CN111046025B - Unmanned aerial vehicle signal detection method and device - Google Patents
Unmanned aerial vehicle signal detection method and device Download PDFInfo
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Abstract
The invention discloses a signal detection method and device for an unmanned aerial vehicle, wherein the method comprises the following steps: constructing an unmanned aerial vehicle characteristic parameter database; acquiring a sampling signal and processing the sampling signal into a plurality of sections of signals; calculating to obtain characteristic parameters corresponding to each section of signals remaining after the interference signals are removed from the sections of signals; adding the rest signals of each section into a database to be compared, and grouping the signals belonging to the same unmanned aerial vehicle into a group according to the characteristic parameters corresponding to the rest signals of each section and the characteristic parameters corresponding to the signals stored in the database to be compared; repeating the steps to obtain a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments; and determining the model of each unmanned aerial vehicle based on the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments and the characteristic parameter database of the unmanned aerial vehicle. According to the invention, the unmanned aerial vehicle signal can be detected in real time, and the unmanned aerial vehicle model is determined according to the detected signal.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle signal detection method and device.
Background
In recent years, with the rapid development of unmanned aerial vehicle technology and industry in the low-altitude field, the unmanned aerial vehicle is widely applied and is commonly used in the industries of aerial mapping, disaster relief, monitoring and inspection, environment-friendly detection, electric power overhaul, agricultural plant protection and the like, but the potential safety hazard in low-altitude is also outstanding, so that a large number of unmanned aerial vehicle 'black flight' events frequently occur. Therefore, there is a need for an unmanned aerial vehicle signal detection method for timely detecting unmanned aerial vehicle signals, so as to reduce low-altitude potential safety hazards.
Disclosure of Invention
The main purpose of the present invention is to solve the above-mentioned technical problems existing in the prior art.
In order to achieve the above object, the present invention provides a method for detecting signals of an unmanned aerial vehicle, the method comprising:
step S10, an unmanned aerial vehicle characteristic parameter database is constructed, and the unmanned aerial vehicle model and standard characteristic parameters corresponding to the unmanned aerial vehicle model are stored in the unmanned aerial vehicle characteristic parameter database in an associated mode;
step S20, obtaining a sampling signal, and processing the sampling signal to obtain a plurality of sections of signals;
step S30, determining the signal category of each section of signals in the multi-section signals, eliminating the interference signals when at least one section of signals is the interference signals, and calculating to obtain the characteristic parameters corresponding to each section of signals remaining after the interference signals are eliminated in the multi-section signals;
step S40, adding the rest signals to a database to be compared, and grouping the signals belonging to the same unmanned aerial vehicle into a group according to the characteristic parameters corresponding to the rest signals and the characteristic parameters corresponding to the signals stored in the database to be compared;
step S50, repeating the steps S20 to S40 to obtain a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments;
And step S60, determining the model of each unmanned aerial vehicle based on a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments and the characteristic parameter database of the unmanned aerial vehicle.
Optionally, the step S10 includes:
collecting target signals of unmanned aerial vehicles with known models, wherein the target signals comprise image transmission signals and/or remote control signals;
calculating to obtain standard characteristic parameters corresponding to the target signals;
and storing the model of the unmanned aerial vehicle and the standard characteristic parameters in an unmanned aerial vehicle characteristic parameter database in an associated mode.
Optionally, the step S20 includes:
acquiring a sampling signal, and carrying out power spectrum estimation on the sampling information to obtain a power spectrum;
logarithm is carried out on the power spectrum to obtain a logarithmic spectrum;
carrying out smoothing filter treatment on the logarithmic spectrum, and carrying out statistical average on spectrum data obtained after the smoothing filter treatment to obtain average power;
taking the average power as the background noise power, and superposing a preset signal-to-noise ratio threshold value to obtain a noise power threshold value;
extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal;
calculating the frequency point interval difference corresponding to the effective signal, and dividing and combining the effective signal by utilizing the frequency point interval difference to obtain a multi-section signal.
Optionally, the step S60 includes:
removing the characteristic parameters with low confidence coefficient from the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments to obtain effective characteristic parameters corresponding to each unmanned aerial vehicle;
and determining the model of each unmanned aerial vehicle based on the effective characteristic parameters corresponding to each unmanned aerial vehicle and the unmanned aerial vehicle characteristic parameter database.
Optionally, after the step S60, the method further includes:
selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with determined models;
collecting signals of a plurality of channels of the target unmanned aerial vehicle;
and when the acquisition time reaches a preset duration or the number of the acquired data points reaches a preset number, executing a direction finding algorithm based on the acquired signals to obtain a direction finding result of the target unmanned aerial vehicle.
In addition, in order to achieve the above object, the present invention also provides an unmanned aerial vehicle signal detection device, the device comprising:
the system comprises a construction module, a control module and a control module, wherein the construction module is used for constructing an unmanned aerial vehicle characteristic parameter database, and the unmanned aerial vehicle characteristic parameter database is stored with unmanned aerial vehicle models and standard characteristic parameters corresponding to the unmanned aerial vehicle models in an associated mode;
the segmentation module is used for acquiring sampling signals and processing the sampling signals to obtain a plurality of sections of signals;
The processing module is used for determining the signal category of each section of signal in the multi-section signals, eliminating the interference signal when at least one section of signal is the interference signal, and calculating to obtain the characteristic parameters corresponding to each section of signal which is remained in the multi-section signal after the interference signal is eliminated;
the grouping module is used for adding the remaining signals of each section into a database to be compared, and grouping the signals belonging to the same unmanned aerial vehicle into a group according to the characteristic parameters corresponding to the remaining signals of each section and the characteristic parameters corresponding to the signals stored in the database to be compared;
the repeating module is used for repeating the steps executed by the building module, the dividing module, the processing module and the grouping module to obtain a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments;
and the identification module is used for determining the model of each unmanned aerial vehicle based on the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments and the characteristic parameter database of the unmanned aerial vehicle.
Optionally, the construction module is configured to:
collecting target signals of unmanned aerial vehicles with known models, wherein the target signals comprise image transmission signals and/or remote control signals;
calculating to obtain standard characteristic parameters corresponding to the target signals;
And storing the model of the unmanned aerial vehicle and the standard characteristic parameters in an unmanned aerial vehicle characteristic parameter database in an associated mode.
Optionally, the splitting module is configured to:
acquiring a sampling signal, and carrying out power spectrum estimation on the sampling information to obtain a power spectrum;
logarithm is carried out on the power spectrum to obtain a logarithmic spectrum;
carrying out smoothing filter treatment on the logarithmic spectrum, and carrying out statistical average on spectrum data obtained after the smoothing filter treatment to obtain average power;
taking the average power as the background noise power, and superposing a preset signal-to-noise ratio threshold value to obtain a noise power threshold value;
extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal;
calculating the frequency point interval difference corresponding to the effective signal, and dividing and combining the effective signal by utilizing the frequency point interval difference to obtain a multi-section signal.
Optionally, the identification module is configured to:
removing the characteristic parameters with low confidence coefficient from the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments to obtain effective characteristic parameters corresponding to each unmanned aerial vehicle;
and determining the model of each unmanned aerial vehicle based on the effective characteristic parameters corresponding to each unmanned aerial vehicle and the unmanned aerial vehicle characteristic parameter database.
Optionally, the apparatus further includes:
the direction finding module is used for selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with determined models; collecting signals of a plurality of channels of the target unmanned aerial vehicle; and when the acquisition time reaches a preset duration or the number of the acquired data points reaches a preset number, executing a direction finding algorithm based on the acquired signals to obtain a direction finding result of the target unmanned aerial vehicle.
According to the method, an unmanned aerial vehicle characteristic parameter database is constructed, and the unmanned aerial vehicle model and standard characteristic parameters corresponding to the unmanned aerial vehicle model are stored in the unmanned aerial vehicle characteristic parameter database in an associated mode; acquiring a sampling signal, and processing the sampling signal to obtain a plurality of sections of signals; determining the signal category of each section of signals in the multi-section signals, eliminating the interference signals when at least one section of signals is the interference signals, and calculating to obtain the characteristic parameters corresponding to each section of signals remaining after the interference signals are eliminated in the multi-section signals; adding each remaining section of signals into a database to be compared, and grouping signals belonging to the same unmanned aerial vehicle into a group according to the characteristic parameters corresponding to each remaining section of signals and the characteristic parameters corresponding to the signals stored in the database to be compared; repeating the steps to obtain a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments; and determining the model of each unmanned aerial vehicle based on the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments and the characteristic parameter database of the unmanned aerial vehicle. According to the invention, the unmanned aerial vehicle target signal can be accurately analyzed and the characteristic parameters of the unmanned aerial vehicle target signal can be extracted by adopting the signal processing method combining the time domain and the frequency domain, the characteristic parameter estimation precision is high, the target false detection rate and the omission factor are low, the accuracy is high, the unmanned aerial vehicle target signal can be easily integrated on hardware platforms such as ARM or DSP, the real-time performance is good, the expansibility is good, and meanwhile, a plurality of effective targets can be detected and identified in one processing process in the frequency band to be detected.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for detecting unmanned aerial vehicle signals according to the present invention;
FIG. 2 is a schematic flow chart of constructing a database of unmanned aerial vehicle characteristic parameters in an embodiment of the unmanned aerial vehicle signal detection method of the present invention;
FIG. 3 is a flowchart illustrating another embodiment of a signal detection method of the unmanned aerial vehicle according to the present invention;
FIG. 4 is a schematic diagram of a detection system according to an embodiment of the signal detection method of the present invention;
FIG. 5 is a schematic diagram of a detection system according to another embodiment of the signal detection method of the present invention;
fig. 6 is a schematic functional block diagram of an embodiment of a signal detection device of an unmanned aerial vehicle according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
Referring to fig. 1, fig. 1 is a flowchart of an embodiment of a method for detecting a signal of an unmanned aerial vehicle according to the present invention. In an embodiment, the unmanned aerial vehicle signal detection method includes:
step S10, an unmanned aerial vehicle characteristic parameter database is constructed, and the unmanned aerial vehicle model and standard characteristic parameters corresponding to the unmanned aerial vehicle model are stored in the unmanned aerial vehicle characteristic parameter database in an associated mode;
in this embodiment, an unmanned aerial vehicle characteristic parameter database is constructed, so that the collected unmanned aerial vehicle signals are conveniently identified based on the content in the unmanned aerial vehicle characteristic parameter database, and the unmanned aerial vehicle numbers corresponding to the unmanned aerial vehicle signals are determined. Fig. 2 is a schematic flow chart of constructing a characteristic parameter database of the unmanned aerial vehicle according to an embodiment of the unmanned aerial vehicle signal detection method of the present invention. As shown in fig. 2, after a graphic transmission or remote control signal of the unmanned aerial vehicle sample is collected, the characteristic parameters of the unmanned aerial vehicle sample are extracted through an STFT (short-time Fourier transform ) analysis method, and then the model and the characteristic parameters of the unmanned aerial vehicle sample are recorded and put in storage.
In one embodiment, step S10 includes:
collecting target signals of unmanned aerial vehicles with known models, wherein the target signals comprise image transmission signals and/or remote control signals; calculating to obtain standard characteristic parameters corresponding to the target signals; and storing the model of the unmanned aerial vehicle and the standard characteristic parameters in an unmanned aerial vehicle characteristic parameter database in an associated mode.
In this embodiment, a target signal of a unmanned aerial vehicle with a known model is collected, where the target signal includes a graphical signal and/or a remote control signal. The image transmission signal and the remote control signal can be simultaneously acquired according to actual needs, and the image transmission signal or the remote control signal can also be independently acquired. After the target signal is collected, a Short-time Fourier transform (STFT) technology is adopted to analyze the collected target signal from the angles of a time domain and a frequency domain, and characteristic parameters (such as parameters including signal bandwidth, duration and the like) of the target signal are extracted, and are called standard characteristic parameters. It is easy to understand that the standard characteristic parameters of the target signals of the unmanned aerial vehicles of different models are different. And finally, storing the model and the standard characteristic parameters of the unmanned aerial vehicle in a characteristic parameter database of the unmanned aerial vehicle in an associated mode.
In this embodiment, if N target signals of different unmanned aerial vehicles with known models are collected, standard feature parameters corresponding to each unmanned aerial vehicle are obtained, and then the model of each unmanned aerial vehicle and the standard feature parameters corresponding to each unmanned aerial vehicle are associated and stored in an unmanned aerial vehicle feature parameter database. For example, N is 5, then model 1 is associated with standard feature parameter 1 to the unmanned aerial vehicle feature parameter database, model 2 is associated with standard feature parameter 2 to the unmanned aerial vehicle feature parameter database, model 3 is associated with standard feature parameter 3 to the unmanned aerial vehicle feature parameter database, model 4 is associated with standard feature parameter 4 to the unmanned aerial vehicle feature parameter database, and model 5 is associated with standard feature parameter 5 to the unmanned aerial vehicle feature parameter database. If the signal of the unmanned aerial vehicle is collected subsequently and the characteristic parameter is extracted according to the signal, if the characteristic parameter is the same as or has small difference with any one of the standard characteristic parameter 1 to the standard characteristic parameter 5, for example, the characteristic parameter is the same as or has small difference with the standard characteristic parameter 3, the model of the unmanned aerial vehicle to which the currently collected unmanned aerial vehicle signal belongs can be determined to be the unmanned aerial vehicle model corresponding to the standard characteristic parameter 3.
Step S20, obtaining a sampling signal, and processing the sampling signal to obtain a plurality of sections of signals;
in this embodiment, acquiring the sampling signal includes two modes, i.e., active acquisition and passive acquisition. Wherein the active acquisition mode is also called active detection and the passive acquisition mode is also called passive detection. Active detection, such as radar detection, is implemented by actively transmitting a signal to receive an echo signal reflected from a target and analyzing and processing the echo signal, but for a low-altitude unmanned aerial vehicle target with typical characteristics of low speed, complex mobility, small radar reflection sectional area and the like, the radar is difficult to effectively detect the target; furthermore, actively transmitted electromagnetic wave signals may even affect the communication equipment and signal quality in certain sensitive areas, making these areas exclusive to radar detection. Compared with active detection, passive detection is a passive detection technology, only a receiver is needed without actively sending signals, and detection purposes are achieved by analyzing and processing received signals of the unmanned aerial vehicle (unmanned aerial vehicle image transmission signals and remote control signals sent by a remote controller operated by a remote controller), and the passive detection technology is more suitable for unmanned aerial vehicle detection compared with active detection. Therefore, in this embodiment, the sampling signal is acquired by a passive acquisition manner, and is divided into a plurality of segments.
In one embodiment, step S20 includes:
acquiring a sampling signal, and carrying out power spectrum estimation on the sampling information to obtain a power spectrum;
in this embodiment, after the sampling signal is obtained, a periodic chart method (periodic) or a Welch method may be used to form a power spectrum. For example, a 512-point spectrum estimation algorithm is selected for the sampled signal to obtain a power spectrum. If a periodogram method is used, a windowing technique is used to improve the spectrum leakage problem.
Logarithm is carried out on the power spectrum to obtain a logarithmic spectrum;
in this embodiment, the power spectrum is logarithmized, so that some abnormal burr peak values can be eliminated.
Carrying out smoothing filter treatment on the logarithmic spectrum, and carrying out statistical average on spectrum data obtained after the smoothing filter treatment to obtain average power; taking the average power as the background noise power, and superposing a preset signal-to-noise ratio threshold value to obtain a noise power threshold value;
in this embodiment, the log spectrum is subjected to smoothing filtering, so that the processed log spectrum is smoother and finer. And carrying out statistical average on the spectrum data obtained after the smoothing filtering processing to obtain average power, taking the average power as background noise power, and superposing a preset signal-to-noise ratio threshold value to obtain a noise power threshold value. To accurately estimate the background noise power, a histogram distribution statistical algorithm may be employed.
Extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal;
in this embodiment, according to the calculated noise power threshold value calculated in the above steps, a signal exceeding the noise power threshold value is extracted from the sampled signal as an effective signal.
Calculating the frequency point interval difference corresponding to the effective signal, and dividing and combining the effective signal by utilizing the frequency point interval difference to obtain a multi-section signal.
In this embodiment, considering that the effective signal may be a plurality of signals related to the unmanned aerial vehicle are mixed together, therefore, for the effective signal obtained in the above steps, the interval difference of the frequency points corresponding to the effective signal is calculated, the interval difference is used to segment and combine the signals, and the effective signal is segmented into multiple segments of signals, so as to realize the capability of detecting multiple unmanned aerial vehicles simultaneously.
Step S30, determining the signal category of each section of signals in the multi-section signals, eliminating the interference signals when at least one section of signals is the interference signals, and calculating to obtain the characteristic parameters corresponding to each section of signals remaining after the interference signals are eliminated in the multi-section signals;
in this embodiment, for each segment of the signal obtained in the above step, the signal type of each segment of the signal is determined according to the analyzed characteristic parameter of the unmanned aerial vehicle, that is, whether each segment of the signal is a Remote Control (RC) signal or a picture transmission (Image Transmission, IT) signal is distinguished. Considering the existence of some interference signals, the interference signals need to be removed. For example, the WiFi signal and the WIFI (Wireless Fidelity) signal have similar characteristics (such as bandwidth) to the unmanned aerial vehicle image signal, so that the types of the obtained signals need to be further judged to analyze whether the signals are WiFi signals. The WIFI signal and the unmanned aerial vehicle image transmission signal and the time-frequency characteristics of the remote control signal are compared, different characteristics of the WIFI signal and the unmanned aerial vehicle image transmission signal and the remote control signal can be extracted, and then the WIFI signal is identified by adopting a WIFI interference removal algorithm. When a certain signal type is identified as the WIFI signal, the signal type is eliminated, so that the false alarm rate is reduced. And after the WiFi signals are removed, calculating the signal bandwidth, the center frequency point, the signal to noise ratio, the power and other parameter information corresponding to each section of signals of the multi-section signals, which are remained after the WiFi signals are removed, namely calculating to obtain the characteristic parameters corresponding to each section of signals of the multi-section signals, which are remained after the WiFi signals are removed.
Step S40, adding the rest signals to a database to be compared, and grouping the signals belonging to the same unmanned aerial vehicle into a group according to the characteristic parameters corresponding to the rest signals and the characteristic parameters corresponding to the signals stored in the database to be compared;
in this embodiment, each segment of signal is added to the database to be compared, and at this time, the type of each segment of signal has been determined, that is, the image transmission signal or the remote control signal is added to the database to be compared, and in this process, the signal to be put in the warehouse and the related parameter information of the existing signal in the library need to be subjected to traversal comparison, so as to realize distinguishing signals belonging to different unmanned aerial vehicles. For example, the signal 1 and the signal 7 need to be added to the database to be compared, and data is added to the database to be compared for the first time, so that no data is stored, the signal 1 and the signal 7 are analyzed to find that the corresponding characteristic parameters of the signal 1 and the signal 2 are basically the same, and therefore, the signal 1 and the signal 2 are determined to be the signals belonging to the same unmanned aerial vehicle, and the signal 1 and the signal 2 are grouped into a group; the characteristic parameters corresponding to the signals 3 and 7 are found to be basically the same, so that the signals 3 and 7 are classified into one group when the signals 3 and 7 are determined to belong to the same unmanned aerial vehicle.
Step S50, repeating the steps S20 to S40 to obtain a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments;
in this embodiment, the steps S20 to S40 are repeated to obtain the characteristic parameters of the signals of each unmanned aerial vehicle in different time dimensions, and the information structure is similar to a two-dimensional pattern. Wherein: the abscissa represents the unmanned aerial vehicle targets carrying signals of different characteristic parameters, and the ordinate represents the characteristic parameters of the signals of the unmanned aerial vehicle obtained by detecting the targets in the time dimension, namely, a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments are obtained.
And step S60, determining the model of each unmanned aerial vehicle based on a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments and the characteristic parameter database of the unmanned aerial vehicle.
In this embodiment, after obtaining multiple sets of characteristic parameters of each unmanned aerial vehicle at different moments, for example, the characteristic parameters include bandwidth, center frequency point and power, multiple sets of bandwidth, center frequency point and power corresponding to each unmanned aerial vehicle are obtained. For an unmanned aerial vehicle, if three signals are corresponding, 3 groups of characteristic parameters exist, namely bandwidth 1, center frequency point 1, power 1 and bandwidth 2, center frequency point 2, power 2 and bandwidth 3, center frequency point 3 and power 3 respectively, an average bandwidth value is obtained by calculating the average value of bandwidth 1, bandwidth 2 and bandwidth 3, an average center frequency point is obtained by calculating the average value of center frequency point 1, center frequency point 2 and center frequency point 3, average power is obtained by calculating the average value of power 1, power 2 and power 3, and based on the two-dimensional pattern in the embodiment (wherein the abscissa represents unmanned aerial vehicle targets carrying signals with different characteristic parameters, the ordinate represents the characteristic parameters of the signals of the unmanned aerial vehicle obtained by detecting the targets in the time dimension), if the three signals continuously appear, the duration of the signals corresponding to the unmanned aerial vehicle can be calculated, the average bandwidth value, the average center frequency point, the average power and the duration are used as statistical information corresponding to the unmanned aerial vehicle, the statistical information corresponding to the statistical information is used as the statistical information corresponding to the unmanned aerial vehicle, the statistical information is compared with the statistical information corresponding to the characteristic parameters of the unmanned aerial vehicle, and the statistical information is used as the statistical information corresponding to the characteristic parameters of the unmanned aerial vehicle, and the model is closest to the characteristic parameters of the unmanned aerial vehicle is obtained.
Further, in an embodiment, the step S60 includes:
removing the characteristic parameters with low confidence coefficient from the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments to obtain effective characteristic parameters corresponding to each unmanned aerial vehicle; and determining the model of each unmanned aerial vehicle based on the effective characteristic parameters corresponding to each unmanned aerial vehicle and the unmanned aerial vehicle characteristic parameter database.
In this embodiment, in order to improve accuracy of estimating the feature parameters of the unmanned aerial vehicle, it is necessary to reject feature parameters with low confidence in multiple sets of feature parameters. For example, the corresponding sets of characteristic parameters for a drone include: bandwidth 1, bandwidth 2, and bandwidth 3; center frequency point 1, center frequency point 2 and center frequency point 3; power 1, power 2, and power 3. Firstly, calculating to obtain an average bandwidth value, an average center frequency point and average power, and removing bandwidths with the difference value between the bandwidth value and the average bandwidth value being larger than a preset value in the bandwidths 1, 2 and 3, for example, removing the bandwidth 1 if the difference value between the bandwidth value and the average bandwidth value is larger than the preset value; removing the center frequency points with the difference value between the center frequency point 1 and the average center frequency point being larger than a preset value, namely removing the center frequency point 3 if the difference value between the center frequency point 3 and the average center frequency point is larger than the preset value; and removing the power 1, the power 2 and the power 3, wherein the difference value between the power 3 and the average power is larger than a preset value, for example, the difference value between the power 3 and the average power is larger than the preset value, and removing the power 3, so that the obtained effective characteristic parameters corresponding to the unmanned aerial vehicle comprise: bandwidth 2, bandwidth 3, center frequency point 1, center frequency point 2, power 1, power 2. And then calculating the average value of the bandwidth 2 and the bandwidth 3, calculating the average value of the center frequency point 1 and the center frequency point 2, calculating the average value of the power 1 and the power 2, taking the calculated average bandwidth value, average center frequency point, average power and duration as the corresponding statistical information of the unmanned aerial vehicle based on the duration of the signal corresponding to the unmanned aerial vehicle, comparing and matching the statistical information with each standard characteristic parameter stored in the unmanned aerial vehicle characteristic parameter database to obtain the standard characteristic parameter closest to the statistical information, and taking the unmanned aerial vehicle model number corresponding to the standard characteristic parameter as the model of the unmanned aerial vehicle corresponding to the statistical information. In this embodiment, after the final statistics information of each unmanned aerial vehicle is obtained through the above steps, the power, bandwidth, center frequency point or frequency information in the statistics information can be further combined, and through setting a reasonable threshold, unmanned aerial vehicle targets exceeding a specified threshold can be screened out as unmanned aerial vehicles with high reliability, so that the accuracy of unmanned aerial vehicle detection is improved.
Referring to fig. 3, fig. 3 is a flowchart of another embodiment of a method for detecting a signal of a drone according to the present invention. As shown in fig. 3, in another embodiment, the unmanned aerial vehicle signal detection method includes:
1) Inputting source data: specifically, the sampling signal received and processed by the receiver system is used as a signal source. In this example, 1024 sample point signals are processed each time they are selected.
2) Power spectrum estimation: for the sampled signal of step 1), a periodic chart method (periodic) or a Welch method can be used to form a power spectrum. In this example, a 512-point spectral estimation algorithm is selected for the sampled signal. If a periodogram method is used, a windowing technique is proposed to improve the spectral leakage problem.
3) Calculating a log spectrum: and D), carrying out logarithmization on the power spectrum obtained in the step 2), and eliminating some abnormal burr peak values.
4) Smoothing and filtering: and (3) further smoothing the logarithmic spectrum in the step (3) to enable the spectrum to be smoother and finer.
5) Statistical noise: and (3) carrying out statistical average on the spectrum data in the step (4), taking the average power as the background noise power, and superposing a preset signal-to-noise ratio threshold value to obtain a noise power threshold value. To accurately estimate the background noise power, a histogram distribution statistical algorithm may be employed.
6) Useful signal extraction: extracting a signal exceeding the threshold value as a useful signal in combination with the noise power threshold value calculated in step 5).
7) Splitting/combining signals: and (3) calculating the interval difference of the frequency points corresponding to the useful signals obtained in the step (6), and carrying out signal segmentation and combination by utilizing the interval difference so as to realize the capability of simultaneous detection of multiple targets.
8) Determining the RC/IT signal: and (3) judging the type of the target signal of the unmanned aerial vehicle according to the analyzed characteristic parameters of the unmanned aerial vehicle for each section of signal obtained in the step 7) so as to distinguish whether the signal is a Remote Control (RC) signal or a picture transmission (Image Transmission, IT) signal. Meanwhile, information such as signal types, starting and ending frequency points and the like of each section of signals is recorded, and corresponding parameter information such as signal bandwidth, center frequency points, signal to noise ratio, power and the like is calculated.
9) Removing WIFI interference signals: because the WIFI (Wireless Fidelity) signal has similar characteristics (such as bandwidth) to the unmanned aerial vehicle target image signal, the type of each segment of the determination signal obtained in the step 8) needs to be further determined to analyze whether the signal is a WIFI signal. Different characteristics of the WIFI signal and the unmanned aerial vehicle target signal can be extracted by comparing time-frequency characteristics of the WIFI signal and the unmanned aerial vehicle target signal, and then the WIFI signal is identified by adopting a WIFI interference elimination algorithm. When a certain signal type is identified as the WIFI signal, the signal type is eliminated, so that the false alarm rate is reduced.
10 Target IT/RC signal binning): and adding the final unmanned aerial vehicle target map or remote control signal information obtained in the step 9) into a library. In the process, the target signals to be put in the warehouse and the related parameter information of the existing signals in the warehouse need to be subjected to traversal comparison so as to realize the distinction of different target signals.
11 Statistical signal: and (3) performing multiple times of processing on the steps 1) to 10) to obtain target information of each unmanned aerial vehicle in different time dimensions, wherein the information structure is similar to a two-dimensional pattern. Wherein: the abscissa represents each unmanned aerial vehicle target carrying different parameter information, and the ordinate represents the detection information of each target in the time dimension. And carrying out statistical processing on the target information of the unmanned aerial vehicle to obtain statistical information (such as signal type, bandwidth, center frequency point, signal-to-noise ratio, power, frequency and the like) of each target in the time dimension.
12 Model identification: and (3) combining the statistical information obtained in the step 11) with a recorded unmanned aerial vehicle characteristic parameter library, on one hand, further screening unmanned aerial vehicle target signals with high reliability, on the other hand, matching and identifying the model of the unmanned aerial vehicle, and respectively processing the unmanned aerial vehicle target information (such as: signal type, bandwidth, center frequency, signal to noise ratio, power, number of times, model, etc.) is recorded in the library.
13 Outputting the detection result and combining the detection result with the direction finding: and 4) reporting the target information of each unmanned aerial vehicle obtained in the step 12). The above processing is entirely based on signal processing of the single antenna receive channel. When the combined direction finding device is applied to a multi-array antenna receiving system, the combined direction finding function can be realized.
Further, in an embodiment, after the step S60, the method further includes:
selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with determined models; collecting signals of a plurality of channels of the target unmanned aerial vehicle; and when the acquisition time reaches a preset duration or the number of the acquired data points reaches a preset number, executing a direction finding algorithm based on the acquired signals to obtain a direction finding result of the target unmanned aerial vehicle.
In this embodiment, as shown in fig. 4, fig. 4 is a schematic diagram of a detection system in an embodiment of a signal detection method of an unmanned aerial vehicle according to the present invention. A detection system constructed as shown in fig. 4, which includes two subsystems, target detection and direction finding. Wherein:
1) The detection system comprises: according to the actual detection performance index requirement, an omnidirectional antenna is reasonably designed and adopted for detecting unmanned aerial vehicle target signals in a 360-degree direction range, and the detection method refers to the method from the step S10 to the step S60.
2) Direction finding system: when the detection system detects an effective unmanned aerial vehicle target, the direction-finding system is controlled to work according to the target parameter information. And switching the hardware sampling equipment to the corresponding target signal center frequency point by the direction finding system, setting a proper sampling rate, and collecting target signals of a plurality of channels. After a certain time or data point is collected, the direction finding algorithm function is executed. According to the performance index requirements of an actual system and different antenna array structures, a corresponding direction finding algorithm can be flexibly selected, such as: digital beamforming (Digital Beam Forming, DBF), multiple signal classification (Multiple Signal Classification Algorithm, MUSIC) algorithms, or amplitude-contrast direction finding algorithms, etc.
In another embodiment, as shown in fig. 5, fig. 5 is a schematic diagram of a detection system in another embodiment of the signal detection method of the present invention. As shown in fig. 5, the system can directly use multiple directional antenna arrays for target detection and direction finding simultaneously. The method comprises the following steps:
1) For receiving channel data for a plurality of antennas, the method described in the above steps S10 to S60 is referred to, and a multi-channel detection target result is obtained.
2) And a multi-antenna channel target fusion algorithm is adopted, and according to the parameter information of each target unmanned aerial vehicle (such as: center frequency point, model, etc.), statistical analysis, combination and screening are carried out on the multi-channel detection target results, and a final unmanned aerial vehicle target is obtained.
3) And 2) executing an algorithm flow based on the contrast direction finding by combining the target information output in the step 2). The traditional amplitude comparison direction finding method is to search the largest and next largest amplitude in two adjacent antennas to compare the direction finding, but the accuracy is not high. The system optimizes the amplitude and direction finding algorithm, and one effective method is as follows: firstly, a channel correction algorithm is adopted to ensure the consistency of amplitude and phase among channels, then, a target signal power value (after interference elimination) obtained based on a frequency domain processing method in target information is utilized to conduct amplitude comparison direction finding aiming at the power of a plurality of adjacent antennas (such as 3-4 antennas), and a weighting algorithm is adopted to obtain a final direction finding result, so that the direction finding precision is improved.
4) And outputting and reporting the final detection and direction finding results of each target.
In the embodiment, an unmanned aerial vehicle characteristic parameter database is constructed, and the unmanned aerial vehicle model and standard characteristic parameters corresponding to the unmanned aerial vehicle model are stored in the unmanned aerial vehicle characteristic parameter database in an associated mode; acquiring a sampling signal, and processing the sampling signal to obtain a plurality of sections of signals; determining the signal category of each section of signals in the multi-section signals, eliminating the interference signals when at least one section of signals is the interference signals, and calculating to obtain the characteristic parameters corresponding to each section of signals remaining after the interference signals are eliminated in the multi-section signals; adding each remaining section of signals into a database to be compared, and grouping signals belonging to the same unmanned aerial vehicle into a group according to the characteristic parameters corresponding to each remaining section of signals and the characteristic parameters corresponding to the signals stored in the database to be compared; repeating the steps to obtain a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments; and determining the model of each unmanned aerial vehicle based on the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments and the characteristic parameter database of the unmanned aerial vehicle. According to the embodiment, the signal processing method combining the time domain and the frequency domain is adopted, the unmanned aerial vehicle target signal can be accurately analyzed, the characteristic parameters of the unmanned aerial vehicle target signal can be extracted, the characteristic parameter estimation precision is high, the target false detection rate and the omission factor are low, the accuracy is high, the unmanned aerial vehicle target signal can be easily integrated on hardware platforms such as ARM or DSP, the real-time performance is good, the expansibility is good, and meanwhile, in the frequency band to be detected, a plurality of effective targets can be detected and identified at the same time in one processing process.
Referring to fig. 6, fig. 6 is a schematic functional block diagram of an embodiment of a signal detection device of a drone according to the present invention. In an embodiment, the unmanned aerial vehicle signal detection device includes:
the construction module 10 is configured to construct an unmanned aerial vehicle characteristic parameter database, where an unmanned aerial vehicle model and a standard characteristic parameter corresponding to the unmanned aerial vehicle model are stored in the unmanned aerial vehicle characteristic parameter database in an associated manner;
the segmentation module 20 is configured to obtain a sampling signal, and process the sampling signal to obtain a multi-segment signal;
the processing module 30 is configured to determine a signal class of each signal in the multiple segments of signals, reject the interference signal when at least one segment of signal is the interference signal, and calculate to obtain a feature parameter corresponding to each segment of signal remaining after the interference signal is rejected in the multiple segments of signals;
the grouping module 40 is configured to add the remaining signals of each segment to a database to be compared, and group the signals belonging to the same unmanned aerial vehicle into a group according to the characteristic parameters corresponding to the remaining signals of each segment and the characteristic parameters corresponding to the signals stored in the database to be compared;
the repeating module 50 is configured to repeat the steps performed by the constructing module, the dividing module, the processing module, and the grouping module to obtain multiple sets of characteristic parameters of each unmanned aerial vehicle at different moments;
The identification module 60 is configured to determine a model of each unmanned aerial vehicle based on a plurality of sets of characteristic parameters of each unmanned aerial vehicle at different moments and the unmanned aerial vehicle characteristic parameter database.
Further, in an embodiment, the building module 10 is configured to:
collecting target signals of unmanned aerial vehicles with known models, wherein the target signals comprise image transmission signals and/or remote control signals;
calculating to obtain standard characteristic parameters corresponding to the target signals;
and storing the model of the unmanned aerial vehicle and the standard characteristic parameters in an unmanned aerial vehicle characteristic parameter database in an associated mode.
Further, in an embodiment, the dividing module 20 is configured to:
acquiring a sampling signal, and carrying out power spectrum estimation on the sampling information to obtain a power spectrum;
logarithm is carried out on the power spectrum to obtain a logarithmic spectrum;
carrying out smoothing filter treatment on the logarithmic spectrum, and carrying out statistical average on spectrum data obtained after the smoothing filter treatment to obtain average power;
taking the average power as the background noise power, and superposing a preset signal-to-noise ratio threshold value to obtain a noise power threshold value;
extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal;
Calculating the frequency point interval difference corresponding to the effective signal, and dividing and combining the effective signal by utilizing the frequency point interval difference to obtain a multi-section signal.
Further, in an embodiment, the identification module 60 is configured to:
removing the characteristic parameters with low confidence coefficient from the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments to obtain effective characteristic parameters corresponding to each unmanned aerial vehicle;
and determining the model of each unmanned aerial vehicle based on the effective characteristic parameters corresponding to each unmanned aerial vehicle and the unmanned aerial vehicle characteristic parameter database.
Further, in an embodiment, the apparatus further comprises:
the direction finding module is used for selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with determined models; collecting signals of a plurality of channels of the target unmanned aerial vehicle; and when the acquisition time reaches a preset duration or the number of the acquired data points reaches a preset number, executing a direction finding algorithm based on the acquired signals to obtain a direction finding result of the target unmanned aerial vehicle.
The specific embodiment of the unmanned aerial vehicle signal detection device is basically the same as each embodiment of the unmanned aerial vehicle signal detection method, and is not described herein.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some or all of the components in accordance with embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
Claims (8)
1. A method of unmanned aerial vehicle signal detection, the method comprising:
step S10, an unmanned aerial vehicle characteristic parameter database is constructed, and the unmanned aerial vehicle model and standard characteristic parameters corresponding to the unmanned aerial vehicle model are stored in the unmanned aerial vehicle characteristic parameter database in an associated mode;
Step S20, obtaining a sampling signal, and processing the sampling signal to obtain a multi-segment signal, which specifically includes: acquiring a sampling signal, and carrying out power spectrum estimation on the sampling signal to obtain a power spectrum; logarithm is carried out on the power spectrum to obtain a logarithmic spectrum; carrying out smoothing filter treatment on the logarithmic spectrum, and carrying out statistical average on spectrum data obtained after the smoothing filter treatment to obtain average power; taking the average power as the background noise power, and superposing a preset signal-to-noise ratio threshold value to obtain a noise power threshold value; extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal; calculating frequency point interval differences corresponding to the effective signals, and dividing and combining the effective signals by utilizing the frequency point interval differences to obtain multi-section signals;
step S30, determining the signal category of each section of signals in the multi-section signals, eliminating the interference signals when at least one section of signals is the interference signals, and calculating to obtain the characteristic parameters corresponding to each section of signals remaining after the interference signals are eliminated in the multi-section signals;
step S40, adding each remaining section of signals into a database to be compared, and grouping signals belonging to the same unmanned aerial vehicle into a group according to the characteristic parameters corresponding to each remaining section of signals and the characteristic parameters corresponding to the signals stored in the database to be compared;
Step S50, repeating the steps S20 to S40 to obtain a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments;
and step S60, determining the model of each unmanned aerial vehicle based on a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments and the characteristic parameter database of the unmanned aerial vehicle.
2. The method according to claim 1, wherein the step S10 includes:
collecting target signals of unmanned aerial vehicles with known models, wherein the target signals comprise image transmission signals and/or remote control signals;
calculating to obtain standard characteristic parameters corresponding to the target signals;
and storing the model of the unmanned aerial vehicle and the standard characteristic parameters in an unmanned aerial vehicle characteristic parameter database in an associated mode.
3. The method according to claim 1, wherein the step S60 includes:
removing the characteristic parameters with low confidence coefficient from the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments to obtain effective characteristic parameters corresponding to each unmanned aerial vehicle;
and determining the model of each unmanned aerial vehicle based on the effective characteristic parameters corresponding to each unmanned aerial vehicle and the unmanned aerial vehicle characteristic parameter database.
4. A method according to any one of claims 1 to 3, further comprising, after said step S60:
Selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with determined models;
collecting signals of a plurality of channels of the target unmanned aerial vehicle;
and when the acquisition time reaches a preset duration or the number of the acquired data points reaches a preset number, executing a direction finding algorithm based on the acquired signals to obtain a direction finding result of the target unmanned aerial vehicle.
5. An unmanned aerial vehicle signal detection device, the device comprising:
the system comprises a construction module, a control module and a control module, wherein the construction module is used for constructing an unmanned aerial vehicle characteristic parameter database, and the unmanned aerial vehicle characteristic parameter database is stored with unmanned aerial vehicle models and standard characteristic parameters corresponding to the unmanned aerial vehicle models in an associated mode;
the segmentation module is used for acquiring sampling signals and processing the sampling signals to obtain a plurality of sections of signals, and specifically comprises the following steps: acquiring a sampling signal, and carrying out power spectrum estimation on the sampling signal to obtain a power spectrum; logarithm is carried out on the power spectrum to obtain a logarithmic spectrum; carrying out smoothing filter treatment on the logarithmic spectrum, and carrying out statistical average on spectrum data obtained after the smoothing filter treatment to obtain average power; taking the average power as the background noise power, and superposing a preset signal-to-noise ratio threshold value to obtain a noise power threshold value; extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal; calculating frequency point interval differences corresponding to the effective signals, and dividing and combining the effective signals by utilizing the frequency point interval differences to obtain multi-section signals;
The processing module is used for determining the signal category of each section of signal in the multi-section signals, eliminating the interference signal when at least one section of signal is the interference signal, and calculating to obtain the characteristic parameters corresponding to each section of signal which is remained in the multi-section signal after the interference signal is eliminated;
the grouping module is used for adding the remaining signals of each section into a database to be compared, and grouping the signals belonging to the same unmanned aerial vehicle into a group according to the characteristic parameters corresponding to the remaining signals of each section and the characteristic parameters corresponding to the signals which are put in storage in the database to be compared;
the repeating module is used for repeating the steps executed by the building module, the dividing module, the processing module and the grouping module to obtain a plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments;
and the identification module is used for determining the model of each unmanned aerial vehicle based on the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments and the characteristic parameter database of the unmanned aerial vehicle.
6. The apparatus of claim 5, wherein the build module is to:
collecting target signals of unmanned aerial vehicles with known models, wherein the target signals comprise image transmission signals and/or remote control signals;
Calculating to obtain standard characteristic parameters corresponding to the target signals;
and storing the model of the unmanned aerial vehicle and the standard characteristic parameters in an unmanned aerial vehicle characteristic parameter database in an associated mode.
7. The apparatus of claim 5, wherein the identification module is to:
removing the characteristic parameters with low confidence coefficient from the plurality of groups of characteristic parameters of each unmanned aerial vehicle at different moments to obtain effective characteristic parameters corresponding to each unmanned aerial vehicle;
and determining the model of each unmanned aerial vehicle based on the effective characteristic parameters corresponding to each unmanned aerial vehicle and the unmanned aerial vehicle characteristic parameter database.
8. The apparatus according to any one of claims 5 to 7, wherein the apparatus further comprises:
the direction finding module is used for selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with determined models; collecting signals of a plurality of channels of the target unmanned aerial vehicle; and when the acquisition time reaches a preset duration or the number of the acquired data points reaches a preset number, executing a direction finding algorithm based on the acquired signals to obtain a direction finding result of the target unmanned aerial vehicle.
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