CN116381717A - Unmanned aerial vehicle positioning device and positioning method based on Kalman filtering algorithm - Google Patents

Unmanned aerial vehicle positioning device and positioning method based on Kalman filtering algorithm Download PDF

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CN116381717A
CN116381717A CN202310454196.0A CN202310454196A CN116381717A CN 116381717 A CN116381717 A CN 116381717A CN 202310454196 A CN202310454196 A CN 202310454196A CN 116381717 A CN116381717 A CN 116381717A
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陈树烽
林宜锋
文成林
张�杰
崔永锋
柯宗杰
陈静
陈冰琳
郑灿龙
陈静君
余广炼
黄嘉敏
林彤
蔺松鹤
麦正乐
李依妮
阮俞铭
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Guangdong University of Petrochemical Technology
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Abstract

The invention discloses an unmanned aerial vehicle positioning device and a positioning method based on a Kalman filtering algorithm, and relates to the field of unmanned aerial vehicle positioning. By adopting the structure, the unmanned aerial vehicle sound source positioning algorithm and the unmanned aerial vehicle voiceprint recognition algorithm optimized by the microcontroller are used for detecting whether the unmanned aerial vehicle invades or not; acquiring a three-dimensional space position of the unmanned aerial vehicle through a sound source positioning algorithm; the laser radar positioning result is fused through the Kalman filtering algorithm, so that the positioning result has higher accuracy and stability, and the unmanned aerial vehicle is positioned.

Description

Unmanned aerial vehicle positioning device and positioning method based on Kalman filtering algorithm
Technical Field
The invention relates to the field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle positioning device and method based on a Kalman filtering algorithm.
Background
Along with the development of science and technology, unmanned aerial vehicle industry rises, and unmanned aerial vehicle's price is civilian constantly, and the function is diversified, slowly becomes the conventional toy in teenager's hand, the necessity that uses in adult's work. The unmanned plane needs to know the position information of the unmanned plane in the flight and landing processes so as to be controlled, and the existing unmanned plane positioning technology mainly adopts a radar positioning technology; radio positioning technology; photoelectric recognition positioning technology; passive sound localization techniques, etc.
The photoelectric recognition positioning technology needs to train an image library in advance and is easily affected by the environment, and the recognition capability is almost lost in the night; the radar positioning technology is difficult to identify objects with smaller targets, and the low-altitude blind area is large; the radio positioning technology is complex in technology, high in cost and high in interference signal, and is not suitable for long-time watching; radar, radio and photoelectric identification positioning technologies all need to transmit detection signals, and are easy to capture by targets so as to make interference; the traditional sound source positioning technology has the defects of large identification error, easiness in environmental influence and inapplicability to long-time work.
Therefore, it is necessary to provide a positioning device and a positioning method for an unmanned aerial vehicle based on a kalman filter algorithm to solve the above problems.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle positioning device and method based on a Kalman filtering algorithm, which solve the problems that the traditional sound source positioning technology has large recognition error, is easily affected by environment and is not suitable for long-time work.
In order to achieve the above purpose, the invention provides an unmanned aerial vehicle positioning device based on a Kalman filtering algorithm, which comprises a microphone, a microphone array bracket and a metal shell, wherein the microphone and the microphone array bracket form a microphone array, a laser radar is arranged at the top of the microphone array bracket, the microphone and the microphone array bracket are both arranged at the top of the metal shell, and the bottom of the metal shell is connected with the bottom bracket.
Preferably, the inside of metal casing is provided with insulating support, insulating support from the top down sets up to the three-layer, is first insulating support, second insulating support and third insulating support respectively, be provided with microphone integrated chip on the first insulating support, be provided with display screen control chip on the second insulating support, be provided with the battery on the third insulating support.
Preferably, the metal casing includes roof, bottom plate and sets up the roof with curb plate between the bottom plate, the roof with the bottom plate all sets up square structure, the curb plate includes preceding curb plate, posterior lateral plate, left side board and right side board, preceding curb plate with posterior lateral plate symmetry sets up, left side board with right side board symmetry sets up.
Preferably, the front side plate is provided with a safety door, the rear side plate is provided with a display screen, and the bottom plate is provided with an electric wire reserved hole.
Preferably, the display screen is connected with the display screen control chip, the microphone is connected with the microphone integrated chip, and the display screen control chip is connected with the microphone integrated chip.
Preferably, the microphone array is configured as a top array, a middle array and a bottom array, wherein the top array and the middle array each comprise one microphone, the bottom array comprises five microphones, and the five microphones are arranged in a cross shape.
A unmanned aerial vehicle positioning method based on a Kalman filtering algorithm comprises the following steps:
s1: after the unmanned aerial vehicle enters a monitoring range, carrying out sound pickup through seven microphones arranged on a microphone array bracket;
s2: the picked sound is transmitted into the microphone integrated chip;
s3: separating out unmanned aerial vehicle sound through an unmanned aerial vehicle sound separation algorithm, obtaining angles and distances on the unmanned aerial vehicle space through an unmanned aerial vehicle sound source positioning algorithm, and calculating the three-dimensional space position of the unmanned aerial vehicle;
s4: the laser radar acquires a scanning result of the whole space region;
s5: the result obtained by the microphone integrated chip and the laser radar is transmitted to a Kalman filter, data fusion is carried out through a data fusion algorithm, and the measured spatial position data of the unmanned aerial vehicle is used for estimating the real position of the unmanned aerial vehicle;
s6: iterating the estimated value and the obtained measurement value to correct the position state of the unmanned aerial vehicle;
s7: returning to the prediction process when the ending condition is not reached; when the end condition is reached, outputting an estimation result;
s8: the final unmanned aerial vehicle positioning result is obtained through fusion calculation of a multi-sensor fusion algorithm and a laser radar positioning result;
s9: and transmitting the data and the positioning result into a display screen control chip, and displaying the spatial position and the motion state of the unmanned aerial vehicle on the display screen in real time.
Preferably, in step S3, the acoustic recognition model of the unmanned aerial vehicle in the acoustic separation algorithm is a blind source separation model, expressed as a process of extracting source signals from a set of observed signals, and when the independence of the linear combination reaches the maximum, the corresponding linear combination of the observed signals is regarded as a separated "source signal"; and setting an objective function, maximizing the objective function to solve, and selecting the negative entropy in the maximized non-Gaussian property as the objective function for measuring the independence, wherein the simplified definition of the negative entropy is as follows:
J(y)∝(E{G(y)}-E{G(v)}) 2
wherein J (y) is the negative entropy of y, v is zero-mean unit Gaussian random variable, G is any actual non-quadratic function, and E is expected;
let the microphone array gather ambient sound signal S (t), separate a unmanned aerial vehicle sound signal X (t) from S (t), the two relation can be expressed as:
S(t)＝X(t)×A
wherein A is a mixing matrix, A is obtained, an unmanned aerial vehicle sound signal X (t) is separated from a sound signal S (t), and a nonlinear function g is selected by using a fixed point algorithm in the mixing matrix A; the observed signal is preprocessed, including centering and whitening, the centering being denoted as:
Figure BDA0004198505990000041
Figure BDA0004198505990000042
in the method, the average value of the environmental sound signal S (t) is firstly obtained according to the row, N is the sampling point number of the microphone,
Figure BDA0004198505990000043
zero-averaged data;
the whitening process is as follows: calculating a covariance matrix of the ambient sound signal S (t), Σ= cov (S (t)),
the eigenvalue λ and eigenvector v are found from the covariance matrix Σ:
Σ＝vλv T
wherein v is T Is the transpose of the feature vector v.
Calculating a whitening matrix A:
Figure BDA0004198505990000044
calculating an orthogonal matrix Z:
Z＝A×S(t)
after the pretreatment, iterating according to the following formula, normalizing after each iteration,
w＝E{zg(w T z)}-E{g′(w T z)}w
wherein g' is the derivative of g; w is a vector with unit norms selected randomly, and z is a whitened sound signal;
converging in the iteration process, finishing the iteration, taking out the separated unmanned aerial vehicle sound signals, performing cross-correlation operation with unmanned aerial vehicle sound features in an unmanned aerial vehicle sound feature database, determining that unmanned aerial vehicles exist in the environment sound signals when an operation result reaches a threshold value, and calling a sound source positioning algorithm to determine the space coordinate position of the unmanned aerial vehicle;
the sound source localization algorithm selects two microphones with space coordinate difference to receive sound signals with the expression of x (t) and y (t), and converts the signals received by the two microphones from time domain to frequency domain signals through Fourier transformation:
Figure BDA0004198505990000051
Figure BDA0004198505990000052
where τ is the frequency, e is the natural constant, j is the unit vector:
solving cross-power spectral density function
Figure BDA0004198505990000053
Figure BDA0004198505990000054
In the method, in the process of the invention,
Figure BDA0004198505990000055
is F x Conjugation of (τ);
for a pair of
Figure BDA0004198505990000056
Performing Fourier inverse transformation to obtain generalized cross-correlation function between two signals>
Figure BDA0004198505990000057
Figure BDA0004198505990000058
Taking out
Figure BDA0004198505990000059
The maximum value and the median value in the signal are obtained, and the difference value between the maximum value and the median value is obtained to obtain the time delay difference t between the signals x (t), y (t) xy ；
According to the triangular relation of the microphone array on the plane, the analytical solution of all unknowns of the linear structure is determined as follows:
Figure BDA00041985059900000510
Figure BDA00041985059900000511
Figure BDA00041985059900000512
Figure BDA00041985059900000513
Figure BDA0004198505990000061
Figure BDA0004198505990000062
y1 (k), y2 (k) and y3 (k) are a group of microphones with linear structures on the microphone array, and the distance between the microphones is d; s (k) is the position of the unmanned aerial vehicle to be positioned; r is (r) 1 、r 2 、r 3 The linear distances of the microphones to S (k), respectively; obtaining microphones y3 (k) -y using time delay difference calculation method 1 (k)、y 2 (k)-y 1 (k) Time delay differences t13 and t12 between the two are converted into a distance difference L according to sound velocity v measured by environment 13 ＝t 13 v＝r3-r1、L 12 ＝t 12 v=r2-r 1, solving for θ 1 、r 2 As the planar polar position of the drone S (k) to be positioned.
Preferably, in step S5, the position state quantity p, the speed state quantity v, the acceleration a, and the state prediction equation of the unmanned aerial vehicle to be positioned:
Figure BDA0004198505990000063
Figure BDA0004198505990000064
representing state vectors by matrix
Figure BDA0004198505990000065
Figure BDA0004198505990000066
Wherein F is k To predict matrix, B k Called control matrix, u k Called control vector;
using covariance matrix P k Representing a state quantity prediction model and a covariance prediction model:
Figure BDA0004198505990000067
Figure BDA0004198505990000068
the units of the sound source positioning algorithm result and the laser radar sensor ranging result are different from the unmanned aerial vehicle prediction model, the sound source positioning algorithm result and the laser radar sensor ranging result are converted into the same by using linear transformation, and the linear transformation is described by using a matrix Hk:
Figure BDA0004198505990000069
Figure BDA00041985059900000610
Figure BDA0004198505990000071
is the mean vector; p (P) k Is a covariance matrix, representing uncertainty at time K;
describing sensor noise, introducing noise distribution Rk, wherein Rk mean value is the same as observed value Zk, and product parameters of two Gaussian distributions are expressed in a matrix form:
Figure BDA0004198505990000072
Figure BDA0004198505990000073
Figure BDA0004198505990000074
wherein is sigma 1 、∑ 2 Covariance matrices of prediction and observation respectively, obtain prediction distribution:
Figure BDA0004198505990000075
Figure BDA0004198505990000076
observation distribution:
Figure BDA0004198505990000077
∑1＝R k
combining the state quantity prediction model and the covariance prediction model of the observation and model prediction to obtain an optimal estimation model:
Figure BDA0004198505990000078
P′ k ＝P k -K′P k
after the sound source positioning algorithm operation is completed and the laser radar ranging result is obtained, constructing a matrix by using the two results, and transmitting the matrix into the optimal estimation model for algorithm iteration to obtain the unmanned plane positioning result after data fusion and filtering.
Therefore, the unmanned aerial vehicle positioning device and the unmanned aerial vehicle positioning method based on the Kalman filtering algorithm have the following beneficial effects;
(1) The bottom bracket can enable the microphone array to be placed at a higher position so as to increase the monitoring range of the unmanned aerial vehicle.
(2) The microphone array and the laser radar positioning technology of the invention perform data fusion, and can improve the accuracy of the positioning result
(3) The metal shell can protect the internal equipment of the device, so as to adapt to complex and diverse environments.
(4) The invention upgrades and overhauls the internal equipment directly through the safety door.
(5) The invention uses the sound source positioning technology to monitor and position the invasive unmanned aerial vehicle, is suitable for various environments, and the technology is used for receiving the sound source signal, does not need to transmit detection signals, and is not easy to be captured and interfered by the other side.
(6) According to the unmanned aerial vehicle voice print recognition method, the unmanned aerial vehicle voice print recognition technology is used for judging whether the unmanned aerial vehicle invades or not, and the unmanned aerial vehicle voice print recognition method is not influenced by the size of the unmanned aerial vehicle.
(7) According to the invention, a Kalman filter is introduced to perform data fusion with a laser radar positioning result, so that the accuracy of the positioning result is improved.
(8) The invention supports cellular networking and enlarges the positioning range.
(9) The invention designs upper computer software, and records the state of the device and the data of the invasive unmanned aerial vehicle in real time.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
Fig. 1 is a schematic diagram of the overall structure of an unmanned aerial vehicle positioning device based on a kalman filtering algorithm;
FIG. 2 is an internal structure diagram of an unmanned aerial vehicle positioning device based on a Kalman filtering algorithm;
FIG. 3 is a graph showing the relationship of a microphone array of an unmanned aerial vehicle positioning device on a plane based on a Kalman filtering algorithm;
FIG. 4 is a flowchart of an unmanned aerial vehicle positioning system based on a Kalman filtering algorithm according to the present invention;
reference is made to the accompanying drawings
1. A laser radar; 2. a microphone array mount; 3. a microphone; 4. a metal housing; 41. a bottom plate; 410. a wire preformed hole; 42. a front side plate; 43. a left side plate; 44. a right side plate; 45. a rear side plate; 451. a display screen; 5. a safety door; 6. a bottom bracket; 7. a third insulating support; 71. a battery; 8. a first insulating support; 81. a microphone integrated chip; 9. a second insulating support; 91. and the display screen controls the chip.
Detailed Description
The technical scheme of the invention is further described below through the attached drawings and the embodiments.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
Examples
In this embodiment, the invention provides an unmanned aerial vehicle positioning device based on a kalman filtering algorithm, as shown in fig. 1-2, the unmanned aerial vehicle positioning device comprises a microphone 3, a microphone array support 2 and a metal shell 4, wherein the microphone 3 and the microphone array support 2 form a microphone array, a laser radar 1 is arranged at the top of the microphone array support 2, the microphone 3 and the microphone array support 2 are arranged at the top of the metal shell 4, and the bottom of the metal shell 4 is connected with a bottom support 6. To increase the monitoring range of the unmanned aerial vehicle.
The microphone array is arranged into a top array, a middle array and a bottom array, wherein the top array and the middle array comprise one microphone, the bottom array comprises five microphones, and the five microphones are arranged in a cross shape.
The metal casing 4 can protect the internal equipment of the device, so as to adapt to complex and diverse environments. The inside of metal casing 4 is provided with insulating support, and insulating support from the top down sets up to the three-layer, is first insulating support 8, second insulating support 9 and third insulating support 7 respectively, is provided with microphone integrated chip 81 on the first insulating support 8, is provided with display screen control chip 91 on the second insulating support 9, is provided with battery 71 on the third insulating support 7. The display 451 is connected to the display control chip 91, the microphone 3 is connected to the microphone integrated chip 81, and the display control chip 91 is connected to the microphone integrated chip 81.
The metal casing 4 includes a top plate, a bottom plate 41, and side plates disposed between the top plate and the bottom plate 41, the top plate and the bottom plate 41 are each disposed in a square structure, the side plates include a front side plate 42, a rear side plate 45, a left side plate 43, and a right side plate 44, the front side plate 42 and the rear side plate 45 are symmetrically disposed, and the left side plate 43 and the right side plate 44 are symmetrically disposed. The front side plate 42 is provided with a safety door 5, the rear side plate 45 is provided with a display screen 451, and the bottom plate 41 is provided with an electric wire reserved hole 410. The internal devices are directly upgraded and overhauled through the safety door 5, and the electric wire reserved holes 410 can charge the device, connect the external display devices and reserve subsequent updating and upgrading.
As shown in fig. 4, a positioning method of an unmanned aerial vehicle based on a kalman filtering algorithm includes the following steps:
s1: after the unmanned aerial vehicle enters the monitoring range, sound pickup is carried out through seven microphones arranged on the microphone array support.
S2: the picked-up sound is transmitted into the microphone integrated chip.
S3: the unmanned aerial vehicle sound is separated through an unmanned aerial vehicle sound separation algorithm, the angle and the distance on the unmanned aerial vehicle space are obtained through an unmanned aerial vehicle sound source positioning algorithm, and the three-dimensional space position of the unmanned aerial vehicle is calculated.
In step S3, the unmanned aerial vehicle acoustic recognition model in the acoustic separation algorithm is a blind source separation model, expressed as a process of extracting a source signal from a set of observed signals, and when the independence of the linear combination reaches the maximum, the corresponding linear combination of the observed signals is regarded as a separated "source signal"; and setting an objective function, maximizing the objective function to solve, and selecting the negative entropy in the maximized non-Gaussian property as the objective function for measuring the independence, wherein the simplified definition of the negative entropy is as follows:
J(y)∝(E{G(y)}-E{G(v)}) 2
wherein J (y) is the negative entropy of y, v is zero-mean unit Gaussian random variable, G is any actual non-quadratic function, and E is expected;
let the microphone array gather ambient sound signal S (t), separate a unmanned aerial vehicle sound signal X (t) from S (t), the two relation can be expressed as:
S(t)＝X(t)×A
wherein A is a mixing matrix, A is obtained, an unmanned aerial vehicle sound signal X (t) is separated from a sound signal S (t), the mixing matrix A uses a dead point algorithm to select a nonlinear function G, namely the derivative of the function G; the observed signal is preprocessed, including centering and whitening, the centering being denoted as:
Figure BDA0004198505990000111
Figure BDA0004198505990000112
in the method, the average value of the environmental sound signal S (t) is firstly obtained according to the row, N is the sampling point number of the microphone,
Figure BDA0004198505990000113
zero-averaged data;
the whitening process is as follows: calculating a covariance matrix of the ambient sound signal S (t), Σ= cov (S (t)),
the eigenvalue lambda and eigenvector v are found from the covariance matrix sigma:
∑＝vλv T
wherein v is T Is the transpose of the feature vector v.
Calculating a whitening matrix A:
Figure BDA0004198505990000114
calculating an orthogonal matrix Z:
Z＝A×S(t)
after the pretreatment, iterating according to the following formula, normalizing after each iteration,
w＝E{zg(w T z)}-E{g′(w T z)}w
wherein g' is the derivative of g; w is a vector with unit norms selected randomly, and z is a whitened sound signal;
converging in the iteration process, finishing the iteration, taking out the separated unmanned aerial vehicle sound signals, performing cross-correlation operation with unmanned aerial vehicle sound features in an unmanned aerial vehicle sound feature database, determining that unmanned aerial vehicles exist in the environment sound signals when an operation result reaches a threshold value, and calling a sound source positioning algorithm to determine the space coordinate position of the unmanned aerial vehicle;
the sound source localization algorithm selects two microphones with space coordinate difference to receive sound signals with the expression of x (t) and y (t), and converts the signals received by the two microphones from time domain to frequency domain signals through Fourier transformation:
Figure BDA0004198505990000121
Figure BDA0004198505990000122
where τ is the frequency, e is the natural constant, j is the unit vector:
solving cross-power spectral density function
Figure BDA0004198505990000123
Figure BDA0004198505990000124
In the method, in the process of the invention,
Figure BDA0004198505990000125
is F x Conjugation of (τ);
for a pair of
Figure BDA0004198505990000126
Performing Fourier inverse transformation to obtain generalized cross-correlation function between two signals>
Figure BDA0004198505990000127
Figure BDA0004198505990000128
Taking out
Figure BDA0004198505990000129
The maximum value and the median value in the signal are obtained, and the difference value between the maximum value and the median value is obtained to obtain the time delay difference t between the signals x (t), y (t) xy ；
As shown in fig. 3, from the triangular relationship of the microphone array on the plane, the analytical solution for determining all unknowns of the straight line structure is as follows:
Figure BDA0004198505990000131
Figure BDA0004198505990000132
Figure BDA0004198505990000133
Figure BDA0004198505990000134
Figure BDA0004198505990000135
Figure BDA0004198505990000136
y 1 (k)、y 2 (k)、y 3 (k) Is a group of microphones with linear structures on a microphone array, and the distance between the microphones is d; s (k) is the position of the unmanned aerial vehicle to be positioned; r is (r) 1 、r 2 、r 3 The linear distances of the microphones to S (k), respectively; obtaining microphone y using time delay difference calculation method 3 (k)-y 1 (k)、y 2 (k)-y 1 (k) Time delay differences t13 and t12 between the two are converted into a distance difference L according to sound velocity v measured by environment 13 ＝t 13 v＝r3-r1、L 12 ＝t 12 v=r2-r 1, solving for θ 1 、r 2 As the planar polar position of the drone S (k) to be positioned.
S4: the laser radar acquires a scanning result of the whole spatial region.
S5: the result obtained by the microphone integrated chip and the laser radar is transmitted to a Kalman filter, data fusion is carried out through a data fusion algorithm, and the measured spatial position data of the unmanned aerial vehicle is used for estimating the real position of the unmanned aerial vehicle; in step S5, the position state quantity p, the speed state quantity v, the acceleration a, and the state prediction equation of the unmanned aerial vehicle to be positioned:
Figure BDA0004198505990000137
Figure BDA0004198505990000138
representing state vectors by matrix
Figure BDA0004198505990000139
Figure BDA00041985059900001310
Wherein F is k To predict matrix, B k Called control matrix, u k Called control vector;
using covariance matrix P k Representing a state quantity prediction model and a covariance prediction model:
Figure BDA0004198505990000141
Figure BDA0004198505990000142
the units of the sound source positioning algorithm result and the laser radar sensor ranging result are different from the unmanned aerial vehicle prediction model, the sound source positioning algorithm result and the laser radar sensor ranging result are converted into the same by using linear transformation, and the linear transformation is described by using a matrix Hk:
Figure BDA0004198505990000143
Figure BDA0004198505990000144
Figure BDA0004198505990000145
is the mean vector; p (P) k Is a covariance matrix, representing uncertainty at time K;
describing sensor noise, introducing noise distribution Rk, wherein Rk mean value is the same as observed value Zk, and product parameters of two Gaussian distributions are expressed in a matrix form:
Figure BDA0004198505990000146
Figure BDA0004198505990000147
Figure BDA0004198505990000148
wherein is sigma 1 、∑ 2 Covariance matrices of prediction and observation respectively, obtain prediction distribution:
Figure BDA0004198505990000149
Figure BDA00041985059900001410
observation distribution:
Figure BDA00041985059900001411
∑1＝R k
combining the state quantity prediction model and the covariance prediction model of the observation and model prediction to obtain an optimal estimation model:
Figure BDA0004198505990000151
P k ′＝P k -K′P k
after the sound source positioning algorithm operation is completed and the laser radar ranging result is obtained, constructing a matrix by using the two results, and transmitting the matrix into the optimal estimation model for algorithm iteration to obtain the unmanned plane positioning result after data fusion and filtering.
S6: and iterating the estimated value and the acquired measurement value to correct the position state of the unmanned aerial vehicle.
S7: returning to the prediction process when the ending condition is not reached; and when the end condition is reached, outputting an estimation result.
S8: and carrying out fusion calculation on the multi-sensor fusion algorithm and the laser radar positioning result to obtain a final unmanned aerial vehicle positioning result.
S9: and transmitting the data and the positioning result into a display screen control chip, and displaying the spatial position and the motion state of the unmanned aerial vehicle on the display screen in real time.
In the specific use process:
1. sound pickup is performed by seven microphones 3 mounted on the microphone array holder 2;
2. the picked-up sound is transmitted into the microphone integrated chip 81;
3. the laser radar acquires a scanning result of the whole space region;
4. the result obtained by the microphone integrated chip and the laser radar is transmitted to a Kalman filter; obtaining the finally output space position of the unmanned aerial vehicle;
5. the display screen control chip converts the obtained data and results into images to be displayed on a display screen.
Therefore, the unmanned aerial vehicle positioning device and the unmanned aerial vehicle positioning method based on the Kalman filtering algorithm adopt the structure, a microphone array is formed by the microphone array bracket and the microphone, the microphone array and the laser radar positioning technology are used for data fusion, the accuracy of positioning results is improved, the metal shell can protect the internal equipment of the device, so that the unmanned aerial vehicle positioning device is suitable for complex and various environments, the safety door is arranged on the metal shell, and the internal equipment is updated and overhauled through the safety door.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.

Claims (9)

1. Unmanned aerial vehicle positioner based on kalman filter algorithm, including microphone, microphone array support and metal casing, its characterized in that: the microphone with the microphone array support constitutes the microphone array, the top of microphone array support is provided with laser radar, the microphone with the microphone array support all sets up the top of metal casing, the bottom and the bottom leg joint of metal casing.
2. The unmanned aerial vehicle positioning device based on a Kalman filtering algorithm according to claim 1, wherein: the inside of metal casing is provided with insulating support, insulating support from the top down sets up to the three-layer, is first insulating support, second insulating support and third insulating support respectively, be provided with microphone integrated chip on the first insulating support, be provided with display screen control chip on the second insulating support, be provided with the battery on the third insulating support.
3. The unmanned aerial vehicle positioning device based on a Kalman filtering algorithm according to claim 1, wherein: the metal shell comprises a top plate, a bottom plate and side plates arranged between the top plate and the bottom plate, wherein the top plate and the bottom plate are all arranged into square structures, the side plates comprise a front side plate, a rear side plate, a left side plate and a right side plate, the front side plate and the rear side plate are symmetrically arranged, and the left side plate and the right side plate are symmetrically arranged.
4. A unmanned aerial vehicle positioning device based on a kalman filter algorithm according to claim 3, wherein: the safety door is arranged on the front side plate, the display screen is arranged on the rear side plate, and the electric wire reserved holes are formed in the bottom plate.
5. The unmanned aerial vehicle positioning device based on the Kalman filtering algorithm according to claim 2, wherein: the display screen is connected with the display screen control chip, the microphone is connected with the microphone integrated chip, and the display screen control chip is connected with the microphone integrated chip.
6. The unmanned aerial vehicle positioning device based on a Kalman filtering algorithm according to claim 1, wherein: the microphone array is arranged into a top array, a middle array and a bottom array, wherein the top array and the middle array both comprise one microphone, the bottom array comprises five microphones, and the five microphones are arranged in a cross shape.
7. The unmanned aerial vehicle positioning method based on the Kalman filtering algorithm is characterized by comprising the following steps of:
s1: after the unmanned aerial vehicle enters a monitoring range, carrying out sound pickup through seven microphones arranged on a microphone array bracket;
s2: the picked sound is transmitted into the microphone integrated chip;
s3: separating out unmanned aerial vehicle sound through an unmanned aerial vehicle sound separation algorithm, obtaining angles and distances on the unmanned aerial vehicle space through an unmanned aerial vehicle sound source positioning algorithm, and calculating the three-dimensional space position of the unmanned aerial vehicle;
s4: the laser radar acquires a scanning result of the whole space region;
s5: the result obtained by the microphone integrated chip and the laser radar is transmitted to a Kalman filter, data fusion is carried out through a data fusion algorithm, and the measured spatial position data of the unmanned aerial vehicle is used for estimating the real position of the unmanned aerial vehicle;
s6: iterating the estimated value and the obtained measurement value to correct the position state of the unmanned aerial vehicle;
s7: returning to the prediction process when the ending condition is not reached; when the end condition is reached, outputting an estimation result;
s8: the final unmanned aerial vehicle positioning result is obtained through fusion calculation of a multi-sensor fusion algorithm and a laser radar positioning result;
s9: and transmitting the data and the positioning result into a display screen control chip, and displaying the spatial position and the motion state of the unmanned aerial vehicle on the display screen in real time.
8. The unmanned aerial vehicle positioning method based on the Kalman filtering algorithm according to claim 7, wherein the unmanned aerial vehicle positioning method is characterized in that: in step S3, the unmanned aerial vehicle acoustic recognition model in the acoustic separation algorithm is a blind source separation model, expressed as a process of extracting a source signal from a set of observed signals, and when the independence of the linear combination reaches the maximum, the corresponding linear combination of the observed signals is regarded as a separated "source signal"; and setting an objective function, maximizing the objective function to solve, and selecting the negative entropy in the maximized non-Gaussian property as the objective function for measuring the independence, wherein the simplified definition of the negative entropy is as follows:
J(y)∝(E{G(y)}-E{G(v)}) 2
wherein J (y) is the negative entropy of y, v is zero-mean unit Gaussian random variable, G is any actual non-quadratic function, and E is expected;
let the microphone array gather ambient sound signal S (t), separate a unmanned aerial vehicle sound signal X (t) from S (t), the two relation can be expressed as:
S(t)＝X(t)×A
wherein A is a mixing matrix, A is obtained, an unmanned aerial vehicle sound signal X (t) is separated from a sound signal S (t), and a nonlinear function g is selected by using a fixed point algorithm in the mixing matrix A; the observed signal is preprocessed, including centering and whitening, the centering being denoted as:
Figure FDA0004198505980000031
Figure FDA0004198505980000032
in the method, the average value of the environmental sound signal S (t) is firstly obtained according to the row, N is the sampling point number of the microphone,
Figure FDA0004198505980000033
zero-averaged data;
the whitening process is as follows: calculating a covariance matrix of the ambient sound signal S (t), Σ= cov (S (t)),
the eigenvalue lambda and eigenvector v are found from the covariance matrix sigma:
∑＝vλv T
wherein v is T Is the transpose of the feature vector v,
calculating a whitening matrix A:
Figure FDA0004198505980000034
calculating an orthogonal matrix Z:
Z＝A×S(t)
after the pretreatment, iterating according to the following formula, normalizing after each iteration,
w＝E{zg(w T z)}-E{g′(w T z)}w
wherein g' is the derivative of g; w is a vector with unit norms selected randomly, and z is a whitened sound signal;
converging in the iteration process, finishing the iteration, taking out the separated unmanned aerial vehicle sound signals, performing cross-correlation operation with unmanned aerial vehicle sound features in an unmanned aerial vehicle sound feature database, determining that unmanned aerial vehicles exist in the environment sound signals when an operation result reaches a threshold value, and calling a sound source positioning algorithm to determine the space coordinate position of the unmanned aerial vehicle;
the sound source localization algorithm selects two microphones with space coordinate difference to receive sound signals with the expression of x (t) and y (t), and converts the signals received by the two microphones from time domain to frequency domain signals through Fourier transformation:
Figure FDA0004198505980000041
Figure FDA0004198505980000042
where τ is the frequency, e is the natural constant, j is the unit vector:
solving cross-power spectral density function
Figure FDA0004198505980000043
Figure FDA0004198505980000044
In the method, in the process of the invention,
Figure FDA0004198505980000045
is F x Conjugation of (τ);
for a pair of
Figure FDA0004198505980000046
Performing Fourier inverse transformation to obtain generalized cross-correlation function between two signals>
Figure FDA0004198505980000049
Figure FDA0004198505980000047
Taking out
Figure FDA0004198505980000048
The maximum value and the median value in the signal are obtained, and the difference value between the maximum value and the median value is obtained to obtain the time delay difference t between the signals x (t), y (t) xy ；
According to the triangular relation of the microphone array on the plane, the analytical solution of all unknowns of the linear structure is determined as follows:
Figure FDA0004198505980000051
Figure FDA0004198505980000052
Figure FDA0004198505980000053
Figure FDA0004198505980000054
Figure FDA0004198505980000055
Figure FDA0004198505980000056
y 1 (k)、y 2 (k)、y 3 (k) Is a group of microphones with linear structures on a microphone array, and the distance between the microphones is d; s (k) is the position of the unmanned aerial vehicle to be positioned; r is (r) 1 、r 2 、r 3 The linear distances of the microphones to S (k), respectively; obtaining microphone y using time delay difference calculation method 3 (k)-y 1 (k)、y 2 (k)-y 1 (k) Time delay differences t13 and t12 between the two are converted into a distance difference L according to sound velocity v measured by environment 13 ＝t 13 v＝r3-r1、L 12 ＝t 12 v=r2-r 1, solving for θ 1 、r 2 As the planar polar position of the drone S (k) to be positioned.
9. The unmanned aerial vehicle positioning method based on the Kalman filtering algorithm according to claim 8, wherein the unmanned aerial vehicle positioning method is characterized in that: in step S5, the position state quantity p, the speed state quantity v, the acceleration a, and the state prediction equation of the unmanned aerial vehicle to be positioned:
Figure FDA0004198505980000057
Figure FDA0004198505980000058
representing state vectors by matrix
Figure FDA0004198505980000059
Figure FDA00041985059800000510
Wherein F is k To predict matrix, B k Called controlMatrix formation, u k Called control vector;
using covariance matrix P k Representing a state quantity prediction model and a covariance prediction model:
Figure FDA0004198505980000061
Figure FDA0004198505980000062
the units of the sound source positioning algorithm result and the laser radar sensor ranging result are different from the unmanned aerial vehicle prediction model, the sound source positioning algorithm result and the laser radar sensor ranging result are converted into the same by using linear transformation, and the linear transformation is described by using a matrix Hk:
Figure FDA0004198505980000063
Figure FDA0004198505980000064
Figure FDA0004198505980000065
is the mean vector; p (P) k Is a covariance matrix, representing uncertainty at time K;
describing sensor noise, introducing noise distribution Rk, wherein Rk mean value is the same as observed value Zk, and product parameters of two Gaussian distributions are expressed in a matrix form:
Figure FDA0004198505980000066
Figure FDA0004198505980000067
Figure FDA0004198505980000068
wherein Sigma 1 and Sigma 2 are covariance matrices of prediction and observation respectively, and a prediction distribution is obtained:
Figure FDA0004198505980000069
Figure FDA00041985059800000610
observation distribution:
Figure FDA00041985059800000611
∑1＝R k
combining the state quantity prediction model and the covariance prediction model of the observation and model prediction to obtain an optimal estimation model:
Figure FDA00041985059800000612
P k ′＝P k -K′P k
after the sound source positioning algorithm operation is completed and the laser radar ranging result is obtained, constructing a matrix by using the two results, and transmitting the matrix into the optimal estimation model for algorithm iteration to obtain the unmanned plane positioning result after data fusion and filtering.
CN202310454196.0A 2023-04-25 2023-04-25 Unmanned aerial vehicle positioning device and positioning method based on Kalman filtering algorithm Pending CN116381717A (en)

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Publication number Priority date Publication date Assignee Title
CN117289208A (en) * 2023-11-24 2023-12-26 北京瑞森新谱科技股份有限公司 Sound source positioning method and device
CN117451033A (en) * 2023-12-21 2024-01-26 广东石油化工学院 Synchronous positioning and map construction method, device, terminal and medium
CN117452336A (en) * 2023-10-25 2024-01-26 东北大学 Unmanned aerial vehicle sound event detection positioning device

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117452336A (en) * 2023-10-25 2024-01-26 东北大学 Unmanned aerial vehicle sound event detection positioning device
CN117452336B (en) * 2023-10-25 2024-06-04 东北大学 Unmanned aerial vehicle sound event detection positioning device
CN117289208A (en) * 2023-11-24 2023-12-26 北京瑞森新谱科技股份有限公司 Sound source positioning method and device
CN117289208B (en) * 2023-11-24 2024-02-20 北京瑞森新谱科技股份有限公司 Sound source positioning method and device
CN117451033A (en) * 2023-12-21 2024-01-26 广东石油化工学院 Synchronous positioning and map construction method, device, terminal and medium
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