CN115941082B - Distributed cooperative interference identification method for unmanned aerial vehicle communication system - Google Patents
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Abstract
The invention provides a distributed cooperative interference identification method of an unmanned aerial vehicle communication system, which comprises the steps of establishing signal feature libraries of different interference patterns; capturing an environmental signal through distributed nodes of the unmanned aerial vehicle network; detecting the existence of interference to the environment signal through the distributed node; if interference exists, calculating a high-order accumulated quantity of the environmental signal through the distributed node; and according to the high-order accumulation amount and the signal feature library, performing interference pattern discrimination through the distributed node to obtain a local interference pattern recognition result of the distributed node. By the method provided by the invention, the interference signals in the unmanned aerial vehicle network can be analyzed in time, the interference signals can be accurately identified, and then specific defensive measures aiming at the current interference are adopted, so that the interference can be avoided and suppressed more efficiently and to the maximum extent.
Description
Technical Field
The invention relates to a distributed cooperative interference identification method of an unmanned aerial vehicle communication system, and belongs to the technical field of communication interference identification.
Background
At present, the application of the unmanned aerial vehicle communication network in military operation guarantee and civil mobile communication systems is rapidly increased, and the unmanned aerial vehicle communication network has the remarkable advantages of maneuverability, intelligence and track controllability. The inter-machine communication network is a foundation for supporting the cooperative tasks of the unmanned aerial vehicle, and it is important to establish an efficient and reliable inter-machine communication network. However, the working environment of the unmanned aerial vehicle is more open and complex, so that the unmanned aerial vehicle is more vulnerable to attack such as deception, interference and eavesdropping, and serious threat is caused to the reliability guarantee of network communication of the unmanned aerial vehicle. When the unmanned aerial vehicle suffers serious interference, reliable connection between the unmanned aerial vehicle or between the unmanned aerial vehicle and a control site is difficult to establish, so that the network communication quality is reduced, and even the current requirements cannot be met, and the task fails. The traditional anti-interference method mainly based on frequency hopping and spread spectrum not only needs a large amount of frequency spectrum resources as support, but also has passive anti-interference capability, and is difficult to obtain good effects in the real-time dynamic change scene of the transmission environment such as the unmanned aerial vehicle network. Therefore, under the conditions of complex electromagnetic environment and noise interference, if the interference signal can be analyzed in time and accurately identified, and then specific defensive measures aiming at the current interference are adopted, the interference can be effectively and maximally avoided and suppressed.
However, most of the existing interference recognition researches are directed against traditional static wireless communication networks, so that the method is suitable for interference monitoring and recognition of a single site to a wireless environment, and an unmanned aerial vehicle network is composed of a plurality of distributed unmanned aerial vehicles, and under a complex environment, the unmanned aerial vehicle network needs to rely on self-perception and calculation capability to realize network anti-interference effects in a cooperative manner.
Based on the above-mentioned problems, it is necessary to provide a low-complexity and high-accuracy unmanned aerial vehicle distributed cooperative interference identification method.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent.
Therefore, the invention aims to provide a distributed cooperative interference identification method of an unmanned aerial vehicle communication system, which is used for realizing the distributed cooperative interference identification of the unmanned aerial vehicle with low complexity and high accuracy.
To achieve the above objective, an embodiment of a first aspect of the present invention provides a distributed cooperative interference identification method for an unmanned aerial vehicle communication system, including:
Establishing signal feature libraries of different interference patterns;
capturing an environmental signal through distributed nodes of the unmanned aerial vehicle network;
detecting the existence of interference to the environment signal through the distributed node;
If interference exists, calculating a high-order accumulated quantity of the environmental signal through the distributed node;
and according to the high-order accumulation amount and the signal feature library, performing interference pattern discrimination through the distributed node to obtain a local interference pattern recognition result of the distributed node.
In addition, the method for identifying distributed cooperative interference of the unmanned aerial vehicle communication system according to the embodiment of the invention may further have the following additional technical features:
Further, in an embodiment of the present invention, the signal characteristics of the different interference patterns include: single tone interference CWI, multi-tone interference SCWI, swept frequency interference LFM.
Further, in one embodiment of the present invention, after capturing the environmental signal through the distributed nodes of the unmanned network, further comprising
Preprocessing the environment signal, specifically including:
Define the received signal as x (t) =s (t) +n (t) +j (t),
Wherein s (t) is a useful signal, N (t) is additive Gaussian white noise, J (t) is an interference signal, the number of sampling points is N, and the envelope A (N) of each sampling point of the signal to be identified can be obtained after N-point FFT conversion:
further, in an embodiment of the present invention, the performing, by the distributed node, interference presence detection on the environmental signal includes
The method for detecting the interference existence of the environmental signal by adopting a double-threshold FCME algorithm specifically comprises the following steps:
sequencing spectral lines of the environmental signals according to a descending order rule;
Selecting the smallest one or a part of the smallest one to perform initial spectral line mean value calculation, setting two interference detection thresholds with different sizes by presetting two false alarm probabilities with different sizes, determining a subset of non-interference frequency points of a received signal by using a small interference detection threshold, and finally determining a subset of interference frequency points by using a large interference detection threshold;
And finally, judging the existence of interference according to the detection set of the interference frequency points.
Further, in an embodiment of the present invention, the performing, by the distributed node, interference pattern discrimination to obtain a local interference pattern recognition result of the distributed node includes:
Judging whether the multi-tone interference exists or not according to the high-order accumulation amount;
if not, further distinguishing the single-tone interference from the sweep interference.
Further, in an embodiment of the present invention, the determining whether the signal is a polyphonic interference according to the characteristic of the higher order cumulative amount includes:
And when the value of the high-order accumulation amount is larger than 0, the high-order accumulation amount is multitone interference, otherwise, the high-order accumulation amount is single-tone interference or sweep frequency interference.
Further, in one embodiment of the present invention, the further distinguishing the single tone interference from the sweep interference includes
Distinguishing the single-tone interference signal and the sweep frequency interference signal by the number of the interference frequency points through a secondary identification method specifically comprises the following steps:
if the two times of identification interference detection frequency points are the same in the continuous time, the single-tone interference is identified, and if the plurality of interference detection frequency points are different, the sweep frequency interference is identified.
Further, in one embodiment of the present invention, after obtaining the local interference pattern recognition result of the distributed node, the method further includes:
And sending the local interference pattern recognition result to the main node through a standby link, and carrying out global judgment by the main node.
To achieve the above objective, an embodiment of a second aspect of the present invention provides a distributed cooperative interference identification device for an unmanned aerial vehicle communication system, including:
The construction module is used for establishing signal feature libraries of different interference patterns;
the acquisition module is used for acquiring environmental signals through distributed nodes of the unmanned aerial vehicle network;
the detection module is used for detecting the existence of interference on the environment signal through the distributed node;
the calculation module is used for calculating the higher-order accumulation amount of the environment signal through the distributed node if interference exists;
And the local recognition module is used for judging the interference pattern through the distributed node according to the high-order accumulation amount and the signal characteristic library to obtain a local interference pattern recognition result of the distributed node.
Further, in one embodiment of the present invention, the method further includes a global identification module for:
And sending the local interference pattern recognition result to the main node through a standby link, and carrying out global judgment by the main node.
The distributed cooperative interference identification method for the unmanned aerial vehicle communication system provided by the embodiment of the invention solves the defects of the traditional single-node interference identification method in the aspects of identification precision and efficiency by utilizing the distributed characteristics of the unmanned aerial vehicle network and the local sensing and computing capability of the nodes, and can realize the identification of various interference patterns such as single-tone interference, multi-tone interference, sweep-frequency interference and the like with lower complexity by utilizing the characteristics of the high-order cumulant and the frequency point of each signal. Compared with the prior art, the method has the advantages of low complexity, high identification accuracy, easy realization of engineering and the like.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic diagram of a distributed cooperative interference identification method of an unmanned aerial vehicle communication system according to an embodiment of the present invention.
FIG. 2 is a flow chart of multi-node cooperative interference identification.
Fig. 3 is a graph showing the high-order cumulative characteristics of three interference signals at different interference-to-noise ratios.
Fig. 4 is a comparison of multi-node interference recognition probability and single-node interference recognition probability under different interference-to-noise ratio conditions.
Fig. 5 is a schematic diagram of a distributed cooperative interference identification device of an unmanned aerial vehicle communication system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The distributed cooperative interference identification method of the unmanned aerial vehicle communication system according to the embodiment of the invention is described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a distributed cooperative interference identification method of an unmanned aerial vehicle communication system according to an embodiment of the present invention.
As shown in fig. 1, the distributed cooperative interference identification method of the unmanned aerial vehicle communication system comprises the following steps:
S1: establishing signal feature libraries of different interference patterns;
further, in one embodiment of the present invention, the signal characteristics of the different interference patterns include: single tone interference CWI, multi-tone interference SCWI, swept frequency interference LFM.
Specifically, in S101, in order to achieve the effect of rapid identification, first, the characteristic values of the analog signals of different interference patterns are extracted by performing simulation on the signal samples of different interference patterns. The signal feature library is initialized and loaded on each unmanned aerial vehicle node, can be continuously updated, and improves the recognition efficiency of different interferences in the future. The method takes the most common three interference patterns of single tone, multitone and sweep frequency as an example to describe the interference characteristics, and can be expanded to identify more interference patterns based on the method. The single tone interference (CWI) signal is an interference transmitted at a certain fixed frequency point, and is a single frequency continuous wave tone because of its simple structure, easy generation, relatively concentrated energy, continuous time domain and single frequency domain. The multi-tone interference (SCWI) signal is continuous wave impulse generated on a plurality of frequency points, which can be formed by combining a plurality of single-tone interferences with specific power, the positions of the interference frequency points are generally randomly distributed, and the multi-tone interference (SCWI) signal can be fixed on a specific certain frequency point, so that the power of the multi-tone interference is more dispersed. The instantaneous frequency of a swept interference (LFM) signal varies linearly with time. Its instantaneous frequency characteristics are the same as a tone, but exhibit dynamic sweep characteristics over a period of time. Based on the above-described differentiated features, the features can be further characterized by constructing higher-order cumulants.
Establishing a conversion formula between a high-order matrix and a high-order cumulant of the signal sample x:
Where p=1, 2, …, k.
The mixing moment is:
Mpq＝E[X(t)p-qX*(t)q] (2)
the fourth-order cumulative amount of x (t) obtained from equation (1) is
The advantage of characterizing different interference features by higher order cumulants is that different signal patterns have different higher order cumulant parameters, which are a smoother value at different signal strengths, as can be seen in fig. 2.
S102, clustering is carried out according to the characteristic values of different interference signal samples, and threshold division is carried out on the characteristic values of different interference patterns. The higher-order cumulants of the three kinds of interference signals in advance are calculated through S101 to obtain a corresponding higher-order cumulant feature library, and since the higher-order cumulant features of the multi-tone interference signals are usually positive, and the higher-order cumulant features of the single-tone interference signals and the sweep-frequency interference signals are usually negative, it is recommended to select a threshold value of 0 to screen multi-tone interference, and when the obtained higher-order cumulant value is larger than 0, the multi-tone interference is the multi-tone interference, otherwise the multi-tone interference is the single-tone interference or the sweep-frequency interference. Because the sweep frequency interference and the single-tone interference have the same characteristics at the instant moment, the method has similar high-order cumulant characteristics, and therefore, a secondary identification method is adopted in the subsequent identification process,
S2: capturing an environmental signal through distributed nodes of the unmanned aerial vehicle network;
Considering that the unmanned network is composed of multiple distributed nodes and generally adopts a master-slave architecture, each distributed node receives and samples environmental signals within the operating frequency band of the unmanned system.
Further, in one embodiment of the present invention, after capturing the environmental signal through the distributed nodes of the unmanned network, further comprising
Preprocessing the environment signal, specifically including:
Define the received signal as x (t) =s (t) +n (t) +j (t),
Wherein s (t) is a useful signal, N (t) is additive Gaussian white noise, J (t) is an interference signal, the number of sampling points is N, and the envelope A (N) of each sampling point of the signal to be identified can be obtained after N-point FFT conversion:
S3: detecting the interference existence of the environmental signal through the distributed node;
Further, in one embodiment of the present invention, the interference presence detection of the environmental signal by the distributed node comprises
The method for detecting the interference existence of the environmental signal by adopting the double-threshold FCME algorithm specifically comprises the following steps:
sequencing spectral lines of the environmental signals according to a descending order rule;
Selecting the smallest one or a part of the smallest one to perform initial spectral line mean value calculation, setting two interference detection thresholds with different sizes by presetting two false alarm probabilities with different sizes, determining a subset of non-interference frequency points of a received signal by using a small interference detection threshold, and finally determining a subset of interference frequency points by using a large interference detection threshold;
and finally, judging the existence of interference according to the detection set of the interference frequency points.
Specifically, a dual threshold FCME algorithm is used to detect the presence of interference to the received signal. The method comprises the following steps: the received signal spectral lines are ordered according to descending order rules, the smallest one or a part of the smallest spectral line is selected to calculate initial spectral line mean value, two different interference detection thresholds are set through presetting two different false alarm probabilities, a small interference detection threshold is used for determining a non-interference frequency point subset of the received signal, then a large interference detection threshold is used for finally determining an interference frequency point subset, and finally the existence of interference is judged according to an interference frequency point detection set. Specifically:
S301, according to the envelope A (n) of the S2 received signal, all spectral line values of the A (n) are arranged in a descending order, two empty sets I (n) and J (n) are set, I (n) is a non-interference frequency point set, J (n) is an interference frequency point set, q spectral lines with the smallest value are defined as an original non-interference frequency point set I (n), and a spectral line mean E { I (n) } of the q spectral lines is calculated.
S302, setting low false alarm probability P fl, calculating a threshold factor a l by the formula (6), and calculating an original low threshold T l by the formula (7).
Where P f is the false alarm probability of the detection algorithm.
Tl＝al×E{I(n)} (7)
S303, comparing the I (N) in S301 with the T l in S302, updating the elements of which the I (I) is more than T l (0.ltoreq.i.ltoreq.N-1) into an interference frequency point set J (N), and updating the elements of which the I (I) is more than T l (0.ltoreq.i.ltoreq.N-1) into a non-interference frequency point set I (N).
S304, repeating S301 to S303 until I (N) and J (N) are not updated any more, and finally, setting the spectral line in the non-interference frequency point set I (N) as p, and then, the spectral line number N-p in the interference frequency point set J (N).
S305, solving a mean value E { I (n) } of p spectral lines in I (n);
s306, setting low false alarm probability P fh, calculating a threshold factor a h by the formula (6), and calculating an original high threshold T h by the formula (8).
Th＝ah×E{I(n)} (8)
S307, comparing the I (N) in S304 with the T h in S306, updating the elements of I (I) > T h (0.ltoreq.i.ltoreq.N-1) into the interference frequency point set J (N), and updating the elements of I (I) < T h (0.ltoreq.i.ltoreq.N-1) into the non-interference frequency point set I (N).
S308, completing interference detection to obtain an interference frequency point set J (n).
S4: if interference exists, calculating a high-order accumulated quantity of the environmental signal through the distributed nodes;
specifically: s401, establishing a conversion formula between a high-order matrix and a high-order cumulant:
Where p=1, 2, …, k.
S402, calculating a mixing moment:
Mpq＝E[X(t)p-qX*(t)q] (10)
s403, obtaining the fourth-order cumulative amount of the signal x (t) to be identified as
S5: and according to the high-order accumulation amount and the signal feature library, performing interference pattern discrimination through the distributed nodes to obtain a local interference pattern recognition result of the distributed nodes.
Further, in an embodiment of the present invention, performing interference pattern discrimination by a distributed node to obtain a local interference pattern recognition result of the distributed node includes:
judging whether the multi-tone interference exists or not according to the high-order accumulation quantity;
if not, further distinguishing the single-tone interference from the sweep interference.
Further, in an embodiment of the present invention, the determining whether the signal is a polyphonic interference according to the characteristic of the high-order cumulative amount includes:
When the value of the higher-order accumulation amount is larger than 0, the multi-tone interference is generated, otherwise, the single-tone interference or the sweep frequency interference is generated.
Further, in one embodiment of the present invention, the method further distinguishes between single tone interference and swept frequency interference, including
The number of the interference frequency points is used for distinguishing the single-tone interference signals from the sweep frequency interference signals by a secondary identification method, and the method specifically comprises the following steps:
if the two times of identification interference detection frequency points are the same in the continuous time, the single-tone interference is identified, and if the plurality of interference detection frequency points are different, the sweep frequency interference is identified.
As shown in fig. 3. In particular, the method comprises the steps of,
S501, judging whether the multi-tone interference is caused by the threshold value set in S1 according to the high-order accumulation quantity characteristic obtained in S4; if not, the process proceeds to step S502.
S502, further distinguishing the single-tone interference and the sweep frequency interference according to the high-order cumulant parameter obtained in the S4 and combining the interference frequency point distinguishing result obtained in the S3.
Although the high-order accumulation quantity characteristics of the single-tone interference signal and the sweep-frequency interference signal at the same moment are the same, the single-tone interference signal generally refers to audio interference with only one frequency point, sweep-frequency interference is complex, dynamic scanning is characterized in that the instantaneous frequency of the single-tone interference signal is always changed along with the change of time, a certain linear relationship is always present between the frequency and the time, the difference of the frequency points of the single-tone interference signal and the sweep-frequency interference signal on the same frequency band is considered, and the single-tone interference signal and the sweep-frequency interference signal can be distinguished through a secondary identification method by combining the number of the interference frequency points obtained in the step S3. If the frequency points of the secondary identification interference detection are the same in the continuous time, the single-tone interference is identified, and if the frequency points are different, the sweep frequency interference is identified.
Further, in one embodiment of the present invention, after obtaining the local interference pattern recognition result of the distributed node, the method further includes:
And sending the identification result of the local interference pattern to the main node through the standby link, and carrying out global judgment by the main node.
Specifically, each unmanned aerial vehicle node obtains respective interference recognition results through the above local recognition of the interference signals. When the unmanned aerial vehicle serving as the main node determines the global result, the result of the coincidence of at least any two nodes is taken as the final determination result.
The above is a flow of a method for identifying distributed cooperative interference of a complete unmanned aerial vehicle communication system, and fig. 4 is a schematic diagram of a technical route of the present invention.
The distributed cooperative interference identification method for the unmanned aerial vehicle communication system provided by the embodiment of the invention solves the defects of the traditional single-node interference identification method in the aspects of identification precision and efficiency by utilizing the distributed characteristics of the unmanned aerial vehicle network and the local sensing and computing capability of the nodes, and can realize the identification of various interference patterns such as single-tone interference, multi-tone interference, sweep-frequency interference and the like with lower complexity by utilizing the characteristics of the high-order cumulant and the frequency point of each signal. Compared with the prior art, the method has the advantages of low complexity, high identification accuracy, easy realization of engineering and the like.
In order to achieve the above embodiment, the invention further provides a distributed cooperative interference identification device of the unmanned aerial vehicle communication system.
Fig. 5 is a schematic structural diagram of a distributed cooperative interference identification device of an unmanned aerial vehicle communication system according to an embodiment of the present invention.
As shown in fig. 5, the distributed cooperative interference identification device of the unmanned aerial vehicle communication system includes: a construction module 100, a capture module 200, a detection module 300, a calculation module 400, a local identification module 500, wherein,
The construction module is used for establishing signal feature libraries of different interference patterns;
the acquisition module is used for acquiring environmental signals through distributed nodes of the unmanned aerial vehicle network;
The detection module is used for detecting the existence of interference on the environmental signal through the distributed nodes;
The calculation module is used for calculating the high-order accumulated quantity of the environmental signals through the distributed nodes if the interference exists;
and the local recognition module is used for judging the interference patterns through the distributed nodes according to the high-order accumulation and the signal characteristic library to obtain a local interference pattern recognition result of the distributed nodes.
Further, in one embodiment of the present invention, the method further includes a global identification module for:
And sending the local interference pattern recognition result to the main node through the standby link, and carrying out global judgment by the main node.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (8)
1. The distributed cooperative interference identification method of the unmanned aerial vehicle communication system is characterized by comprising the following steps of: establishing signal feature libraries of different interference patterns;
capturing an environmental signal through distributed nodes of the unmanned aerial vehicle network;
detecting the existence of interference to the environment signal through the distributed node;
If interference exists, calculating a high-order accumulated quantity of the environmental signal through the distributed node;
According to the high-order accumulation amount and the signal feature library, performing interference pattern discrimination through the distributed node to obtain a local interference pattern recognition result of the distributed node;
If interference exists, calculating a higher order accumulated quantity of the environmental signal by the distributed node, including:
Establishing a conversion formula between the high-order matrix and the high-order cumulant;
wherein p=1, 2, …, k;
Calculating a mixing moment:
Mpq＝E[X(t)p-qX*(t)q]
The fourth-order cumulative amount of the signal x (t) to be identified is obtained as follows:
After obtaining the local interference pattern recognition result of the distributed node, the method further comprises the following steps:
And sending the local interference pattern recognition result to the main node through a standby link, and carrying out global judgment by the main node.
2. The method of claim 1, wherein the signal characteristics of the different interference patterns comprise: single tone interference CWI, multi-tone interference SCWI, swept frequency interference LFM.
3. The method of claim 1, further comprising, after capturing the environmental signal by the distributed nodes of the drone network
Preprocessing the environment signal, specifically including:
Define the received signal as x (t) =s (t) +n (t) +j (t),
Wherein s (t) is a useful signal, N (t) is additive Gaussian white noise, J (t) is an interference signal, the number of sampling points is N, and the envelope A (N) of each sampling point of the signal to be identified can be obtained after N-point FFT conversion:
4. The method of claim 1, wherein said detecting the presence of interference to the ambient signal by the distributed node comprises
The method for detecting the interference existence of the environmental signal by adopting a double-threshold FCME algorithm specifically comprises the following steps:
sequencing spectral lines of the environmental signals according to a descending order rule;
Selecting the smallest one or a part of the smallest one to perform initial spectral line mean value calculation, setting two interference detection thresholds with different sizes by presetting two false alarm probabilities with different sizes, determining a subset of non-interference frequency points of a received signal by using a small interference detection threshold, and finally determining a subset of interference frequency points by using a large interference detection threshold;
And finally, judging the existence of interference according to the detection set of the interference frequency points.
5. The method of claim 4, wherein the performing, by the distributed node, the interference pattern discrimination to obtain the local interference pattern recognition result of the distributed node includes:
Judging whether the multi-tone interference exists or not according to the high-order accumulation amount;
if not, further distinguishing the single-tone interference from the sweep interference.
6. The method of claim 5, wherein said determining whether it is a polyphonic interference based on the characteristics of the higher order cumulants comprises:
And when the value of the high-order accumulation amount is larger than 0, the high-order accumulation amount is multitone interference, otherwise, the high-order accumulation amount is single-tone interference or sweep frequency interference.
7. The method of claim 5, wherein said further distinguishing between single tone interference and swept frequency interference comprises
Distinguishing the single-tone interference signal and the sweep frequency interference signal by the number of the interference frequency points through a secondary identification method specifically comprises the following steps:
if the two times of identification interference detection frequency points are the same in the continuous time, the single-tone interference is identified, and if the plurality of interference detection frequency points are different, the sweep frequency interference is identified.
8. The distributed cooperative interference identification device of the unmanned aerial vehicle communication system is characterized by comprising the following modules:
The construction module is used for establishing signal feature libraries of different interference patterns;
the acquisition module is used for acquiring environmental signals through distributed nodes of the unmanned aerial vehicle network;
the detection module is used for detecting the existence of interference on the environment signal through the distributed node;
the calculation module is used for calculating the higher-order accumulation amount of the environment signal through the distributed node if interference exists;
the local recognition module is used for judging the interference patterns through the distributed nodes according to the high-order accumulation amount and the signal characteristic library to obtain a local interference pattern recognition result of the distributed nodes;
The computing module is used for:
Establishing a conversion formula between the high-order matrix and the high-order cumulant;
wherein p=1, 2, …, k;
Calculating a mixing moment:
Mpq＝E[X(t)p-qX*(t)q]
The fourth-order cumulative amount of the signal x (t) to be identified is obtained as follows:
the device further comprises a global identification module for:
And sending the local interference pattern recognition result to the main node through a standby link, and carrying out global judgment by the main node.
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