CN219676293U - Unmanned aerial vehicle positioner based on Kalman filtering algorithm - Google Patents
Unmanned aerial vehicle positioner based on Kalman filtering algorithm Download PDFInfo
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Abstract
The utility model discloses an unmanned aerial vehicle positioning device based on a Kalman filtering algorithm, which relates to the field of unmanned aerial vehicle positioning and 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. According to the utility model, a microphone array is formed by the microphone array bracket and the microphone, the microphone array and the laser radar positioning technology are subjected to data fusion, and whether an unmanned aerial vehicle invades or not is detected by an unmanned aerial vehicle sound source positioning algorithm and an unmanned aerial vehicle voiceprint recognition algorithm optimized by a microcontroller; 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 a Kalman filtering algorithm, so that the positioning result has higher accuracy and stability.
Description
Technical Field
The utility model relates to the field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle positioning device 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 for unmanned aerial vehicle based on the kalman filter algorithm to solve the above-mentioned problems.
Disclosure of Invention
The utility model aims to provide an unmanned aerial vehicle positioning device based on a Kalman filtering algorithm, which solves 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 utility model 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.
Therefore, the unmanned aerial vehicle positioning device based on the Kalman filtering algorithm has 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 utility model 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 utility model upgrades and overhauls the internal equipment directly through the safety door.
(5) The utility model 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 utility model, 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 utility model supports cellular networking and enlarges the positioning range.
(9) The utility model 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 utility model 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 utility model; 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 utility model 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 utility model 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 utility model 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:
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,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:
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:
where τ is the frequency, e is the natural constant, j is the unit vector:
solving cross-power spectral density function
In the method, in the process of the utility model,is F x Conjugation of (τ);
for a pair ofPerforming Fourier inverse transformation to obtain generalized cross-correlation function between two signals>
Taking outThe 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:
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:
representing state vectors by matrix
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:
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:
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:
wherein Sigma 1 and Sigma 2 are covariance matrices of prediction and observation respectively, and a prediction distribution is obtained:
observation distribution:
∑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:
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 based on the Kalman filtering algorithm adopts 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 subjected to data fusion, the accuracy of a positioning result is improved, the metal shell can protect the internal equipment of the device so as to adapt to complex and various environments, and the metal shell is provided with the safety door which is used for upgrading and overhauling the internal equipment.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present utility model and not for limiting it, and although the present utility model 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 utility model 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 utility model.
Claims (6)
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.
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| 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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| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
| CN117451033B (en) * | 2023-12-21 | 2024-05-14 | 广东石油化工学院 | Synchronous positioning and map construction method, device, terminal and medium |
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