Periodic diagram and incoming wave direction finding fused multi-unmanned aerial vehicle signal identification method and system

Abstract

The invention discloses a periodogram and incoming wave direction finding fused multi-unmanned aerial vehicle signal identification method and system, and relates to a signal processing technology, and the method comprises the steps: obtaining to-be-identified signals with time domain aliasing and frequency domain non-aliasing; suppressing the noise of the to-be-identified signal by using an average periodogram method and smooth filtering, and calculating a bottom noise value; according to the calculated bottom noise value, setting a judgment threshold to detect a target unmanned aerial vehicle signal; estimating the start-stop frequency and bandwidth of the detected target unmanned aerial vehicle signal; performing signal separation by using an FIR filter according to the estimated starting and ending frequency and bandwidth; and executing incoming wave lateral direction based on the separated target unmanned aerial vehicle signal so as to complete signal identification. According to the method, unmanned aerial vehicle signal detection is completed through an average periodogram method, unmanned aerial vehicle weak signals which are sparsely distributed in space are further obtained through a direction finding method, and weak signal unmanned aerial vehicles which are far in distance are detected.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a multi-UAV signal recognition method and system that integrates periodogram and incoming wave direction finding. Background Art

[0002] The illegal flying of drones poses a serious threat to public safety. Traditional drone radio detection first detects drone signals, then distinguishes drones through a "black and white list" to determine whether the current drone is illegally flying. When there are multiple drones at the same time, it is necessary to first correctly detect the number of drones in the current airspace, and then further determine which drone or drones are illegally flying in the current airspace.

[0003] Currently, drone radio detection on the market uses power spectrum estimation, which has poor detection of weak signals at a long distance. Some drone detection equipment also uses direction finding methods such as amplitude comparison direction finding and interferometer direction finding. The detection and identification effect is relatively obvious for multiple drones with sparse spatial distribution, but it cannot distinguish drones with dense spatial distribution and close distance. Summary of the invention

[0004] The embodiments of the present application provide a multi-UAV signal recognition method and system that integrates periodogram and incoming wave direction finding. UAV signal detection is completed by averaging the periodogram method, and weak UAV signals with relatively sparse spatial distribution are further obtained by direction finding, thereby realizing the detection of weak signal UAVs with long distances.

[0005] The present application embodiment provides a multi-UAV signal recognition method by fusing a periodogram with an incoming wave direction finding, comprising:

[0006] Obtain the signal to be identified which is aliased in the time domain but not aliased in the frequency domain;

[0007] The average periodogram method and smoothing filter are used to suppress noise for the signal to be identified, and the noise floor value is calculated;

[0008] According to the calculated background noise value, the decision threshold is set to detect the target drone signal;

[0009] Estimate the start and end frequencies and bandwidth of the detected target drone signals;

[0010] Signal separation is performed using FIR filters based on the estimated start and end frequencies and bandwidth;

[0011] Based on the separated target drone signal, the incoming wave is lateralized to complete the signal recognition.

[0012] Optionally, the signal to be identified is subjected to noise suppression using an average periodogram method and smoothing filtering, including:

[0013] Divide the signal x(n) to be identified into multiple signal segments, and each signal segment has the same length;

[0014] Calculate the periodogram of any signal segment, and perform power spectrum analysis based on the calculated periodogram using the Bartlett averaging method to obtain the power spectrum M(k);

[0015] The power spectrum M(k) is smoothed to obtain the smoothed power spectrum G(k).

[0016] Optionally, according to the calculated background noise value, setting a decision threshold to detect the target drone signal includes:

[0017] Based on the smoothed power spectrum G(k), an iterative algorithm is used to find the approximate value of the noise amplitude in the frequency domain to calculate the decision threshold;

[0018] According to the calculated decision threshold, if there is a point greater than the decision threshold in the detection frequency domain within the bandwidth, it is determined that the target drone signal exists.

[0019] Optionally, estimating the start and end frequencies and bandwidth of the detected target drone signal includes:

[0020] For any target drone signal, calculate the start and end frequencies of each signal to detect the number of signals; and,

[0021] Estimate the bandwidth and center frequency of each target drone signal.

[0022] Optionally, signal separation using an FIR filter based on the estimated start and end frequencies and bandwidth includes:

[0023] A filter with a corresponding bandwidth is set according to the estimated bandwidth and center frequency of each target UAV signal;

[0024] The filters are spliced ​​together to use the spliced ​​filters to sort the target drone signals.

[0025] Optionally, the following steps are performed based on the separated target drone signal to perform the following signal identification:

[0026] According to the number N of antennas receiving the signal to be identified, the angle θ of adjacent antennas is determined. s =360° / N, the azimuth direction of each antenna is:

[0027] F i (θ) = F(θ-iθ s )i=0,1,…,N-1

[0028] The signal received by each antenna undergoes its own amplitude response K iThe receiving channel of the output pulse has a logarithmic envelope signal of:

[0029] s i =lg[KF(θ-iθ s )A(t)]

[0030] Where A(t) is the amplitude modulation of the signal.

[0031] Optionally, performing the incoming wave lateralization based on the separated target drone signal to complete the signal identification also includes:

[0032] Let the antenna pattern be symmetrical in amplitude direction, that is, F(θ) = F(-θ);

[0033] When the signal direction is between any two antennas and deviates from the equal signal direction of the two antennas, the angle is The corresponding channel output signals S1(t), S2(t) are:

[0034]

[0035] After subtraction, the logarithmic voltage ratio R is:

[0036]

[0037] When K1=K2, we can get:

[0038] or

[0039] Among them, θ r is the half-power beamwidth of F(θ);

[0040] The incident angle of the target UAV signal calculated based on the measured power ratio R Complete signal recognition.

[0041] An embodiment of the present application also proposes a multi-UAV signal recognition system that integrates a periodogram and incoming wave direction finding, including a processor and a memory, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the multi-UAV signal recognition method that integrates a periodogram and incoming wave direction finding as described above are implemented.

[0042] The embodiment of the present application completes the drone signal detection through the method of averaging periodogram, and further obtains the weak drone signal with relatively sparse distribution in space through the direction finding method, so as to realize the detection of weak signal drones with long distance.

[0043] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0045] Figure 1 This is a basic flow diagram of the multi-UAV signal recognition method according to an embodiment of the present application;

[0046] Figure 2 This is a schematic diagram of the signal detection process of the multi-UAV signal recognition method according to an embodiment of the present application;

[0047] Figure 3 This is a schematic diagram of the signal separation process of the multi-UAV signal recognition method according to an embodiment of the present application;

[0048] Figure 4 This is a schematic diagram of a 127-order bandpass filter with a bandwidth of 20M used in the application example of this application;

[0049] Figure 5 This is a schematic diagram of a 255-order bandpass filter with a bandwidth of 10M for an application example of this application;

[0050] Figure 6 This is an illustration of the 20M signal bandwidth of the drone image transmission used in this application example;

[0051] Figure 7 This is a schematic diagram of the 10M signal bandwidth of the drone image transmission used in this application example;

[0052] Figure 8 This is a schematic diagram of the amplitude direction of adjacent antennas in an application example of this application. DETAILED DESCRIPTION

[0053] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0054] The embodiment of the present application provides a multi-UAV signal recognition method that integrates periodogram and incoming wave direction finding. The signal separation and detection process of the present application is as follows: Figure 1 As shown, the time-domain aliased signal and the frequency-domain unaliased signal are subjected to noise suppression by the average periodogram method and smoothing filtering, and then the noise floor value is calculated, and the decision threshold is set according to the noise floor value to detect the signal. If there is a signal, the start and end frequencies and bandwidth of each signal are estimated, and the FIR filter is designed according to the start and end frequencies. The signal is separated after passing through the filter. The specific method of the embodiment of the present application includes the following steps:

[0055] In step S101, a signal to be identified that is aliased in the time domain and not aliased in the frequency domain is obtained, for example, a drone signal to be identified is obtained.

[0056] In step S102, the average periodogram method and smoothing filtering are used to suppress noise of the signal to be identified, and the noise floor value is calculated.

[0057] In step S103, according to the calculated noise floor value, a decision threshold is set to detect the target drone signal;

[0058] In step S104, the start and end frequencies and bandwidth of the detected target drone signal are estimated;

[0059] In step S105, a FIR filter is used to separate the signal according to the estimated start and end frequencies and bandwidth;

[0060] In step S106, the incoming wave lateralization is performed based on the separated target drone signal to complete signal recognition.

[0061] The embodiment of the present application completes the drone signal detection through the method of averaging periodogram, and further obtains the weak drone signal with relatively sparse distribution in space through the direction finding method, so as to realize the detection of weak signal drones with long distance.

[0062] In some embodiments, Figure 2 As shown, the average periodogram method and smoothing filtering are used to suppress noise for the signal to be identified, including:

[0063] The signal to be identified x(n) is divided into multiple signal segments, and each signal segment has the same length. For example, in some examples, N=32768*8 data are taken out for the signal to be identified, and N is divided into 8 segments, each segment having Ns=32768 points.

[0064] Calculate the periodogram of any signal segment, and perform power spectrum analysis based on the calculated periodogram using the Bartlett averaging method to obtain the power spectrum M(k). The basic principle of the specific periodogram is to directly perform Fourier transform on the observed data, and then take the square of the modulus to obtain the power spectrum. In the specific example, take a finite number of observation points x(0), x(1), ....x(n-1) of a stationary random signal x(n), then the Fourier transform is:

[0065]

[0066] Perform spectral estimation:

[0067]

[0068] In order to reduce random fluctuations, the N-point signal sequence x(n) is first divided into L segments, each segment is M in length.

[0069] Calculate the periodogram for each segment, and then take the average of each periodogram as the estimate of the power spectrum. The power spectrum value is:

[0070]

[0071] The power spectrum M(k) is smoothed to obtain the smoothed power spectrum G(k).

[0072] In some embodiments, according to the calculated background noise value, setting a decision threshold to detect the target drone signal includes:

[0073] Based on the smoothed power spectrum G(k), an iterative algorithm is used to find the approximate value of the noise amplitude in the frequency domain to calculate the decision threshold. Specifically, the Filter function in Matlab can be used to filter the power spectrum value data to obtain the initial threshold value.

[0074] According to the calculated decision threshold, if there is a point greater than the decision threshold in the detection frequency domain within the bandwidth, it is determined that the target drone signal exists. Specifically, the embodiment of the present application is aimed at multi-signal detection without aliasing in the frequency domain, and adopts the frequency domain peak detection method to increase the detection of the number of signals and the rough estimation of the bandwidth and start and end frequencies of each signal.

[0075] In some embodiments, estimating the start and end frequencies and bandwidth of the detected target drone signal includes:

[0076] For any target drone signal, calculate the start and end frequencies of each signal to detect the number of signals; and,

[0077] Estimate the bandwidth and center frequency of each target drone signal.

[0078] In some embodiments, Figure 3As shown, signal separation using FIR filter based on estimated start and end frequencies and bandwidth includes:

[0079] A filter with a corresponding bandwidth is set according to the estimated bandwidth and center frequency of each target UAV signal;

[0080] For multiple signals that are not aliased in the input frequency domain, the premise for identifying the modulation mode of each signal is to separate these signals that are not aliased in the frequency domain. The signal separation method is mainly to separate each signal from the mixed signal by dynamically designing a filter.

[0081] For example Figure 4 , Figure 5 As shown in the figure, the signal with a bandwidth of 20MHz is directly filtered out by a 127-order bandpass filter, and the signal with a bandwidth of 10MHz is filtered out by a 255-order bandpass filter after resampling. The FIR bandpass filter is mainly used for signal separation. On the basis of roughly estimating the start and end frequencies of the signal, the FIR bandpass filter is reasonably designed according to the signals of different bandwidths to achieve the separation of multiple signals without aliasing in the frequency domain, thereby achieving the purpose of identifying multiple drone signals.

[0082] The filters are spliced ​​together to sort the target drone signals using the spliced ​​filters. Figure 4 , Figure 5 , multiple 10M or 20M bandwidth filters are spliced ​​together to achieve frequency domain non-aliasing signal sorting.

[0083] like Figure 6 , Figure 7 As shown, currently in the 2.4G or 5.8G frequency band, the modulation method of most drone data or image transmission is OFDM, which contains multiple subcarriers. The subcarriers are orthogonal to each other, and the total signal bandwidth generally does not exceed 20M.

[0084] After AD sampling, the signal bandwidth is estimated using the Bartlett averaging method, and the start and end frequencies of the drone signal are sorted through a polyphase filter. Then, the received drone data and image transmission signals without frequency aliasing are judged one by one until all drone signals within the drone detection range are detected and sorted.

[0085] On this basis, the amplitude comparison direction finding method is used to find the direction of the incoming wave signal. In some embodiments, performing the incoming wave lateral direction finding based on the separated target drone signal to complete the signal identification includes:

[0086] According to the number N of antennas receiving the signal to be identified, the angle θ of adjacent antennas is determined. s =360° / N, Figure 8The figure shows the principle of a 4-antenna omnidirectional amplitude single pulse direction finding. The azimuth directions of the antennas in the figure are:

[0087] F i (θ) = F(θ-iθ s )i=0,1,…,N-1

[0088] The signal received by each antenna undergoes its own amplitude response K i The receiving channel of the output pulse has a logarithmic envelope signal of:

[0089] s i =lg[KF(θ-iθ s )A(t)]

[0090] Where A(t) is the amplitude modulation of the signal. After the signal is sent to the signal processor, the signal processor can generate an angle estimate corresponding to the pulse.

[0091] For the same signal, there is always a pair of adjacent beams that output the strongest and second strongest signals respectively. By comparing the relative size of the envelope amplitude of the output signals of this pair of adjacent beams, the direction of the incoming wave can be determined. Regardless of the number of antennas, their working principles are basically similar. The difference is that the direction finding accuracy and the number of equipment increase with the increase of antennas. In some embodiments, performing the lateral direction of the incoming wave based on the separated target drone signal to complete the signal identification also includes:

[0092] Let the antenna pattern be symmetrical in amplitude direction, that is, F(θ) = F(-θ), and assume that the antenna pattern satisfies Figure 8 The amplitude direction symmetry is shown.

[0093] When the signal direction is between any two antennas and deviates from the equal signal direction of the two antennas, the angle is The corresponding channel output signals S1(t), S2(t) are:

[0094]

[0095] After subtraction, the logarithmic voltage ratio R is:

[0096]

[0097] When K1=K2, we can get:

[0098] or

[0099] Among them, θ r is the half-power beamwidth of F(θ);

[0100] The incident angle of the target UAV signal calculated based on the measured power ratio R Complete signal identification. Specifically, according to the above formula, it can be used as a reference for other antenna functions to measure adjacent amplitude ratio angles. That is, the incident signal angle is calculated based on the measured power ratio R.

[0101] The method of the present application uses the average periodogram method to complete the detection of all signals in the airspace where multiple drones are located, and then obtains the weak signals of drones with relatively sparse spatial distribution through the direction finding method. Through this method, weak signal drones with long distances can be detected. The above method realizes accurate detection of drone signals through judgments in different dimensions of airspace and frequency domain.

[0102] An embodiment of the present application also proposes a multi-UAV signal recognition system that integrates a periodogram and incoming wave direction finding, including a processor and a memory, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the multi-UAV signal recognition method that integrates a periodogram and incoming wave direction finding as described above are implemented.

[0103] It should be noted that in the various embodiments of the present application, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0104] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0105] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0106] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A multi-UAV signal recognition method based on periodogram and incoming wave direction finding fusion, characterized in that: include: Obtain the signal to be identified which is aliased in the time domain but not aliased in the frequency domain; The average periodogram method and smoothing filter are used to suppress noise for the signal to be identified, and the noise floor value is calculated; According to the calculated background noise value, the decision threshold is set to detect the target drone signal; Estimate the start and end frequencies and bandwidth of the detected target drone signals; Signal separation is performed using FIR filters based on the estimated start and end frequencies and bandwidth; Based on the separated target drone signal, the incoming wave is lateralized to complete the signal recognition.

2. The multi-UAV signal recognition method of the periodogram and incoming wave direction finding fusion as claimed in claim 1 is characterized in that: The use of average periodogram method and smoothing filtering to suppress noise on the signal to be identified includes: Divide the signal x(n) to be identified into multiple signal segments, and each signal segment has the same length; Calculate the periodogram of any signal segment, and perform power spectrum analysis based on the calculated periodogram using the Bartlett averaging method to obtain the power spectrum M(k); The power spectrum M(k) is smoothed to obtain the smoothed power spectrum G(k).

3. The multi-UAV signal recognition method of the periodogram and the incoming wave direction finding fusion as claimed in claim 2 is characterized in that: According to the calculated noise floor value, the decision threshold is set to detect the target drone signal including: Based on the smoothed power spectrum G(k), an iterative algorithm is used to find the approximate value of the noise amplitude in the frequency domain to calculate the decision threshold; According to the calculated decision threshold, if there is a point greater than the decision threshold in the detection frequency domain within the bandwidth, it is determined that the target drone signal exists.

4. The multi-UAV signal recognition method of the periodogram and the incoming wave direction finding fusion as claimed in claim 3 is characterized in that: The estimated start and end frequencies and bandwidth of the detected target drone signal include: For any target drone signal, calculate the start and end frequencies of each signal to detect the number of signals; and, Estimate the bandwidth and center frequency of each target drone signal.

5. The multi-UAV signal recognition method of the periodogram and incoming wave direction finding fusion as claimed in claim 4 is characterized in that: Signal separation using FIR filters based on estimated start and end frequencies and bandwidth includes: A filter with a corresponding bandwidth is set according to the estimated bandwidth and center frequency of each target UAV signal; The filters are spliced ​​together to use the spliced ​​filters to sort the target drone signals.

6. The multi-UAV signal recognition method of the periodogram and incoming wave direction finding fusion as claimed in claim 4 is characterized in that: Based on the separated target drone signal, the incoming wave lateralization is performed to complete the signal identification including: According to the number N of antennas receiving the signal to be identified, the angle θ of adjacent antennas is determined. s =360° / N, the azimuth direction of each antenna is: F i (θ)=F(θ-iθ s ) i=0,1,...,N-1 The signal received by each antenna undergoes its own amplitude response K i The receiving channel of the output pulse has a logarithmic envelope signal of: s i =lg[KF(θ-iθ s )A(t)] Where A(t) is the amplitude modulation of the signal.

7. The multi-UAV signal recognition method of the periodogram and incoming wave direction finding fusion as claimed in claim 6 is characterized in that: Based on the separated target drone signal, the following steps are performed to perform the following signal identification: Let the antenna pattern be symmetrical in amplitude direction, that is, F(θ) = F(-θ); When the signal direction is between any two antennas and deviates from the equal signal direction of the two antennas, the angle is The corresponding channel output signals S1(t), S2(t) are: After subtraction, the logarithmic voltage ratio R is: When K1=K2, we can get: or Among them, θ r is the half-power beamwidth of F(θ); The incident angle of the target UAV signal calculated based on the measured power ratio R Complete signal recognition.

8. A multi-UAV signal recognition system integrating periodogram and incoming wave direction finding, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the multi-UAV signal recognition method according to any one of claims 1 to 7 are implemented by fusing the periodogram with the incoming wave direction finding.

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