CN111046025A - Unmanned aerial vehicle signal detection method and device - Google Patents
Unmanned aerial vehicle signal detection method and device Download PDFInfo
- Publication number
- CN111046025A CN111046025A CN201911311333.5A CN201911311333A CN111046025A CN 111046025 A CN111046025 A CN 111046025A CN 201911311333 A CN201911311333 A CN 201911311333A CN 111046025 A CN111046025 A CN 111046025A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- signals
- aerial vehicle
- signal
- characteristic parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a method and a device for detecting unmanned aerial vehicle signals, 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 left after the interference signals are eliminated from the plurality of sections of signals; adding the rest of each section of signals 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 of each section of signals and the characteristic parameters corresponding to the signals which are put in a 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 multiple groups of characteristic parameters of each unmanned aerial vehicle at different moments and the unmanned aerial vehicle characteristic parameter database. By the invention, the signal of the unmanned aerial vehicle can be detected in real time, and the model of the unmanned aerial vehicle can be determined according to the detected signal.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a signal detection method and device for an unmanned aerial vehicle.
Background
In recent years, with the rapid development of unmanned aerial vehicle technology and industry in the low-altitude field, small unmanned aerial vehicles are widely applied and commonly used in industries such as aerial photography surveying and mapping, disaster rescue, monitoring and patrolling, environmental protection detection, electric power overhaul and agricultural plant protection, but the potential safety hazard of low altitude is prominent, so that a large number of unmanned aerial vehicle 'black flying' events occur frequently. Therefore, an unmanned aerial vehicle signal detection method is needed to detect an unmanned aerial vehicle signal in time, so that the potential safety hazard of low altitude is reduced.
Disclosure of Invention
The main object of the present invention is to solve the above technical problems 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, constructing an unmanned aerial vehicle characteristic parameter database, wherein the unmanned aerial vehicle characteristic parameter database is stored with the unmanned aerial vehicle model and the standard characteristic parameter corresponding to the unmanned aerial vehicle model in a correlated manner;
step S20, acquiring a sampling signal, and processing the sampling signal to obtain a plurality of sections of signals;
step S30, determining the signal type of each section of signals in the multiple sections of signals, eliminating the interference signals when at least one section of signals is interference signals, and calculating to obtain the characteristic parameters corresponding to each section of signals left after the interference signals are eliminated in the multiple sections of signals;
step S40, adding the remaining signals 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 and the characteristic parameters corresponding to the signals which are put into 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 the multiple groups of characteristic parameters of each unmanned aerial vehicle at different moments and the unmanned aerial vehicle characteristic parameter database.
Optionally, the step S10 includes:
acquiring 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 into an unmanned aerial vehicle characteristic parameter database in a correlation manner.
Optionally, the step S20 includes:
acquiring a sampling signal, and performing power spectrum estimation on the sampling information to obtain a power spectrum;
carrying out logarithm conversion on the power spectrum to obtain a logarithm spectrum;
carrying out smooth filtering processing on the log spectrum, and carrying out statistical averaging on spectrum data obtained after the smooth filtering processing to obtain average power;
taking the average power as a background noise power, and superposing a preset signal-to-noise ratio threshold to obtain a noise power threshold;
extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal;
and calculating the frequency point interval difference corresponding to the effective signals, and dividing and combining the effective signals by using the frequency point interval difference to obtain a plurality of sections of signals.
Optionally, the step S60 includes:
eliminating characteristic parameters with low confidence coefficient in a 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 the determined models;
acquiring signals of a plurality of channels for 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 further provides an apparatus for detecting signals of an unmanned aerial vehicle, the apparatus comprising:
the unmanned aerial vehicle system comprises a building module, a database and a control module, wherein the building module is used for building an unmanned aerial vehicle characteristic parameter database, and the unmanned aerial vehicle characteristic parameter database is stored with an unmanned aerial vehicle model and standard characteristic parameters corresponding to the unmanned aerial vehicle model in a correlated manner;
the segmentation module is used for acquiring a sampling signal and processing the sampling signal to obtain a plurality of sections of signals;
the processing module is used for determining the signal category of each section of signals in the multiple sections of 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 left after the interference signals are eliminated in the multiple sections of signals;
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 into 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 multiple groups of characteristic parameters of each unmanned aerial vehicle at different moments and the unmanned aerial vehicle characteristic parameter database.
Optionally, the building module is configured to:
acquiring 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 into an unmanned aerial vehicle characteristic parameter database in a correlation manner.
Optionally, the segmentation module is configured to:
acquiring a sampling signal, and performing power spectrum estimation on the sampling information to obtain a power spectrum;
carrying out logarithm conversion on the power spectrum to obtain a logarithm spectrum;
carrying out smooth filtering processing on the log spectrum, and carrying out statistical averaging on spectrum data obtained after the smooth filtering processing to obtain average power;
taking the average power as a background noise power, and superposing a preset signal-to-noise ratio threshold to obtain a noise power threshold;
extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal;
and calculating the frequency point interval difference corresponding to the effective signals, and dividing and combining the effective signals by using the frequency point interval difference to obtain a plurality of sections of signals.
Optionally, the identification module is configured to:
eliminating characteristic parameters with low confidence coefficient in a 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 comprises:
the direction-finding module is used for selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with the determined models; acquiring signals of a plurality of channels for 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 the invention, an unmanned aerial vehicle characteristic parameter database is established, wherein 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 a correlated manner; 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 multiple sections of signals, eliminating the interference signals when at least one section of signals is an interference signal, and calculating to obtain characteristic parameters corresponding to each section of signals left after the interference signals are eliminated in the multiple sections of signals; adding the rest of each section of signals 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 of each section of signals and the characteristic parameters corresponding to the signals which are put into a 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 multiple groups of characteristic parameters of each unmanned aerial vehicle at different moments and the unmanned aerial vehicle characteristic parameter database. By adopting the signal processing method combining the time domain and the frequency domain, the target signal of the unmanned aerial vehicle can be accurately analyzed and the characteristic parameters of the target signal of the unmanned aerial vehicle can be extracted, the estimation precision of the characteristic parameters is high, the false detection rate and the missed detection rate of the target are low, the accuracy is high, the method 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 simultaneously detected and identified in one processing process within a frequency band to be detected.
Drawings
Fig. 1 is a schematic flow chart of an embodiment of a method for detecting signals of an unmanned aerial vehicle according to the present invention;
fig. 2 is a schematic flow chart of constructing a characteristic parameter database of the unmanned aerial vehicle in an embodiment of the unmanned aerial vehicle signal detection method of the present invention;
fig. 3 is a schematic flow chart of another embodiment of the method for detecting signals of an unmanned aerial vehicle according to the present invention;
fig. 4 is a schematic structural diagram of a detection system in an embodiment of the unmanned aerial vehicle signal detection method of the present invention;
fig. 5 is a schematic structural diagram of a detection system in another embodiment of the unmanned aerial vehicle signal detection method of the present invention;
fig. 6 is a schematic functional module diagram of an embodiment of the signal detection device of the unmanned aerial vehicle according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a signal detection method for an unmanned aerial vehicle according to the present invention. In one embodiment, the drone signal detection method includes:
step S10, constructing an unmanned aerial vehicle characteristic parameter database, wherein the unmanned aerial vehicle characteristic parameter database is stored with the unmanned aerial vehicle model and the standard characteristic parameter corresponding to the unmanned aerial vehicle model in a correlated manner;
in this embodiment, establish unmanned aerial vehicle characteristic parameter database, be convenient for follow-up content based on in the unmanned aerial vehicle characteristic parameter database, discern the unmanned aerial vehicle signal of gathering to supply to confirm the unmanned aerial vehicle model that the unmanned aerial vehicle signal corresponds. As shown in fig. 2, fig. 2 is a schematic flow chart of constructing a characteristic parameter database of an unmanned aerial vehicle according to an embodiment of the method for detecting a signal of an unmanned aerial vehicle of the present invention. As shown in fig. 2, after the map transmission or remote control signal of the sample of the unmanned aerial vehicle is collected, the characteristic parameters of the sample of the unmanned aerial vehicle are extracted and obtained by an STFT (short-time fourier transform) analysis method, and then the model and the characteristic parameters of the sample of the unmanned aerial vehicle are recorded and stored.
In one embodiment, step S10 includes:
acquiring 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 into an unmanned aerial vehicle characteristic parameter database in a correlation manner.
In this embodiment, what gather is the target signal of the unmanned aerial vehicle of known model, and the target signal includes picture transmission signal and/or remote control signal. The image transmission signals and the remote control signals can be simultaneously acquired according to actual needs, and the image transmission signals or the remote control signals can also be independently acquired. After the target signal is acquired, a Short-time fourier transform (STFT) technology is adopted to analyze the acquired target signal from the angles of time domain and frequency domain, and characteristic parameters (such as signal bandwidth, duration and the like) of the target signal are extracted, which are referred to as standard characteristic parameters. It is easily understood that the standard characteristic parameters of the target signals of different models of drones are different. And finally, storing the model of the unmanned aerial vehicle and the standard characteristic parameters in an unmanned aerial vehicle characteristic parameter database in a correlation manner.
In this embodiment, if the target signals of N unmanned aerial vehicles of which the models are known and different are acquired, the standard characteristic parameters corresponding to each unmanned aerial vehicle are obtained, and the model of each unmanned aerial vehicle and the corresponding standard characteristic parameters are stored in the unmanned aerial vehicle characteristic parameter database in an associated manner. For example, if N is 5, the model 1 and the standard characteristic parameter 1 are stored in the feature parameter database of the unmanned aerial vehicle in a correlated manner, the model 2 and the standard characteristic parameter 2 are stored in the feature parameter database of the unmanned aerial vehicle in a correlated manner, the model 3 and the standard characteristic parameter 3 are stored in the feature parameter database of the unmanned aerial vehicle in a correlated manner, the model 4 and the standard characteristic parameter 4 are stored in the feature parameter database of the unmanned aerial vehicle in a correlated manner, and the model 5 and the standard characteristic parameter 5 are stored in the feature parameter database of the unmanned aerial vehicle in a correlated manner. If an unmanned aerial vehicle signal is subsequently acquired, and a characteristic parameter is extracted according to the signal, if the characteristic parameter is the same as or has a 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 a small difference with the standard characteristic parameter 3, the model of the unmanned aerial vehicle to which the currently acquired unmanned aerial vehicle signal belongs can be determined to be the unmanned aerial vehicle model corresponding to the standard characteristic parameter 3.
Step S20, acquiring 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, namely active acquisition and passive acquisition. 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, actively sends a signal to receive an echo signal reflected from a target, and analyzes and processes the echo signal to achieve the detection purpose, but for a low-altitude unmanned target with the typical characteristics of low speed and small size, the radar is difficult to effectively detect the target due to the characteristics of low target speed, complex maneuverability, small radar reflection sectional area and the like; in addition, the actively transmitted electromagnetic wave signals may affect the communication equipment and signal quality in some sensitive areas, making these areas repulsive to radar detection. Compared with active detection, passive detection is a passive detection technology, only a receiver is needed, active signal transmission is not needed, and the detection purpose is realized by analyzing and processing the 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), so that the passive detection is more suitable for unmanned aerial vehicle detection compared with active detection. Therefore, in this embodiment, the sampling signal is obtained by passive obtaining, and the sampling signal is divided into multiple segments of signals.
In one embodiment, step S20 includes:
acquiring a sampling signal, and performing power spectrum estimation on the sampling information to obtain a power spectrum;
in this embodiment, after the sampling signal is obtained, a power spectrum may be formed by using a Periodogram method (Periodogram) or a Welch method. For example, a spectrum estimation algorithm with 512 points is selected for the sampled signal, and a power spectrum is obtained. If the periodogram method is adopted, a windowing technology is adopted to improve the frequency spectrum leakage problem.
Carrying out logarithm conversion on the power spectrum to obtain a logarithm spectrum;
in this embodiment, the power spectrum is logarithmized to eliminate some abnormal spike peaks.
Carrying out smooth filtering processing on the log spectrum, and carrying out statistical averaging on spectrum data obtained after the smooth filtering processing to obtain average power; taking the average power as a background noise power, and superposing a preset signal-to-noise ratio threshold to obtain a noise power threshold;
in this embodiment, the log spectrum is subjected to smooth filtering processing, so that the processed log spectrum is smoother and finer. And carrying out statistical averaging on the spectrum data obtained after the smoothing filtering processing to obtain average power, taking the average power as the background noise power, and overlapping a preset signal-to-noise ratio threshold value to further 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 step, a signal exceeding the noise power threshold value is extracted from the sampling signal as an effective signal.
And calculating the frequency point interval difference corresponding to the effective signals, and dividing and combining the effective signals by using the frequency point interval difference to obtain a plurality of sections of signals.
In this embodiment, it is considered that the effective signals may be signals related to a plurality of unmanned aerial vehicles, and therefore, for the effective signals obtained in the above steps, the interval difference of the frequency points corresponding to the effective signals is calculated, the interval difference is used to segment and merge the signals, and the effective signals are divided into a plurality of sections of signals, so as to achieve the capability of simultaneously detecting a plurality of unmanned aerial vehicles.
Step S30, determining the signal type of each section of signals in the multiple sections of signals, eliminating the interference signals when at least one section of signals is interference signals, and calculating to obtain the characteristic parameters corresponding to each section of signals left after the interference signals are eliminated in the multiple sections of signals;
in this embodiment, the signal type of each segment of signals obtained in the above steps is determined according to the analyzed characteristic parameters of the unmanned aerial vehicle, that is, whether each segment of signals is a Remote Control (RC) signal or an Image Transmission (IT) signal is distinguished. Considering the existence of some interference signals, the interference signals need to be eliminated. For example, WiFi (wireless fidelity) signals have similar characteristics (e.g., bandwidth) to those of the unmanned aerial vehicle image-transmitted signals, and therefore, it is necessary to further determine the types of the obtained signals to analyze whether the signals are WiFi signals. Different characteristics of the WIFI signal, the unmanned aerial vehicle image transmission signal and the remote control signal can be extracted by comparing time-frequency characteristics of the WIFI signal, the unmanned aerial vehicle image transmission signal and the remote control signal, and then the WIFI signal is identified by adopting a WIFI interference removing algorithm. When a certain signal type is identified as a WIFI signal, the WIFI signal type is eliminated, so that the false alarm rate is reduced. And after the WiFi signals are removed, calculating parameter information such as signal bandwidth, central frequency point, signal-to-noise ratio, power and the like corresponding to each section of signals left after the WiFi signals are removed from the plurality of sections of signals, namely calculating to obtain characteristic parameters corresponding to each section of signals left after the WiFi signals are removed from the plurality of sections of signals.
Step S40, adding the remaining signals 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 and the characteristic parameters corresponding to the signals which are put into the database to be compared;
in this embodiment, each segment of signals is added to the database to be compared, at this time, the type of each segment of signals is determined, that is, the image-transmitted signals or the remote control signals are added to the database to be compared, and in this process, the signals to be stored in the database and the related parameter information of the existing signals in the database need to be traversed and compared, so as to distinguish the signals belonging to different unmanned aerial vehicles. For example, the signal 1 and the signal 7 need to be added to a database to be compared, and data is added to the database to be compared for the first time currently, so that there is no data already put in storage, and by analyzing the signal 1 and the signal 7, it is found that characteristic parameters corresponding to the signal 1 and the signal 2 are substantially the same, and therefore, it is determined that the signal 1 and the signal 2 are signals belonging to the same unmanned aerial vehicle, and the signal 1 and the signal 2 are grouped into one group; the characteristic parameters corresponding to the signals 3 and 7 are found to be substantially the same, and therefore, if the signals 3 and 7 are determined to be signals belonging to the same drone, the signals 3 and 7 are grouped together.
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 above steps S20 to S40 are repeated, so that the characteristic parameters of the signals of each drone in different time dimensions are obtained, and the information structure is similar to a two-dimensional pattern. Wherein: the abscissa represents each unmanned aerial vehicle target carrying signals of different characteristic parameters, and the ordinate represents the characteristic parameters of the signal of the unmanned aerial vehicle obtained by detecting each target in the time dimension, so that 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 the multiple groups of characteristic parameters of each unmanned aerial vehicle at different moments and the unmanned aerial vehicle characteristic parameter database.
In this embodiment, after obtaining multiple sets of characteristic parameters of each unmanned aerial vehicle at different times, 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 may be obtained. For an unmanned aerial vehicle, if three signals are corresponded, 3 sets of characteristic parameters exist, namely bandwidth 1, central frequency point 1, power 1 and bandwidth 2, central frequency point 2, power 2 and bandwidth 3, central frequency point 3 and power 3, an average bandwidth value is obtained by calculating the average value of bandwidth 1, bandwidth 2 and bandwidth 3, an average central frequency point is obtained by calculating the average value of central frequency point 1, central frequency point 2 and central frequency point 3, and an average power is obtained by calculating the average value of power 1, power 2 and power 3, and based on the two-dimensional pattern described in the above embodiment (wherein the abscissa represents each unmanned aerial vehicle target carrying signals with different characteristic parameters, and the ordinate represents the characteristic parameters of the signal of the unmanned aerial vehicle obtained by detecting each target in the time dimension), if the three signals appear continuously, then can also calculate the duration that obtains the signal that this unmanned aerial vehicle corresponds, then regard as the statistical information that this unmanned aerial vehicle corresponds with average bandwidth value, average center frequency point, average power and duration, compare this statistical information and each standard feature parameter of storage in the unmanned aerial vehicle feature parameter database and match, obtain the standard feature parameter that is the closest with this statistical information to regard as the model of the unmanned aerial vehicle that this statistical information corresponds with the unmanned aerial vehicle model that this standard feature parameter corresponds.
Further, in an embodiment, the step S60 includes:
eliminating characteristic parameters with low confidence coefficient in a 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 the accuracy of estimating the feature parameters of the unmanned aerial vehicle, the feature parameters with low confidence in the multiple sets of feature parameters need to be removed. For example, a corresponding set of characteristic parameters includes: bandwidth 1, bandwidth 2, and bandwidth 3; a central frequency point 1, a central frequency point 2 and a central 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 rejecting bandwidths with difference values larger than a preset value from among the bandwidth 1, the bandwidth 2 and the bandwidth 3, for example, rejecting the bandwidth 1 if the difference value between the bandwidth 1 and the average bandwidth value is larger than the preset value; removing the central frequency points 1, 2 and 3 with the difference value with the average central frequency point larger than a preset value, for example, removing the central frequency point 3 with the difference value with the average central frequency point 3 larger than the preset value; rejecting power with the difference value between the average power and the power 1, the power 2 and the power 3 being greater than a preset value, for example, rejecting the power 3 with the difference value between the average power and the power 3 being greater than the preset value, so as to obtain an effective characteristic parameter corresponding to the unmanned aerial vehicle, including: bandwidth 2, bandwidth 3, center frequency point 1, center frequency point 2, power 1, and power 2. Then calculate bandwidth 2, the average value of bandwidth 3, calculate central frequency point 1, the average value of central frequency point 2, calculate power 1, the average value of power 2, and the duration based on the signal that this unmanned aerial vehicle corresponds, with the average bandwidth value that obtains of calculation, average central frequency point, average power and duration are as the statistical information that this unmanned aerial vehicle corresponds, compare this statistical information and each standard characteristic parameter of storage in the unmanned aerial vehicle characteristic parameter database and match, obtain the standard characteristic parameter that is the closest with this statistical information, and regard the unmanned aerial vehicle model that this standard characteristic parameter corresponds as the model of the unmanned aerial vehicle that this statistical information corresponds. In this embodiment, after obtaining final each unmanned aerial vehicle's statistical information through above-mentioned step, can further combine power, bandwidth, central frequency point or number of times information in the statistical information, through setting up reasonable threshold value, can select the unmanned aerial vehicle target that exceeds appointed threshold value as the unmanned aerial vehicle that the credibility is high to this accuracy that promotes unmanned aerial vehicle and detect.
Referring to fig. 3, fig. 3 is a schematic flow chart of another embodiment of the unmanned aerial vehicle signal detection method of the present invention. As shown in fig. 3, in another embodiment, the method for detecting the drone signal includes:
1) inputting source data: in particular, a sampled signal received and processed by a receiver system is used as a signal source. In this example, 1024 sample signals are processed for each selection.
2) And (3) power spectrum estimation: for the sampling signal in step 1), a power spectrum can be formed by adopting a Periodogram method (Periodogram) or a Welch method. In this example, a 512-point spectrum estimation algorithm is selected for the sampled signal. If the periodogram method is used, windowing is suggested to improve the spectrum leakage problem.
3) Calculating a log spectrum: the power spectrum obtained in the step 2) is subjected to logarithm treatment, and some abnormal burr sharp peaks can be eliminated.
4) Smoothing and filtering: and 3) further performing smooth filtering on the log spectrum in the step 3) to enable the spectrum to become smoother and finer.
5) Noise statistics: and 4) carrying out statistical averaging on the spectrum data in the step 4), taking the average power as the background noise power, and overlapping a preset signal-to-noise ratio threshold value to further 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: combining the noise power threshold value calculated in the step 5), and extracting the signal exceeding the threshold value as a useful signal.
7) Split/combine signal: and (3) calculating the corresponding frequency point interval difference of the useful signals obtained in the step 6), and segmenting and combining the signals by using the interval difference so as to realize the capability of simultaneously detecting multiple targets.
8) Determining the RC/IT signal: 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 signals obtained in the step 7) so as to distinguish whether the signals are Remote Control (RC) signals or Image Transmission (IT) signals. And simultaneously, recording the information of the signal type, the starting frequency point, the ending frequency point and the like of each section of signal, and calculating the corresponding parameter information of the signal bandwidth, the central frequency point, the signal-to-noise ratio, the power and the like.
9) Eliminating WIFI interference signals: because the WIFI (wireless fidelity) signal has similar characteristics (such as bandwidth) to the target image transmission signal of the drone, the type of each segment of the determination signal obtained in 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 removing algorithm. When a certain signal type is identified as a WIFI signal, the WIFI signal type is eliminated, so that the false alarm rate is reduced.
10) Target IT/RC signal storage: adding the final unmanned aerial vehicle target image transmission or remote control signal information obtained in the step 9) into a library. In the process, the target signal to be stored in the library and the related parameter information of the existing signal in the library need to be traversed and compared, so as to distinguish different target signals.
11) And (3) counting signals: and (4) processing the steps 1) to 10) for multiple times 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 detection information of each target in the time dimension. Statistical information (such as signal type, bandwidth, central frequency point, signal-to-noise ratio, power, times and the like) of each target on the time dimension is obtained by performing statistical processing on the target information of the unmanned aerial vehicle.
12) Model identification: combining the statistical information obtained in the step 11) with the recorded characteristic parameter library of the unmanned aerial vehicle, on one hand, an unmanned aerial vehicle target signal with high credibility can be further screened out, on the other hand, the model of the unmanned aerial vehicle can be matched and identified, and the target information of the unmanned aerial vehicle (such as: signal type, bandwidth, center frequency point, signal-to-noise ratio, power, frequency, model, etc.) are recorded in the library.
13) Outputting a detection result and combining direction finding: and reporting and alarming the target information of each unmanned aerial vehicle obtained in the step 12). The above processing procedure is completely a signal processing procedure based on a single antenna receiving channel. When the method is applied to a multi-array antenna receiving system, the function of joint direction finding 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 the determined models; acquiring signals of a plurality of channels for 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 structural diagram of a detection system in an embodiment of the method for detecting signals of an unmanned aerial vehicle according to the present invention. A detection system constructed as shown in fig. 4, which includes two subsystems of object 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 the target signal of the unmanned aerial vehicle in the direction range of 360 degrees, and the detection method refers to the method from the step S10 to the step S60.
2) The direction-finding system comprises: 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 the direction-finding system switches the hardware sampling equipment to the corresponding target signal center frequency point, sets a proper sampling rate and acquires the target signals of a plurality of channels. When a certain time or data point is collected, the function of the direction-finding algorithm is executed. According to the performance index requirements of the actual system and different antenna array structures, the corresponding direction-finding algorithm can be flexibly selected, such as: digital Beam Forming (DBF), Multiple Signal classification (MUSIC) algorithm, or amplitude-to-direction algorithm, etc.
In another embodiment, as shown in fig. 5, fig. 5 is a schematic structural diagram of a detection system in another embodiment of the method for detecting signals of an unmanned aerial vehicle according to the present invention. As shown in fig. 5, the system can directly use multiple directional antenna arrays for object detection and direction finding at the same time. The method comprises the following steps:
1) for the multiple antennas receiving the channel data, the multi-channel detection target result is obtained by referring to the method described in the above steps S10 to S60.
2) And (3) adopting a multi-antenna channel target fusion algorithm, and according to the parameter information of each target unmanned aerial vehicle (such as: center frequency point, model, etc.), and carrying out statistical analysis, merging and screening on the multi-channel detection target results to obtain the final unmanned aerial vehicle target.
3) And (3) combining the target information output in the step 2) to execute an algorithm process based on amplitude and direction finding. The traditional direction finding method by comparing amplitudes of the maximum and the second maximum in two adjacent antennas is used for comparing direction finding, but the precision is not high. The system is optimized aiming at the amplitude comparison direction finding algorithm, and an effective method is as follows: firstly, a channel correction algorithm is adopted to ensure the amplitude phase consistency among channels, then, the target signal power value (after interference elimination) obtained based on a frequency domain processing method in target information is utilized, amplitude comparison and direction finding are carried out aiming at the power of a plurality of adjacent antennas (such as 3-4 antennas), and a weighting algorithm is adopted to obtain the 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 established, wherein 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 a correlated manner; 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 multiple sections of signals, eliminating the interference signals when at least one section of signals is an interference signal, and calculating to obtain characteristic parameters corresponding to each section of signals left after the interference signals are eliminated in the multiple sections of signals; adding the rest of each section of signals 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 of each section of signals and the characteristic parameters corresponding to the signals which are put into a 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 multiple groups of characteristic parameters of each unmanned aerial vehicle at different moments and the unmanned aerial vehicle characteristic parameter database. By the embodiment, the signal processing method combining the time domain and the frequency domain is adopted, the target signal of the unmanned aerial vehicle can be accurately analyzed, the characteristic parameters of the target signal of the unmanned aerial vehicle can be extracted, the estimation precision of the characteristic parameters is high, the false detection rate and the missed detection rate of the target are low, the accuracy is high, the target signal processing method can be easily integrated on hardware platforms such as an ARM (advanced RISC machine) or a DSP (digital signal processor), the real-time performance is good, the expansibility is good, and meanwhile, in a frequency band to be detected, a plurality of effective targets can.
Referring to fig. 6, fig. 6 is a functional module schematic diagram of an embodiment of the signal detection device of the unmanned aerial vehicle according to the present invention. In one embodiment, the drone signal detection device comprises:
the unmanned aerial vehicle model establishing module is used for establishing an unmanned aerial vehicle characteristic parameter database, wherein the unmanned aerial vehicle characteristic parameter database is stored with an unmanned aerial vehicle model and standard characteristic parameters corresponding to the unmanned aerial vehicle model in a correlated manner;
the segmentation module 20 is configured to acquire a sampling signal and process the sampling signal to obtain multiple segments of signals;
the processing module 30 is configured to determine a signal type of each segment of signals in the multiple segments of signals, and when at least one segment of signals is an interference signal, remove the interference signal, and calculate to obtain a feature parameter corresponding to each segment of signals remaining after the interference signal is removed from 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 a characteristic parameter corresponding to the remaining signals of each segment and a characteristic parameter corresponding to a signal that has been put in storage in the database to be compared;
a repeating module 50, configured to repeat the steps executed by the building 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 times;
and the identification module 60 is configured to determine the model of each unmanned aerial vehicle based on the multiple sets of characteristic parameters of each unmanned aerial vehicle at different times and the unmanned aerial vehicle characteristic parameter database.
Further, in an embodiment, the building module 10 is configured to:
acquiring 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 into an unmanned aerial vehicle characteristic parameter database in a correlation manner.
Further, in an embodiment, the segmentation module 20 is configured to:
acquiring a sampling signal, and performing power spectrum estimation on the sampling information to obtain a power spectrum;
carrying out logarithm conversion on the power spectrum to obtain a logarithm spectrum;
carrying out smooth filtering processing on the log spectrum, and carrying out statistical averaging on spectrum data obtained after the smooth filtering processing to obtain average power;
taking the average power as a background noise power, and superposing a preset signal-to-noise ratio threshold to obtain a noise power threshold;
extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal;
and calculating the frequency point interval difference corresponding to the effective signals, and dividing and combining the effective signals by using the frequency point interval difference to obtain a plurality of sections of signals.
Further, in an embodiment, the identifying module 60 is configured to:
eliminating characteristic parameters with low confidence coefficient in a 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 includes:
the direction-finding module is used for selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with the determined models; acquiring signals of a plurality of channels for 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 signal detection device of the unmanned aerial vehicle of the invention is basically the same as the embodiments of the signal detection method of the unmanned aerial vehicle, and the details are not repeated herein.
In the description provided herein, numerous specific details are set forth. It is understood, however, 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 interpreted as reflecting an intention that: that the invention as claimed 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 device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. 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. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements 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 included in other embodiments, rather than other features, 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 may be used in any combination.
The 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 a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components in accordance with embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or 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 usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (10)
1. A method for signal detection of a drone, the method comprising:
step S10, constructing an unmanned aerial vehicle characteristic parameter database, wherein the unmanned aerial vehicle characteristic parameter database is stored with the unmanned aerial vehicle model and the standard characteristic parameter corresponding to the unmanned aerial vehicle model in a correlated manner;
step S20, acquiring a sampling signal, and processing the sampling signal to obtain a plurality of sections of signals;
step S30, determining the signal type of each section of signals in the multiple sections of signals, eliminating the interference signals when at least one section of signals is interference signals, and calculating to obtain the characteristic parameters corresponding to each section of signals left after the interference signals are eliminated in the multiple sections of signals;
step S40, adding the remaining signals 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 and the characteristic parameters corresponding to the signals which are put into 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 the multiple groups of characteristic parameters of each unmanned aerial vehicle at different moments and the unmanned aerial vehicle characteristic parameter database.
2. The method of claim 1, wherein the step S10 includes:
acquiring 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 into an unmanned aerial vehicle characteristic parameter database in a correlation manner.
3. The method of claim 1, wherein the step S20 includes:
acquiring a sampling signal, and performing power spectrum estimation on the sampling information to obtain a power spectrum;
carrying out logarithm conversion on the power spectrum to obtain a logarithm spectrum;
carrying out smooth filtering processing on the log spectrum, and carrying out statistical averaging on spectrum data obtained after the smooth filtering processing to obtain average power;
taking the average power as a background noise power, and superposing a preset signal-to-noise ratio threshold to obtain a noise power threshold;
extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal;
and calculating the frequency point interval difference corresponding to the effective signals, and dividing and combining the effective signals by using the frequency point interval difference to obtain a plurality of sections of signals.
4. The method of claim 1, wherein the step S60 includes:
eliminating characteristic parameters with low confidence coefficient in a 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.
5. The method according to any one of claims 1 to 4, further comprising, after the step S60:
selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with the determined models;
acquiring signals of a plurality of channels for 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.
6. An unmanned aerial vehicle signal detection device, characterized in that the device includes:
the unmanned aerial vehicle system comprises a building module, a database and a control module, wherein the building module is used for building an unmanned aerial vehicle characteristic parameter database, and the unmanned aerial vehicle characteristic parameter database is stored with an unmanned aerial vehicle model and standard characteristic parameters corresponding to the unmanned aerial vehicle model in a correlated manner;
the segmentation module is used for acquiring a sampling signal and processing the sampling signal to obtain a plurality of sections of signals;
the processing module is used for determining the signal category of each section of signals in the multiple sections of 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 left after the interference signals are eliminated in the multiple sections of signals;
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 into 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 multiple groups of characteristic parameters of each unmanned aerial vehicle at different moments and the unmanned aerial vehicle characteristic parameter database.
7. The apparatus of claim 6, wherein the build module is to:
acquiring 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 into an unmanned aerial vehicle characteristic parameter database in a correlation manner.
8. The apparatus of claim 6, wherein the partitioning module is to:
acquiring a sampling signal, and performing power spectrum estimation on the sampling information to obtain a power spectrum;
carrying out logarithm conversion on the power spectrum to obtain a logarithm spectrum;
carrying out smooth filtering processing on the log spectrum, and carrying out statistical averaging on spectrum data obtained after the smooth filtering processing to obtain average power;
taking the average power as a background noise power, and superposing a preset signal-to-noise ratio threshold to obtain a noise power threshold;
extracting a signal exceeding the noise power threshold value from the sampling signal as an effective signal;
and calculating the frequency point interval difference corresponding to the effective signals, and dividing and combining the effective signals by using the frequency point interval difference to obtain a plurality of sections of signals.
9. The apparatus of claim 6, wherein the identification module is to:
eliminating characteristic parameters with low confidence coefficient in a 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.
10. The apparatus of any of claims 6 to 9, further comprising:
the direction-finding module is used for selecting at least one target unmanned aerial vehicle from the unmanned aerial vehicles with the determined models; acquiring signals of a plurality of channels for 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911311333.5A CN111046025B (en) | 2019-12-18 | 2019-12-18 | Unmanned aerial vehicle signal detection method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911311333.5A CN111046025B (en) | 2019-12-18 | 2019-12-18 | Unmanned aerial vehicle signal detection method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111046025A true CN111046025A (en) | 2020-04-21 |
| CN111046025B CN111046025B (en) | 2023-09-19 |
Family
ID=70237157
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911311333.5A Active CN111046025B (en) | 2019-12-18 | 2019-12-18 | Unmanned aerial vehicle signal detection method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111046025B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111709385A (en) * | 2020-06-22 | 2020-09-25 | 电子科技大学 | Low, small and slow aircraft target detection system and identity identification method thereof |
| CN113271161A (en) * | 2021-05-17 | 2021-08-17 | 湖南坤雷科技有限公司 | Method and device for screening electromagnetic signals |
| CN114499710A (en) * | 2022-04-02 | 2022-05-13 | 成都爱瑞无线科技有限公司 | Background noise change measuring method, background noise change measuring device, background noise change measuring system, electronic device, and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130097111A1 (en) * | 2011-10-13 | 2013-04-18 | Edward B. Loewenstein | Vector Smoothing of Complex-Valued Cross Spectra to Estimate Power Spectral Density of a Noise Signal |
| US20170032175A1 (en) * | 2015-07-31 | 2017-02-02 | Hon Hai Precision Industry Co., Ltd. | Unmanned aerial vehicle detection method and unmanned aerial vehicle using same |
| CN106774422A (en) * | 2017-02-28 | 2017-05-31 | 成都紫瑞青云航空宇航技术有限公司 | A kind of unmanned plane recognition methods and system |
| CN107911183A (en) * | 2017-11-23 | 2018-04-13 | 成都中星世通电子科技有限公司 | A kind of unmanned plane recognition methods and system based on live signal signature analysis |
| US20180107182A1 (en) * | 2016-10-13 | 2018-04-19 | Farrokh Mohamadi | Detection of drones |
| CN108768562A (en) * | 2018-05-07 | 2018-11-06 | 西北工业大学 | A kind of illegal unmanned plane recognition methods based on remote control with figure communication number |
| CN108957445A (en) * | 2018-07-30 | 2018-12-07 | 四川九洲空管科技有限责任公司 | A kind of low-altitude low-velocity small targets detection system and its detection method |
| CN110334591A (en) * | 2019-05-24 | 2019-10-15 | 西华大学 | A kind of detection of unmanned plane Frequency Hopping Signal and recognition methods based on clustering |
-
2019
- 2019-12-18 CN CN201911311333.5A patent/CN111046025B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130097111A1 (en) * | 2011-10-13 | 2013-04-18 | Edward B. Loewenstein | Vector Smoothing of Complex-Valued Cross Spectra to Estimate Power Spectral Density of a Noise Signal |
| US20170032175A1 (en) * | 2015-07-31 | 2017-02-02 | Hon Hai Precision Industry Co., Ltd. | Unmanned aerial vehicle detection method and unmanned aerial vehicle using same |
| US20180107182A1 (en) * | 2016-10-13 | 2018-04-19 | Farrokh Mohamadi | Detection of drones |
| CN106774422A (en) * | 2017-02-28 | 2017-05-31 | 成都紫瑞青云航空宇航技术有限公司 | A kind of unmanned plane recognition methods and system |
| CN107911183A (en) * | 2017-11-23 | 2018-04-13 | 成都中星世通电子科技有限公司 | A kind of unmanned plane recognition methods and system based on live signal signature analysis |
| CN108768562A (en) * | 2018-05-07 | 2018-11-06 | 西北工业大学 | A kind of illegal unmanned plane recognition methods based on remote control with figure communication number |
| CN108957445A (en) * | 2018-07-30 | 2018-12-07 | 四川九洲空管科技有限责任公司 | A kind of low-altitude low-velocity small targets detection system and its detection method |
| CN110334591A (en) * | 2019-05-24 | 2019-10-15 | 西华大学 | A kind of detection of unmanned plane Frequency Hopping Signal and recognition methods based on clustering |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111709385A (en) * | 2020-06-22 | 2020-09-25 | 电子科技大学 | Low, small and slow aircraft target detection system and identity identification method thereof |
| CN111709385B (en) * | 2020-06-22 | 2023-07-25 | 电子科技大学 | Low-altitude and slow-altitude aircraft target detection system and identity recognition method thereof |
| CN113271161A (en) * | 2021-05-17 | 2021-08-17 | 湖南坤雷科技有限公司 | Method and device for screening electromagnetic signals |
| CN113271161B (en) * | 2021-05-17 | 2022-06-03 | 湖南坤雷科技有限公司 | Method and device for screening electromagnetic signals |
| CN114499710A (en) * | 2022-04-02 | 2022-05-13 | 成都爱瑞无线科技有限公司 | Background noise change measuring method, background noise change measuring device, background noise change measuring system, electronic device, and storage medium |
| CN114499710B (en) * | 2022-04-02 | 2022-06-21 | 成都爱瑞无线科技有限公司 | Background noise change measuring method, background noise change measuring device, background noise change measuring system, electronic device, and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111046025B (en) | 2023-09-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9157992B2 (en) | Knowledge aided detector | |
| CN111046025A (en) | Unmanned aerial vehicle signal detection method and device | |
| JP6363637B2 (en) | System and method for detecting spoofing of radio signals | |
| US10879946B1 (en) | Weak signal processing systems and methods | |
| CN108038419B (en) | Wi-Fi-based indoor personnel passive detection method | |
| KR100824552B1 (en) | System and method for detection and feature extraction in passive coherent location applications | |
| CN107462873A (en) | A kind of radar chaff method for quickly identifying | |
| KR102162284B1 (en) | Cloud data processing gnss jamming monitoring method and system | |
| KR20140049394A (en) | Gps aoa choosing system and the method | |
| CN110837079B (en) | Target detection method and device based on radar | |
| Kaplan et al. | Measurement based FHSS–type drone controller detection at 2.4 GHz: An STFT approach | |
| CN112782685A (en) | Multi-sound-source positioning and sound reconstruction method and system based on MIMO radar | |
| CN108155959B (en) | Unmanned aerial vehicle measurement and control signal detection method under complex network environment | |
| CN116359876B (en) | Radar signal processing method | |
| JP7313018B2 (en) | sensor | |
| RU2723145C1 (en) | Method and device for detecting noisy objects in the sea with onboard antenna | |
| CN111812634A (en) | Method, device and system for monitoring warning line target | |
| Müller et al. | Acoustic flow perception in cf-bats: extraction of parameters | |
| KR101497557B1 (en) | Single-ping-clutter removing technique of active-sonar using estimation of multidimensional-feature-vector | |
| US9739866B2 (en) | Method and apparatus for radio direction finding | |
| TW202030496A (en) | Signal processing method | |
| US4958361A (en) | Edge effect reduction by smoothing in digital receivers | |
| US10480932B2 (en) | Automated computation of a dimension of a moving platform | |
| CN114584227A (en) | Automatic burst signal detection method | |
| CN109358324B (en) | Target detection method based on ground monitoring radar spectrum data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |