Non-cooperative unmanned aerial vehicle image transmission signal detection method

Abstract

The invention discloses a non-cooperative unmanned aerial vehicle image transmission signal detection method. The method comprises the steps of obtaining a to-be-detected signal x (n); performing segmentation processing and segmentation cancellation processing on the to-be-detected signal to obtain a cancellation ratio r (1), judging the r (1), forming a judgment array R (1) by a judgment result, and further performing target coarse detection to obtain a coarse detection result; the content of the coarse detection result comprises whether a target signal exists in the specified frequency band; if the target information exists in the specified frequency band, performing short-time Fourier transform on the to-be-detected signal x (n), converting the to-be-detected signal x (n) into a time-frequency domain, intercepting frequency band data in the time-frequency domain as a shaping frequency band, and performing square wave shaping; performing cross-correlation operation on a square wave shaping result and a local standard square wave, and judging whether an unmanned aerial vehicle image transmission signal exists or not according to an operation result. According to the technical scheme, the image transmission signal of the non-cooperative low-slow small unmanned aerial vehicle which adopts a non-public protocol and has a periodic burst characteristic can be quickly and accurately detected.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic reconnaissance, and in particular, to a method for detecting non-cooperative UAV video transmission signals. Background Art

[0002] Unmanned aerial vehicles (UAVs) are developing towards high speed, small size, and miniaturization. UAV swarms have strong battlefield survival and penetration capabilities. With the development of materials science, UAVs' ability to avoid radar and infrared detection and reduce noise has been greatly enhanced, and it is difficult for a complete range of air defense systems to intercept UAVs effectively. Among them, the survival space and detection performance of active detection systems face severe challenges. Passive detection technology can achieve stealth detection and first-strike attack, is not limited by UAV materials, and has the advantages of low cost and reconfigurability.

[0003] The existing UAV detection technologies based on passive detection methods mainly include the following three: First, for UAVs using open protocols, mainly the Wi-Fi protocol, UAV signal detection is carried out through protocol matching. This method is only limited to UAVs with known protocols; Second, for UAVs with unknown protocols, UAV signal detection is carried out through the method of blind parameter estimation. This method requires a large amount of prior information and has a large amount of calculation; Third, for UAVs with unknown protocols, the motion characteristics of UAVs are used to determine whether a UAV appears. This method has poor effects when the UAV is flying at a long distance, low speed, or in a silent state. Generally speaking, there are significant limitations in the current non-cooperative UAV detection technologies based on passive detection methods.

[0004] Therefore, there is an urgent need for a detection method that can adapt to complex electromagnetic environments, is fast and accurate, and is applicable to UAV video transmission signals with unknown communication protocols, to provide a solution for countering low, slow, and small UAVs. Summary of the Invention

[0005] To achieve the above object, the present application provides a method for detecting non-cooperative UAV video transmission signals, including the following steps:

[0006] Obtain the signal x(n) to be detected;

[0007] Perform segmented processing and segmented cancellation processing on the signal to be detected to obtain the cancellation ratio r(l); make a decision on the cancellation ratio r(l), and form a decision array R(l) with the decision results;

[0008] Perform rough target detection according to the decision array R(l) to obtain the rough detection result; the content of the rough detection result includes: whether there is a target signal in the specified frequency band;

[0009] If there is target information in the specified frequency band, perform short-time Fourier transform on the signal x(n) to be detected to convert it to the time-frequency domain, and determine the bandwidth, starting frequency point, and ending frequency point occupied by the target signal;

[0010] Intercept the frequency band data in the time-frequency domain according to the bandwidth, starting frequency point and cut-off frequency point as the shaping frequency band for square wave shaping;

[0011] Perform cross-correlation operation on the square wave shaping result and the local standard square wave, and judge whether there is a UAV video transmission signal according to the operation result.

[0012] Among them, the segmented processing includes: intercepting U frames from the signal to be detected x(n), and the number of signal points per frame is M; calculating the power spectrum F u (k) of each frame of data x u (n), and the calculation method is:

[0013] The segmented cancellation processing refers to: smoothing the power spectrum F u (k) to generate the power spectrum F ave (k), and the smoothing processing method is:

[0014] Furthermore, divide the power spectrum F ave (k) into L segments, and each segment contains Q spectral lines; among them,

[0015] Calculate the sum of all spectral lines F ave (k) of the power spectrum F all and the sum of the intensities of each segment of spectral lines F l ;

[0016] Perform segmented cancellation calculation to obtain the cancellation ratio r(l), which is expressed as:

[0017] Among them, the calculation method of the sum of all spectral lines F all is:

[0018] The sum of the intensities of each segment of spectral lines F l is calculated as:

[0019] Furthermore, the judgment of r(l) refers to: comparing the ratio r(l) with the detection threshold. If r(l) is greater than or equal to the detection threshold, assign the judgment array R(l) to 1; otherwise, assign R(l) to 0.

[0020] If R(l) = 0, there is no target signal in the specified frequency band; otherwise, if the number of segments with R(l) = 1 is greater than the specified value, there is no target signal in the specified frequency band; otherwise, there is a target signal in the specified frequency band.

[0021] Among them, the method of converting to the time-frequency domain is:

[0022] Among them, \(t\) is the frequency component in the transformed time-frequency domain, \(\Omega\) is the frequency component in the transformed time-frequency domain, \(\tau\) is the time variable when the window function is multiplied by the signal, and \(e\) -jΩτ is the basis function of the short-time Fourier transform, and the window function \(g(\tau)\) uses the Hann window with relatively fast sidelobe attenuation.

[0023] Square wave shaping means: binaryizing the accumulated result of the shaping frequency band level data into a square wave, setting it to 1 when the accumulated value is greater than the threshold, and setting it to -1 when the accumulated value is less than the threshold;

[0024] Among them, the method for determining the threshold is: arranging the accumulated result of the level data in descending order, and finding the median value between the high level and the low level in the sorted result as the threshold.

[0025] Furthermore, judging whether there is a UAV video transmission signal according to the operation result means: in the operation result, when the number of peaks is within the range threshold, there is a UAV video transmission signal.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1) It can quickly and accurately detect non-cooperative low-slow-small UAV video transmission signals with periodic burst characteristics using non-public protocols, such as UAVs using Lightbridge video transmission technology. And the UAV video transmission signal is still effective when the signal strength is -63 dBm;

[0028] 2) In the broadband signal detection process based on the power cancellation algorithm, the decision threshold has nothing to do with the level and variance of the node noise, and can effectively overcome the influence of noise uncertainty on the detection performance;

[0029] 3) In the process of discriminating UAV video transmission signals, the square wave shaping threshold is adjusted in real time according to the signal strength, further reducing the influence brought by the change of the electromagnetic environment;

[0030] 4) Combining the information of time and frequency in the time-frequency domain of the signal for signal analysis, eliminating the interference of interference signals such as Bluetooth, Zigbee, and Wi-Fi on the detection result, and improving the anti-interference ability of the method;

[0031] 5) The present invention is effective when the UAV video transmission signal strength is as low as -63 dB. Brief Description of the Drawings

[0032] Figure 1 is a flowchart of a non-cooperative UAV video transmission signal detection method provided according to an embodiment of the present invention;

[0033] Figure 2 is the power spectrum segmented cancellation algorithm process during the detection process provided according to an embodiment of the present invention;

[0034] Figure 3 It is a schematic diagram of the smoothing results of the signal power spectra of three signal power levels of -40 dBm, -52 dBm, and -63 dBm provided according to an embodiment of the present invention;

[0035] Figure 4 It is a statistical histogram of the segmented ratio of the signal power spectra of three signal intensities of -40 dBm, -52 dBm, and -63 dBm provided according to an embodiment of the present invention;

[0036] Figure 5 It is a schematic diagram of the time-domain waveform of the signal to be detected, the signal power spectrum, the smoothing processing result, and the broadband signal detection result provided according to an embodiment of the present invention;

[0037] Figure 6 It is a result diagram of the short-time Fourier transform of the signal to be measured provided according to an embodiment of the present invention;

[0038] Figure 7 It is a result diagram of the interception of a specific frequency band provided according to an embodiment of the present invention;

[0039] Figure 8 It is a result diagram of the accumulation of a specific frequency band provided according to an embodiment of the present invention;

[0040] Figure 9 It is a result diagram of the descending order arrangement of the accumulation result and the threshold calculation result provided according to an embodiment of the present invention;

[0041] Figure 10 It is a binarization schematic diagram of the accumulation result of the data in a specific frequency band provided according to an embodiment of the present invention;

[0042] Figure 11 It is a result diagram of the square wave matching provided according to an embodiment of the present invention;

[0043] Figure 12 It is a result diagram of the time-domain waveform of the collected signal, the signal power spectrum, the result after smoothing processing, the result of intercepting a specific frequency band, the result of accumulation and binarization of a specific frequency band, and the result of square wave matching when the intensity of the UAV video transmission signal is -63 dBm provided according to an embodiment of the present invention. Detailed implementation manner

[0044] For non-cooperative UAV video transmission signals, in the case of not knowing the UAV communication protocol, the present invention provides a signal detection method. First, the power segmentation cancellation method is used to roughly detect broadband signals in a specified frequency band. If there is a broadband signal, the short-time Fourier transform is further used to convert the signal to the time-frequency domain, and it is judged whether it is a UAV video transmission signal according to the periodic characteristics of the time-frequency domain.

[0045] The following describes in detail the specific implementation manner of the present invention with reference to the accompanying drawings of the specification.

[0046] Figure 1 The specific steps of the non - cooperative UAV video transmission signal detection method are provided. As shown in the figure, they include:

[0047] Step S100: Obtain the signal x(n) to be detected;

[0048] The present invention provides an embodiment, collecting signals of three different intensities of - 40dBm, - 52dBm, and - 63dBm for 10,000 times of random sampling detection.

[0049] Step S110: Perform segmented processing and segmented cancellation processing on the signal to be detected, obtain the cancellation ratio r(l), make a decision on the r(l), and form a decision array R(l) with the decision results;

[0050] 1) The process of segmented processing includes: intercepting U frames from the signal x(n) to be detected, with the number of signal points per frame being M (for example: M = 4096);

[0051] Calculate the power spectrum F u (k) of each frame of data x u (n). Since the power spectrum has symmetry, only the positive - frequency part is taken in the calculation process. Specifically, the calculation method is:

[0052] 2) After the segmented processing is completed, perform segmented cancellation processing. The specific power - spectrum segmented cancellation algorithm process is as Figure 2 shown:

[0053] First, perform smoothing processing on the power spectrum F u (k) to generate a power spectrum F ave (k). The smoothing processing method is:

[0054] Next, divide the power spectrum F ave (k) into L segments, and each segment contains Q spectral lines; among them, On this basis, calculate the sum of all spectral lines F ave (k) of the power spectrum F all and the sum of the intensities of each segment of spectral lines F l ;

[0055] The calculation method of the sum of all spectral lines F all is:

[0056] The calculation method of the sum of the intensities of each segment of spectral lines F l is:

[0057] Divide the sum of all spectral lines F alland the cumulative sum F of the intensities of each spectral line l Perform segmented cancellation to obtain the cancellation ratio r(l), expressed as:

[0058] In the embodiment of the present invention, the specific implementation process of the power spectrum segmented cancellation algorithm is as follows. Take U = 30 frames of the collected signal and perform non-coherent accumulation to achieve power spectrum smoothing; then the cumulative sum F of all spectral lines all and the cumulative sum F of the intensities of each spectral line l Perform ratio calculation to achieve segmented cancellation.

[0059] 3) Make a decision on r(l): Compare the ratio r(l) with the detection threshold. If r(l) is greater than or equal to the detection threshold, assign the decision array R(l) to 1; otherwise, assign R(l) to 0, expressed as:

[0060]

[0061] In this step, the decision threshold in the power spectrum segmented cancellation algorithm is processed by the Monte Carlo method for three different intensity signals collected. The power spectrum smoothing results of the three signals are as Figure 3 shown;

[0062] The statistical histogram of the power spectrum segmented ratios of the three intensity signals is as Figure 4 shown. The left blue part is the case where there is no video transmission signal, i.e., H0, and the right orange part is the case where there is a video transmission signal, i.e., H1. The false alarm probability and miss detection probability corresponding to different threshold values are shown in Table 1. After statistical analysis, the threshold value is obtained as 0.01565. The cumulative value of the false alarm probability and the miss alarm probability corresponding to this threshold value is the smallest, which is 0.037453. The false alarm probability of this threshold is relatively high, but since after threshold detection, it will be further determined whether it is a video transmission signal, and the misjudgment values caused by false alarms can be eliminated to some extent during the judgment process. For the above two reasons, 0.01565 is selected as the threshold value. It can be seen that this detection threshold has nothing to do with the level and variance of the node noise, so it can effectively overcome the influence of noise uncertainty on the detection performance.

[0063] Table 1 False alarm probability and miss detection probability corresponding to four threshold values

[0064]

[0065] In the embodiment of the present invention, compare the cancellation result with the threshold value of 0.01565 to achieve the detection of wideband signals within the frequency band. The time-domain waveform of the signal to be detected, the signal power spectrum and the result after smoothing processing, and the wideband signal detection result are as Figure 5 shown.

[0066] Step S120: Perform target rough detection based on the decision array R(l) to obtain the rough detection result; the rough detection result includes whether there is a target signal in the specified frequency band;

[0067] Specifically, if R(l) = 0, there is no target signal in the specified frequency band; otherwise, if the number of segments where R(l) = 1 is greater than the specified value, there is also no target signal in the specified frequency band; otherwise, there is a target signal in the specified frequency band.

[0068] If there is target information in the specified frequency band, determine the bandwidth, starting frequency point, and ending frequency point occupied by the target signal; and perform short-time Fourier transform on the signal to be detected x(n) to convert it to the time-frequency domain. Among them, the window function g(τ) uses a hann window with faster side lobe attenuation, which can reduce spectral leakage, lower the side lobe level, reduce the influence of false alarms and interference, and facilitate subsequent interception of time-frequency domain data for further processing.

[0069] The method of converting to the time-frequency domain is:

[0070] where t is the frequency component in the transformed time-frequency domain, Ω is the frequency component in the transformed time-frequency domain, τ is the time variable for multiplying the window function and the signal, and e -jΩτ is the basis function of the short-time Fourier transform.

[0071] In the embodiment, perform short-time Fourier transform on the collected signal, and the window function uses a hann window with the fastest side lobe attenuation. The result after transformation is as Figure 6 shown: It can be seen that the bandwidth of the UAV video transmission signal is fixed and has the characteristic of periodic bursts. The present invention uses this as the judgment basis for UAVs.

[0072] Step S130: Intercept the frequency band data in the time-frequency domain as the shaping frequency band according to the bandwidth, starting frequency point, and ending frequency point, and perform square wave shaping; and further perform cross-correlation operation on the square wave shaping result and the local standard square wave, and judge whether there is a UAV video transmission signal according to the operation result.

[0073] In this step, obtain the starting frequency point, ending frequency point, and center frequency of the broadband signal according to the power spectrum segmentation cancellation algorithm, and intercept the data corresponding to the frequency band in the short-time Fourier transform result of the signal. In the embodiment of the present invention, the interception result of a specific frequency band in the time-frequency domain is as Figure 7 shown. Accumulate the extracted specific frequency band data column by column, and the accumulation result is as Figure 8 shown. It can be seen that when the UAV sends a video transmission signal, the amplitude is about 13×10 11 or so, and the amplitude when not sending is near 0, and the amplitude of the frequency hopping signal is about half of the video transmission signal.

[0074] Among them, square wave shaping means: binaryizing the accumulation result of the shaping frequency band level data into a square wave, setting it to 1 when the accumulation result is greater than the threshold, and setting it to -1 when the accumulation result is less than the threshold.

[0075] Among them, the method for determining the threshold is: sorting the accumulation result of the level data in descending order, and finding the median value of the high level and the low level in the sorting result, that is, taking the average value of the high level and the low level as the threshold. Compared with a fixed threshold, this method can adjust the threshold according to the signal strength and can reduce the influence brought by the change of the electromagnetic environment. In the embodiment, the result of calculating the threshold is shown in Figure 9 as shown, where the threshold is the straight line in the figure.

[0076] The result of square wave shaping is shown in Figure 10 as shown. Subsequently, perform a cross-correlation operation between the shaping result and the local standard square wave. When the signal completely matches the local square wave, a spike close to 1 appears. When the spike does not exist or the number does not meet the conditions, it can be determined that there is no UAV video transmission signal; if the number of spikes is within the specified range, it is determined that there is a UAV video transmission signal.

[0077] According to the embodiment of the present invention, the square wave matching result after performing a cross-correlation operation between the shaping result and the local standard square wave is shown in Figure 11 as shown. Since the cross-correlation result has symmetry, only the second half of the result is taken to reduce the calculation amount. Finally, perform spike search. When 12 ≤ the number of spikes ≤ 16, there is a UAV video transmission signal.

[0078] The UAV video transmission signal detection method provided by the present invention realizes signal detection based on power segment cancellation and short-time Fourier transform, and can also achieve effective results when the UAV video transmission signal strength is -63 dBm. When the UAV video transmission signal strength is -63 dBm, the time-domain waveform of the collected signal, the signal power spectrum and the results after smoothing processing, the intercepted result of the specific frequency band, the accumulation and binaryization result of the specific frequency band, and the square wave matching result are shown in Figure 12 as shown.

[0079] Compared with the prior art, the UAV video transmission signal detection method provided by the present invention can quickly and accurately detect non-cooperative low, slow, and small UAV video transmission signals with periodic burst characteristics using non-public protocols, such as UAVs using Lightbridge video transmission technology, and is still effective when the UAV video transmission signal strength is -63 dBm; in the broadband signal detection process based on the power cancellation algorithm of the present invention, the decision threshold has nothing to do with the level and variance of the node noise, and can effectively overcome the influence of noise uncertainty on the detection performance; in the process of discriminating UAV video transmission signals, the square wave shaping threshold is adjusted in real time according to the signal strength, further reducing the influence brought by the change of the electromagnetic environment; further, by combining the information of the two dimensions of time and frequency in the time-frequency domain of the signal for signal analysis, interference signals such as Bluetooth, Zigbee, and Wi-Fi can be excluded from interfering with the detection result, improving the anti-interference ability; at the same time, the method described in this application is effective when the UAV video transmission signal strength is as low as -63 dB.

[0080] The above are only several specific embodiments of the present invention disclosed, however, the present invention is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A non-cooperative UAV video transmission signal detection method, characterized in that It includes the following steps: Obtain the signal x(n) to be detected; Perform segmentation processing and segmented cancellation processing on the signal to be detected, obtain the cancellation ratio r(l), make a decision on the cancellation ratio r(l), and form the decision result into a decision array R(l); Perform rough target detection according to the decision array R(l) and obtain the rough detection result; The content of the rough detection result includes: whether there is a target signal in the specified frequency band; If there is target information in the specified frequency band, perform short-time Fourier transform on the signal x(n) to be detected to convert it to the time-frequency domain, and determine the bandwidth, starting frequency point, and cut-off frequency point occupied by the target signal; Intercept the frequency band data in the time-frequency domain as the shaping frequency band according to the bandwidth, starting frequency point, and cut-off frequency point, and perform square wave shaping; Perform cross-correlation operation on the square wave shaping result and the local standard square wave, and judge whether there is a UAV video transmission signal according to the operation result.

2. The non-cooperative UAV video transmission signal detection method according to claim 1, wherein The segmentation processing includes: Intercept U frames from the signal x(n) to be detected, and the number of signal points per frame is M; Calculate the data x for each frame u The power spectrum F of (n) u (k), and the calculation method is as follows:

3. The non-cooperative UAV video transmission signal detection method according to claim 2, wherein The segmented cancellation processing refers to: Smoothing the power spectrum F u (k) to generate a power spectrum F ave (k), and the smoothing method is as follows: Divide the power spectrum F ave (k) into L segments, each containing Q spectral lines; where Calculate the power spectrum F ave (k) of all spectral line summations F all and the summation of each segment of spectral line intensities F l ; Perform segmented cancellation calculation to obtain the cancellation ratio r(l), expressed as:

4. The non-cooperative UAV video transmission signal detection method according to claim 3, wherein The sum F of all the spectral lines accumulated all is calculated as follows: The cumulative sum F of the intensities of each spectral line l is calculated as follows:

5. The non-cooperative UAV video transmission signal detection method according to claim 1, characterized in that, Making a decision on the r(l) means: comparing the ratio r(l) with the detection threshold. If r(l) is greater than or equal to the detection threshold, assign the decision array R(l) to 1; otherwise, assign R(l) to 0.

6. The non-cooperative UAV video transmission signal detection method according to claim 1, characterized in that, If R(l)=0, there is no target signal in the specified frequency band; Otherwise, if the number of segments with R(l)=1 is greater than the specified value, there is no target signal in the specified frequency band; otherwise, there is a target signal in the specified frequency band.

7. The non-cooperative UAV video transmission signal detection method according to claim 1, characterized in that The method for converting to the time-frequency domain is: Among them, \(t\) is the frequency component in the transformed time-frequency domain, \(\Omega\) is the frequency component in the transformed time-frequency domain, \(\tau\) is the time variable when the window function is multiplied by the signal, and \(e\) -jΩr is the basis function of the short-time Fourier transform, and the window function \(g(\tau)\) uses the Hann window with relatively fast sidelobe attenuation.

8. The non-cooperative UAV video transmission signal detection method according to claim 1, characterized in that The square wave shaping refers to: binaryizing the accumulated result of the shaping frequency band level data into a square wave. When the accumulated result is greater than the threshold, set it to 1, and when the accumulated value result is less than the threshold, set it to -1; Among them, the method for determining the threshold is: arranging the accumulated result of the level data in descending order, and finding the middle value between the high level and the low level in the sorting result as the threshold.

9. The non-cooperative UAV video transmission signal detection method according to claim 1, wherein The judging whether there is a UAV video transmission signal according to the operation result means: when the number of spikes in the operation result is within the range threshold, there is a UAV video transmission signal.

Images