene and an indoor and outdoor juncture scene; The positioning selection module is used for adaptively selecting a corresponding fusion positioning mode according to scene types, activating an RTK and INS tightly combined positioning mode if the scene is an outdoor scene, activating a UWB and INS tightly combined positioning mode if the scene is an indoor scene, and activating an RTK, UWB and INS fully-source fusion positioning mode if the scene is an indoor and outdoor boundary scene; the positioning accuracy module is used for constructing a system state model taking an INS error equation as a state variable, respectively constructing corresponding system observation models according to the selected fusion positioning mode, calculating a spatial accuracy factor of RTK positioning and a horizontal accuracy factor of UWB positioning in real time, and taking the spatial accuracy factor and the horizontal accuracy factor of UWB positioning as dynamic indexes for measuring the current positioning accuracy of each sub-positioning system; The positioning correction module is used for dynamically calculating information distribution coefficients of all sub-filters in the Federal Kalman filter according to the space precision factor and the horizontal precision factor, wherein the information distribution coefficients are positively correlated with the positioning precision of the corresponding sub-positioning system, injecting the information distribution coefficients into the Federal Kalman filter, carrying out self-adaptive fusion on local state estimation of all the sub-filters to obtain a global optimal state estimation value, carrying out real-time correction on accumulated errors of the INS by using the global optimal state estimation value, and outputting a fusion positioning result after correction.
9. A control apparatus, characterized in that the apparatus comprises: a memory and a processor, said memory having stored thereon a computer program capable of being loaded by said processor and performing the method according to any of claims 1 to 7.
10. A computer readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which performs the method according to any of claims 1 to 7.

Description

Indoor and outdoor integrated positioning navigation method, device, equipment and storage medium Technical Field The application relates to the technical field of satellite positioning, in particular to an indoor and outdoor integrated positioning navigation method, device, equipment and storage medium. Background With the continuous acceleration of modern smart city construction and the rapid development of internet of things, the demands of people for positioning navigation services have been expanded from a single outdoor environment to an indoor and outdoor full scene. In an outdoor scene, the global navigation satellite system can provide all-weather and global coverage position services, and is widely applied to the fields of vehicle navigation, personnel tracking and the like. However, in indoor or indoor-like scenes such as underground parking lots, bridge tunnels, shopping malls, etc., satellite signals are extremely vulnerable to occlusion and interference, resulting in an inability of the GNSS to efficiently locate. At present, china patent application with publication number of CN110234072A proposes an indoor positioning method based on Wi-Fi fingerprint and pedestrian dead reckoning fusion. The method comprises the steps of collecting Wi-Fi signal intensity of each indoor reference point in an off-line stage, constructing a fingerprint map database, in an on-line positioning stage, obtaining preliminary position estimation through matching of the Wi-Fi signal intensity collected in real time with the fingerprint database, meanwhile, conducting pedestrian track calculation by utilizing an inertial sensor built in a smart phone, and fusing Wi-Fi positioning results with a PDR track through a Kalman filter so as to improve positioning continuity and stability. The method relieves the problem that the positioning of the pure Wi-Fi is easily affected by signal fluctuation to a certain extent, and also overcomes the defect of long-time accumulated drift of PDR. Aiming at the technology, the scene is simple to consider, the recognition accuracy is low at indoor and outdoor junctions, signal shielding environments or special weather conditions, reliable prepositive information is difficult to provide for a fusion positioning algorithm, and secondly, the weight distribution of Wi-Fi positioning and PDR in the fusion positioning process usually adopts a fixed value, the real-time positioning quality change of different positioning systems in a complex environment cannot be fully considered, and when the signal quality of a certain subsystem is reduced, errors are introduced into a final positioning result by the fusion mode of the fixed weight. Based on the above, the application provides an indoor and outdoor integrated positioning navigation method, device, equipment and storage medium. Disclosure of Invention In order to improve scene consideration simplicity, the recognition accuracy is low at indoor and outdoor junctions, signal shielding environments or special weather conditions, reliable front information is difficult to provide for a fusion positioning algorithm, and secondly, the weight distribution of Wi-Fi positioning and PDR in the fusion positioning process usually adopts a fixed value, the real-time positioning quality change of different positioning systems in a complex environment cannot be fully considered, and when the signal quality of a certain subsystem is reduced, the fusion mode of the fixed weight can introduce errors into the final positioning result. In a first aspect, the application provides an indoor and outdoor integrated positioning navigation method, which adopts the following technical scheme that: acquiring light sensor data, temperature sensor data, satellite signal data, UWB ranging data and inertial measurement unit data in real time, acquiring multi-source data, and performing time alignment and coordinate system integrated processing on the multi-source data; According to the real-time credibility of the sensor data, the voting weight of each sensor in the scene judgment is dynamically adjusted, the preliminary judgment result of each sensor is fused through a weighted voting mechanism, and the scene category of the user is determined, wherein the scene category comprises an outdoor scene, an indoor scene and an indoor and outdoor juncture scene; According to scene categories, the corresponding fusion positioning mode is selected in a self-adaptive mode, and if the scene is an outdoor scene, the RTK and INS tightly combined positioning mode is activated, if the scene is an indoor scene, the UWB and INS tightly combined positioning mode is activated, and if the scene is an indoor and outdoor boundary scene, the RTK, UWB and INS fully-source fusion positioning mode is activated; The method comprises the steps of establishing a system state model taking an INS error equation as a state variable, respectively establishing a corresponding system observation model accor