CN112505620A - Rotary direction finding method for unmanned aerial vehicle detection - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
- G01S3/14—Systems for determining direction or deviation from predetermined direction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Abstract
The invention discloses a rotary direction finding method for unmanned aerial vehicle detection, which is based on a rotary direction finding system for unmanned aerial vehicle detection, and comprises an antenna servo turntable, a low-noise amplifier, a real-time spectrum analyzer, a main control board and an upper computer which are sequentially connected, wherein a high-gain large-bandwidth log periodic antenna is arranged on the antenna servo turntable, the antenna servo turntable is also connected with the main control board, and the rotary direction finding method is implemented according to the following steps: firstly, initialization: the method comprises the steps of calibrating a turntable zero angle, setting detection initial parameters and training a neural network, wherein an antenna servo turntable drives a high-gain large-bandwidth log-periodic antenna to perform circular motion detection on peripheral space radio signals, a real-time spectrum analyzer performs Fast Fourier Transform (FFT) processing on the detected signals, converts time domain signals into frequency domain signals and finally performs classification and identification on the neural network.
Description
Technical Field
The invention belongs to the technical field of radio detection, and particularly relates to a rotary direction finding method for unmanned aerial vehicle detection.
Background
With the gradual opening of low-altitude airspace facing civilian use, the low-altitude unmanned aerial vehicle is widely applied and, as a new industry, the low-altitude unmanned aerial vehicle industry is expanded year by year. But as drones are applied in more and more fields, various security events due to the drones are frequent.
Because unmanned aerial vehicle characteristics such as small, flight noise are little, there is certain technical degree of difficulty at present in unmanned aerial vehicle detection, direction finding field. At present, radio detection technical means are mainly adopted for the detection and direction finding of the unmanned aerial vehicle. Radio detection realizes the detection and the direction finding of unmanned aerial vehicle through the radio signal of analysis peripheral free space. Because the information volume that radio wave carried is big, accessible frequency spectrum information and demodulation etc. technique realize the detection to information such as unmanned aerial vehicle model, frequency channel, azimuth, provide the reference for unmanned aerial vehicle counteraction.
Because unmanned aerial vehicle is small, flight noise is little, radio emission power characteristics such as little, there is certain technical degree of difficulty at present in unmanned aerial vehicle detection, direction finding field. The existing wireless electric detection means has the problems of low direction detection precision, high false alarm rate and the like.
Disclosure of Invention
The invention aims to provide a rotary direction finding method for unmanned aerial vehicle detection, and solves the problems of low direction finding precision and high false alarm rate of a radio detection means in the prior art.
The invention adopts the technical scheme that a rotary direction finding method for unmanned aerial vehicle detection is implemented according to the following steps:
step 1, initialization: calibrating a zero angle of a rotary table, setting detection initial parameters and training a neural network, and specifically comprising the following steps:
the neural network structure: using a radio ML2016.10a data set and sampling data transmission signals of unmanned aerial vehicles of different models as a training data set, wherein a neural network adopts a two-layer convolution neural network structure and comprises an input layer, a hidden layer and an output layer, and the input layer is an 8 x 1 matrix; the hidden layer comprises 50 neurons for classification calculation; the output layer comprises a classification result set of unmanned aerial vehicle signals, WiFi signals, base station signals, satellite signals, clutter signals and interference signals; the activation function selects a ReLU function which is used for eliminating the linear characteristic of the neural network;
training data: carrying out different combinations by using I, Q and A, P parameters of a self-carried sample signal of a radio ML2016.10a data set and a data transmission signal of an unmanned aerial vehicle sampling different models to obtain eight parameter combinations, namely IQ, AP, IQA, IQP, API, APQ, IQAP and APIQ, assigning the eight different parameter combinations to an input layer of a convolutional neural network, carrying out neural network training, wherein the assignment corresponding relationship is as follows: IQ parameters are assigned to a 1 st row and a 1 st column of an input layer, AP parameters are assigned to a 2 nd row and a 1 st column, IQA parameters are assigned to a 3 rd row and a 1 st column, IQP parameters are assigned to a 4 th row and a 1 st column, API parameters are assigned to a 5 th row and a 1 st column, APQ parameters are assigned to a 6 th row and a 1 st column, IQAP parameters are assigned to a 7 th row and a 1 st column, and APIQ parameters are assigned to an 8 th row and a 1 st column, wherein I represents a real part of a complex representation form of a radio signal; q represents the imaginary part of the complex representation of the radio signal, a represents the amplitude of the radio signal; p represents the phase of the radio signal;
step 2, the antenna servo turntable drives the high-gain large-bandwidth log periodic antenna to perform circular motion to detect a peripheral space radio signal;
step 3, the real-time spectrum analyzer carries out Fast Fourier Transform (FFT) processing on the detected signal, converts a time domain signal into a frequency domain signal, and extracts I, Q parameters of the signal, wherein the I, Q parameters comprise the frequency, the bandwidth, the signal intensity, the modulation mode, the frequency hopping speed and the frequency hopping frequency point of the detected radio signal;
step 4, the upper computer receives I, Q parameters extracted by the real-time spectrum analyzer, and classification and identification are carried out through the neural network trained in the step 1, so as to judge whether the unmanned aerial vehicle exists in the peripheral space;
and 5, if the identification result shows that unmanned aerial vehicle signals exist around the unmanned aerial vehicle, starting a direction finding process: resetting detection parameters of a real-time spectrum analyzer according to frequency point and bandwidth information of a frequency band used by the unmanned aerial vehicle detected by the system, and determining an azimuth angle of the antenna servo turntable when the intensity of the frequency band is maximum, wherein the azimuth angle is an azimuth angle of the current unmanned aerial vehicle relative to a detection position;
and 6, the upper computer sends out an alarm and gives information of the frequency band, the model, the bandwidth, the direction and the signal residence time of the unmanned aerial vehicle.
The present invention is also characterized in that,
and 3, receiving the signal from the high-gain large-bandwidth log periodic antenna by the real-time spectrum analyzer, and performing time-frequency FFT (fast Fourier transform) conversion on the signal according to the set frequency sweep frequency band, acquisition bandwidth, dynamic range, display bandwidth VBW and resolution bandwidth RBW parameters.
In the step 3, the setting range of the sweep frequency band of the real-time spectrum analyzer is 2400MHz-2500MHz and 5725 MHz-5850 MHz, the setting range of the acquisition bandwidth of the real-time spectrum analyzer is 5-20MHz, the dynamic range is-20 db-0 db, the display bandwidth VBW is 300KHz, and the resolution bandwidth RBW is 1 MHz.
In step 3, the sweep frequency adopts a binary search method, and the sweep range of the frequency spectrum is set as
，
Represents the lower limit of the scanning range of the spectrum,
represents the upper limit of the scanning range of the spectrum; swept bandwidth
Left side frequency during first scan
Frequency of right side
Intermediate frequency of
；
During subsequent scans, the left-hand frequencies are adjacent to the scanned band according to the presence signal
Or right side frequency
Or an intermediate frequency
Adjusting the bandwidth of the next sweep
And the next time in betweenFrequency of
。
The frequency sweeping process in the step 3 is specifically calculated as follows:
if the signal is close to the left frequency, then
If the signal is close to the right frequency, then
If the signal is close to the intermediate frequency, then
Wherein the content of the first and second substances,
to represent
The bandwidth set by the next sweep of frequencies,
to represent
The bandwidth set by the frequency sweep is swept the next time,
to represent
The next time the set intermediate frequency is swept,
to represent
The next time the set intermediate frequency is swept.
The neural network classification and identification process in the step 4 is as follows:
step 4.1, inputting data: i, Q parameters of the radio signal obtained by the real-time spectrum analyzer processing in the step 3;
step 4.2, data conversion: deriving the amplitude of the radio signal from the I, Q parameter
The phase of the radio signal being
Amplitude a and phase P are the AP parameters of the radio signal; different combinations of I, Q and A, P parameters of the detected radio signal are carried out to obtain eight parameter combinations, namely IQ, AP, IQA, IQP, API, APQ, IQAP and APIQ;
4.3, classification and identification: inputting the eight parameter combinations IQ, AP, IQA, IQP, API, APQ, IQAP and APIQ obtained in the step 4.2 into a trained neural network input layer, identifying and classifying the radio signals of the eight parameter combinations, outputting the identification and classification results by the neural network output layer, and enabling the classification results to correspond to unmanned aerial vehicle signals, WiFi signals, base station signals, satellite signals, clutter signals and interference signals.
The invention has the beneficial effects that the direction is measured by adopting a rotating antenna method, the antenna rotates and steps by 0.8 degrees, the width of a horizontal lobe is 8 degrees, the direction measurement adopts an amplitude comparison method, and the actual measurement of the direction measurement precision (RMS) is less than 3 degrees. The problems that the direction finding cost of the antenna array method is high, the direction finding is inaccurate, the direction of the antenna needs to be regularly calibrated and the like are effectively solved. The invention adopts the technology of neural network identification and the like. The detected radio frequency spectrum characteristic information is input into the trained neural network for identification, so that the false alarm rate of the system can be obviously reduced. The invention solves the problems that the alarm information of the radio detection technology is inaccurate, the unknown unmanned aerial vehicle cannot be identified and the like.
Drawings
FIG. 1 is a diagram of a system for a rotary direction finding method for unmanned aerial vehicle detection according to the present invention;
FIG. 2 is a flow chart of the operation of a rotary direction finding method for unmanned aerial vehicle detection of the present invention;
FIG. 3 is a diagram of neural network classification recognition;
fig. 4 is a diagram of a neural network structure.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a rotary direction finding method for unmanned aerial vehicle detection, which is based on a rotary direction finding system for unmanned aerial vehicle detection, and as shown in figure 1, the rotary direction finding system for unmanned aerial vehicle detection comprises an antenna servo turntable, a low-noise amplifier, a real-time spectrum analyzer, a main control board, an upper computer and a high-gain large-bandwidth log periodic antenna. The high-gain large-bandwidth log periodic antenna is fixed on the antenna servo turntable and is connected with the input end of the low-noise amplifier through a radio frequency cable and a reversing slip ring; the radio frequency output end of the low-noise amplifier is connected with the input end of the real-time spectrum analyzer through a radio frequency cable; the real-time spectrum analyzer is connected with the main control board through a USB port; the main control board is connected with the upper computer through an RJ45 network cable; the main control board is connected with the antenna servo turntable through an RS485 bus. The flow chart of the invention is shown in fig. 2, and is implemented specifically according to the following steps:
step 1, initialization: calibrating a zero angle of a rotary table, setting detection initial parameters and training a neural network, and specifically comprising the following steps:
as shown in fig. 4, the neural network structure: using a radio ML2016.10a data set and sampling data transmission signals of unmanned aerial vehicles of different models as a training data set, wherein a neural network adopts a two-layer convolution neural network structure and comprises an input layer, a hidden layer and an output layer, and the input layer is an 8 x 1 matrix; the hidden layer comprises 50 neurons for classification calculation; the output layer comprises a classification result set of unmanned aerial vehicle signals, WiFi signals, base station signals, satellite signals, clutter signals and interference signals; the activation function selects a ReLU function which is used for eliminating the linear characteristic of the neural network;
training data: carrying out different combinations by using I, Q and A, P parameters of a self-carried sample signal of a radio ML2016.10a data set and a data transmission signal of an unmanned aerial vehicle sampling different models to obtain eight parameter combinations, namely IQ, AP, IQA, IQP, API, APQ, IQAP and APIQ, assigning the eight different parameter combinations to an input layer of a convolutional neural network, carrying out neural network training, wherein the assignment corresponding relationship is as follows: IQ parameters are assigned to a 1 st row and a 1 st column of an input layer, AP parameters are assigned to a 2 nd row and a 1 st column, IQA parameters are assigned to a 3 rd row and a 1 st column, IQP parameters are assigned to a 4 th row and a 1 st column, API parameters are assigned to a 5 th row and a 1 st column, APQ parameters are assigned to a 6 th row and a 1 st column, IQAP parameters are assigned to a 7 th row and a 1 st column, and APIQ parameters are assigned to an 8 th row and a 1 st column, wherein I represents a real part of a complex representation form of a radio signal; q represents the imaginary part of the complex representation of the radio signal, a represents the amplitude of the radio signal; p represents the phase of the radio signal; the trained neural network has the signal classification and identification capability and can be used for subsequent signal classification and identification;
step 2, the antenna servo turntable drives the high-gain large-bandwidth log periodic antenna to perform circular motion to detect a peripheral space radio signal;
in the invention, the high-gain large-bandwidth log periodic antenna is connected with a reversing slip ring on an antenna servo turntable, and the high-gain large-bandwidth log periodic antenna sends a detected radio signal to a low-noise amplifier for amplification.
Step 3, a real-time spectrum analyzer receives a signal from a high-gain large-bandwidth log-periodic antenna, performs time-frequency FFT (fast Fourier transform) conversion on the signal according to set frequency sweep frequency band, acquisition bandwidth, dynamic range, display bandwidth VBW (visual basic) and resolution bandwidth RBW (radial basis) parameters, converts a time domain signal into a frequency domain signal, and extracts I, Q parameters of the signal, wherein I, Q parameters comprise the frequency, bandwidth, signal strength, modulation mode, frequency hopping speed and frequency hopping frequency point of the detected radio signal;
in the step 3, the setting range of the sweep frequency band of the real-time spectrum analyzer is 2400MHz-2500MHz and 5725 MHz-5850 MHz, the setting range of the acquisition bandwidth of the real-time spectrum analyzer is 5-20MHz, the dynamic range is-20 db-0 db, the display bandwidth VBW is 300KHz, and the resolution bandwidth RBW is 1 MHz.
In step 3, the sweep frequency adopts a binary search method, and the sweep range of the frequency spectrum is set as
，
Represents the lower limit of the scanning range of the spectrum,
represents the upper limit of the scanning range of the spectrum; swept bandwidth
Left side frequency during first scan
Frequency of right side
Intermediate frequency of
；
During subsequent scans, the left-hand frequencies are adjacent to the scanned band according to the presence signal
Or right side frequency
Or an intermediate frequency
Adjusting the bandwidth of the next sweep
And the next intermediate frequency
。
The frequency sweeping process in the step 3 is specifically calculated as follows:
if the signal is close to the left frequency, then
If the signal is close to the right frequency, then
If the signal is close to the intermediate frequency, then
Wherein the content of the first and second substances,
to represent
The bandwidth set by the next sweep of frequencies,
to represent
The bandwidth set by the frequency sweep is swept the next time,
to represent
The next time the set intermediate frequency is swept,
to represent
The next time the set intermediate frequency is swept.
Step 4, the upper computer receives I, Q parameters extracted by the real-time spectrum analyzer, and classification and identification are carried out through the neural network trained in the step 1, so as to judge whether the unmanned aerial vehicle exists in the peripheral space;
the neural network classification and identification process in the step 4 is as follows:
step 4.1, inputting data: i, Q parameters of the radio signal obtained by the real-time spectrum analyzer processing in the step 3;
step 4.2, data conversion: deriving the amplitude of the radio signal from the I, Q parameter
The phase of the radio signal being
Amplitude a and phase P are the AP parameters of the radio signal; different combinations of I, Q and A, P parameters of the detected radio signal are carried out to obtain eight parameter combinations, namely IQ, AP, IQA, IQP, API, APQ, IQAP and APIQ;
as shown in fig. 4, step 4.3, classification and identification: combining the eight parameters obtained in the step 4.2 with IQ, AP, IQA, IQP, API, APQ, IQAP and APIQ, inputting the parameters into a trained neural network input layer, and setting the corresponding relation as follows: IQ parameters are assigned to a 1 st row and a 1 st column of a neural network input layer, AP parameters are assigned to a 2 nd row and a 1 st column, IQA parameters are assigned to a 3 rd row and a 1 st column, IQP parameters are assigned to a 4 th row and a 1 st column, API parameters are assigned to a 5 th row and a 1 st column, APQ parameters are assigned to a 6 th row and a 1 st column, IQAP parameters are assigned to a 7 th row and a 1 st column, APIQ parameters are assigned to a 8 th row and a 1 st column, radio signals combined by the eight parameters are identified and classified, and the result of identification and classification is output by a neural network output layer, as shown in figure 4, the classification result corresponds to unmanned aerial vehicle signals, WiFi signals, base station signals, satellite signals, clutter signals and interference signals.
And 5, if the identification result shows that unmanned aerial vehicle signals exist around the unmanned aerial vehicle, starting a direction finding process: resetting detection parameters of a real-time spectrum analyzer according to frequency point and bandwidth information of a frequency band used by the unmanned aerial vehicle detected by the system, and determining an azimuth angle of the antenna servo turntable when the intensity of the frequency band is maximum, wherein the azimuth angle is an azimuth angle of the current unmanned aerial vehicle relative to a detection position;
and 6, the upper computer sends out an alarm and gives information of the frequency band, the model, the bandwidth, the direction and the signal residence time of the unmanned aerial vehicle.
Examples
When the frequency point of the input signal of the neural network is between 5.725GHz and 5.85 GHz, the signal bandwidth is 20MHz, the frequency hopping speed is 1200hop/s, and the frequency hopping signal bandwidth is 1MHz, the neural network can identify the signal as a Dajiang eidolon 4Pro unmanned aerial vehicle signal. When the frequency point of the input signal of the neural network is between 2.4GHz and 2.482 GHz, the signal bandwidth is 10MHz, the frequency hopping speed is 800hop/s, and the frequency hopping signal bandwidth is 1MHz, the neural network can identify the signal as an inspire1 unmanned aerial vehicle signal in Xinjiang. When the frequency point of the input signal of the neural network is between 2.4 GHz-2.482 GHz, the signal bandwidth is 10MHz, the frequency hopping speed is 280 hops/s, and the frequency hopping signal bandwidth is 1.3MHz, the neural network can identify the signal as a space-ground flying model unmanned aerial vehicle signal.
In the present invention, a radio signal is an electromagnetic wave that propagates information in free space using a radio wave as a carrier.
The main control board is used for converting a communication protocol of the real-time spectrum analyzer and the upper computer, providing drive for the antenna servo turntable, collecting the rotation angle of the antenna servo turntable and the like. The main control chip adopts STM32F103RC, and a CH563 chip is used for real-time conversion of a communication protocol between the spectrum analyzer and an upper computer. The PWM output channel of STM32F103RC is used to control the rotation angle of the turntable while the current antenna orientation azimuth value can be read according to the turntable encoder state.
The upper computer is a common PC computer, a windows7 system is carried, and data from the main control board is received through the network port. And (3) extracting frequency spectrum characteristic information of a sensitive frequency band, such as frequency points, bandwidth, strength and the like, by the upper computer software, inputting the radio frequency spectrum characteristic information into the trained neural network for identification, and judging whether the unmanned aerial vehicle exists around.
If the identification result shows that the unmanned aerial vehicle signal exists around, starting a direction finding process: and resetting the detection parameters of the real-time spectrum analyzer according to information such as frequency points, bandwidth and the like of the frequency band of the unmanned aerial vehicle, and measuring the azimuth angle of the rotary table when the intensity of the frequency band is maximum, wherein the azimuth angle is the azimuth angle of the current unmanned aerial vehicle relative to the detection position.
According to the invention, through external field actual measurement, when the flying height of the unmanned aerial vehicle is 100 meters and the horizontal distance from the unmanned aerial vehicle to the equipment is 300 meters, the azimuth angle errors of the unmanned aerial vehicle detected by the system are respectively as follows: the direction-finding speed of the unmanned aerial vehicle can be improved to be within 3 degrees (root mean square value) by the aid of the method, the angle of the horizontal lobe is 2.1 degrees, 1.5 degrees, 1.3 degrees, 2.8 degrees, 1.7 degrees, 2.1 degrees, 1.5 degrees, 2.7 degrees and 1.1 degrees, the rotation of the antenna is stepped by 0.8 degree, and the width of the horizontal lobe is 8 degrees. The method is used for testing in an office building with more WiFi hotspots, and the comprehensive false alarm rate of the system is within 3%.
Claims (6)
1. A rotary direction finding method for unmanned aerial vehicle detection is characterized by being implemented according to the following steps:
step 1, initialization: calibrating a zero angle of a rotary table, setting detection initial parameters and training a neural network, and specifically comprising the following steps:
the neural network structure: using a radio ML2016.10a data set and sampling data transmission signals of unmanned aerial vehicles of different models as a training data set, wherein a neural network adopts a two-layer convolution neural network structure and comprises an input layer, a hidden layer and an output layer, and the input layer is an 8 x 1 matrix; the hidden layer comprises 50 neurons for classification calculation; the output layer outputs the classification result; the activating function is a ReLU function;
training data: carrying out different combinations by using a sample signal carried by a radioML2016.10a data set and I, Q and A, P parameters for sampling data transmission signals of unmanned aerial vehicles of different models to obtain eight parameter combinations, namely IQ, AP, IQA, IQP, API, APQ, IQAP and APIQ, assigning the eight different parameter combinations to an input layer of a convolutional neural network, and carrying out neural network training, wherein I represents a real part of a radio signal complex representation form; q represents the imaginary part of the complex representation of the radio signal, a represents the amplitude of the radio signal; p represents the phase of the radio signal;
step 2, the antenna servo turntable drives the high-gain large-bandwidth log periodic antenna to perform circular motion to detect a peripheral space radio signal;
step 3, the real-time spectrum analyzer carries out Fast Fourier Transform (FFT) processing on the detected signal, converts a time domain signal into a frequency domain signal, and extracts I, Q parameters of the signal, wherein the I, Q parameters comprise the frequency, the bandwidth, the signal intensity, the modulation mode, the frequency hopping speed and the frequency hopping frequency point of the detected radio signal;
step 4, the upper computer receives I, Q parameters extracted by the real-time spectrum analyzer, and classification and identification are carried out through the neural network trained in the step 1, so as to judge whether the unmanned aerial vehicle exists in the peripheral space;
and 5, if the identification result shows that unmanned aerial vehicle signals exist around the unmanned aerial vehicle, starting a direction finding process: resetting detection parameters of a real-time spectrum analyzer according to frequency point and bandwidth information of a frequency band used by the unmanned aerial vehicle detected by the system, and determining an azimuth angle of the antenna servo turntable when the intensity of the frequency band is maximum, wherein the azimuth angle is an azimuth angle of the current unmanned aerial vehicle relative to a detection position;
and 6, the upper computer sends out an alarm and gives information of the frequency band, the model, the bandwidth, the direction and the signal residence time of the unmanned aerial vehicle.
2. The method as claimed in claim 1, wherein the real-time spectrum analyzer in step 3 receives the signal from the log periodic antenna with high gain and large bandwidth, and performs time-frequency FFT conversion on the signal according to the set parameters of frequency sweep band, acquisition bandwidth, dynamic range, display bandwidth VBW and resolution bandwidth RBW.
3. The rotary direction finding method for unmanned aerial vehicle detection as claimed in claim 2, wherein in step 3, the frequency band of the frequency sweep is set to 2400MHz-2500MHz and 5725 MHz-5850 MHz, the acquisition bandwidth of the real-time spectrum analyzer is set to 5-20MHz, the dynamic range is-20 db to 0db, the display bandwidth VBW is 300KHz, and the resolution bandwidth RBW is 1 MHz.
4. The method according to claim 3, wherein the sweep in step 3 is performed by binary search, and the sweep range of the spectrum is defined as
,
Represents the lower limit of the scanning range of the spectrum,
represents the upper limit of the scanning range of the spectrum; swept bandwidth
Left side frequency during first scan
Frequency of right side
Intermediate frequency of
；
In the subsequent scanning process, the rootAccording to the presence of signals close to the left frequency of the scanning band
Or right side frequency
Or an intermediate frequency
Adjusting the bandwidth of the next sweep
And the next intermediate frequency
。
5. The method according to claim 4, wherein the frequency sweeping process in step 3 is calculated as follows:
if the signal is close to the left frequency, then
If the signal is close to the right frequency, then
If the signal is close to the intermediate frequency, then
Wherein the content of the first and second substances,
to represent
The bandwidth set by the next sweep of frequencies,
to represent
The bandwidth set by the frequency sweep is swept the next time,
to represent
The next time the set intermediate frequency is swept,
to represent
The next time the set intermediate frequency is swept.
6. The rotary direction finding method for unmanned aerial vehicle detection according to claim 5, wherein the neural network classification identification process in the step 4 is specifically as follows:
step 4.1, inputting data: i, Q parameters of the radio signal obtained by the real-time spectrum analyzer processing in the step 3;
step 4.2, data conversion: deriving the amplitude of the radio signal from the I, Q parameter
The phase of the radio signal being
Amplitude a and phase P are the AP parameters of the radio signal; different combinations of I, Q and A, P parameters of the detected radio signal are carried out to obtain eight parameter combinations, namely IQ, AP, IQA, IQP, API, APQ, IQAP and APIQ;
4.3, classification and identification: inputting the eight parameter combinations IQ, AP, IQA, IQP, API, APQ, IQAP and APIQ obtained in the step 4.2 into a trained neural network input layer, identifying and classifying the radio signals of the eight parameter combinations, outputting the identification and classification results by the neural network output layer, and enabling the classification results to correspond to unmanned aerial vehicle signals, WiFi signals, base station signals, satellite signals, clutter signals and interference signals.
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| SE2200059A1 (en) * | 2022-06-02 | 2023-12-03 | Saab Ab | DETECTION AND CLASSIFICATION OF UAVs |
| WO2023234841A1 (en) * | 2022-06-02 | 2023-12-07 | Saab Ab | DETECTION AND CLASSIFICATION OF UAVs |
| SE545861C2 (en) * | 2022-06-02 | 2024-02-27 | Saab Ab | DETECTION AND CLASSIFICATION OF UAVs |
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