CN112698665A - Unmanned aerial vehicle detection positioning method - Google Patents
Unmanned aerial vehicle detection positioning method Download PDFInfo
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- CN112698665A CN112698665A CN202011580536.7A CN202011580536A CN112698665A CN 112698665 A CN112698665 A CN 112698665A CN 202011580536 A CN202011580536 A CN 202011580536A CN 112698665 A CN112698665 A CN 112698665A
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- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
Abstract
The invention provides an unmanned aerial vehicle detection positioning method, which comprises the following steps: s1: arranging an acoustic receiver array and establishing a space coordinate system; s2: collecting acoustic signal data of a target object; s3: calculating the time delay of the received signal, and determining the coordinate of the target object in a space coordinate system; s4: judging the flying direction of the target object relative to the acoustic wave receiver array; s5: extracting the characteristics of the sound wave signal data, and judging whether the target object is an unmanned aerial vehicle or not; s6: starting a camera to align the flight direction to acquire an image of a target object; s7: carrying out image preprocessing and image compression on the image, and judging whether the target object is an unmanned aerial vehicle or not through image identification; s8: finally, whether the target object is the unmanned aerial vehicle is judged; s9: and judging the state of the unmanned aerial vehicle according to the sound wave frequency change of the sound wave signal data. The unmanned aerial vehicle detection positioning method has the characteristics of high judgment precision, low cost and low computational power, and can be used in heavy fog weather.
Description
Technical Field
The invention relates to the technical field of detection and positioning, in particular to a detection and positioning method for an unmanned aerial vehicle.
Background
In recent years, it is often reported that an unmanned aerial vehicle flies near an airport so that the airplane cannot fly at the right moment. Along with the development of unmanned aerial vehicle technique, unmanned aerial vehicle is more and more popular, and because the more mature of present technique, unmanned aerial vehicle has been towards the miniaturization, the development of miniaturization, miniature unmanned aerial vehicle is small, and easy operation, flight height is low, ground object shelters from many, make unmanned aerial vehicle fly at the place that forbids flight difficult discovery, and in addition big fog weather, it is more difficult to the detection of unmanned aerial vehicle, take precautions against and handle unmanned aerial vehicle's interference and destruction, become the boundary, dangerous place, military place and the on-the-spot difficult problem of great security activity, the concrete performance is difficult management and control, difficult detection, difficult handling. At present, the detection of the unmanned aerial vehicle includes artificial observation, receiver detection, radio detection, image detection and the like. The manual observation mode is to rely on the vision and the hearing of people to sense and judge, under normal conditions, the hearing of people is 800 meters, the visibility is 0.25 square meter of weather, the vision of people to objects in the air is 2-3 kilometers, moreover, the energy of people is limited, the duration of concentration is limited, the subjective intention of people is easily influenced by the weather, the monitoring for 24 hours a day cannot be met, the accuracy of the manual observation judgment is unstable, and the labor cost is higher. The receiver detection is to receive the echo information of the unmanned aerial vehicle through the receiver of the array, obtain the characteristic information such as the speed, the position of the unmanned aerial vehicle to identify the unmanned aerial vehicle, and the array size of the single receiver has certain influence on the detection accuracy of the unmanned aerial vehicle. Especially when detecting drones with drones. Because unmanned aerial vehicle self's size for under the less condition in array space on unmanned aerial vehicle, can influence unmanned aerial vehicle's detection precision, and to the unmanned aerial vehicle detection of the volume less, low noise comparatively difficult. Radio detection surveys and discerns unmanned aerial vehicle for the frequency of use, but then is difficult to survey to the unmanned aerial vehicle that does not emit radio signal, and image detection gathers unmanned aerial vehicle's image data through the camera and carries out the detection discernment that characteristic extraction carried out unmanned aerial vehicle, and this method surveys comparatively difficultly to the miniature unmanned aerial vehicle that is in heavy fog weather or night, and requires highly to the computing power of hardware.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the unmanned aerial vehicle detection positioning method which has the characteristics of high judgment precision, low cost and small calculation power, and can be used in the heavy fog weather.
In order to achieve the purpose, the invention provides an unmanned aerial vehicle detection and positioning method, which comprises the following steps:
s1: setting an acoustic receiver array and establishing a space coordinate system, wherein the acoustic receiver array comprises a plurality of acoustic receivers;
s2: collecting acoustic signal data of a target object by using the acoustic receiver array;
s3: calculating the time delay of the received signal of each sound wave receiver according to the sound wave signal data, and determining the coordinate of the target object in the space coordinate system according to the time delay;
s4: judging the flying direction of the target object relative to the acoustic wave receiver array according to the time delay;
s5: extracting characteristics of the sound wave signal data, judging whether the target object is an unmanned aerial vehicle or not, and obtaining a first judgment result;
s6: starting a camera to acquire an image of the target object aiming at the flight direction;
s7: performing image preprocessing and image compression on the image, and judging whether the target object is an unmanned aerial vehicle or not through image identification to obtain a second judgment result;
s8: finally judging whether the target object is an unmanned aerial vehicle or not by combining the first judgment result and the second judgment result to obtain a final judgment result;
s9: and when the final judgment result shows that the target object is an unmanned aerial vehicle, judging the state of the unmanned aerial vehicle according to the sound wave frequency change of the sound wave signal data, wherein the state comprises hovering, approaching and departing.
Preferably, said sonic receiver array comprises five of said sonic receivers, one of said sonic receivers being centrally located, the remaining four of said sonic receivers being symmetrically arranged around said centrally located sonic receiver and forming a rectangle; the space coordinate system takes the central position as a coordinate center.
Preferably, in the step S3, an equation between the coordinates of the target object and the time delay τ of the sound wave signal data received by the sound wave receiver two by two is established by using the spatial coordinate system; and calculating the time delay tau by utilizing a mutual generalized correlation algorithm, and substituting the time delay tau into the equation to calculate the coordinate of the target object in the space coordinate system.
Preferably, in the step S4, the flying direction of the target object relative to the acoustic wave receiver array is determined by comparing the positive and negative of the time delay.
Preferably, in the step S5, the sound wave signal data is subjected to feature extraction by using a mel-frequency cepstrum coefficient method; outputting a judgment result whether the sound wave signal data is the noise of the unmanned aerial vehicle or not by using a BP neural network; the activation function of the BP neural network adopts a sigmoid function; and judging whether the target object is an unmanned aerial vehicle according to the judgment result to obtain the first judgment result.
Preferably, in the step S7, the image is preprocessed by using an FFA-Net defogging new network algorithm; performing image compression on the image subjected to image preprocessing by using singular value decomposition; and performing image recognition on the compressed image by using a Faster R-CNN algorithm to judge whether the target object is an unmanned aerial vehicle or not, and obtaining a second judgment result.
Preferably, the method further comprises the steps of: and when one of the first judgment result and the second judgment result judges that the target object is the unmanned aerial vehicle, sending out an early warning signal.
Preferably, in the step S9, according to the doppler effect, when the change in the sound wave frequency is within a threshold range, it is determined that the state of the drone is hovering; when the sound wave frequency exceeds the threshold range and becomes larger, judging that the unmanned aerial vehicle approaches the sound wave receiver, wherein the state is close; when the sound wave frequency exceeds the threshold range and becomes smaller, the unmanned aerial vehicle is judged to be far away from the sound wave receiver, and the state is far away.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
accomplish the real-time detection to unmanned aerial vehicle with lower cost, the price can let the light burden of user, even if unmanned on duty, also can survey unmanned aerial vehicle, when detecting the unmanned aerial vehicle target, the user can obtain the early warning signal that unmanned aerial vehicle exists, is convenient for counter-act to the unmanned aerial vehicle of banning. Prevent the potential danger that unmanned aerial vehicle's flight of breaking away brought, reduce the casualty accident.
Adopt sound wave sensor and vision sensor to fuse the mode of surveying, avoid the not high limitation of single sensor detection precision, for example single sound wave sensor can receive the distance influence and make the signal that detects less, and vision sensor can receive the influence of light. The Bayesian probability is utilized, and the advantages of the Bayesian probability and the Bayesian probability are combined, so that the detection accuracy of the unmanned aerial vehicle can be improved.
Adopt the sound wave to survey the direction of unmanned aerial vehicle back and rotate the camera to unmanned aerial vehicle's direction again and carry out image acquisition, avoid the camera to the big problem of storage data volume that omnidirectional detection brought, simultaneously, carry out singular value decomposition to the image of gathering, reduced the calculation power of hardware.
Drawings
Fig. 1 is a flowchart of an unmanned aerial vehicle detection and positioning method according to an embodiment of the present invention.
Detailed Description
The following description of the preferred embodiment of the present invention, in accordance with the accompanying drawings of which 1 is presented to enable a better understanding of the invention as to its functions and features.
Referring to fig. 1, a method for detecting and positioning an unmanned aerial vehicle according to an embodiment of the present invention includes:
s1: setting an acoustic receiver array and establishing a space coordinate system, wherein the acoustic receiver array comprises a plurality of acoustic receivers;
s2: collecting acoustic signal data of a target object by using an acoustic receiver array;
s3: calculating the time delay of the received signal of each sound wave receiver according to the sound wave signal data, and determining the coordinate of the target object in a space coordinate system according to the time delay;
s4: judging the flight direction of the target object relative to the acoustic wave receiver array according to the time delay;
s5: extracting characteristics of the sound wave signal data, judging whether a target object is an unmanned aerial vehicle or not, and obtaining a first judgment result;
s6: starting a camera to align the flight direction to acquire an image of a target object;
s7: carrying out image preprocessing and image compression on the image, and judging whether the target object is an unmanned aerial vehicle or not through image identification to obtain a second judgment result;
s8: finally judging whether the target object is the unmanned aerial vehicle or not by combining the first judgment result and the second judgment result to obtain a final judgment result;
s9: and when the final judgment result shows that the target object is the unmanned aerial vehicle, judging the state of the unmanned aerial vehicle according to the sound wave frequency change of the sound wave signal data, wherein the state comprises hovering, approaching and departing.
The sound wave receiver array is a quinary array and comprises five sound wave receivers, one sound wave receiver is positioned at the central position, and the other four sound wave receivers are symmetrically arranged around the sound wave receiver positioned at the central position to form a rectangle; the spatial coordinate system has the center position as a coordinate center.
In the step of S3, establishing an equation between the coordinates of the target object and the time delay tau of the sound wave signal data received by the sound wave receiver pairwise by using a space coordinate system; and calculating the time delay tau by utilizing a mutual generalized correlation algorithm, and substituting the time delay tau into an equation to calculate the coordinate of the target object in a space coordinate system.
In step S4, the flight direction of the target object with respect to the acoustic wave receiver array is determined by comparing the positive and negative of the time delay.
In the step of S5, a Mel cepstrum coefficient method is used for carrying out feature extraction on sound wave signal data, so that unmanned aerial vehicles, natural noise and automobile noise can be classified; whether the sound wave signal data output by the BP neural network is the judgment result of the noise of the unmanned aerial vehicle or not is judged; the activation function of the BP neural network adopts a sigmoid function; and judging whether the target object is the unmanned aerial vehicle according to the judgment result to obtain a first judgment result.
In the step of S7, carrying out image preprocessing on the image by using an FFA-Net defogging new network algorithm; performing image compression on the image after image preprocessing by using singular value decomposition; and performing image recognition on the compressed image by using a Faster R-CNN algorithm to judge whether the target object is the unmanned aerial vehicle or not, and obtaining a second judgment result.
Further comprising the steps of: and when one of the first judgment result and the second judgment result judges that the target object is the unmanned aerial vehicle, sending out an early warning signal.
In the step S9, according to the doppler effect, when the change of the sound wave frequency is within a threshold range, it is determined that the unmanned aerial vehicle is hovering; when the sound wave frequency exceeds the threshold range and becomes larger, judging that the unmanned aerial vehicle approaches the sound wave receiver, and judging that the state is approaching; when the sound wave frequency exceeds the threshold range and becomes small, the unmanned aerial vehicle is judged to be far away from the sound wave receiver, and the state is far away.
For example: the time for the sound wave of the unmanned aerial vehicle to reach the first sound wave receiver, the second sound wave receiver, the third sound wave receiver, the fourth sound wave receiver and the fifth sound wave receiver is t1、t2、t3、t4、t5. Time delay between two sonic receivers is tauij＝ti-tjAnd t isi＞tj,τijThe calculation can be carried out according to the frequency domain cross-correlation function of the sound wave signals received by the ith sound wave receiver and the jth sound wave receiver. Before calculating the coordinates of the drone, it is necessary to pair τ1jJ is 2,3,4,5, and finds out the smallest two, if the two with the smallest time delay are neighbors, then directly determining which quadrant the drone is in to determine which coordinate system the drone is in, and then determining the direction of the drone relative to the coordinates, such as τ14And τ15If the data is two minimum delay data, the unmanned aerial vehicle is in the first quadrant, and if the non-adjacent delays are equal, the size of the delay data between equal times can be judged again, such as tau12And τ14At a minimum, and the two data are equal, then τ can be seen again3And τ5If τ is greater than τ3If the coordinate value of the unmanned aerial vehicle in the first quadrant and the coordinate values (x) of the fourth sound wave receiver and the fifth sound wave receiver are calculated, the unmanned aerial vehicle is in the positive direction of the y axis, otherwise, the unmanned aerial vehicle is in the negative direction of the y axis, the coordinate of the unmanned aerial vehicle on the coordinate axis is easy to calculate, and the coordinate values (x) of the unmanned aerial vehicle in the first quadrant and the coordinate values (x) of the4,y4,z4) And (x)5，y5,z5) For known data, assume coordinate values of the drone are (x, y, z). The following equation can be derived.
Wherein tau is14、τ15、τ45The generalized cross-correlation function of the data collected by the first sound wave receiver, the fourth sound wave receiver, the first sound wave receiver, the fifth sound wave receiver, the fourth sound wave receiver and the fifth sound wave receiver can be used for solving the problem that v is the sound propagation speed of the air.
According to the Doppler effect, when a sound source is close to the sound wave receiver, the larger the signal frequency received by the sound wave receiver is, when the unmanned aerial vehicle is far away from the sound wave receiver, the smaller the signal frequency received by the sound wave receiver is, and if the unmanned aerial vehicle is in a hovering state, the frequency change received by the sound wave receiver is not large. Therefore, according to the analysis of the time domain waveform of the signal received by the first sound wave receiver and the extraction of the wave crest of the waveform, if the distance between the wave crest and the wave crest is smaller, the frequency is larger and larger, and the unmanned aerial vehicle is approaching. Otherwise, the distance is far away.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An unmanned aerial vehicle detection positioning method comprises the following steps:
s1: setting an acoustic receiver array and establishing a space coordinate system, wherein the acoustic receiver array comprises a plurality of acoustic receivers;
s2: collecting acoustic signal data of a target object by using the acoustic receiver array;
s3: calculating the time delay of the received signal of each sound wave receiver according to the sound wave signal data, and determining the coordinate of the target object in the space coordinate system according to the time delay;
s4: judging the flying direction of the target object relative to the acoustic wave receiver array according to the time delay;
s5: extracting characteristics of the sound wave signal data, judging whether the target object is an unmanned aerial vehicle or not, and obtaining a first judgment result;
s6: starting a camera to acquire an image of the target object aiming at the flight direction;
s7: performing image preprocessing and image compression on the image, and judging whether the target object is an unmanned aerial vehicle or not through image identification to obtain a second judgment result;
s8: finally judging whether the target object is an unmanned aerial vehicle or not by combining the first judgment result and the second judgment result to obtain a final judgment result;
s9: and when the final judgment result shows that the target object is an unmanned aerial vehicle, judging the state of the unmanned aerial vehicle according to the sound wave frequency change of the sound wave signal data, wherein the state comprises hovering, approaching and departing.
2. The method according to claim 1, wherein said sonic receiver array comprises five sonic receivers, one of said sonic receivers is located at a central position, and the other four sonic receivers are symmetrically arranged around said centrally located sonic receiver to form a rectangle; the space coordinate system takes the central position as a coordinate center.
3. The unmanned aerial vehicle detection and positioning method according to claim 2, wherein in the step S3, an equation between the coordinates of the target object and the time delay τ of the sound wave signal data received by the sound wave receiver two by two is established by using the spatial coordinate system; and calculating the time delay tau by utilizing a mutual generalized correlation algorithm, and substituting the time delay tau into the equation to calculate the coordinate of the target object in the space coordinate system.
4. The method according to claim 3, wherein in step S4, the flying direction of the target object relative to the sonic receiver array is determined by comparing the positive and negative of the time delay.
5. The unmanned aerial vehicle detection and positioning method according to claim 1, wherein in the step S5, the sound wave signal data is subjected to feature extraction by using mel-frequency cepstrum coefficient method; outputting a judgment result whether the sound wave signal data is the noise of the unmanned aerial vehicle or not by using a BP neural network; the activation function of the BP neural network adopts a sigmoid function; and judging whether the target object is an unmanned aerial vehicle according to the judgment result to obtain the first judgment result.
6. The method according to claim 1, wherein in step S7, the images are pre-processed by using a FFA-Net defogging new network algorithm; performing image compression on the image subjected to image preprocessing by using singular value decomposition; and performing image recognition on the compressed image by using a Faster R-CNN algorithm to judge whether the target object is an unmanned aerial vehicle or not, and obtaining a second judgment result.
7. The unmanned aerial vehicle detection and positioning method according to claim 1, further comprising the steps of: and when one of the first judgment result and the second judgment result judges that the target object is the unmanned aerial vehicle, sending out an early warning signal.
8. The method according to claim 1, wherein in step S9, when the change in the sound wave frequency is within a threshold range according to doppler effect, it is determined that the state of the drone is hovering; when the sound wave frequency exceeds the threshold range and becomes larger, judging that the unmanned aerial vehicle approaches the sound wave receiver, wherein the state is close; when the sound wave frequency exceeds the threshold range and becomes smaller, the unmanned aerial vehicle is judged to be far away from the sound wave receiver, and the state is far away.
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