CN114993299A - Ultra-wideband-based unmanned aerial vehicle auxiliary positioning method and system - Google Patents

Ultra-wideband-based unmanned aerial vehicle auxiliary positioning method and system Download PDF

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CN114993299A
CN114993299A CN202210562134.7A CN202210562134A CN114993299A CN 114993299 A CN114993299 A CN 114993299A CN 202210562134 A CN202210562134 A CN 202210562134A CN 114993299 A CN114993299 A CN 114993299A
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ultra
wideband
positioning module
ranging
wideband positioning
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CN114993299B (en
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安帅
王世勇
李茂�
雷超
谢鸿辉
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China South Industries Group Automation Research Institute
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G01C21/1652—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with ranging devices, e.g. LIDAR or RADAR
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/024—Guidance services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
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Abstract

The invention discloses an unmanned aerial vehicle auxiliary positioning method and system based on ultra wide band, the method comprises the steps of obtaining attitude data of a first ultra wide band positioning module and a distance measurement value between the first ultra wide band positioning module and a second ultra wide band positioning module; and fusing the attitude data and the ranging value through an extended Kalman filtering algorithm. Under the condition that the distance between the ultra-wideband base stations is limited, the ultra-wideband positioning precision is improved by optimizing the two stages of ranging and positioning. The ultra-wideband ranging and attitude data are fused by adopting an extended Kalman filtering algorithm, the statistical characteristic of the measurement noise is dynamically adjusted, the ultra-wideband ranging error is reduced, the ultra-wideband ranging and positioning precision is improved, the dependence of the unmanned aerial vehicle on satellite signals is reduced, the ultra-wideband ranging and inertial navigation positioning are integrated, and the problem that the tethered unmanned aerial vehicle cannot be positioned in the satellite signal rejection environment is solved.

Description

Ultra-wideband-based unmanned aerial vehicle auxiliary positioning method and system
Technical Field
The invention relates to the technical field of navigation methods, in particular to an unmanned aerial vehicle auxiliary positioning method and system based on ultra wide band.
Background
An unmanned aerial vehicle, or simply "unmanned aerial vehicle" ("UAV"), is an unmanned aerial vehicle that is operated by a radio remote control device and a self-contained program control device. Compared with manned aircraft, it has the advantages of small volume, low cost, convenient use, low requirement on the operational environment, strong battlefield viability and the like. The unmanned aerial vehicle navigation refers to a method and a process for determining the position and the direction of an unmanned aerial vehicle in the flight process, and the performance of a navigation system is directly related to whether a flight route task can be completed. Since the drone can only rely on the flight control system to achieve automatic flight, the feedback input to the flight control system comes from the navigation signal, i.e. the onboard computer's estimate of the current position and velocity.
With the development of science and technology, the outdoor positioning and navigation technology of the unmanned aerial vehicle is basically mature at present. When the unmanned aerial vehicle flies in an outdoor open area, a global navigation satellite system and an inertial navigation system are mostly adopted to provide position navigation information, and the precision of the unmanned aerial vehicle can reach the meter level. The cooperation is at the fixed difference basic station of ground deployment, and unmanned aerial vehicle's positioning accuracy can reach centimetre level.
Although the global navigation satellite system and the inertial navigation system are adopted to provide position navigation, the method has the advantages of high navigation precision, suitability for long-time flight and the like, but the dependence of the navigation mode on the satellite signal intensity is larger. Especially, some unmanned aerial vehicles of special form because the satellite signal easily receives the interference in its use scene, often appear because the satellite signal loses and causes unmanned aerial vehicle out of control or can't take off the scheduling problem.
Mooring unmanned aerial vehicle also known as mooring unmanned aerial vehicle, for many rotor unmanned aerial vehicle's a special form, use the ground power supply through mooring cable transmission as power source, replace traditional lithium cell, the leading features is long-time stagnation suspension ability. The tethered unmanned aerial vehicle is usually provided with a photoelectric pod, a communication relay and other loads to carry out tasks in mountainous regions, jungles, urban streets and other regions, satellite signals are easily affected by environment and artificially and maliciously interfered, signal loss is caused, the unmanned aerial vehicle is out of control or cannot take off, and although the problem of positioning in a short time without the satellite signals can be solved through inertial navigation, the problem of positioning drift in a long time cannot be solved.
Therefore, how to provide an auxiliary positioning method can assist positioning of the unmanned aerial vehicle in a satellite signal rejection environment, effectively reduce dependence of the unmanned aerial vehicle on satellite signals, and improve application capability of the unmanned aerial vehicle is a technical problem which needs to be solved by technical personnel in the field urgently.
Disclosure of Invention
The invention provides an unmanned aerial vehicle auxiliary positioning method and system based on an ultra wide band.
The invention provides the following scheme:
an ultra-wideband-based unmanned aerial vehicle auxiliary positioning method comprises the following steps:
acquiring attitude data of a first ultra-wideband positioning module and a distance measurement value between the first ultra-wideband positioning module and a second ultra-wideband positioning module; the first ultra-wideband positioning module is arranged on the unmanned aerial vehicle; the second ultra-wideband positioning module is arranged on the ground workstation;
fusing the attitude data and the ranging values through an extended Kalman filtering algorithm;
calculating to obtain a resolving distance between the first ultra-wideband positioning module and the ground workstation through the attitude data; calculating to obtain a deviation between the ranging value and the resolving distance, and updating the statistical characteristic of measurement noise in the extended Kalman filtering algorithm through the deviation so as to update the extended Kalman filtering algorithm to obtain a posterior updated value of the state variable; the state variables comprise the position and the speed of the first ultra-wideband positioning module in the X, Y, Z axis direction under the ultra-wideband coordinate system;
and acquiring a position estimation value of the first ultra-wideband positioning module under the ultra-wideband coordinate system from the posterior updated value, and taking the position estimation value as position data of the unmanned aerial vehicle relative to the ground workstation.
Preferably: the posture data of obtaining the first ultra-wideband positioning module comprises the following steps:
measuring and acquiring attitude data of the first ultra-wideband positioning module through the inertial navigation module; the attitude data includes an attitude angle, a quaternion, and an acceleration.
Preferably: the second ultra-wideband positioning module comprises a plurality of modules;
the range finding value of acquireing between first ultra wide band location module and the second ultra wide band location module includes:
respectively obtaining a ranging value between the first ultra-wideband positioning module and each second ultra-wideband positioning module to form a ranging value group; fusing the attitude data and the distance measuring value group through an extended Kalman filtering algorithm;
the calculating obtains the deviation between the ranging value and the resolving distance, and comprises the following steps:
and respectively calculating and obtaining the deviation between each ranging value contained in the ranging value group and the resolving distance to form a deviation group, and updating the measurement noise statistical characteristic in the extended Kalman filtering algorithm through the deviation group.
Preferably: the unmanned aerial vehicle comprises a tethered unmanned aerial vehicle, and the ground workstation comprises a ground control vehicle; and the second ultra-wideband positioning modules are arranged at the top of the ground control vehicle.
Preferably: the first ultra-wideband positioning module and the second ultra-wideband positioning module are ultra-wideband positioning modules which are calibrated by linear fitting in combination with a laser range finder.
Preferably: the calibration method comprises the following steps:
in a line-of-sight environment, acquisition
Figure 832898DEST_PATH_IMAGE001
Distance between groups
Figure 358557DEST_PATH_IMAGE002
Data therein, wherein
Figure 250421DEST_PATH_IMAGE003
Is the first
Figure 482819DEST_PATH_IMAGE004
The first ultra-wideband positioning module and the second ultra-wideband positioning module
Figure 785624DEST_PATH_IMAGE005
The value of the secondary distance measurement is,
Figure 113094DEST_PATH_IMAGE006
is the measured value corresponding to the laser range finder; after calibration the first
Figure 57916DEST_PATH_IMAGE004
The group distance measuring is
Figure 879242DEST_PATH_IMAGE007
Wherein the parameters
Figure 369260DEST_PATH_IMAGE008
And
Figure 666249DEST_PATH_IMAGE009
and (3) solving according to least square normal linear fitting, wherein the calculation formula is as follows:
Figure 132871DEST_PATH_IMAGE010
。
preferably: the range finding value of acquireing between first ultra wide band location module and the second ultra wide band location module includes:
measuring and obtaining a calibrated ranging value between the first ultra-wideband positioning module and the second ultra-wideband positioning module by adopting a bilateral two-way ranging method;
the bilateral two-way ranging method comprises the following steps:
an ultra-wideband positioning module A
Figure 74283DEST_PATH_IMAGE011
Constantly sends out a ranging signal, and the ultra-wideband positioning module B is
Figure 781207DEST_PATH_IMAGE012
A ranging signal is received at time A
Figure 175280DEST_PATH_IMAGE013
Sends out ranging signals all the time, and the ultra-wideband positioning module A is
Figure 947058DEST_PATH_IMAGE014
The ranging signal of the ultra-wideband positioning module B is received constantly
Figure 867609DEST_PATH_IMAGE015
Sends out ranging signals all the time, and the ultra-wideband positioning module B is
Figure 886381DEST_PATH_IMAGE016
Receiving a ranging signal of the ultra-wideband positioning module A at any time; the one-way transmission time of the ranging signal between the ultra-wideband positioning module A and the ultra-wideband positioning module B is as follows:
Figure 347843DEST_PATH_IMAGE017
wherein, the first and the second end of the pipe are connected with each other,
Figure 703737DEST_PATH_IMAGE018
，
Figure 619741DEST_PATH_IMAGE019
、
Figure 622463DEST_PATH_IMAGE020
、
Figure 381341DEST_PATH_IMAGE021
(ii) a The distance between the ultra-wideband positioning module A and the ultra-wideband positioning module B is
Figure 993456DEST_PATH_IMAGE022
Wherein
Figure 498387DEST_PATH_IMAGE023
Is light ofAnd (4) speed.
Preferably: fusing the attitude data and the ranging value through an extended Kalman filtering algorithm; the method comprises the following steps:
calculating a rotation matrix from a body coordinate system to a navigation coordinate system according to the attitude data
Figure 186857DEST_PATH_IMAGE024
(ii) a Calculating a rotation matrix from a navigation coordinate system to the ultra-wideband coordinate system according to the attitude angle and the quaternion of the second ultra-wideband positioning module
Figure 183763DEST_PATH_IMAGE025
；
Selecting the state variables as the position and the speed in the X, Y, Z axis direction under the ultra-wideband coordinate system
Figure 84723DEST_PATH_IMAGE026
The system state equation is:
Figure 820991DEST_PATH_IMAGE027
（3）
the corresponding matrix is in the form of
Figure 414784DEST_PATH_IMAGE028
；
Wherein the content of the first and second substances,
Figure 492461DEST_PATH_IMAGE029
is a process noise covariance matrix that is,
Figure 479003DEST_PATH_IMAGE030
is an iteration cycle of the extended kalman filter algorithm;
Figure 552001DEST_PATH_IMAGE031
is the X-axis component of the position of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 628279DEST_PATH_IMAGE032
is the Y-axis component of the position of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 521149DEST_PATH_IMAGE033
a Z-axis component of the position of the first ultra-wideband positioning module at the k moment under the ultra-wideband coordinate system;
Figure 780223DEST_PATH_IMAGE034
is the X-axis component of the velocity of the first UWB positioning module at the time k under the UWB coordinate system,
Figure 973307DEST_PATH_IMAGE035
is the velocity Y-axis component of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 883801DEST_PATH_IMAGE036
a speed Z-axis component of the first ultra-wideband positioning module at the k moment under an ultra-wideband coordinate system;
Figure 873754DEST_PATH_IMAGE037
is the X-axis component of the position of the first ultra-wideband positioning module at the time k-1 under the ultra-wideband coordinate system,
Figure 185787DEST_PATH_IMAGE038
is the acceleration X-axis component of the first UWB positioning module at the time k-1 under the UWB coordinate system,
Figure 639902DEST_PATH_IMAGE039
the process noise of the X-axis component of the position of the first ultra-wideband positioning module at the moment k-1 is obtained;
Figure 261245DEST_PATH_IMAGE040
is the Y-axis component of the position of the first ultra-wideband positioning module at the time k-1 in the ultra-wideband coordinate system,
Figure 191024DEST_PATH_IMAGE041
is the acceleration Y-axis component of the first ultra-wideband positioning module at the time k-1 under the ultra-wideband coordinate system,
Figure 57480DEST_PATH_IMAGE042
the process noise of the Y-axis component of the position of the first ultra-wideband positioning module at the moment k-1 is obtained;
Figure 959577DEST_PATH_IMAGE043
is the Z-axis component of the position of the first ultra-wideband positioning module at the time k-1 in the ultra-wideband coordinate system,
Figure 754751DEST_PATH_IMAGE044
is the acceleration Z-axis component of the first UWB positioning module at the time k-1 under the UWB coordinate system,
Figure 843929DEST_PATH_IMAGE045
the process noise of the Z-axis component of the position of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 514076DEST_PATH_IMAGE046
process noise of a velocity X-axis component at the time of k-1 for the first ultra-wideband positioning module;
Figure 677204DEST_PATH_IMAGE047
the process noise of the velocity Y-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 187820DEST_PATH_IMAGE048
the process noise of the speed Z-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 701978DEST_PATH_IMAGE049
is the state matrix of the system at time k-1,
Figure 408772DEST_PATH_IMAGE050
is a system inThe input matrix at time k-1,
Figure 19882DEST_PATH_IMAGE051
is the control vector of the system at time k-1,
Figure 452131DEST_PATH_IMAGE052
is the state variable of the system at the time k;
and combining the attitude data, and the system input quantity is as follows:
Figure 515902DEST_PATH_IMAGE053
wherein g is gravity acceleration;
Figure 763737DEST_PATH_IMAGE054
an acceleration X-axis component of the first ultra-wideband positioning module at the k-1 moment under a machine body coordinate system is obtained;
the observation equation is a first ultra-wideband positioning module and a second ultra-wideband positioning module
Figure 494933DEST_PATH_IMAGE055
（
Figure 566925DEST_PATH_IMAGE055
=1, …, n);
Figure 117992DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure 589425DEST_PATH_IMAGE057
is the measurement noise of the ranging value of the system at time k,
Figure 424395DEST_PATH_IMAGE058
is to measure the noise of the measurement,
Figure 650977DEST_PATH_IMAGE059
is a second ultra-wideband positioning module
Figure 502389DEST_PATH_IMAGE060
Coordinates under an ultra-wideband coordinate system;
predicting initial values from state variables
Figure 839829DEST_PATH_IMAGE061
Estimating an error covariance matrix initial value
Figure 952142DEST_PATH_IMAGE062
And initial value of system input quantity
Figure 601822DEST_PATH_IMAGE063
Calculating the prior predicted value of the state variable by combining the following formula
Figure 924219DEST_PATH_IMAGE064
Estimate error covariance matrix prior prediction
Figure 816083DEST_PATH_IMAGE065
；
Figure 48481DEST_PATH_IMAGE066
。
Preferably: updating the statistical characteristics of the measurement noise in the extended Kalman filtering algorithm through the deviation so as to update the extended Kalman filtering algorithm to obtain a posterior updated value of a state variable; the method comprises the following steps:
according to the deviation between the distance measurement value and the resolving distance
Figure 351287DEST_PATH_IMAGE067
Adjusting the measurement noise covariance matrix
Figure 879089DEST_PATH_IMAGE068
（
Figure 355070DEST_PATH_IMAGE068
>0) System for modifying observed noiseMeasuring the characteristic; wherein
Figure 520603DEST_PATH_IMAGE069
Is the calibrated range value;
Figure 931993DEST_PATH_IMAGE070
wherein the coefficients
Figure 963403DEST_PATH_IMAGE071
Value range of
Figure 710253DEST_PATH_IMAGE072
Calculating the Kalman gain according to
Figure 979560DEST_PATH_IMAGE073
；
Figure 827430DEST_PATH_IMAGE074
In the formula (I), the compound is shown in the specification,
Figure 300131DEST_PATH_IMAGE075
is the transpose of the system's observation matrix at time k,
Figure 586756DEST_PATH_IMAGE076
an observation matrix of the system at the time k;
carrying out posterior updating on the state variable and the estimation error covariance matrix according to the following formula;
Figure 225416DEST_PATH_IMAGE077
updating values from a posteriori of state variables
Figure 837663DEST_PATH_IMAGE078
Obtaining a position estimation value of the first ultra-wideband positioning module under the ultra-wideband coordinate system.
An ultra-wideband based drone assisted positioning system, the system comprising:
the data acquisition unit is used for acquiring attitude data of a first ultra-wideband positioning module and a distance measurement value between the first ultra-wideband positioning module and a second ultra-wideband positioning module; the first ultra-wideband positioning module is arranged on the unmanned aerial vehicle; the second ultra-wideband positioning module is arranged on the ground workstation;
the data fusion unit is used for fusing the attitude data and the ranging value through an extended Kalman filtering algorithm;
an update unit; the calculation distance between the first ultra-wideband positioning module and the ground workstation is obtained through calculation of the attitude data; calculating to obtain a deviation between the ranging value and the resolving distance, and updating the statistical characteristic of measurement noise in the extended Kalman filtering algorithm through the deviation so as to update the extended Kalman filtering algorithm to obtain a posterior updated value of the state variable; the state variables comprise the position and the speed of the first ultra-wideband positioning module in the X, Y, Z axis direction under the ultra-wideband coordinate system;
and the position data determining unit is used for acquiring a position estimation value of the first ultra-wideband positioning module in the ultra-wideband coordinate system from the posterior updated value, and taking the position estimation value as the position data of the unmanned aerial vehicle relative to the ground workstation.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the unmanned aerial vehicle auxiliary positioning method based on the ultra wide band, under the condition that the distance between ultra wide band base stations is limited, the two stages of ranging and positioning are optimized, and therefore ultra wide band positioning accuracy is improved. The ultra-wideband ranging and attitude data are fused by adopting an extended Kalman filtering algorithm, the statistical characteristic of the measurement noise is dynamically adjusted, the ultra-wideband ranging error is reduced, the ultra-wideband ranging and positioning precision is improved, the dependence of the unmanned aerial vehicle on satellite signals is reduced, the ultra-wideband ranging and inertial navigation positioning are integrated, and the problem that the tethered unmanned aerial vehicle cannot be positioned in the satellite signal rejection environment is solved.
In addition, in a preferred embodiment, each ultra-wideband tag-base station can be calibrated based on least square normal fitting according to the measurement value of the laser range finder, and a bilateral two-way ranging method is adopted to suppress ranging errors caused by antenna delay and clock drift.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a flowchart of an ultra-wideband-based unmanned aerial vehicle-assisted positioning method according to an embodiment of the present invention;
fig. 2 is a schematic deployment diagram of an ultra-wideband positioning module according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a bilateral two-way UWB ranging process provided by an embodiment of the invention;
FIG. 4 is a diagram of a positioning solution XY plane trajectory (height 25 meters) provided by an embodiment of the present invention;
FIG. 5 is a diagram of a position solution X-axis position estimate (height 25 meters) provided by an embodiment of the present invention;
FIG. 6 is a diagram of a position solution Y-axis position estimate (height 25 meters) provided by an embodiment of the present invention;
FIG. 7 is a diagram of a positioning solution XY plane trajectory (height 195 meters) provided by an embodiment of the present invention;
FIG. 8 is a diagram of a position solution X-axis position estimate (195 meters in height) provided by an embodiment of the present invention;
FIG. 9 is a diagram of a position solution Y-axis position estimate (195 meters in height) provided by an embodiment of the present invention;
fig. 10 is a schematic diagram of an ultra-wideband-based drone-assisted positioning system provided by an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
In the wireless communication technology, ultra-wideband (UWB) signals have the advantages of high transmission, low power consumption, high time resolution, strong anti-interference capability and the like, are applied to the fields of indoor communication, position positioning and the like, and can provide a high-reliability and low-complexity solution for indoor positioning of an unmanned aerial vehicle. The UWB positioning technology is a common method for solving the problem of positioning of the unmanned aerial vehicle in the satellite signal rejection environment at present. It uses nanosecond to microsecond non-sine wave narrow pulse to transmit data. The RF frequency conversion required by the conventional narrow-band modulation is not needed, and the pulse can be directly sent to an antenna for transmission after being shaped. The ultra-wideband signal has very low radiation, usually only one thousandth of the radiation of a mobile phone, so that when the ultra-wideband signal is applied in the industry, the ultra-wideband signal does not have the problem of interference on other instruments. During the use, the location basic station of certain quantity is installed in the region that needs the location and is let the region that needs the location of location basic station signal coverage, and unmanned aerial vehicle carries the location label just can realize the decimetre level accurate positioning to the target object.
By adopting the method, good effect can be obtained in the indoor unmanned aerial vehicle navigation. However, the arrangement of the UWB positioning base stations has certain limitations, and the distance between the UWB positioning base stations is required to meet certain requirements. If the distance between UWB location basic station is undersized, can seriously influence the precision of location at unmanned aerial vehicle flight in-process.
Due to the limitation of the using environment and the using method of some special forms of unmanned aerial vehicles, the area of the area where the positioning base stations can be arranged is limited, and the distance between the arranged positioning base stations is difficult to expand. For example, a tethered drone needs to be connected by a cable to a ground station through which it is powered, although ground stations come in many forms, such as control vehicles and other forms of take-off and landing platforms. Whether controlling a vehicle or a take-off and landing platform, the area available for deploying a positioning base station is limited. Limited by vehicle size, the UWB positioning base station of the vehicle-mounted tethered drone can only be deployed on ground control vehicles with small areas. Different with unmanned aerial vehicle's UWB indoor location, UWB location basic station interval on the vehicle is less relatively and the flying height of mooring unmanned aerial vehicle is higher, leads to UWB positioning accuracy low, can't fix a position even.
The method provided by the embodiment of the application adopts the extended Kalman filtering algorithm to fuse the UWB ranging value and IMU (inertial Measurement Unit) data, namely the attitude data output by the inertial Measurement unit, dynamically adjusts the statistical characteristic of the Measurement noise, reduces the UWB ranging error, further promotes the UWB ranging positioning precision, reduces the dependence of the unmanned aerial vehicle on satellite signals, and solves the problem that the unmanned aerial vehicle with limited positioning base station arrangement cannot be positioned in the satellite signal rejection environment.
Referring to fig. 1, an ultra-wideband-based unmanned aerial vehicle-assisted positioning method provided in an embodiment of the present invention, as shown in fig. 1, may include:
s101: acquiring attitude data of a first ultra-wideband positioning module and a ranging value between the first ultra-wideband positioning module and a second ultra-wideband positioning module; the first ultra-wideband positioning module is arranged on the unmanned aerial vehicle; the second ultra-wideband positioning module is arranged on the ground workstation; in practical application, the attitude data can be obtained in various ways, for example, in one implementation way, the attitude data of the first ultra-wideband positioning module can be obtained by measuring through the inertial navigation module; the attitude data includes an attitude angle, a quaternion, and an acceleration.
S102: fusing the attitude data and the ranging values through an extended Kalman filtering algorithm;
s103: calculating to obtain a resolving distance between the first ultra-wideband positioning module and the ground workstation through the attitude data; calculating to obtain a deviation between the ranging value and the resolving distance, and updating the statistical characteristic of measurement noise in the extended Kalman filtering algorithm through the deviation so as to update the extended Kalman filtering algorithm to obtain a posterior updated value of the state variable; the state variables comprise the position and the speed of the first ultra-wideband positioning module in the X, Y, Z axis direction under the ultra-wideband coordinate system; the ultra-wideband coordinate system can be established by taking the arrangement position of the second ultra-wideband positioning module as a coordinate center and taking the installation surface of the second ultra-wideband positioning module on the ground workstation as a reference surface.
S104: and acquiring a position estimation value of the first ultra-wideband positioning module under the ultra-wideband coordinate system from the posterior updated value, and taking the position estimation value as position data of the unmanned aerial vehicle relative to the ground workstation.
Because UWB ranging is relatively independent, there is no accumulated error, but influenced by external environment, will worsen the precision of ranging; the positioning based on IMU is not influenced by external environment, but has error accumulation. According to the method provided by the embodiment of the application, the attitude data of the first ultra-wideband positioning module and the ranging value between the first ultra-wideband positioning module and the second ultra-wideband positioning module are fused by adopting the extended Kalman filtering algorithm, so that advantage complementation can be realized.
It can be understood that the first ultra-wideband positioning module that this application embodiment provided arranges and unmanned aerial vehicle is last for form the label mode, and the second ultra-wideband positioning module arranges on ground workstation, is used for forming the basic station mode. In order to further improve the positioning effect, a plurality of second ultra-wideband positioning modules can be arranged, that is, a plurality of base station modes are formed, so as to eliminate errors in ranging. Specifically, the second ultra-wideband positioning module comprises a plurality of modules;
the range finding value of acquireing between first ultra wide band location module and the second ultra wide band location module includes:
respectively obtaining a ranging value between the first ultra-wideband positioning module and each second ultra-wideband positioning module to form a ranging value group; fusing the attitude data and the ranging value group through an extended Kalman filtering algorithm;
the calculating to obtain the deviation between the ranging value and the resolving distance comprises:
and respectively calculating and obtaining the deviation between each ranging value contained in the ranging value group and the resolving distance to form a deviation group, and updating the measurement noise statistical characteristic in the extended Kalman filtering algorithm through the deviation group.
In practical application, the method provided by the embodiment of the application is suitable for assisting the unmanned aerial vehicle in navigation in various environments where satellite signals are possibly rejected. Especially useful for mooring unmanned aerial vehicle. Specifically, the unmanned aerial vehicle comprises a tethered unmanned aerial vehicle, and the ground workstation comprises a ground control vehicle; and the second ultra-wideband positioning modules are arranged at the top of the ground control vehicle. When the number of the second ultra-wideband positioning modules is specifically selected, the number of the second ultra-wideband positioning modules is usually not less than 4, and the distance between the two ultra-wideband positioning modules is increased as much as possible.
It can be understood that the method provided by the embodiment of the application is also suitable for the use of the non-tethered unmanned aerial vehicle with the following function. When concrete operation, can arrange first ultra wide band location module on unmanned aerial vehicle, arrange the second ultra wide band location module on the vehicle that it was followed. For example, the user can arrange a plurality of second ultra wide band location modules at the roof of private car according to the demand, and the second ultra wide band location module that sets up can form the basic station mode, adopts the method that this application embodiment provided, can effectually improve unmanned aerial vehicle's following position precision.
In practical application, in order to suppress the ranging error caused by the hardware difference such as the ultra-wideband positioning module antenna. The embodiment of the application can also provide that the first ultra-wideband positioning module and the second ultra-wideband positioning module are ultra-wideband positioning modules which are calibrated and calibrated by linear fitting in combination with a laser range finder. A plurality of base stations and a label need to be planned before a first ultra-wideband positioning module and a second ultra-wideband positioning module are calibrated, a measurement value of a laser range finder is used as a reference standard, each label-base station is calibrated based on least square normal fitting, and ranging errors caused by difference of hardware such as an antenna are restrained.
The calibration method comprises the following steps:
in a line-of-sight environment, acquisition
Figure 391136DEST_PATH_IMAGE079
Group distance
Figure 936911DEST_PATH_IMAGE080
Data therein, wherein
Figure 977548DEST_PATH_IMAGE081
Is the first
Figure 980270DEST_PATH_IMAGE082
The first ultra-wideband positioning module and the second ultra-wideband positioning module
Figure 942410DEST_PATH_IMAGE083
The value of the secondary distance measurement is,
Figure 820105DEST_PATH_IMAGE084
is the measured value corresponding to the laser range finder; after calibration the first
Figure 918511DEST_PATH_IMAGE082
The group distance measuring is
Figure 357714DEST_PATH_IMAGE085
Wherein the parameters
Figure 541570DEST_PATH_IMAGE086
And
Figure 225886DEST_PATH_IMAGE087
and (3) solving according to least square normal linear fitting, wherein the calculation formula is as follows:
Figure 709957DEST_PATH_IMAGE088
。
in order to suppress ranging errors generated by clock drift of a tag and a base station in the ultra-wideband positioning module, the embodiment of the application can provide a method for measuring and obtaining a calibrated ranging value between the first ultra-wideband positioning module and the second ultra-wideband positioning module by adopting a bilateral two-way ranging method;
the bilateral two-way ranging method comprises the following steps:
an ultra-wideband positioning module A
Figure 54482DEST_PATH_IMAGE089
Sends out ranging signals all the time, and the ultra-wideband positioning module B is
Figure 991214DEST_PATH_IMAGE090
A ranging signal is received at time A
Figure 210711DEST_PATH_IMAGE091
Sends out ranging signals all the time, and the ultra-wideband positioning module A is
Figure 690234DEST_PATH_IMAGE092
The ranging signal of the ultra-wideband positioning module B is received constantly
Figure 720507DEST_PATH_IMAGE093
Sends out ranging signals all the time, and the ultra-wideband positioning module B is
Figure 364109DEST_PATH_IMAGE094
Receiving a ranging signal of the ultra-wideband positioning module A at any time; the one-way transmission time of the ranging signal between the ultra-wideband positioning module A and the ultra-wideband positioning module B is as follows:
Figure 138030DEST_PATH_IMAGE095
wherein the content of the first and second substances,
Figure 583311DEST_PATH_IMAGE096
，
Figure 784486DEST_PATH_IMAGE097
、
Figure 712122DEST_PATH_IMAGE098
、
Figure 289733DEST_PATH_IMAGE099
(ii) a The distance between the ultra-wideband positioning module A and the ultra-wideband positioning module B is
Figure 321012DEST_PATH_IMAGE100
Wherein
Figure 693088DEST_PATH_IMAGE101
Is the speed of light; calculating the calibrated range value of
Figure 232653DEST_PATH_IMAGE102
。
Fusing the attitude data and the ranging value through an extended Kalman filtering algorithm; the method comprises the following steps:
calculating a rotation matrix from a body coordinate system to a navigation coordinate system according to the attitude data
Figure 833530DEST_PATH_IMAGE103
(ii) a Calculating a rotation matrix from a navigation coordinate system to the ultra-wideband coordinate system according to the attitude angle and the quaternion of the second ultra-wideband positioning module
Figure 532365DEST_PATH_IMAGE104
；
Selecting the state variables as the position and the speed in the X, Y, Z axis direction under the ultra-wideband coordinate system
Figure 315820DEST_PATH_IMAGE105
The system state equation is:
Figure 670578DEST_PATH_IMAGE106
（3）
the corresponding matrix is in the form of
Figure 730938DEST_PATH_IMAGE107
In which
Figure 300591DEST_PATH_IMAGE108
Is a process noise covariance matrix that is,
Figure 14469DEST_PATH_IMAGE109
is an iteration cycle of the extended kalman filter algorithm;
Figure 263048DEST_PATH_IMAGE110
is the X-axis component of the position of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 704262DEST_PATH_IMAGE111
is the Y-axis component of the position of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 580951DEST_PATH_IMAGE112
a Z-axis component of the position of the first ultra-wideband positioning module at the k moment under the ultra-wideband coordinate system;
Figure 216463DEST_PATH_IMAGE113
is the X-axis component of the velocity of the first UWB positioning module at the time k under the UWB coordinate system,
Figure 342551DEST_PATH_IMAGE114
is the velocity Y-axis component of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 590386DEST_PATH_IMAGE115
a Z-axis component of the speed of the first ultra-wideband positioning module at the k moment under the ultra-wideband coordinate system;
Figure 993685DEST_PATH_IMAGE116
is the X-axis component of the position of the first ultra-wideband positioning module at the time k-1 under the ultra-wideband coordinate system,
Figure 783787DEST_PATH_IMAGE117
is the acceleration X-axis component of the first UWB positioning module at the time k-1 under the UWB coordinate system,
Figure 147903DEST_PATH_IMAGE118
the process noise of the X-axis component of the position of the first ultra-wideband positioning module at the moment k-1 is obtained;
Figure 947232DEST_PATH_IMAGE119
is the Y-axis component of the position of the first ultra-wideband positioning module at the time k-1 in the ultra-wideband coordinate system,
Figure 205038DEST_PATH_IMAGE120
is the acceleration Y-axis component of the first ultra-wideband positioning module at the time k-1 under the ultra-wideband coordinate system,
Figure 415308DEST_PATH_IMAGE121
the process noise of the Y-axis component of the position of the first ultra-wideband positioning module at the moment k-1 is obtained;
Figure 515988DEST_PATH_IMAGE122
is the Z-axis component of the position of the first ultra-wideband positioning module at the time k-1 in the ultra-wideband coordinate system,
Figure 604161DEST_PATH_IMAGE123
is the acceleration Z-axis component of the first UWB positioning module at the time k-1 under the UWB coordinate system,
Figure 716474DEST_PATH_IMAGE124
the process noise of the Z-axis component of the position of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 910695DEST_PATH_IMAGE125
the process noise of the speed X-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 422972DEST_PATH_IMAGE126
the process noise of the velocity Y-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 829683DEST_PATH_IMAGE127
the process noise of the speed Z-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 875130DEST_PATH_IMAGE128
is the state matrix of the system at time k-1,
Figure 115619DEST_PATH_IMAGE129
to input the matrix for the system at time k-1,
Figure 128574DEST_PATH_IMAGE130
is the control vector of the system at time k-1,
Figure 119401DEST_PATH_IMAGE131
is the state variable of the system at the time k;
and combining the attitude data, and the system input quantity is as follows:
Figure 737465DEST_PATH_IMAGE132
wherein g is the acceleration of gravity;
Figure 493062DEST_PATH_IMAGE133
the acceleration X-axis component of the first ultra-wideband positioning module at the moment of k-1 under a machine body coordinate system is obtained;
the observation equation is a first ultra-wideband positioning module and a second ultra-wideband positioning module
Figure 993314DEST_PATH_IMAGE134
（
Figure 790762DEST_PATH_IMAGE134
=1,…,n）A ranging value therebetween;
Figure 794490DEST_PATH_IMAGE135
wherein the content of the first and second substances,
Figure 642360DEST_PATH_IMAGE136
is the measurement noise of the ranging value of the system at time k,
Figure 115061DEST_PATH_IMAGE137
is to measure the noise of the measurement,
Figure 401686DEST_PATH_IMAGE138
is a second ultra-wideband positioning module
Figure 837084DEST_PATH_IMAGE134
Coordinates under an ultra-wideband coordinate system;
predicting initial values from state variables
Figure 918172DEST_PATH_IMAGE139
Estimating an error covariance matrix initial value
Figure 143749DEST_PATH_IMAGE140
And initial value of system input quantity
Figure 234064DEST_PATH_IMAGE141
Calculating the prior predicted value of the state variable by combining the following formula
Figure 945142DEST_PATH_IMAGE142
Estimate error covariance matrix prior prediction
Figure 462711DEST_PATH_IMAGE143
；
Figure 910004DEST_PATH_IMAGE144
。
Updating the statistical characteristics of the measurement noise in the extended Kalman filtering algorithm through the deviation so as to update the extended Kalman filtering algorithm to obtain a posterior updated value of the state variable; the method comprises the following steps:
according to the deviation between the distance measurement value and the resolving distance
Figure 335169DEST_PATH_IMAGE145
Adjusting the measurement noise covariance matrix
Figure 948422DEST_PATH_IMAGE146
（
Figure 636892DEST_PATH_IMAGE146
>0) Modifying the statistical characteristics of the observation noise; wherein
Figure 305902DEST_PATH_IMAGE147
Is the calibrated range value;
Figure 410125DEST_PATH_IMAGE148
wherein the coefficients
Figure 628616DEST_PATH_IMAGE149
Value range of
Figure 474606DEST_PATH_IMAGE150
，
Figure 942496DEST_PATH_IMAGE149
(ii) a Calculating the Kalman gain according to
Figure 397880DEST_PATH_IMAGE151
。
Figure 470878DEST_PATH_IMAGE152
In the formula (I), the compound is shown in the specification,
Figure 750418DEST_PATH_IMAGE153
is the transpose of the system's observation matrix at time k,
Figure 580971DEST_PATH_IMAGE154
an observation matrix of the system at the time k;
carrying out posterior updating on the state variable and the estimation error covariance matrix according to the following formula;
Figure 886050DEST_PATH_IMAGE155
updating values from a posteriori of state variables
Figure 564288DEST_PATH_IMAGE156
Obtaining a position estimation value of the first ultra-wideband positioning module under the ultra-wideband coordinate system. Because this first ultra wide band location module is installed on unmanned aerial vehicle, consequently the location estimation of ultra wide band location module can regard as unmanned aerial vehicle's location data to use.
The method provided by the application embodiment is explained in detail by taking a ground control vehicle as a ground workstation (6 base stations are arranged on the workstation) and taking a tethered unmanned aerial vehicle as an example for the unmanned aerial vehicle.
Before first ultra wide band location module and second ultra wide band location module are installed at unmanned aerial vehicle and ground workstation respectively, can calibrate first ultra wide band location module and second ultra wide band location module and mark the range finding error that hardware differences such as restraines ultra wide band location module self UWB antenna lead to. During specific operation, under the sight distance environment, the laser range finder is combined, linear fitting calibration is carried out on each UWB ranging module, and ranging errors caused by hardware differences such as UWB antennas are restrained. Wherein, every UWB ranging module contains two ultra wide band location modules. 6 base stations and a label need to be planned before a UWB ranging module is calibrated, a measured value of a laser range finder is used as a reference standard, each label-base station is calibrated based on least square normal fitting, and ranging errors caused by hardware differences such as antennas are restrained.
Specifically, seven UWB positioning modules are divided into six positioning base stations and one positioning tag. And in a line-of-sight environment, the measurement value of the laser range finder is used as a standard reference value, and the least square method is used for carrying out linear fitting calibration on the ranging of each UWB tag-base station. To the first
Figure 765462DEST_PATH_IMAGE157
(
Figure 866666DEST_PATH_IMAGE157
=1, …,6) group tag-base station, acquisition
Figure 444278DEST_PATH_IMAGE158
Group distance
Figure 773760DEST_PATH_IMAGE159
Measurement value data, wherein
Figure 145835DEST_PATH_IMAGE160
Is the first
Figure 528144DEST_PATH_IMAGE157
Group tag-to-base station
Figure 112709DEST_PATH_IMAGE161
The sub-UWB ranging value is determined to be,
Figure 765538DEST_PATH_IMAGE162
is the measurement value corresponding to the laser rangefinder. After calibration the first
Figure 42936DEST_PATH_IMAGE157
Group UWB ranging as
Figure 712208DEST_PATH_IMAGE163
. Computing scaling factors by minimizing mean square error
Figure 366043DEST_PATH_IMAGE164
And offset
Figure 607800DEST_PATH_IMAGE165
. Mean square error of
Figure 259361DEST_PATH_IMAGE166
（1）
According to the formula
Figure 835836DEST_PATH_IMAGE167
（2）
Calculate out
Figure 542629DEST_PATH_IMAGE168
And
Figure 216056DEST_PATH_IMAGE169
is composed of
Figure 585989DEST_PATH_IMAGE170
（3）。
After the first ultra-wideband positioning module and the second ultra-wideband positioning module are calibrated and calibrated, the ultra-wideband positioning module can be installed to a corresponding position. Specifically, deploy 6 ultra wide band location modules and set up to the basic station mode on the ground control vehicle, an ultra wide band location module is deployed and is set up to the label mode on mooring unmanned aerial vehicle.
The specific arrangement mode can be that, as shown in fig. 2, an ultra wide band positioning module is respectively deployed at two ends of the top of a cab of a vehicle head and is set as a positioning base station, an ultra wide band positioning module is respectively deployed at the periphery of the top of a cargo compartment of a vehicle tail and is set as a positioning base station, and an ultra wide band positioning module is deployed on a tethered unmanned aerial vehicle and is set as a label. The deployment plane area of the six positioning base stations is 6 meters by 2.4 meters, wherein the length of a cockpit is 3.3 meters, and the length of a cargo hold is 2.7 meters.
After the ultra-wideband positioning module is installed, the auxiliary positioning can be performed on the unmanned aerial vehicle in taking off and landing and in the flying process. During specific implementation, each ranging module (one base station and one tag) measures the distance between the tag and each base station by adopting a bilateral two-way ranging method so as to inhibit ranging errors caused by clock drift of the tag and the base stations, and the IMU module measures attitude data of the ultra-wideband positioning module.
The attitude of the ultra-wideband positioning module is measured by an IMU module of the ultra-wideband positioning module and is output in the form of quaternion and attitude angle; the specific method of bilateral two-way ranging is as follows:
as shown in FIG. 3, the UWB positioning module A is
Figure 321864DEST_PATH_IMAGE171
Constantly sends out a ranging signal, and the ultra-wideband positioning module B is
Figure 379818DEST_PATH_IMAGE172
The ranging signal of the ultra-wideband positioning module A is received constantly
Figure 351493DEST_PATH_IMAGE173
Sends out ranging signals all the time, and the ultra-wideband positioning module A is
Figure 141594DEST_PATH_IMAGE174
A ranging signal of B is received at
Figure 364765DEST_PATH_IMAGE175
Sends out ranging signals all the time, and the ultra-wideband positioning module B is
Figure 914826DEST_PATH_IMAGE176
And receiving the ranging signal of the ultra-wideband positioning module A at any time. The signal one-way transmission time between the ultra-wideband positioning module A and the ultra-wideband positioning module B is as follows:
Figure 297266DEST_PATH_IMAGE177
（4）
wherein
Figure 38695DEST_PATH_IMAGE178
，
Figure 749162DEST_PATH_IMAGE179
，
Figure 821023DEST_PATH_IMAGE180
And
Figure 74281DEST_PATH_IMAGE181
. The distance between the ultra-wideband positioning module A and the ultra-wideband positioning module B is
Figure 206185DEST_PATH_IMAGE182
In which
Figure 403948DEST_PATH_IMAGE183
Is the speed of light. Calculating the calibrated range according to the scaling factor and the offset in the formula (3)
Figure 594014DEST_PATH_IMAGE184
。
And through the extended Kalman filtering algorithm, the UWB ranging value and IMU data are fused, and the measurement noise statistical characteristic in the extended Kalman filtering algorithm is dynamically updated according to the deviation between the inertial navigation resolving distance and the UWB ranging value.
The specific method for the fusion of the UWB ranging value and the IMU data and the adjustment of the statistical characteristics of the measurement noise is as follows:
and an IMU module in the UWB module outputs data such as attitude angle, quaternion, acceleration and the like. Calculating a rotation matrix from a body coordinate system to a navigation coordinate system according to the attitude angle and quaternion of the tag ultra-wideband positioning module
Figure 888730DEST_PATH_IMAGE185
. Calculating a rotation matrix from a navigation coordinate system to an ultra-wideband coordinate system according to the attitude angle and quaternion of the base station ultra-wideband positioning module
Figure 394797DEST_PATH_IMAGE186
。
Ultra-wideband ranging is relatively independent, does not have accumulated errors, but is affected by external environment, and the ranging precision is deteriorated; the positioning based on IMU is not influenced by external environment, but has error accumulation. By expandingAnd the extended Kalman filter fuses the data of the extended Kalman filter and the data of the extended Kalman filter to realize advantage complementation. The state variables selected by the extended Kalman filter are the position and the speed in the X, Y and Z axis directions under an ultra wide band coordinate system
Figure 158485DEST_PATH_IMAGE187
The system state equation is:
Figure 680471DEST_PATH_IMAGE188
(5)
corresponding matrix form
Figure 95272DEST_PATH_IMAGE189
Wherein
Figure 850869DEST_PATH_IMAGE190
（
Figure 616700DEST_PATH_IMAGE191
) Is a process noise covariance matrix that is,
Figure 351831DEST_PATH_IMAGE192
is the iteration cycle of the extended kalman filter.
Figure 621139DEST_PATH_IMAGE193
Is the X-axis component of the position of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 547637DEST_PATH_IMAGE194
is the Y-axis component of the position of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 207289DEST_PATH_IMAGE195
a Z-axis component of the position of the first ultra-wideband positioning module at the k moment under the ultra-wideband coordinate system;
Figure 71077DEST_PATH_IMAGE196
locating a first ultra-widebandThe velocity X-axis component of the module at time k in the ultra-wideband coordinate system,
Figure 866995DEST_PATH_IMAGE197
is the velocity Y-axis component of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 292291DEST_PATH_IMAGE198
a Z-axis component of the speed of the first ultra-wideband positioning module at the k moment under the ultra-wideband coordinate system;
Figure 439239DEST_PATH_IMAGE199
is the X-axis component of the position of the first ultra-wideband positioning module at the time k-1 under the ultra-wideband coordinate system,
Figure 529555DEST_PATH_IMAGE200
is the acceleration X-axis component of the first UWB positioning module at the time k-1 under the UWB coordinate system,
Figure 353548DEST_PATH_IMAGE201
the process noise of the X-axis component of the position of the first ultra-wideband positioning module at the moment k-1 is obtained;
Figure 277641DEST_PATH_IMAGE202
is the Y-axis component of the position of the first ultra-wideband positioning module at the time k-1 in the ultra-wideband coordinate system,
Figure 724934DEST_PATH_IMAGE203
is the acceleration Y-axis component of the first UWB positioning module at the time k-1 under the UWB coordinate system,
Figure 618941DEST_PATH_IMAGE204
the process noise of the Y-axis component of the position of the first ultra-wideband positioning module at the moment k-1 is obtained;
Figure 232194DEST_PATH_IMAGE205
is the Z-axis component of the position of the first ultra-wideband positioning module at the time k-1 in the ultra-wideband coordinate system,
Figure 733713DEST_PATH_IMAGE206
is the acceleration Z-axis component of the first UWB positioning module at the time k-1 under the UWB coordinate system,
Figure 855253DEST_PATH_IMAGE207
process noise of a Z-axis component of a position of the first ultra-wideband positioning module at the time of k-1;
Figure 21792DEST_PATH_IMAGE208
the process noise of the speed X-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 51121DEST_PATH_IMAGE209
the process noise of the velocity Y-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 707230DEST_PATH_IMAGE210
the process noise of the speed Z-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 863536DEST_PATH_IMAGE211
is the state matrix of the system at time k-1,
Figure 99345DEST_PATH_IMAGE212
to input the matrix for the system at time k-1,
Figure 421611DEST_PATH_IMAGE213
is the control vector of the system at time k-1,
Figure 123988DEST_PATH_IMAGE214
is the state variable of the system at time k.
Combine attitude angle, quaternion, the acceleration data of IMU module output in the ultra wide band module, the system input is:
Figure 16857DEST_PATH_IMAGE215
(6)
wherein g is the acceleration of the weight of the material,
Figure 338248DEST_PATH_IMAGE216
an acceleration X-axis component of the first ultra-wideband positioning module at the k-1 moment under a machine body coordinate system is obtained; .
The observation equation is an ultra-wideband tag and an ultra-wideband base station
Figure 174DEST_PATH_IMAGE217
（
Figure 607873DEST_PATH_IMAGE217
Distance measurement of =1, …,6)
Figure 302553DEST_PATH_IMAGE218
(7)
Wherein the content of the first and second substances,
Figure 349006DEST_PATH_IMAGE219
is the measurement noise of the ranging value of the system at time k,
Figure 68700DEST_PATH_IMAGE220
is to measure the noise of the measurement,
Figure 925929DEST_PATH_IMAGE221
is an ultra-wideband base station
Figure 855708DEST_PATH_IMAGE217
Coordinates in an ultra-wideband coordinate system.
In the prediction stage of the extended Kalman filter, an initial value is predicted according to the state variables
Figure 955120DEST_PATH_IMAGE222
Estimating an error covariance matrix initial value
Figure 263741DEST_PATH_IMAGE223
And initial value of system input quantity
Figure 337877DEST_PATH_IMAGE224
Calculating the prior predicted value of the state variable by combining the following formula
Figure 364739DEST_PATH_IMAGE225
Estimate error covariance matrix prior prediction
Figure 769306DEST_PATH_IMAGE226
。
Figure 322647DEST_PATH_IMAGE227
(8)
In the extended Kalman filter correction stage, firstly, the deviation of the distance is calculated according to ultra-wideband ranging and inertial navigation
Figure 85460DEST_PATH_IMAGE228
Adjusting the measurement noise covariance matrix
Figure 599618DEST_PATH_IMAGE229
（
Figure 604614DEST_PATH_IMAGE229
>0) And modifying the statistical characteristics of the observation noise and reducing the model error. Wherein
Figure 950145DEST_PATH_IMAGE230
Is the calibrated ultra-wideband ranging value. When one or more (less than 6) ultra-wideband ranging values are invalid (the validity and the invalidity are determined by a set precision factor), then
Figure 772608DEST_PATH_IMAGE231
The corresponding element in the tree is not updated and does not participate in the operation.
Figure 85646DEST_PATH_IMAGE232
(9)
Wherein the coefficients
Figure 346863DEST_PATH_IMAGE233
Value range of
Figure 359950DEST_PATH_IMAGE234
，
Figure 884472DEST_PATH_IMAGE235
The larger the value, the larger the impact of the new ranging data on the system. Calculating the Kalman gain according to
Figure 750053DEST_PATH_IMAGE236
。
Figure 283803DEST_PATH_IMAGE237
(10)
In the formula (I), the compound is shown in the specification,
Figure 620237DEST_PATH_IMAGE238
is the transpose of the system's observation matrix at time k,
Figure 112399DEST_PATH_IMAGE239
an observation matrix of the system at the time k;
a posteriori update of state variable and estimation error covariance matrices as follows
Figure 400029DEST_PATH_IMAGE240
(11)
Posterior updating of values from state variables
Figure 409574DEST_PATH_IMAGE241
The estimated position of the tag under the ultra-wideband coordinate system is obtained and used as the position data of the mooring unmanned aerial vehicle relative to the ground control vehicle. The method adopts a method of fusing ultra wide band distance measurement values and attitude data, and ensures that the auxiliary positioning system is still available under the condition of less available ranging signals in the environment of satellite signal rejection.
In order to verify the effectiveness of positioning of the vehicle-mounted tethered unmanned aerial vehicle in the satellite signal rejection environment, the method provided by the embodiment of the application is further explained by the following simulation experiment.
In the simulation, the coordinates of 6 positioning base stations in the ultra-wideband coordinate system are (0, 0, 0), (0, 1.85, 0.22), (0, 4.69, 0.22), (-1.78, 0, 0), (-2.3, 1.85, 0.22), (-2.3, 4.69, 0.22), and the ultra-wideband ranging accuracy is 10 cm.
The unmanned aerial vehicle carries the positioning tag to fly at the height of 25 meters above the ground control vehicle, and the simulation experiment result is shown in fig. 4, 5 and 6. When the data fusion algorithm of the extended Kalman filter algorithm is converged, the positioning error in the X-axis direction is
Figure 912099DEST_PATH_IMAGE242
Within 30 cm, the positioning error in the Y-axis direction is
Figure 60315DEST_PATH_IMAGE242
Within 18 cm.
The unmanned aerial vehicle carries the positioning tag to fly at the position 195 meters above the ground control vehicle, and the simulation experiment result is shown in fig. 7, 8 and 9. When the data fusion algorithm of the extended Kalman filter algorithm is converged, the positioning error in the X-axis direction is
Figure 585974DEST_PATH_IMAGE242
Within 70 cm, the positioning error in the Y-axis direction is
Figure 133630DEST_PATH_IMAGE242
Within 40 centimeters. The positioning error in the X-axis direction is larger because the maximum distance between base stations in the X-axis direction is smaller than the maximum distance between base stations in the Y-axis direction.
In a word, the utility model provides an unmanned aerial vehicle assistance-localization real-time method based on ultra wide band, according to laser range finder measured value, can effectual calibration ultra wide band location module, use bilateral two-way range finding method to measure the distance of unmanned aerial vehicle and each basic station, fuse ultra wide band range finding and gesture data according to the extended Kalman filtering algorithm, dynamic adjustment measures noise statistical property, reduce ultra wide band range finding error, and then promoted ultra wide band range finding positioning accuracy, unmanned aerial vehicle's dependence to satellite signal has been reduced, the problem that tethered unmanned aerial vehicle can't fix a position under the satellite signal environment of refusing is solved.
Under the condition that the distance between the ultra-wideband base stations is limited, the ultra-wideband positioning precision is improved by optimizing two stages of ranging and positioning. And calibrating each ultra-wideband tag-base station based on least square normal linear fitting according to the measured value of the laser range finder, and inhibiting the ranging error caused by antenna delay and clock drift by adopting a bilateral two-way ranging method. Based on the extended Kalman filtering algorithm, ultra-wideband ranging and attitude data are fused, the statistical characteristics of measurement noise can be dynamically adjusted, and the error of a positioning model is reduced. The ultra-wideband ranging and inertial navigation positioning are integrated, and the problem that the tethered unmanned aerial vehicle cannot be positioned in a satellite signal rejection environment is solved.
Referring to fig. 10, corresponding to the ultra-wideband-based unmanned aerial vehicle assisted positioning method provided in the embodiment of the present application, as shown in fig. 10, an embodiment of the present application further provides an ultra-wideband-based unmanned aerial vehicle assisted positioning system, where the system specifically may include:
the data acquisition unit 201 is configured to acquire attitude data of a first ultra-wideband positioning module and a ranging value between the first ultra-wideband positioning module and a second ultra-wideband positioning module; the first ultra-wideband positioning module is arranged on the unmanned aerial vehicle; the second ultra-wideband positioning module is arranged on the ground workstation;
a data fusion unit 202, configured to fuse the attitude data and the distance measurement value through an extended kalman filter algorithm;
an updating unit 203; the calculation distance between the first ultra-wideband positioning module and the ground workstation is obtained through calculation of the attitude data; calculating to obtain a deviation between the ranging value and the resolving distance, and updating the statistical characteristic of measurement noise in the extended Kalman filtering algorithm through the deviation so as to update the extended Kalman filtering algorithm to obtain a posterior updated value of the state variable; the state variables comprise the position and the speed of the first ultra-wideband positioning module in the X, Y, Z axis direction under the ultra-wideband coordinate system;
and the position data determining unit 204 is configured to obtain a position estimation value of the first ultra-wideband positioning module in the ultra-wideband coordinate system from the posterior updated value, and use the position estimation value as position data of the unmanned aerial vehicle relative to the ground workstation.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments, which are substantially similar to the method embodiments, are described in a relatively simple manner, and reference may be made to some descriptions of the method embodiments for relevant points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (10)

1. An ultra-wideband-based unmanned aerial vehicle auxiliary positioning method is characterized by comprising the following steps:
acquiring attitude data of a first ultra-wideband positioning module and a ranging value between the first ultra-wideband positioning module and a second ultra-wideband positioning module; the first ultra-wideband positioning module is arranged on the unmanned aerial vehicle; the second ultra-wideband positioning module is arranged on the ground workstation;
fusing the attitude data and the ranging values through an extended Kalman filtering algorithm;
calculating to obtain a resolving distance between the first ultra-wideband positioning module and the ground workstation through the attitude data; calculating to obtain a deviation between the ranging value and the resolving distance, and updating a measurement noise statistical characteristic in the extended Kalman filtering algorithm through the deviation so as to update the extended Kalman filtering algorithm to obtain a posterior updated value of a state variable; the state variables comprise the position and the speed of the first ultra-wideband positioning module in the X, Y, Z axial direction under the ultra-wideband coordinate system;
and acquiring a position estimation value of the first ultra-wideband positioning module under the ultra-wideband coordinate system from the posterior updated value, and taking the position estimation value as position data of the unmanned aerial vehicle relative to the ground workstation.
2. The ultra-wideband-based unmanned aerial vehicle-assisted positioning method according to claim 1, wherein the obtaining attitude data of the first ultra-wideband positioning module comprises:
measuring and acquiring attitude data of the first ultra-wideband positioning module through the inertial navigation module; the attitude data includes an attitude angle, a quaternion, and an acceleration.
3. The ultra-wideband-based unmanned aerial vehicle-assisted positioning method of claim 1, wherein the second ultra-wideband positioning module comprises a plurality of modules;
the range finding value of acquireing between first ultra wide band location module and the second ultra wide band location module includes:
respectively obtaining ranging values between the first ultra-wideband positioning module and each second ultra-wideband positioning module to form a ranging value group; fusing the attitude data and the ranging value group through an extended Kalman filtering algorithm;
the calculating to obtain the deviation between the ranging value and the resolving distance comprises:
and respectively calculating and obtaining the deviation between each ranging value contained in the ranging value group and the resolving distance to form a deviation group, and updating the measurement noise statistical characteristic in the extended Kalman filtering algorithm through the deviation group.
4. The ultra-wideband-based drone assisted positioning method of claim 3, wherein the drone includes a tethered drone, the ground station includes a ground control vehicle; and the second ultra-wideband positioning modules are arranged at the top of the ground control vehicle.
5. The ultra-wideband-based unmanned aerial vehicle auxiliary positioning method according to claim 1, wherein the first ultra-wideband positioning module and the second ultra-wideband positioning module are ultra-wideband positioning modules which are calibrated by linear fitting in combination with a laser range finder.
6. The ultra-wideband-based unmanned aerial vehicle-assisted positioning method according to claim 5, wherein the calibration method comprises:
in a line-of-sight environment, acquisition
Figure 112011DEST_PATH_IMAGE001
Group distance
Figure 763572DEST_PATH_IMAGE002
Data therein, wherein
Figure 386052DEST_PATH_IMAGE003
Is the first
Figure 250103DEST_PATH_IMAGE004
Set the first ultra-wideband positioning module and the second ultra-wideband positioning module
Figure 657951DEST_PATH_IMAGE005
The value of the secondary distance measurement is,
Figure 949255DEST_PATH_IMAGE006
is the measured value corresponding to the laser range finder; after calibration the first
Figure 763758DEST_PATH_IMAGE004
Group ranging as
Figure 556134DEST_PATH_IMAGE007
Wherein the parameters
Figure 693854DEST_PATH_IMAGE008
And
Figure 532890DEST_PATH_IMAGE009
and (3) solving according to least square normal linear fitting, wherein the calculation formula is as follows:
Figure 756061DEST_PATH_IMAGE010
。
7. the ultra-wideband-based unmanned aerial vehicle-assisted positioning method of claim 1, wherein the obtaining of the ranging value between the first ultra-wideband positioning module and the second ultra-wideband positioning module comprises:
measuring and obtaining a calibrated ranging value between the first ultra-wideband positioning module and the second ultra-wideband positioning module by adopting a bilateral two-way ranging method;
the bilateral two-way ranging method comprises the following steps:
an ultra-wideband positioning module A
Figure 352128DEST_PATH_IMAGE011
Sends out ranging signals all the time, and the ultra-wideband positioning module B is
Figure 344355DEST_PATH_IMAGE012
A ranging signal is received at time A
Figure 118407DEST_PATH_IMAGE013
Sends out ranging signals all the time, and the ultra-wideband positioning module A is
Figure 156770DEST_PATH_IMAGE014
Ultra-wideband positioning module capable of receiving data at any momentRanging signals of group B, and
Figure 900735DEST_PATH_IMAGE015
sends out ranging signals all the time, and the ultra-wideband positioning module B is
Figure 386949DEST_PATH_IMAGE016
Constantly receiving a ranging signal of the ultra-wideband positioning module A; the one-way transmission time of the ranging signal between the ultra-wideband positioning module A and the ultra-wideband positioning module B is as follows:
Figure 456536DEST_PATH_IMAGE017
wherein, the first and the second end of the pipe are connected with each other,
Figure 982195DEST_PATH_IMAGE018
，
Figure 405217DEST_PATH_IMAGE019
、
Figure 372036DEST_PATH_IMAGE020
、
Figure 737159DEST_PATH_IMAGE021
(ii) a The distance between the ultra-wideband positioning module A and the ultra-wideband positioning module B is
Figure 422218DEST_PATH_IMAGE022
Wherein
Figure 162115DEST_PATH_IMAGE023
Is the speed of light.
8. The ultra-wideband-based drone assisted positioning method of claim 7,
fusing the attitude data and the ranging value through an extended Kalman filtering algorithm; the method comprises the following steps:
calculating a rotation matrix from a body coordinate system to a navigation coordinate system according to the attitude data
Figure 983440DEST_PATH_IMAGE024
(ii) a Calculating a rotation matrix from a navigation coordinate system to the ultra-wideband coordinate system according to the attitude angle and the quaternion of the second ultra-wideband positioning module
Figure 722726DEST_PATH_IMAGE025
；
Selecting the state variables as the position and the speed in the X, Y, Z axis direction under the ultra-wideband coordinate system
Figure 504868DEST_PATH_IMAGE026
The system state equation is:
Figure 659906DEST_PATH_IMAGE027
（3）
the corresponding matrix is in the form of
Figure 725951DEST_PATH_IMAGE028
；
Wherein the content of the first and second substances,
Figure 308242DEST_PATH_IMAGE029
is a process noise covariance matrix that is,
Figure 279478DEST_PATH_IMAGE030
is an iteration cycle of the extended kalman filter algorithm;
Figure 97261DEST_PATH_IMAGE031
is the X-axis component of the position of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 893179DEST_PATH_IMAGE032
is the Y-axis component of the position of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 521738DEST_PATH_IMAGE033
a Z-axis component of the position of the first ultra-wideband positioning module at the k moment under the ultra-wideband coordinate system;
Figure 403106DEST_PATH_IMAGE034
is the X-axis component of the velocity of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 24580DEST_PATH_IMAGE035
is the velocity Y-axis component of the first UWB positioning module at the k-time under the UWB coordinate system,
Figure 675004DEST_PATH_IMAGE036
a Z-axis component of the speed of the first ultra-wideband positioning module at the k moment under the ultra-wideband coordinate system;
Figure 913612DEST_PATH_IMAGE037
is the X-axis component of the position of the first ultra-wideband positioning module at the time k-1 under the ultra-wideband coordinate system,
Figure 406910DEST_PATH_IMAGE038
is the acceleration X-axis component of the first UWB positioning module at the time k-1 under the UWB coordinate system,
Figure 441863DEST_PATH_IMAGE039
the process noise of the X-axis component of the position of the first ultra-wideband positioning module at the moment k-1 is obtained;
Figure 87739DEST_PATH_IMAGE040
for the position of the first UWB positioning module at the k-1 moment under the UWB coordinate systemThe component of the Y-axis is set,
Figure 182734DEST_PATH_IMAGE041
is the acceleration Y-axis component of the first ultra-wideband positioning module at the time k-1 under the ultra-wideband coordinate system,
Figure 163328DEST_PATH_IMAGE042
process noise of a Y-axis component of a position of the first ultra-wideband positioning module at the time of k-1;
Figure 1971DEST_PATH_IMAGE043
is the Z-axis component of the position of the first ultra-wideband positioning module at the time k-1 in the ultra-wideband coordinate system,
Figure 938572DEST_PATH_IMAGE044
is the acceleration Z-axis component of the first UWB positioning module at the time k-1 under the UWB coordinate system,
Figure 329102DEST_PATH_IMAGE045
the process noise of the Z-axis component of the position of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 406779DEST_PATH_IMAGE046
the process noise of the speed X-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 924479DEST_PATH_IMAGE047
the process noise of the velocity Y-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 669582DEST_PATH_IMAGE048
the process noise of the speed Z-axis component of the first ultra-wideband positioning module at the moment of k-1 is obtained;
Figure 231013DEST_PATH_IMAGE049
is the state matrix of the system at time k-1,
Figure 795986DEST_PATH_IMAGE050
is the input matrix of the system at time k-1,
Figure 556525DEST_PATH_IMAGE051
is the control vector of the system at time k-1,
Figure 15189DEST_PATH_IMAGE052
is the state variable of the system at the time k;
and combining the attitude data, wherein the system input quantity is as follows:
Figure 622887DEST_PATH_IMAGE053
wherein g is gravity acceleration;
Figure 550523DEST_PATH_IMAGE054
an acceleration X-axis component of the first ultra-wideband positioning module at the k-1 moment under a machine body coordinate system is obtained;
the observation equation is a first ultra-wideband positioning module and a second ultra-wideband positioning module
Figure 534660DEST_PATH_IMAGE055
（
Figure 113409DEST_PATH_IMAGE055
Distance measurement values between =1, …, n);
Figure 892009DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure 805476DEST_PATH_IMAGE057
is the measurement noise of the ranging value of the system at time k,
Figure 655620DEST_PATH_IMAGE058
is to measure the noise of the measurement,
Figure 229821DEST_PATH_IMAGE059
is a second ultra-wideband positioning module
Figure 54689DEST_PATH_IMAGE060
Coordinates under an ultra-wideband coordinate system;
predicting initial values from state variables
Figure 815971DEST_PATH_IMAGE061
Estimating an initial value of an error covariance matrix
Figure 266544DEST_PATH_IMAGE062
And initial value of system input quantity
Figure 9766DEST_PATH_IMAGE063
Calculating the prior predicted value of the state variable by combining the following formula
Figure 130169DEST_PATH_IMAGE064
Estimate error covariance matrix prior predictor
Figure 34540DEST_PATH_IMAGE065
；
Figure 898590DEST_PATH_IMAGE066
。
9. The ultra-wideband-based unmanned aerial vehicle assisted positioning method according to claim 8, wherein the measurement noise statistical characteristic in the extended kalman filter algorithm is updated by the deviation, so that the extended kalman filter algorithm is updated to obtain a posterior updated value of the state variable; the method comprises the following steps:
according to the deviation between the distance measurement value and the resolving distance
Figure 791591DEST_PATH_IMAGE067
Adjusting the measurement noise covariance matrix
Figure 676371DEST_PATH_IMAGE068
（
Figure 412245DEST_PATH_IMAGE068
>0) Modifying the statistical characteristics of the observation noise; wherein
Figure 453888DEST_PATH_IMAGE069
Is the calibrated range value;
Figure 326029DEST_PATH_IMAGE070
wherein the coefficients
Figure 178448DEST_PATH_IMAGE071
Value range of
Figure 667198DEST_PATH_IMAGE072
Calculating the Kalman gain according to
Figure 748418DEST_PATH_IMAGE073
；
Figure 68541DEST_PATH_IMAGE074
In the formula (I), the compound is shown in the specification,
Figure 967226DEST_PATH_IMAGE075
is the transpose of the system's observation matrix at time k,
Figure 42806DEST_PATH_IMAGE076
an observation matrix of the system at the time k;
carrying out posterior updating on the state variable and the estimation error covariance matrix according to the following formula;
Figure 52350DEST_PATH_IMAGE077
updating values from a posteriori of state variables
Figure 289296DEST_PATH_IMAGE078
Obtaining a position estimation value of the first ultra-wideband positioning module under the ultra-wideband coordinate system.
10. An ultra-wideband-based unmanned aerial vehicle-assisted positioning system, the system comprising:
the data acquisition unit is used for acquiring attitude data of a first ultra-wideband positioning module and a distance measurement value between the first ultra-wideband positioning module and a second ultra-wideband positioning module; the first ultra-wideband positioning module is arranged on the unmanned aerial vehicle; the second ultra-wideband positioning module is arranged on the ground workstation;
the data fusion unit is used for fusing the attitude data and the ranging value through an extended Kalman filtering algorithm;
an update unit; the calculation distance between the first ultra-wideband positioning module and the ground workstation is obtained through calculation of the attitude data; calculating to obtain a deviation between the ranging value and the resolving distance, and updating the statistical characteristic of measurement noise in the extended Kalman filtering algorithm through the deviation so as to update the extended Kalman filtering algorithm to obtain a posterior updated value of the state variable; the state variables comprise the position and the speed of the first ultra-wideband positioning module in the X, Y, Z axial direction under the ultra-wideband coordinate system;
and the position data determining unit is used for acquiring a position estimation value of the first ultra-wideband positioning module in the ultra-wideband coordinate system from the posterior updated value, and taking the position estimation value as the position data of the unmanned aerial vehicle relative to the ground workstation.
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