CN112346104B - Unmanned aerial vehicle information fusion positioning method - Google Patents
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- CN112346104B CN112346104B CN202010953807.2A CN202010953807A CN112346104B CN 112346104 B CN112346104 B CN 112346104B CN 202010953807 A CN202010953807 A CN 202010953807A CN 112346104 B CN112346104 B CN 112346104B
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- 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
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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- 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
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention discloses an unmanned aerial vehicle information fusion positioning method, which is used for solving the problems that the detection capability of infrared waves is greatly reduced and the detection probability is low and the false alarm rate is high when the existing unmanned aerial vehicle is positioned under a non-sunny climate condition, and calculating the preliminary position coordinates of the unmanned aerial vehicle in a ground coordinate system by using an SINS/GPS integrated navigation system; the method comprises the steps of calculating the position coordinates of the unmanned aerial vehicle in multi-machine cross positioning, respectively performing difference calculation by combining the position coordinates of the unmanned aerial vehicle output by the SINS/GPS integrated navigation system, then estimating the error of the SINS/GPS integrated navigation system through Kalman filtering, correcting the error, and outputting the corrected position coordinates of the target unmanned aerial vehicle, so that the unmanned aerial vehicles can perform information fusion under the condition of loose integrated navigation system and multi-machine passive cross direction finding positioning, and a better positioning effect is obtained in a complex climatic environment.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle information fusion positioning method.
Background
Unmanned aerial vehicles are receiving increasing attention as important reconnaissance tools and attack weapons in future battlefields. When the unmanned aerial vehicle executes a task, the on-board photoelectric equipment of the unmanned aerial vehicle has higher requirements on the positioning precision of the unmanned aerial vehicle. At present, some solutions exist for accurately positioning unmanned aerial vehicle formation;
patent CN2020100538363 proposes a multi-unmanned aerial vehicle co-location method based on laser radar and location vector matching, but the laser radar is used as an active device and is affected by weather and atmosphere, if the detection capability of infrared waves is greatly reduced and reduced under the condition of non-clear weather such as heavy rain, snowing, dense fog and the like, at the moment, the problems of low detection probability, high false alarm rate and the like exist. The combined navigation system based on the Strapdown Inertial Navigation System (SINS) and the Global Positioning System (GPS) can perform preliminary positioning on unmanned aerial vehicle observation data, then combine the passive direction-finding cross positioning technology to perform information fusion, and finally calculate the accurate unmanned aerial vehicle position information.
Disclosure of Invention
The invention aims to solve the problems that the detection capability of infrared waves is greatly reduced and the detection probability is low and the false alarm rate is high when the existing unmanned aerial vehicle is positioned under a non-sunny weather condition, and provides an unmanned aerial vehicle information fusion positioning method.
The aim of the invention can be achieved by the following technical scheme: an unmanned aerial vehicle information fusion positioning method comprises the following steps:
step one: measuring the relative position information of each unmanned aerial vehicle through a direction-finding cross positioning method and an UAV vision sensor system; the unmanned aerial vehicle is an unmanned aerial vehicle;
step two: calculating a preliminary position coordinate of the unmanned aerial vehicle in a ground coordinate system through an SINS/GPS integrated navigation system; the method comprises the following steps: at one moment, preliminary position information of the target unmanned aerial vehicle is obtained in real time through the SINS/GPS loose combination navigation systemAttitude angle information (θ) 2 γ 2 ψ)；
Step three: calculating the position coordinates of the unmanned aerial vehicle in multi-machine cross positioning, respectively performing difference calculation by combining the position coordinates of the unmanned aerial vehicle output by the SINS/GPS integrated navigation system, taking the obtained difference value as a measurement value, then performing Kalman filtering, estimating the error of the SINS/GPS integrated navigation system, and correcting the error to output the corrected position coordinates of the target unmanned aerial vehicle; the multi-machine cross positioning is specifically shown as follows: when a plurality of unmanned aerial vehicles exist, the positioning of each unmanned aerial vehicle is realized through other unmanned aerial vehicles except the combined navigation self-positioning, namely the other unmanned aerial vehicles are used as direction-finding base stations, and a group of pitch angles and azimuth angles are obtained by respectively carrying out direction finding on a certain target unmanned aerial vehicle;
when the number of the plurality of unmanned aerial vehicles is equal to two, analysis is carried out through a double-machine three-dimensional positioning algorithm, and the specific analysis process is as follows: with A (x) 1 ,y 1 ,z 1 )、B(x 2 ,y 2 ,z 2 ) The two unmanned aerial vehicles are direction-finding base stations, respectively direction-finding the unmanned aerial vehicle targets C (x, y, z), and respectively obtain a group of pitch angles and azimuth angles, namely (alpha) 1 ,β 1 ) And (alpha) 2 ,β 2 ) The two are combined to obtain four measurement subsets (alpha) 1 ,β 1 ,α 2 )、(α 1 ,β 1 ,β 2 )、(α 2 ,β 2 ,α 1 ) And (alpha) 2 ,β 2 ,β 1 ) Wherein alpha is i (i=1, 2) is azimuth, the X-axis is set to be north direction, beta i (i=1, 2) is the pitch angle, defined by the azimuth angle α 1 、α 2 And pitch angle beta 1 、β 2 The triangular relationship of (2) can be obtained:
the above equation is expressed in matrix form:
HX＝Z
wherein the method comprises the steps of
X＝(x,y,z) T
The coordinates of the target unmanned aerial vehicle can be obtained by the method are as follows:
X＝H -1 Z
and 3 groups in the equation (1) are selected for combination to solve the position of the target unmanned aerial vehicle.
Preferably, the relative position information includes a relative azimuth angle and a pitch angle; the preliminary location coordinates include longitude L, latitude B, and altitude H.
Preferably, when the number of the plurality of unmanned aerial vehicles is greater than two, observation values of N groups of azimuth angles and pitch angles can be obtained for each unmanned aerial vehicle, positioning calculation is performed through two-to-two crossed positioning, one unmanned aerial vehicle is selected as a direction-finding main station, and the coordinates are A (x 0 ,y 0 ,z 0 ) The method comprises the steps of carrying out a first treatment on the surface of the Other unmanned aerial vehicles are used as auxiliary stations, and the coordinates are A (x i ,y i ,z i ) (i=1, 2,., N-1), and then locating the targets by a combination of the primary station and the secondary station two by two, thereby obtaining N-1 estimated positions of the targets, respectively C (x i ,y i ,z i ) (i=1, 2,., N-1), processing the set of data by clustering to obtain an estimate of the target location coordinates, the specific steps being:
s1: let each sample self-class, i.e., establish N-1 class:the reference numeral (0) represents the state before the cluster operation is started, and a sample is a target position coordinate;
s2: calculating the distance between each kind to obtain an N-1 dimensional square matrix D (0) The distance between the samples is the initial time;
s3: the distance matrix D is obtained in the previous step of clustering operation (n) N is the number of successive clusters, D is calculated (n) Is selected from the group consisting of the smallest elements,let it beAnd->The distance between the two classes will be +.>And->Merging into one kind->Thereby creating a new classification: />
S4: calculating the distance between the new classes after merging, i.e. calculatingCombined with other +.>The distance between them; obtaining D by using gravity center method as distance calculation criterion (n+1) The method comprises the steps of carrying out a first treatment on the surface of the Repeating calculation and merging until the minimum element in the distance matrix exceeds the upper limit of the positioning precision or all elements belong to the same class, and outputting the center of gravity of the class with the largest element as a final output result, namely the estimated value of the target position coordinate.
Preferably, the specific process of Kalman filtering is as follows: the state equation and the measurement equation of the random linear discrete system are respectively:
wherein:t is the iteration period, phi k,k-1 Is a one-step transfer matrix from k-1 to k; Γ -shaped structure k-1 Is a system noise matrix; h k Is a measurement matrix; v (V) k Measuring a noise sequence; w (W) k-1 A system noise matrix at the time of k-1;
the set process noise and the observed noise have the following statistical characteristics:
q in k A variance matrix for the system noise sequence; r is R k Measuring a noise variance matrix;
a basic Kalman Filter (KF) equation for a linear discrete system; state one-step prediction equation:
state estimation equation:
optimal filter gain equation:
finally, a predictive mean square error equation is obtained:
the above is the basic equation of discrete KF, giving an initial valueAnd P 0 Then according to the measurement of k timeValue Z k The state estimate at time k is recursively deduced>
Compared with the prior art, the invention has the beneficial effects that: based on a direction-finding cross positioning method, using a UAV vision sensor system to measure the relative position information of each unmanned aerial vehicle, and using a SINS/GPS integrated navigation system to calculate the preliminary position coordinates of the unmanned aerial vehicle in a ground coordinate system; the method comprises the steps of calculating the position coordinates of the unmanned aerial vehicle in multi-machine cross positioning, respectively performing difference calculation by combining the position coordinates of the unmanned aerial vehicle output by the SINS/GPS integrated navigation system, taking the obtained difference value as a measurement value, then performing Kalman filtering, estimating the error of the SINS/GPS integrated navigation system, correcting the error, and outputting the corrected position coordinates of the target unmanned aerial vehicle, so that the information fusion of the unmanned aerial vehicles can be performed under the condition of loose integrated navigation system and multi-machine passive cross direction finding positioning, and a better positioning effect can be obtained in a complex climate environment.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
Fig. 1 is a schematic view of the unmanned aerial vehicle positioning of the present invention;
FIG. 2 is a flow chart of unmanned aerial vehicle positioning according to the present invention;
FIG. 3 is a schematic diagram of a dual-machine cross-positioning system according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, an unmanned aerial vehicle information fusion positioning method is disclosed, wherein an unmanned aerial vehicle positioning algorithm measures relative azimuth angle alpha and pitch angle beta between unmanned aerial vehicles through a vision sensor system, and combines SITarget unmanned aerial vehicle position (longitude lambda, latitude) given by NS/GPS combined navigation systemAnd the altitude H) and attitude angles (pitch angle theta, roll angle gamma and heading angle phi), and obtaining longitude L, latitude B and altitude H of the unmanned aerial vehicle under a geodetic coordinate system by means of a homogeneous coordinate conversion method;
the method comprises three parts, wherein the first part is to measure the relative position information (relative azimuth angle and pitch angle) of each unmanned aerial vehicle by using a UAV vision sensor system based on a direction-finding cross positioning method; the second part is to calculate the preliminary position coordinates (longitude L, latitude B and altitude H) of the unmanned aerial vehicle in a ground coordinate system by using an SINS/GPS integrated navigation system; the third part is to calculate the position coordinates of the unmanned aerial vehicle in multi-machine cross positioning, respectively calculate the difference by combining the position coordinates of the unmanned aerial vehicle output by the SINS/GPS integrated navigation system, take the obtained difference as a measurement value, estimate the error of the SINS/GPS integrated navigation system by Kalman filtering, and correct the error to output the corrected position coordinates of the target unmanned aerial vehicle; schematic and flow chart of the method are shown in fig. 1 and 2;
example 1
The final output result mainly focused in the positioning system is the position coordinate of the unmanned aerial vehicleThus, the other error sources can be simplified, namely, the measurement errors of the attitude angles (theta, gamma, phi) of the unmanned aerial vehicle are skipped, and meanwhile, the vibration angles (delta theta) of the vibration absorber are processed BA ,Δγ BA ,Δψ BA ) The relative position information (alpha, beta) of the unmanned aerial vehicle is measured by a direction finding method, and the self position coordinates, the relative azimuth angle and the pitch angle of the target unmanned aerial vehicle, which are measured by the rest unmanned aerial vehicle, are approximately assumed as follows:
taking into account the accurate values of (α, β, R) obtained from the track position information, it can be assumed that the parameter errors Δx of the parameters X (x=α, β, R) are all subject to an average value of the accurate values and the variance of the parameters σ during the positioning of the target by the unmanned aerial vehicle ΔX Is a normal distribution of (2); similarly, can be pseudoLet parameter X (x=Δθ) BA ,Δγ BA ,Δψ BA ) The parameter error DeltaX is subject to a mean of 0 and the variance is sigma ΔX The azimuth angle error and the pitch angle error of the base station, the self-positioning error of the direction-finding base station are zero mean value, and the standard deviation is sigma respectively φ 、σ ε 、σ S The errors of the positioning of the direction-finding base station and the azimuth angle errors and pitch angle errors measured by the base station can be considered to be mutually independent; the specific flow is as follows:
(1) At a certain moment, preliminary position information of the target unmanned aerial vehicle is obtained in real time through the SINS/GPS loose combination navigation systemAnd attitude angle information (θ, γ, ψ); the loose combination mode is to obtain measurement information of SINS and GPS respectively, perform difference operation, take the obtained difference value as measurement value of the combined navigation system, estimate the error of SINS through Kalman filtering, and correct SINS;
(2) Correcting the preliminary position coordinates through the target unmanned aerial vehicle position coordinates measured by multi-machine cross positioning to obtain corrected target unmanned aerial vehicle position coordinates; the multi-machine cross positioning is specifically realized in such a way that when a plurality of unmanned aerial vehicles exist, the positioning of each unmanned aerial vehicle can be realized through other unmanned aerial vehicles besides the combined navigation self-positioning, namely, the other unmanned aerial vehicles are used as direction-finding base stations, and a group of pitch angles and azimuth angles are obtained by respectively carrying out direction finding on a certain target unmanned aerial vehicle; the analysis is performed on a double-machine three-dimensional positioning algorithm;
as shown in fig. 3, a (x 1 ,y 1 ,z 1 )、B(x 2 ,y 2 ,z 2 ) The two unmanned aerial vehicles are direction-finding base stations, the unmanned aerial vehicle targets C (x, y, z) are respectively subjected to direction finding, and a group of pitch angles and azimuth angles (alpha) are respectively obtained 1 ,β 1 ) And (alpha) 2 ,β 2 ) Which combine with each other to obtain four sets of measurement subsets (alpha 1 ,β 1 ,α 2 )、(α 1 ,β 1 ,β 2 )、(α 2 ,β 2 ,α 1 ) And (alpha) 2 ,β 2 ,β 1 ) Wherein alpha is i (i=1, 2) is azimuth (assuming that the X-axis is north), β i (i=1, 2) is pitch angle; from azimuth angle alpha 1 、α 2 And pitch angle beta 1 、β 2 The triangular relationship of (2) can be obtained:
the above equation can be expressed in matrix form:
HX＝Z
wherein the method comprises the steps of
X＝(x,y,z) T
The coordinates of the target unmanned aerial vehicle can be obtained by the method are as follows:
X＝H -1 Z
it is easy to know that the position of the target unmanned aerial vehicle can be solved by combining the 4 equations and optionally 3 groups; when being popularized to the situation of N (N is more than 2) unmanned aerial vehicles, N groups of observation values of azimuth angles and pitch angles can be obtained for each target, and when the positioning solution is carried out, two-by-two crossed positioning is adopted; one of the unmanned aerial vehicles is selected as a direction-finding master station, and the coordinates are A (x 0 ,y 0 ,z 0 ) The method comprises the steps of carrying out a first treatment on the surface of the Other unmanned aerial vehicles are used as auxiliary stations, and the coordinates are A (x i ,y i ,z i ) (i=1, 2..a., N-1) and then locating the targets by a combination of primary and secondary stations, two by two, whereby N-1 estimated positions of the targets can be obtained, C (x i ,y i ,z i ) (i=1, 2,., N-1); the group of data is processed by adopting a clustering method, so that an estimated value of the target position coordinate is obtained, and the specific flow is as follows: setting each sample (i.e. a target position coordinate) to be self-class, i.e. setting up N-1 class：(the reference number (0) represents the state before the clustering operation starts), and the distance between each type (the distance between each sample at the beginning) is calculated to obtain an N-1 dimensional square matrix D (0) The method comprises the steps of carrying out a first treatment on the surface of the Then, it is assumed that the distance matrix D has been obtained in the previous clustering operation (n) (n is the number of successive clusters), D is obtained (n) The smallest element in (1) is set as +.>And->The distance between the two classes will be +.>And->Merging into one kind->Thereby creating a new classification: />Finally, the distance between the new categories after merging is calculated, i.e. +.>Combined with other +.>The distance between them, here we use the gravity center method as the distance calculation criterion to obtain D (n+1) The method comprises the steps of carrying out a first treatment on the surface of the Repeating the calculation and the combination until the distance matrix D (n) If the minimum element in the list exceeds the upper limit of the positioning accuracy or all the elements belong to the same class, outputting the center of gravity of the class with the largest element as a final output result, namely, the estimated value of the target position coordinate;
(3) Performing difference calculation on SINS/GPS loose combination positioning and passive direction-finding multi-machine cross positioning results, and estimating and correcting a loose combination navigation system positioning difference value through Kalman filtering; the specific Kalman filtering process in the SINS/GPS integrated navigation system and the passive cross positioning process is as follows:
the state equation and the measurement equation of the random linear discrete system are respectively:
wherein:t is an iteration period; phi k,k-1 Is a one-step transfer matrix from k-1 to k; Γ -shaped structure k-1 Is a system noise matrix; h k Is a measurement matrix; v (V) k Measuring a noise sequence; w (W) k-1 A system noise matrix at the time of k-1; the process noise and the observed noise of the system are assumed to have the following statistical characteristics:
q in k A variance matrix for the system noise sequence; r is R k Measuring a noise variance matrix; the basic Kalman Filtering (KF) equation for a linear discrete system is given below; the basic idea of KF is to update state variables by adopting a state space model of signals and noise and through an estimated value of the previous moment and an observed value of the current moment, and further calculate the estimated value of the current moment; state one-step prediction equation:
state estimation equation:
optimal filter gain equation:
finally, a predictive mean square error equation is obtained:
the above is the basic equation of discrete KF, provided that the initial value is givenAnd P 0 Then according to the measured value Z at the moment k k It is possible to recursively derive the state estimate +.>
When the unmanned aerial vehicle positioning system is used, the relative position information of each unmanned aerial vehicle is measured by using a UAV vision sensor system based on a direction-finding cross positioning method, and the preliminary position coordinates of the unmanned aerial vehicle in a ground coordinate system are calculated by using an SINS/GPS integrated navigation system; the method comprises the steps of calculating the position coordinates of the unmanned aerial vehicle in multi-machine cross positioning, respectively performing difference calculation by combining the position coordinates of the unmanned aerial vehicle output by the SINS/GPS integrated navigation system, taking the obtained difference value as a measurement value, then performing Kalman filtering, estimating the error of the SINS/GPS integrated navigation system, correcting the error, and outputting the corrected position coordinates of the target unmanned aerial vehicle, so that the information fusion of the unmanned aerial vehicles can be performed under the condition of loose integrated navigation system and multi-machine passive cross direction finding positioning, and a better positioning effect can be obtained in a complex climate environment.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (4)
1. The unmanned aerial vehicle information fusion positioning method is characterized by comprising the following steps of:
step one: measuring the relative position information of each unmanned aerial vehicle through a direction-finding cross positioning method and an UAV vision sensor system;
step two: calculating a preliminary position coordinate of the unmanned aerial vehicle in a ground coordinate system through an SINS/GPS integrated navigation system; the method comprises the following steps: at one moment, preliminary position information of the target unmanned aerial vehicle is obtained in real time through the SINS/GPS loose combination navigation systemAttitude angle information (θ) 2 γ 2 ψ)；
Step three: calculating the position coordinates of the unmanned aerial vehicle in multi-machine cross positioning, respectively performing difference calculation by combining the position coordinates of the unmanned aerial vehicle output by the SINS/GPS integrated navigation system, taking the obtained difference value as a measurement value, then performing Kalman filtering, estimating the error of the SINS/GPS integrated navigation system, and correcting the error to output the corrected position coordinates of the target unmanned aerial vehicle; the multi-machine cross positioning is specifically shown as follows: when a plurality of unmanned aerial vehicles exist, the positioning of each unmanned aerial vehicle is realized through other unmanned aerial vehicles except the combined navigation self-positioning, namely the other unmanned aerial vehicles are used as direction-finding base stations, and a group of pitch angles and azimuth angles are obtained by respectively carrying out direction finding on a certain target unmanned aerial vehicle;
when the number of the plurality of unmanned aerial vehicles is equal to two, analysis is carried out through a double-machine three-dimensional positioning algorithm, and the specific analysis process is as follows: with A (x) 1 ,y 1 ,z 1 )、B(x 2 ,y 2 ,z 2 ) The two unmanned aerial vehicles are direction-finding base stations, respectively direction-finding the unmanned aerial vehicle targets C (x, y, z), respectively obtaining a group of pitch angles and azimuth angles, namely(α 1 ,β 1 ) And (alpha) 2 ,β 2 ) The two are combined to obtain four measurement subsets (alpha) 1 ,β 1 ,α 2 )、(α 1 ,β 1 ,β 2 )、(α 2 ,β 2 ,α 1 ) And (alpha) 2 ,β 2 ,β 1 ) Wherein alpha is i (i=1, 2) is azimuth, the X-axis is set to be north direction, beta i (i=1, 2) is the pitch angle, defined by the azimuth angle α 1 、α 2 And pitch angle beta 1 、β 2 The triangular relationship of (2) can be obtained:
the above equation is expressed in matrix form:
HX＝Z
wherein the method comprises the steps of
X＝(x,y,z) T
The coordinates of the target unmanned aerial vehicle can be obtained by the method are as follows:
X＝H -1 Z
and 3 groups in the equation (1) are selected for combination to solve the position of the target unmanned aerial vehicle.
2. The unmanned aerial vehicle information fusion positioning method of claim 1, wherein the relative position information comprises a relative azimuth angle and a pitch angle; the preliminary location coordinates include longitude L, latitude B, and altitude H.
3. The unmanned aerial vehicle information fusion positioning method according to claim 1, wherein the unmanned aerial vehicle information fusion positioning method is characterized in thatWhen the number of the unmanned aerial vehicles is greater than two, N groups of observation values of azimuth angles and pitch angles can be obtained for each unmanned aerial vehicle, positioning calculation is carried out through two-to-two crossed positioning, one unmanned aerial vehicle is selected as a direction-finding main station, and the coordinates are A (x 0 ,y 0 ,z 0 ) The method comprises the steps of carrying out a first treatment on the surface of the Other unmanned aerial vehicles are used as auxiliary stations, and the coordinates are A (x i ,y i ,z i ) (i=1, 2,., N-1), and then locating the targets by a combination of the primary station and the secondary station two by two, thereby obtaining N-1 estimated positions of the targets, respectively C (x i ,y i ,z i ) (i=1, 2,., N-1), processing the set of data by clustering to obtain an estimate of the target location coordinates, the specific steps being:
s1: let each sample self-class, i.e., establish N-1 class:the reference numeral (0) represents the state before the cluster operation is started, and a sample is a target position coordinate;
s2: calculating the distance between each kind to obtain an N-1 dimensional square matrix D (0) The distance between the samples is the initial time;
s3: the distance matrix D is obtained in the previous step of clustering operation (n) N is the number of successive clusters, D is calculated (n) The minimum element of (1) is set asAnd->The distance between the two classes will be +.>And->Merging into one kind->Thereby creating a new classification: />
S4: calculating the distance between the new classes after merging, i.e. calculatingCombined with other +.>…; obtaining D by using gravity center method as distance calculation criterion (n+1) The method comprises the steps of carrying out a first treatment on the surface of the Repeating calculation and merging until the minimum element in the distance matrix exceeds the upper limit of the positioning precision or all elements belong to the same class, and outputting the center of gravity of the class with the largest element as a final output result, namely the estimated value of the target position coordinate.
4. The unmanned aerial vehicle information fusion positioning method of claim 1, wherein the specific process of Kalman filtering is as follows: the state equation and the measurement equation of the random linear discrete system are respectively:
wherein:t is the iteration period, phi k,k-1 Is a one-step transfer matrix from k-1 to k; Γ -shaped structure k-1 Is a system noise matrix; h k Is a measurement matrix; v (V) k Measuring a noise sequence; w (W) k-1 A system noise matrix at the time of k-1;
the set process noise and the observed noise have the following statistical characteristics:
q in k A variance matrix for the system noise sequence; r is R k Measuring a noise variance matrix;
a basic Kalman Filter (KF) equation for a linear discrete system; state one-step prediction equation:
state estimation equation:
optimal filter gain equation:
finally, a predictive mean square error equation is obtained:
the above is the basic equation of discrete KF, giving an initial valueAnd P 0 Then according to the measured value Z at the moment k k The state estimate at time k is recursively deduced>
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| CN113551671B (en) * | 2021-06-10 | 2023-04-11 | 中国科学院西安光学精密机械研究所 | Real-time high-precision measurement method for attitude and position of unmanned aerial vehicle |
| CN113759971B (en) * | 2021-08-30 | 2023-11-07 | 中国人民解放军国防科技大学 | Unmanned plane collaborative reconnaissance-oriented path planning method |
| CN114063647B (en) * | 2021-11-16 | 2023-07-04 | 电子科技大学 | Multi-unmanned aerial vehicle mutual positioning method based on distance measurement |
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