An Unmanned Aerial Vehicle Signal Detection Method and System Based on Frequency-Domain Images

Abstract

The present invention relates to the field of UAV signal detection, and specifically discloses a UAV signal detection method and system based on frequency-domain images. The detection method includes the following steps: a signal acquisition and conversion step: preprocessing the signal to obtain frequency-domain image information; an image information processing step: marking regions; an image information discrimination step: segmenting the frequency-domain image information according to the markings of the regions to obtain a UAV spectrum signal region map; a signal type determination step: performing classification processing to finally obtain the UAV signal type; the detection system includes an antenna array module, a signal receiving module, an array signal processing module, a signal conversion module, an image information processing module, a network communication module, and a power supply module. The present invention extends the detection method from the field of signal processing to the field of image processing with a wider range of methods and applications, can solve problems more comprehensively, and improves the detection accuracy in a complex electromagnetic environment by using an accumulative judgment method to screen stable regions.

Description

Technical Field

[0001] The present invention relates to the field of UAV signal detection, and particularly to a UAV signal detection method and system based on frequency-domain images. Background Art

[0002] In recent years, the UAV industry has developed rapidly, and the applications of UAVs have penetrated into all aspects of daily life. UAV supervision is a necessary means to ensure the orderly operation of UAVs, and UAV spectrum detection is an important technical means for UAV detection in the current urban environment.

[0003] Due to the complex electromagnetic environment in the urban area and the relatively weak UAV signals, the current spectrum detection equipment generally has the problem of low detection accuracy in the complex electromagnetic environment; the traditional signal recognition algorithm mainly completes the comparison by extracting frequency characteristics such as the center frequency and bandwidth of the spectrum signal with the corresponding parameters in the feature library, and there will be a problem that the system cannot detect the signal that can be recognized by the human eye. Summary of the Invention

[0004] Aiming at the defects of the prior art, the purpose of the present invention is to provide a UAV signal detection method and system based on frequency-domain images, aiming to solve the problems.

[0005] To solve the above problems, according to an aspect of the present invention, a UAV signal detection method based on frequency-domain images includes the following steps:

[0006] (1) Signal detection and conversion step: According to the preset values of the sampling rate and the sliding size, preprocess the received radio time-domain signal to obtain frequency-domain image information;

[0007] (2) Image information processing step: Mark the foreground area and the background area of the frequency-domain image information;

[0008] (3) Image information discrimination step: Segment the frequency-domain image information according to the marked areas to obtain a UAV spectrum signal area map;

[0009] (4) Signal type determination step: Mark the connected areas of the UAV spectrum signal area map, and perform classification processing. Compare the feature information of the frequency-domain image information in the classification result with the information in the UAV signal feature database, and finally obtain the UAV signal type.

[0010] Further, step (1) includes the following sub-steps:

[0011] (1.1) According to the preset values of the sampling rate and the sliding size, perform STFT processing on the received radio time-domain signal to obtain a spectrum waterfall map of the UAV signal;

[0012] (1.2) Convert the signal intensity information in the spectral waterfall diagram into frequency-domain image information.

[0013] Further, the information conversion method in step (1.2) is as follows: According to the relevant specifications of the UAV power range, preset the maximum value pix of the frequency intensity max and the minimum value pix min . Set the intensity outside the maximum and minimum value ranges in the spectral waterfall diagram to 0, and for the intensity within the range, perform normalization processing according to the formula round(255 * (pix - pix min ) / (pix max - pix min )) to convert the signal intensity information in the spectral waterfall diagram into frequency-domain image information.

[0014] Further, step (2) includes the following sub-steps:

[0015] (2.1) Accumulate the frequency-domain image information of the preset value quantity, store it in a queue, perform accumulation on all the frequency-domain image information in the queue to obtain an image mean matrix, and calculate the mean of the image mean matrix;

[0016] (2.2) Sequentially extract the frequency-domain image information at the end of the queue and perform a difference operation with the mean of the image mean matrix, compare the difference with a preset threshold to obtain a binary frequency-domain image difference map;

[0017] (2.3) Perform an AND operation on the obtained difference map, and use the non-zero part in the AND result as the stable region in the frequency-domain image;

[0018] (2.4) Calculate the average value of the eight-connected region image of the difference map, perform region marking. When the average value is greater than the mean of the image mean matrix, mark the region as the foreground region, and vice versa as the background region.

[0019] Further, in the image information discrimination step of step (3), the segmentation includes:

[0020] Take out the frequency-domain image information at the end of the queue, use its marked region as the input of the OpenCV graph cut function grub-cut to obtain the segmentation result.

[0021] Further, in the signal type determination step of step (4), the classification processing method is the k-means clustering method.

[0022] According to another aspect of the present invention, a UAV signal detection system based on frequency-domain images is provided. The system includes an antenna array module, a signal reception module, an array signal processing module, a signal conversion module, an image information processing module, a network communication module, and a power supply module;

[0023] The antenna array module receives radio information, then transmits the information to the array signal processing module through the signal receiving module, and finally processes the radio information through the array signal processing module to extract useful signal characteristics and information. The enhanced radio signal is converted into image information through the signal conversion module. The image information processing module processes and determines the image information, and outputs the results of the processing and determination through the network communication module. The power supply module is electrically connected to other modules and is used to supply power to other modules.

[0024] Preferably, the signal conversion module performs STFT processing on the received radio time-domain signal according to the preset values of the sampling rate and the sliding size to obtain the spectral waterfall diagram of the UAV signal, and converts the signal intensity information in the spectral waterfall diagram into frequency-domain image information.

[0025] Preferably, the image information processing module is used to mark the foreground area and the background area of the frequency-domain image information, segment the frequency-domain image information according to the area markings to obtain the UAV spectral signal area diagram, mark the connected areas of the UAV spectral signal area diagram, and perform classification processing.

[0026] Preferably, the image information processing module is used to compare the characteristic information of the frequency-domain image information in the classification result with the information in the UAV signal characteristic database to finally obtain the UAV signal type.

[0027] Generally speaking, compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0028] The present invention extends image processing from the field of signal processing to the field of image processing. There are more ways to process data and it has a wider application. The calculation results during the processing are what you see is what you get. For the situation where the human eye can recognize the signal but the system cannot detect it, it can quickly locate the parameters or logical problems in the algorithm. The present invention further preferably accumulates the method of judging and screening stable areas during the image information processing process, effectively reducing the interference of noise in the single-frame area and improving the detection accuracy in the complex electromagnetic environment. Description of the Drawings

[0029] Figure 1 It is a schematic flowchart of the UAV signal detection method provided by the embodiment of the present invention

[0030] Figure 2 It is a detailed flowchart of the UAV signal detection method provided by the embodiment of the present invention;

[0031] Figure 3 It is a segmentation result diagram provided by the embodiment of the present invention;

[0032] Figure 4 It is a clustering processing result provided by the embodiment of the present invention;

[0033] Figure 5 This is the module diagram of the UAV signal detection system provided by the embodiment of the present invention. Detailed implementation manners

[0034] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0035] As Figure 1 shown, the embodiment of the present invention provides a UAV signal detection method based on frequency-domain images, including the following steps:

[0036] 1. Perform STFT processing on the radio time-domain signal at a specified sampling rate and sliding size to obtain a UAV signal spectrum waterfall diagram;

[0037] 2. According to the relevant specifications of the civil UAV power range, specify the maximum value pix max and the minimum value pix min of the frequency intensity, set the intensity outside the maximum and minimum value ranges in the spectrum waterfall diagram to 0, and perform normalization processing within the range according to the formula round(255*(pix - pix min ) / (pix max - pix min )) so as to convert the signal intensity information in the spectrum waterfall diagram into frequency-domain image information for applying image processing methods to identify UAV signals;

[0038] 3. Accumulate and collect N frames of UAV frequency-domain images and store them in a queue, perform cumulative averaging on all the images in the queue to obtain an image mean matrix, and then calculate the mean pix avg of the mean image matrix;

[0039] 4. Extract the difference operation between the image at the end of the image queue and the mean image in turn, set the part where the difference is greater than the threshold T to 0, and vice versa to 255, so as to obtain a binary difference map;

[0040] 5. Perform an AND operation on the obtained difference map, and the part that is not 0 in the result of the AND operation can be considered as the stable region in the frequency-domain image;

[0041] 6. Calculate the average value of the eight-connected region image of the difference map. As Figure 2 shown, when the average value is greater than the mean pix avg of the mean image pixels, mark this region as the foreground region, and vice versa as the background region;

[0042] 7. Retrieve the image data at the end of the queue, and send the obtained foreground region and background region into the OpenCV graph cut function grub-cut as input to obtain the final accurate spectrum signal segmentation result, i.e., the UAV spectrum signal region;

[0043] 8. As Figure 3 shown, after marking the connected components of the segmented result, perform k-means two-class clustering processing;

[0044] 9. Compare the information such as the center frequency, bandwidth, and action time of the spectrum data in the classification result with the database information to finally obtain the wireless point signal type.

[0045] As Figure 4 shown, an embodiment of the present invention provides a UAV signal detection system based on frequency-domain images. The system includes an antenna array module, a signal receiving module, an array signal processing module, a signal conversion module, an image information processing module, a network communication module, and a power supply module;

[0046] The antenna array module receives radio information, and then passes the information into the array signal processing module through the signal receiving module. Finally, the array signal processing module processes the radio information, extracts useful signal characteristics and information. The enhanced radio signal is converted into image information through the signal conversion module. The image information processing module processes and determines the image information, and outputs the processed and determined result through the network communication module. The power supply module is electrically connected to other modules for supplying power to other modules.

[0047] The signal conversion module performs STFT processing on the received radio time-domain signal according to the preset values of the sampling rate and the sliding size to obtain the spectrum waterfall diagram of the UAV signal, and converts the signal intensity information in the spectrum waterfall diagram into frequency-domain image information.

[0048] The image information processing module is used to mark the foreground region and background region of the frequency-domain image information, segment the frequency-domain image information according to the region marking to obtain the UAV spectrum signal region diagram, mark the connected regions of the UAV spectrum signal region diagram, and perform classification processing.

[0049] The image information processing module is used to compare the characteristic information of the frequency-domain image information in the classification result with the information in the UAV signal characteristic database to finally obtain the UAV signal type.

[0050] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting UAV signals based on frequency-domain images, characterized in that, Including the following steps: (1) Signal detection and conversion step: According to the preset values of the sampling rate and the sliding size, preprocess the received radio time-domain signal to obtain frequency-domain image information; (2) Image information processing step: Mark the foreground area and the background area of the frequency-domain image information; (3) Image information discrimination step: Segment the frequency-domain image information according to the region markings to obtain a UAV spectrum signal region map; (4) Signal type determination step: Mark the connected regions of the UAV spectrum signal region map, perform classification processing, compare the characteristic information of the frequency-domain image information in the classification result with the information in the UAV signal feature database, and finally obtain the UAV signal type; The step (2) includes the following sub-steps: (2.1) Accumulate the frequency-domain image information of the preset value quantity, store it in a queue, perform cumulative averaging on all the frequency-domain image information in the queue to obtain an image mean matrix, and calculate the mean of the image mean matrix; (2.2) Sequentially extract the frequency-domain image information at the end of the queue and perform a difference operation with the mean of the image mean matrix, compare the difference with the preset threshold to obtain a difference map of the binarized frequency-domain image; (2.3) Perform an AND operation on the obtained difference map, and the non-zero part in the AND result is used as the connected region in the frequency-domain image; (2.4) Calculate the average value of the connected region image of the difference map, perform region marking. When the average value is greater than the mean of the image mean matrix, mark this region as the foreground region, otherwise mark it as the background region.

2. The method for detecting a drone signal according to claim 1, wherein The step (1) includes the following sub-steps: (1.1) According to the preset values of the sampling rate and the sliding size, perform STFT processing on the received radio time-domain signal to obtain a spectrum waterfall map of the UAV signal; (1.2) Convert the signal intensity information in the spectrum waterfall map into frequency-domain image information.

3. The drone signal detection method according to claim 2, wherein The information conversion method in the step (1.2) is as follows: according to the relevant specifications of the UAV power range, preset the maximum value and the minimum value . Set the intensity outside the maximum and minimum values in the spectrogram waterfall diagram to 0, and perform normalization processing according to the formula for the intensity within the range, so as to convert the signal intensity information in the spectrogram waterfall diagram into frequency domain image information.

4. The method for detecting a drone signal according to claim 1, wherein, In the step (3) image information discrimination step, the segmentation includes: Take out the frequency-domain image information at the end of the queue, use its marked region as the input of the OpenCV graph cut function grub-cut, and obtain the segmentation result.

5. The drone signal detection method according to claim 1, wherein In the step (4) signal type determination step, the classification processing method is the k-means clustering method.

6. An unmanned aerial vehicle signal detection system based on a frequency-domain image, characterized in that, This system includes an antenna array module, a signal receiving module, an array signal processing module, a signal conversion module, an image information processing module, a network communication module, and a power supply module; The antenna array module receives radio information, then transmits the information to the array signal processing module through the signal receiving module, and finally processes the radio information through the array signal processing module to extract useful signal characteristics and information. The enhanced radio signal is converted into image information through the signal conversion module. The image information processing module processes and determines the image information, and outputs the processed and determined results through the network communication module. The power supply module is electrically connected to other modules and is used to supply power to other modules; The image information processing module is specifically used for: Accumulate the frequency-domain image information of the preset value quantity, store it in a queue, perform cumulative averaging on all the frequency-domain image information in the queue to obtain an image mean matrix, and calculate the mean of the image mean matrix; Extract the frequency-domain image information at the end of the queue in sequence and perform a difference operation with the mean of the image mean matrix, and compare the difference with a preset threshold to obtain a difference map of the binarized frequency-domain image; Perform an AND operation on the obtained difference map, and the non-zero part in the result of the AND operation is used as the connected region in the frequency-domain image; Calculate the average value of the connected region image of the difference map and perform region marking. When the average value is greater than the mean of the image mean matrix, mark this region as the foreground region, and vice versa as the background region.

7. The drone signal detection system according to claim 6, characterized in that, The signal conversion module performs STFT processing on the received radio time-domain signal according to the preset values of the sampling rate and the sliding size to obtain the spectral waterfall diagram of the UAV signal, and converts the signal intensity information in the spectral waterfall diagram into frequency-domain image information.

8. The drone signal detection system according to claim 6, wherein, The image information processing module is used to mark the foreground region and the background region of the frequency-domain image information, segment the frequency-domain image information according to the region marking to obtain the UAV spectral signal region diagram, mark the connected region of the UAV spectral signal region diagram, and perform classification processing.

9. The UAV signal detection system according to claim 8, characterized in that, The image information processing module is used to compare the feature information of the frequency-domain image information in the classification result with the information in the UAV signal feature database to finally obtain the UAV signal type.

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