CN113970331A - Four-rotor positioning method and system based on reconstruction observed quantity - Google Patents

Four-rotor positioning method and system based on reconstruction observed quantity Download PDF

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CN113970331A
CN113970331A CN202111038887.XA CN202111038887A CN113970331A CN 113970331 A CN113970331 A CN 113970331A CN 202111038887 A CN202111038887 A CN 202111038887A CN 113970331 A CN113970331 A CN 113970331A
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efir
vector
rotors
filtering
quad
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徐元
孙明旭
赵顺毅
毕淑慧
申涛
赵钦君
孙斌
马荔瑶
王自鹏
韩春艳
闫雪华
曹靖
冯寄东
李明然
马万封
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University of Jinan
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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
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/46—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being of a radio-wave signal type
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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  • Remote Sensing (AREA)
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  • Automation & Control Theory (AREA)
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  • Navigation (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a four-rotor positioning method and a system based on reconstruction observation quantity, which comprises the following steps: taking the positions of the east direction and the north direction and the speeds of the east direction and the north direction as state vectors; the distance between the four rotors and the UWB reference node measured by the ultra-wideband is used as an observed value; performing EFIR filtering based on the state vector and the observed value, and smoothing the output of an EFIR filtering algorithm; and reconstructing the observation vector of the set time period by using the state vector after smoothing, wherein the reconstructed observation vector is used as the observation vector required by the EFIR forward filtering at the next moment, and the position of the four rotors at the next moment is estimated. The invention utilizes the R-T-S smoothing algorithm to reconstruct the observation vector at the previous moment, effectively improves the precision of the observation vector and further improves the precision of the EFIR filtering algorithm.

Description

Four-rotor positioning method and system based on reconstruction observed quantity
Technical Field
The invention relates to the technical field of combined positioning in a complex environment, in particular to a four-rotor positioning method and system based on reconstruction observation quantity.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The navigation and positioning of the four rotors are increasingly paid attention by various scholars as the basis for providing high-quality service for human beings by the four rotors, and become a research hotspot in the field. However, as the range of four-rotor applications expands, the navigation environment encountered by the four-rotor application also becomes more complex. Especially in indoor environment, the indoor layout, building materials and even space size of a building can influence navigation signals, and further influence positioning accuracy. Meanwhile, the relatively small platform of the quadrotors facing the indoor environment makes it impossible to mount a part of the high-precision navigation apparatus.
Although the precision of small-sized navigation devices has improved to some extent in recent years with the progress of miniaturization of navigation devices, there is still a gap in performance compared with that of conventional large-sized high-precision navigation devices. Under the indoor environment, how to utilize the limited information that obtains to eliminate the influence of indoor complicated navigation environment to the accuracy and the real-time nature that four rotors navigation information obtained, guarantee the continuation stability of four rotors navigation precision under the indoor environment, have important scientific theory meaning and practical application and worth.
Among the existing positioning methods, Global Navigation Satellite System (GNSS) is the most commonly used method. Although the GNSS can continuously and stably obtain the position information with high precision, the application range of the GNSS is limited by the defect that the GNSS is easily influenced by external environments such as electromagnetic interference and shielding, and particularly in some closed and environment-complex scenes such as indoor and underground roadways, GNSS signals are seriously shielded, and effective work cannot be performed. In recent years, uwb (ultra wideband) has shown great potential in the field of short-distance local positioning due to its high positioning accuracy in a complex environment. The researchers proposed UWB-based target tracking for quad-rotor navigation in GNSS failure environments. Although indoor positioning can be realized by the method, because the indoor environment is complicated and changeable, UWB signals are easily interfered to cause the reduction of positioning accuracy and even the unlocking; meanwhile, because the communication technology adopted by the UWB is generally a short-distance wireless communication technology, if a large-range indoor target tracking and positioning is to be completed, a large number of network nodes are required to complete together, which inevitably introduces a series of problems such as network organization structure optimization design, multi-node multi-cluster network cooperative communication, and the like. UWB-based object tracking at the present stage therefore still faces many challenges in the field of indoor navigation.
In the prior art, an finite impulse response filter (EFIR) algorithm utilizes an observed quantity at a latest moment to complete the estimation of the current moment state, thereby effectively improving the robustness of the filter algorithm. On one hand, though the robustness of the filtering algorithm is effectively improved by only depending on the observed quantity at a certain moment, the risk of inaccurate measured data still exists because the observed quantity is randomly acquired; on the other hand, a filter gives a state estimation already in a period of time before the current moment, and compared with an observed value obtained by random measurement, the precision of the observed value estimated by a filtering algorithm is relatively high, but the data is not effectively utilized.
In addition, conventional EFIR requires observations of the filter window size N, especially during the first N-1 moments, which are usually actually measured and not processed, which can affect the accuracy of the filter.
Disclosure of Invention
In order to solve the problems, the invention provides a four-rotor positioning method and system based on reconstruction observed quantity, which are based on a finite impulse response filter (EFIR) algorithm and utilize the output of the algorithm to reconstruct the observed quantity of the filter algorithm at the previous period, so that the navigation estimation precision of the observed quantity participating in the EFIR filter algorithm can be effectively improved, and the whole navigation precision is further improved.
In some embodiments, the following technical scheme is adopted:
a quad-rotor positioning method based on reconstruction observation comprises the following steps:
taking the positions of the east direction and the north direction and the speeds of the east direction and the north direction as state vectors;
the distance between the four rotors and the UWB reference node measured by the ultra-wideband is used as an observed value;
performing EFIR filtering based on the state vector and the observed value, and smoothing the output of an EFIR filtering algorithm;
and reconstructing the observation vector of the set time period by using the state vector after smoothing, wherein the reconstructed observation vector is used as the observation vector required by the EFIR forward filtering at the next moment, and the position of the four rotors at the next moment is estimated.
In other embodiments, the following technical solutions are adopted:
a quad rotor positioning system based on reconstructed observations, comprising:
the EFIR filtering module is used for taking the east and north positions and the east and north speeds as state vectors; the distance between the four rotors and the UWB reference node measured by the ultra-wideband is used as an observed value; performing EFIR filtering based on the state vector and the observed value, and smoothing the output of an EFIR filtering algorithm;
and the position estimation module is used for reconstructing the observation vector of the set time interval by using the smoothed state vector, and estimating the position of the four rotors at the next moment by using the reconstructed observation vector as the observation vector required by the EFIR forward filtering at the next moment.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is configured to store a plurality of instructions adapted to be loaded by the processor and to perform the above-described reconstruction observation based quad-rotor positioning method.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute the above-described reconstruction observation based quad-rotor positioning method.
Compared with the prior art, the invention has the beneficial effects that:
the invention reconstructs the observation vector of the previous period of time by utilizing the R-T-S smoothing algorithm, improves the precision of the data of the observation vector of the N-1 time, and further improves the precision of the EFIR filtering algorithm.
Drawings
Fig. 1 is a schematic structural diagram of a four-rotor UWB positioning system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an EFIR filtering algorithm based on a reconstructed observation according to a first embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, a quad-rotor positioning method based on reconstructed observations is disclosed, the positioning method based on a quad-rotor positioning system as shown in fig. 1; the method specifically comprises the following steps: the UWB target node is fixed on the four rotors and is connected with the data processing unit through a serial port RS 232. Wherein the UWB is used to measure the distance between the quadrotors and the UWB reference node.
Based on the above four-rotor positioning system, referring to fig. 2, the four-rotor positioning method based on reconstruction observation disclosed in this embodiment specifically includes the following processes:
(1) when the sampling time exceeds the filter window length N of the EFIR filterEThen, firstly, estimating the east, north and space positions of the four rotors at each moment through an EFIR filtering algorithm;
(2) the estimated value of the output of the EFIR filtering algorithm is R-T-S smoothed, and the observed value of the filter at the previous N-1 time is reconstructed by using the smoothed data.
(3) And at the next moment, estimating the position of the four rotors by using the reconstructed observed value and the observed value of the current moment acquired at the next moment through the EFIR filtering algorithm to obtain the position estimation of the four rotors at the next moment.
Specifically, an estimate of the position of the quadrotors at each moment is obtained by the following specific process:
using the east, north and sky positions and speeds of the four rotors as state vectors of an EFIR filtering algorithm; and taking the distance between the four rotors measured by the UWB and the UWB reference node as an observation vector of an EFIR filtering algorithm for data fusion to obtain the optimal position prediction of the four rotors.
The state equation of the EFIR filtering algorithm is as follows:
Figure BDA0003248326960000051
wherein (x)d,yd,zd) The positions of four rotors d in the east direction, the north direction and the sky direction at the moment; (Vx)d,Vyd,Vzd) The speed of the four rotors at the moment d in the east direction, the north direction and the sky direction; Δ d is the sampling period, wdThe covariance matrix is Q, which is the system noise at time k.
The observation equation for the EFIR filtering algorithm is:
Figure BDA0003248326960000061
wherein the content of the first and second substances,
Figure BDA0003248326960000062
distance between the four rotors and the ith reference node is measured by UWB at the d moment; v isdFor observing noise, its covariance matrix is R; (x)d,yd,zd) The four rotors d are in east, north and sky positions at the moment.
The iterative equation for the EFIR filtering algorithm is:
d<NEwhen the temperature of the water is higher than the set temperature,
Figure BDA0003248326960000063
Figure BDA0003248326960000064
Figure BDA0003248326960000065
Figure BDA0003248326960000066
Figure BDA0003248326960000067
d>NEtime of flight
mm＝d-NE+1,t＝mm+ME-1
Figure BDA0003248326960000068
Figure BDA0003248326960000069
forb＝mm+ME:d
Figure BDA00032483269600000610
Figure BDA00032483269600000611
Gb＝[HTH+(AGb-1AT)-1]-1
Kb＝GbHT
Figure BDA00032483269600000612
Figure BDA00032483269600000613
end
Wherein the content of the first and second substances,
Figure BDA0003248326960000071
in this embodiment, the process of smoothing the positions of the four rotors in the direction by using the R-T-S smoothing algorithm specifically includes:
Figure BDA0003248326960000072
Figure BDA0003248326960000073
Figure BDA0003248326960000074
Figure BDA0003248326960000075
wherein the content of the first and second substances,
Figure BDA0003248326960000076
for smooth error gain, A is the system matrix, ATIs a transpose of the system matrix,
Figure BDA0003248326960000077
is an error matrix from d-1 to d,
Figure BDA0003248326960000078
Is the error matrix at time d-1,
Figure BDA0003248326960000079
Error gain for smoothing at time d-1,
Figure BDA00032483269600000710
Is the d-1 timeThe smooth vector of,
Figure BDA00032483269600000711
Is the vector at the d-1 time,
Figure BDA00032483269600000712
Is the vector at time d,
Figure BDA00032483269600000713
Is a smoothed error matrix at time d-1,
Figure BDA00032483269600000714
Is the smoothed error matrix at time d.
In this embodiment, the process of reconstructing the observed quantity of the EFIR filtering algorithm by using the R-T-S smoothing algorithm specifically includes:
Figure BDA00032483269600000715
wherein q isE＝NE-1，
Figure BDA00032483269600000716
Is d-qED an observation vector at time d,
Figure BDA00032483269600000717
Is d-qED a smoothed vector at time, h a representation function, NEIs the window length of the EFIR filtering.
Example two
In one or more embodiments, a quad rotor positioning system based on reconstruction observations is disclosed, comprising:
the EFIR filtering module is used for taking the east and north positions and the east and north speeds as state vectors; the distance between the four rotors and the UWB reference node measured by the ultra-wideband is used as an observed value; performing EFIR filtering based on the state vector and the observed value, and smoothing the output of an EFIR filtering algorithm;
and the position estimation module is used for reconstructing the observation vector of the set time interval by using the smoothed state vector, and estimating the position of the four rotors at the next moment by using the reconstructed observation vector as the observation vector required by the EFIR forward filtering at the next moment.
It should be noted that, the specific implementation of the above module has been described in the first embodiment, and is not described again.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed that includes a server including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the reconstruction observation based quad-rotor positioning method of example one when executing the program. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
Example four
In one or more embodiments, a computer-readable storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the reconstruction observation based quad-rotor positioning method described in example one is disclosed.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A quad-rotor positioning method based on reconstruction observation is characterized by comprising the following steps:
taking the positions of the east direction and the north direction and the speeds of the east direction and the north direction as state vectors;
the distance between the four rotors and the UWB reference node measured by the ultra-wideband is used as an observed value;
performing EFIR filtering based on the state vector and the observed value, and smoothing the output of an EFIR filtering algorithm;
and reconstructing the observation vector of the set time period by using the state vector after smoothing, wherein the reconstructed observation vector is used as the observation vector required by the EFIR forward filtering at the next moment, and the position of the four rotors at the next moment is estimated.
2. A method of quad-rotor positioning based on reconstructed observations according to claim 1, wherein the output of the EFIR filtering algorithm is smoothed with an R-T-S smoothing algorithm.
3. A method of quad-rotor positioning based on reconstructed observations according to claim 1, characterized by using the east, north and sky positions and velocities of quad-rotors as state vectors of the EFIR filtering algorithm; and taking the distance between the four rotors measured by the UWB and the UWB reference node as an observation vector of an EFIR filtering algorithm for data fusion to obtain the optimal position prediction of the four rotors.
4. A method for quad-rotor positioning based on reconstructed observations according to claim 1, wherein the state vectors of the EFIR filtering algorithm are specifically:
Figure FDA0003248326950000011
wherein (x)d,yd,zd) The positions of four rotors d in the east direction, the north direction and the sky direction at the moment; (Vx)d,Vyd,Vzd) The speed of the four rotors at the moment d in the east direction, the north direction and the sky direction; Δ d is the sampling period, wd-1The covariance matrix is Q for the system noise at time d-1.
5. A method for quad-rotor positioning based on reconstructed observations according to claim 1, wherein the observations of the EFIR filtering algorithm are:
Figure FDA0003248326950000021
wherein the content of the first and second substances,
Figure FDA0003248326950000022
distance between the four rotors and the ith reference node is measured by UWB at the d moment; v isdFor observing noise, its covariance matrix is R; (x)d,yd,zd) The four rotors d are in east, north and sky positions at the moment.
6. A method for quad-rotor positioning based on reconstructed observations as claimed in claim 1 wherein smoothing the output of the EFIR filtering algorithm with the R-T-S smoothing algorithm specifically comprises:
Figure FDA0003248326950000023
Figure FDA0003248326950000024
Figure FDA0003248326950000025
Figure FDA0003248326950000026
wherein the content of the first and second substances,
Figure FDA0003248326950000027
for smooth error gain, A is the system matrix, ATIs a transpose of the system matrix,
Figure FDA0003248326950000028
is an error matrix from d-1 to d,
Figure FDA0003248326950000029
Is the error matrix at time d-1,
Figure FDA00032483269500000210
Error gain for smoothing at time d-1,
Figure FDA00032483269500000211
Is a smooth vector at the d-1 moment,
Figure FDA00032483269500000212
Is the vector at the d-1 time,
Figure FDA00032483269500000213
Is the vector at time d,
Figure FDA00032483269500000214
Is a smoothed error matrix at time d-1,
Figure FDA00032483269500000215
Is the smoothed error matrix at time d.
7. The method for positioning a quadrotor based on reconstructed observations according to claim 1, wherein reconstructing the observation vector of the set time period by using the state vector after smoothing comprises:
Figure FDA00032483269500000216
wherein q isE＝NE-1，
Figure FDA00032483269500000217
Is d-qED an observation vector at time d,
Figure FDA00032483269500000218
Is d-qED a smoothed vector at time, h a representation function, NEIs the window length of the EFIR filtering.
8. A quad-rotor positioning system based on reconstructed observations, comprising:
the EFIR filtering module is used for taking the east and north positions and the east and north speeds as state vectors; the distance between the four rotors and the UWB reference node measured by the ultra-wideband is used as an observed value; performing EFIR filtering based on the state vector and the observed value, and smoothing the output of an EFIR filtering algorithm;
and the position estimation module is used for reconstructing the observation vector of the set time interval by using the smoothed state vector, and estimating the position of the four rotors at the next moment by using the reconstructed observation vector as the observation vector required by the EFIR forward filtering at the next moment.
9. A terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is configured to store a plurality of instructions adapted to be loaded by the processor and to perform the reconstruction observations-based quad-rotor positioning method of any of claims 1-7.
10. A computer-readable storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the reconstruction observation based quad-rotor positioning method of any of claims 1-7.
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