Unmanned aerial vehicle monitoring method for spectrum aliasing wireless signal identification

Abstract

The invention discloses a spectrum aliasing wireless signal identification-oriented unmanned aerial vehicle monitoring method, which comprises the following steps of: performing short-time Fourier transform on a spectrum mixed signal received by a monitoring node in a certain spectrum sampling time slot to obtain a spectrum signal; performing type identification through non-negative matrix factorization, constructing and solving a target function Q1, and obtaining an estimated value of the number of unknown signal sources; and splicing the to-be-solved coefficient matrix containing the type information under different visual angles and the signal fading template matrix of the monitoring node, constructing and solving an objective function Q2, and obtaining a signal type and transmitting power indication matrix to represent the position and the transmitting power of an unknown signal. According to the invention, three tasks of signal identification, signal position estimation and transmitting power estimation are solved through a depth identification method; and meanwhile, a better monitoring effect can be obtained by moving the UAV near the center of mass.

Description

Technical Field

[0001] The present invention relates to the field of in-depth identification of spectrum aliasing signals, and particularly to a drone monitoring method for identifying spectrum aliasing wireless signals. Background Art

[0002] The application of unmanned aerial vehicles (UAVs) in the monitoring field is developing rapidly, providing innovative solutions for real-time monitoring, data collection, and extensive intelligent analysis. In the context of wireless signal monitoring, deploying UAVs equipped with advanced spectrum analyzers can dynamically analyze and evaluate the radio frequency spectrum in a specific area. These UAVs can not only capture accurate data in complex environments but also assist in spectrum management, interference detection, and overall communication security assessment. In addition, using UAVs for spectrum monitoring has the advantages of high flexibility, wide coverage, and fast response speed. UAVs can quickly respond to sudden signal changes and rapidly adjust their monitoring positions to ensure obtaining key information in the shortest time, playing a crucial role in ensuring the security and stability of communication networks. Especially in the face of an increasingly complex electromagnetic environment, with its mobility and wide coverage, UAVs can provide strong support for the optimal allocation of spectrum resources and the timely detection of communication interference.

[0003] In a radio monitoring system, traditional static monitoring nodes have some inherent limitations due to their fixed positions. The monitoring range of these nodes is often affected by their physical location and the surrounding environment, resulting in a limited signal reception coverage range, thus affecting the overall monitoring efficiency and accuracy. In addition, static nodes cannot flexibly respond to the dynamic changes of signal sources, especially in complex urban environments or areas with large terrain undulations, where their monitoring effects are more easily hindered. The introduction of UAVs has brought significant advantages to the radio monitoring system. With its flexible flight ability and high mobility, UAVs can break through the limitations of traditional static nodes, achieve dynamic monitoring of a wide area, and significantly improve the signal reception coverage range and monitoring accuracy. However, in the case of blind source signals, due to the lack of prior position information of the signal source, UAVs still face challenges in practical applications. How to optimize the flight strategy of UAVs in the case of unknown signal source positions to maximize their effectiveness in the radio monitoring system has become an important topic in current research. This not only involves the planning of flight paths but also the application of various signal processing and optimization algorithms to ensure that UAVs can effectively meet the monitoring requirements in a complex electromagnetic environment.

[0004] In the field of UAV monitoring, the existing technology [1] equips UAVs with RF receivers to detect and identify other UAVs by transmitting RF signals. In the existing technology [2], the UAV swarm transmits wireless data in real time to achieve automatic modulation recognition in V2X (vehicle-to-everything) radio monitoring and optimize the flight trajectory of UAVs to obtain better network performance and monitoring effects. In the existing technology [3], the resource allocation and data collection of the UAV-assisted wireless sensor network can effectively monitor the state of the wireless sensor network.

[0005] In the existing UAV-assisted monitoring systems (such as the existing technologies [1] to [3]), the focus is mainly on optimizing the flight trajectory of UAVs to obtain better network performance and less resource consumption, without considering how to better identify the radio signals in the monitoring area. At present, the identification of spectral aliasing signals focuses on the identification of signal types, and the location and transmission power characteristics of unknown signals are not fully identified.

[0006] Existing technology [1]: Y. Xie, P. Jiang, Y. Gu and X. Xiao, “Dual-Source Detection and Identification System Based on UAV Radio Frequency Signal,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-15, 2021.

[0007] Existing technology [2]: Q. Zhou, S. Wu, C. Jiang, R. Zhang and X. Jing, “Over-the-Air Federated Transfer Learning Over UAV Swarm for Automatic Modulation Recognition in V2X Radio Monitoring,” IEEE Transactions on Vehicular Technology, vol. 73, no. 3, pp. 3597-3607, March 2024.

[0008] Prior Art [3]: T.D.P. Perera, S. Panic, D.N.K. Jayakody, P. Muthuchidambaranathan, and J. Li, “A WPT-enabled UAV-assisted condition monitoring scheme for wireless sensor networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 8, pp. 5112-5126, 2020。 Summary of the Invention

[0009] An object of the present invention is to overcome the disadvantages and deficiencies of the prior art, and provide a UAV monitoring method for identifying spectrum overlapping wireless signals. This method utilizes the flexibility of UAVs to monitor radio, deeply identify the types of spectrum overlapping wireless signals, estimate the positions and transmission powers of signals, and through UAV-assisted radio monitoring of spectrum overlapping signals, adjusting the positions of UAVs can effectively improve the identification effect of spectrum overlapping signals.

[0010] The object of the present invention is achieved by the following technical solutions:

[0011] A UAV monitoring method for identifying spectrum overlapping wireless signals includes the following steps:

[0012] S1. Perform short-time Fourier transform on the spectrum mixed signal received by the monitoring node within a certain spectrum sampling time slot to obtain a spectrum signal; where one UAV serves as one monitoring node;

[0013] S2. For the spectrum signal, perform type identification through non-negative matrix factorization to construct the objective function Q 1 ;

[0014] S3. Solve the objective function Q 1 Derive the update rules for the coefficient matrix containing type information and the signal type consensus matrix to be solved respectively, and finally obtain an estimated value of the number of unknown signal sources;

[0015] S4. Concatenate the coefficient matrix containing type information to be solved from different perspectives with the signal fading template matrix of the monitoring node to construct the objective function Q 2 ;

[0016] S5. Through the proximal alternating linearized minimization algorithm for the objective function Q 2Solve to obtain the update rule of the signal type to be solved and the transmission power indication matrix, and solve to obtain that the signal type and the transmission power indication matrix represent the position and transmission power of the unknown signal.

[0017] In step S1, the spectrum mixed signal s received by the m-th monitoring node in the t-th spectrum sampling time slot m (t) is:

[0018]

[0019] Wherein, J m is the number of frequency point samplings of the m-th monitoring node in this frequency band; is the standard power spectral density function of the n-th 0 unknown signal at J m frequency points, and B s is the standard spectrum dictionary of the signals recognizable by the edge node; is the transmission power of the n-th 0 unknown signal source at time slot t, is the channel gain coefficient from the n-th 0 unknown signal source to the m-th monitoring node, z m (t) is the noise at the m-th monitoring node and follows the Gaussian distribution.

[0020] In step S1, the channel gain coefficient of the spectrum mixed signal is defined as follows:

[0021] Considering the large-scale fading of wireless propagation in an outdoor rich scattering environment, the channel gain coefficient is defined as:

[0022]

[0023] Wherein, the channel constant f is the working center frequency of the unknown signal, c is the speed of light, E r,m and are the antenna gains of the m-th monitoring node and the n-th 0 unknown signal respectively; and are the channel response and distance between the m-th monitoring node and the n-th 0 unknown signal respectively;

[0024] And there is:

[0025]

[0026] Wherein, and (x m , y m , h m ) are the coordinates of the n 0 th unknown signal and the mth UAV in the coordinate system with the edge node as the origin, respectively; the maximum abscissa and maximum ordinate of the signal monitoring range of the monitoring network are ±x max , ±y max ; remain unchanged within T s ; the link loss d g,m from the gth grid to the mth monitoring node is the distance from the gth grid to the mth monitoring node, and the signal fading dictionary W = [W 1 , W 2 … W M G×M , and W m = [q 1,m , q 2,m …… q G,m , where W m is the signal fading template matrix of the mth monitoring node.

[0027] For the M monitored spectrum aliasing signals s m (t), the spectrum signal R m is obtained by short-time Fourier transform, and the species identification is performed by non-negative matrix factorization to construct the objective function Q 1 , and Q 1 is solved;

[0028]

[0029] s.t. A m ≥0, A * ≥0;

[0030] where R m is the spectrum aliasing signal monitored by the mth UAV, B s is the standard spectrum dictionary of the signals that can be recognized by the edge node, A m is the coefficient matrix containing species information to be solved, is the signal species determination matrix, which indicates whether it is this type of signal through 0 and 1, and A * is the signal species consensus matrix;

[0031] Since the problem Q 1 is convex in the alternating solution of A m and A * , A m and A * ​Derive the update rule:

[0032]

[0033]

[0034] Obtain the estimated value of the number of unknown signal sources which is:

[0035]

[0036] where is the nth element in matrix A * in the matrix A

[0037] Concatenate the A m under different perspectives with W m to strengthen the connection under each perspective. A = [A 1 , A 2 … A M N×M , W = [W 1 , W 2 … W M G×M ; Construct the objective function Q 2 which is:

[0038]

[0039] s.t. U ≥ 0, ∥(U) n ∥ 1 = ∥(U) n ∥ 2 ;

[0040] where U is the matrix indicating the types and transmission powers of the signals to be solved;

[0041] Since Q 2 satisfies the KL condition, solve it through the proximal alternating linearization minimization algorithm to obtain the update rule of U:

[0042]

[0043] The obtained U matrix also represents the positions and transmission powers of the unknown signals. The nth row of U represents the position and transmission power of the nth signal. The subscript corresponding to the largest element in this row is the grid number where the nth signal is located, and this largest element is the transmission power of the nth signal.

[0044] The flight strategy of the UAV is as follows:

[0045] First, calculate the corresponding two-dimensional coordinates according to the estimated grid numbers of the unknown signals ​​​ The two-dimensional coordinates with grid number g are represented as:

[0046]

[0047] where X max and Y max are the farthest horizontal and vertical coordinates after the monitoring range is gridded, g is the number of grids, and d 0 is the grid spacing; the centroid coordinates (x 0 , y c ) of N unknown signals are calculated through their two-dimensional coordinates: c )

[0048]

[0049]

[0050] Construct a circle with (x c , y c ) as the center and radius r c . Divide this circle into M equal parts, and the m-th equal point is (x m , y m ):

[0051]

[0052] Each UAV moves to the equal point closest to itself. If there is already a UAV at this equal point, it moves to the next closest equal point to itself; for M UAVs, calculate the distance matrix of the UAVs to the M equal points respectively. The distance matrix of the m-th UAV is D m = [d m,1 , d m,2 ... d m,M ;

[0053] If there is no UAV at the m' position, the m-th UAV moves to the m' position, where m' is:

[0054]

[0055] If there is a UAV at the m' position, then delete d m from D m′,M ;

[0056] After all M UAVs move to the corresponding equal points, monitor the signals and estimate the grid numbers of the unknown signals

[0057] Repeatedly execute this movement method until the estimation result approaches stability.

[0058] Meanwhile, the present invention provides:

[0059] A server, the server includes a processor and a memory, and at least one segment of program is stored in the memory, and the program is loaded and executed by the processor to implement the above-mentioned method for identifying spectrum aliased wireless signals based on an unmanned aerial vehicle.

[0060] A computer-readable storage medium, at least one segment of program is stored in the storage medium, and the program is loaded and executed by a processor to implement the above-mentioned method for identifying spectrum aliased wireless signals based on an unmanned aerial vehicle.

[0061] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0062] 1. The present invention solves the three tasks of signal identification, signal position estimation, and transmission power estimation through a deep identification method.

[0063] 2. When formulating the flight method, the present invention considers the distribution of unknown signals and controls the movement of the UAV through the centroid. Since the centroid of the unknown signal position indicates that uniform unknown signals are distributed near this position, moving the UAV near the centroid can obtain better monitoring effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the monitoring architecture of the UAV monitoring method for identifying spectrum aliased wireless signals.

[0065] Figure 2 It is a comparison diagram of the effects of different methods on the signal type identification task.

[0066] Figure 3 It is a comparison diagram of the effects of different methods on the signal position estimation task.

[0067] Figure 4 It is an operation effect diagram of the UAV flight strategy on the signal type identification task.

[0068] Figure 5 It is an operation effect diagram of the UAV flight strategy on the signal position estimation task.

[0069] Figure 6 It is an operation effect diagram of the UAV flight strategy on the signal transmission power estimation task. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The following further describes the present invention in detail with reference to the embodiments and the drawings, but the embodiments of the present invention are not limited thereto.

[0071] As Figure 1 , the UAV monitoring method for identifying spectrum aliased wireless signals includes the following steps:

[0072] S1. Perform short-time Fourier transform on the spectrum mixed signal received by a monitoring node within a certain spectrum sampling time slot to obtain a spectrum signal; where one unmanned aerial vehicle (UAV) serves as one monitoring node.

[0073] S2. Perform species identification on the spectrum signal through non-negative matrix factorization to construct an objective function Q 1 ;

[0074] S3. Solve the objective function Q 1 Derive the update rules for the coefficient matrix containing species information and the signal species consensus matrix to be solved respectively, and finally obtain an estimated value of the number of unknown signal sources.

[0075] S4. Concatenate the coefficient matrix containing species information to be solved from different perspectives with the signal fading template matrix of the monitoring node to construct an objective function Q 2 ;

[0076] S5. Solve the objective function Q 2 through the proximal alternating linearized minimization algorithm to obtain the update rules of the signal species and transmission power indication matrix to be solved, and solve to obtain that the signal species and transmission power indication matrix represent the position and transmission power of the unknown signal.

[0077] Specifically:

[0078] A UAV monitoring method for identifying spectrally aliased wireless signals includes the following steps:

[0079] Assume that m UAVs serve as m monitoring nodes and are evenly distributed within the communication range, and the spectrum data generated by each monitoring node's spectrum scanning is wirelessly transmitted back to the edge computing node.

[0080] The detection duration of each monitoring node for one frequency band is T s , and the time for transmitting the spectrum data of this frequency band is T d , then the total duration for completing the monitoring task of one frequency band is T 0 = T s + T d ;

[0081] Assume that there are N 0 unknown wireless signal sources in the same frequency band within this monitoring area, and the monitoring node has no prior information about their signal types, positions, and transmission powers.

[0082] The spectrum mixed signal s m (t) received by the m-th monitoring node in the t-th spectrum sampling time slot is:

[0083]

[0084] Among them, J m is the number of frequency point samplings of the m-th monitoring node within this frequency band; is for the n-th 0 unknown signal's standard power spectral density function at J m frequency points, and B s is the standard spectrum dictionary of the signals recognizable by the edge node; is for the n-th 0 unknown signal source's transmission power at time slot t, is for the n-th 0 unknown signal source to the m-th monitoring node's channel gain coefficient, z m (t) is the noise at the m-th monitoring node and follows the Gaussian distribution;

[0085] Considering the large-scale fading of wireless propagation in an outdoor rich-scattering environment, thus the channel gain coefficient is defined as:

[0086]

[0087] Among them, the channel constant f is the operating center frequency of the unknown signal, c is the speed of light, E r,m and are respectively the antenna gains of the m-th monitoring node and the n-th 0 unknown signal; and are respectively the channel response and the distance between the m-th monitoring node and the n-th 0 unknown signal;

[0088] And there is:

[0089]

[0090] Among them, and (x m , y m , h m ) are respectively the coordinates of the n-th 0 unknown signal and the m-th UAV in the coordinate system with the edge node as the origin; the maximum abscissa and maximum ordinate of the signal monitoring range of this monitoring network are ±x max , ±y max ; remains unchanged within T s ; the link loss of the g-th grid to the m-th monitoring node d g,mis the distance from the g-th grid to the m-th monitoring node, and defines the signal fading dictionary within the defined area W = [W 1 , W 2 … W M G×M , and W m = [q 1,m , q 2,m …… q G,m , where W m is the signal fading template matrix of the m-th monitoring node.

[0091] The present invention proposes joint multi-layer NMF recognition (JMNR) for in-depth recognition of spectrum aliasing signals. For the M monitored spectrum aliasing signals s m (t), the spectrum signal R m is obtained through short-time Fourier transform, and the category recognition is performed through non-negative matrix factorization to construct the objective function Q 1 , and Q 1 is solved;

[0092]

[0093] where R m is the spectrum aliasing signal monitored by the m-th UAV, B s is the standard spectrum dictionary of the signals that can be recognized by the edge node, A m is the coefficient matrix containing category information to be solved, is the signal category determination matrix, which indicates whether it is this type of signal through 0 and 1, and A * is the signal category consensus matrix.

[0094] Since the problem Q 1 is convex when alternately solving A m and A * , the derivative is taken for A m and A * respectively to obtain the update rule:

[0095]

[0096]

[0097] The estimated value of the number of unknown signal sources is:

[0098]

[0099] where is the n-th element in the matrix A * .

[0100] ​A from different perspectives m and W m are spliced to strengthen the connection from each perspective. A = [A 1 , A 2 … A M N×M , W = [W 1 , W 2 … W M G×M . Construct the objective function Q 2 :

[0101]

[0102] s.t. U ≥ 0, ∥(U) n ∥ 1 = ∥(U) n ∥ 2 ;

[0103] where U is the matrix of signal types and transmit power indications to be solved.

[0104] Since Q 2 satisfies the KL (Kurdyka - Lojasiewicz) condition, it is solved by the proximal alternating linearization minimization (PLAM) algorithm to obtain the update rule of U:

[0105]

[0106] The solved U matrix also represents the positions and transmit powers of the unknown signals. The nth row of U represents the position and transmit power of the nth signal. The subscript corresponding to the largest element in this row is the grid number where the nth signal is located, and this largest element is the transmit power of the nth signal.

[0107] The present invention also designs a flight method for the drone. By adjusting the position of the drone, better monitoring effects can be obtained. First, according to the estimated grid numbers of the unknown signals calculate the corresponding two - dimensional coordinates The two - dimensional coordinates of grid number g are expressed as:

[0108]

[0109] where X max and Y max are the farthest horizontal and vertical coordinates after the monitoring range is grid - divided, g is the number of grids, d 0 is the grid spacing; calculate their centroid coordinates (x 0 , y c , y c ) from the two - dimensional coordinates of N

[0110] ​​

[0111] Construct a circle with the center at (x c , y c ) and radius r c . Divide this circle into M equal parts, and the m-th equal division point is (x m , y m ):

[0112]

[0113] Each drone moves to the equal division point closest to itself. If there is already a drone at this equal division point, it moves to the next closest equal division point to itself. For M drones, calculate the distance matrix of the drones to the M equal division points respectively. The distance matrix of the m-th drone is D m = [d m,1 , d m,2 ... d m,M ;

[0114] If there is no drone at the m' position, the m-th drone moves to the m' position, where m' is:

[0115]

[0116] If there is a drone at the m' position, delete d m from D m′,M ;

[0117] After all M drones move to the corresponding equal division points, monitor the signal and estimate the grid number of the unknown signal

[0118] Repeatedly execute this movement method until the estimation result approaches stability.

[0119] Meanwhile, the present invention provides:

[0120] A server, the server includes a processor and a memory. At least one segment of program is stored in the memory, and the program is loaded and executed by the processor to implement the above-mentioned method for identifying spectrum aliased wireless signals based on drones.

[0121] A computer-readable storage medium, at least one segment of program is stored in the storage medium, and the program is loaded and executed by a processor to implement the above-mentioned method for identifying spectrum aliased wireless signals based on drones.

[0122] Experimental results show that this flight method can effectively improve the effect of signal type identification, position and transmission power estimation of spectrum aliased signals.

[0123] As Figure 2, in the signal type recognition task, the method proposed by the present invention is superior to the traditional blind source separation method.

[0124] Such as Figure 3 , in the signal position estimation task, the method proposed by the present invention is superior to the traditional trilateration algorithm and the PSO method.

[0125] Such as Figure 4 , as the number of runs of the UAV flight strategy increases, the signal type recognition effect of the method proposed by the present invention is gradually improved. When the monitoring range D = 140m, the signal type recognition effect is improved by 5.2%.

[0126] Such as Figure 5 , as the number of runs of the UAV flight strategy increases, the signal position estimation results of the method proposed by the present invention are comprehensively improved. When the monitoring range D = 60m, the signal position estimation results are improved by 12.3%; when the monitoring range D = 140m, the signal position estimation results are improved by 20.9%;

[0127] Such as Figure 6 , as the number of runs of the UAV flight strategy increases, the signal transmission power estimation results of the method proposed by the present invention are gradually improved. When the monitoring range D = 140m, the signal transmission power estimation results are improved by 25.3%.

[0128] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited by the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A drone monitoring method for spectrum aliasing wireless signal identification, characterized in that: The following steps are involved: S1. Perform short-time Fourier transform on the spectrum mixed signal received by the monitoring node in a spectrum sampling time slot to obtain the spectrum signal; wherein a drone is used as a monitoring node; S2, for the spectrum signal, class identification is performed through non-negative matrix decomposition to construct the objective function q1; S3, solving the objective function q1, respectively derivatizing the coefficient matrix containing the type information and the signal type consensus matrix to be solved to obtain the update rule, and finally obtaining the estimated value of the number of unknown signal sources; S4, concatenating the coefficient matrix containing the type information to be solved under different perspectives with the signal fading template matrix of the monitoring node to construct the objective function q2; S5. Solve the objective function q2 through the proximal alternating linearization minimization algorithm to obtain the update rule of the signal type and transmit power indicator matrix to be solved, and the signal type and transmit power indicator matrix obtained by solving represent the position and transmit power of the unknown signal.

2. The method for monitoring unmanned aerial vehicles for spectrum aliasing wireless signal identification according to claim 1, characterized in that: In step S1, the spectrum mixed signal s received by the mth monitoring node in the tth spectrum sampling time slot m (t) is: in, J m is the number of frequency sampling points of the mth monitoring node in the frequency band; is the n0th unknown signal in J m The standard power spectral density function at the frequency points, and B s A standard spectrum dictionary for edge nodes to identify signals; is the transmission power of the n0th unknown signal source in time slot t, is the channel gain coefficient from the n0th unknown signal source to the mth monitoring node, z m (t) is the noise at the mth monitoring node and obeys Gaussian distribution.

3. The method for monitoring unmanned aerial vehicles for spectrum aliasing wireless signal identification according to claim 1, characterized in that: In step S1, the channel gain coefficient of the spectrum mixing signal is defined as follows: Considering the large-scale fading of wireless propagation in outdoor rich scattering environment, the channel gain coefficient Defined as: Among them, the channel constant f is the operating center frequency of the unknown signal, c is the speed of light, E r,m and are the antenna gains of the mth monitoring node and the n0th unknown signal respectively; and are the channel response and distance between the mth monitoring node and the n0th unknown signal respectively; And there are: in, and (x m ,y m ,h m ) are the coordinates of the n0th unknown signal and the mth UAV in the coordinate system with the edge node as the origin; the maximum horizontal coordinate and maximum vertical coordinate of the signal monitoring range of the monitoring network are ±x max ,±y max ; In T s The link loss from the gth grid to the mth monitoring node remains unchanged; d g,m is the distance from the gth grid to the mth monitoring node, defining the signal fading dictionary within the area W=[W1,W2…W M ] G×M , and W m =[q 1,m ,q2, m ……q G,m ],in W m is the signal fading template matrix of the mth monitoring node.

4. The method for monitoring unmanned aerial vehicles for spectrum aliasing wireless signal identification according to claim 1, characterized in that: The M monitored spectrum aliasing signals s m (t) The spectrum signal R is obtained by short-time Fourier transform m , class identification is performed through non-negative matrix decomposition, the objective function Q1 is constructed, and Q1 is solved; Where R m is the spectrum aliasing signal detected by the mth UAV, B s is the standard spectrum dictionary of signals that can be identified by edge nodes, A m is the coefficient matrix containing species information to be solved, is the signal type determination matrix, which indicates whether it is a certain type of signal through 0 and 1. * is the signal category consensus matrix; Since problem Q1 is solved alternately in A m With A * is convex, respectively for A m With A * The derivative obtains the update rule: Get an estimate of the number of unknown signal sources for: in is the matrix A * The nth element in .

5. The method for monitoring unmanned aerial vehicles for spectrum aliasing wireless signal identification according to claim 4, characterized in that: The A from different perspectives m With W m Splicing is performed to strengthen the connection between different perspectives, A=[A1,A2…A M ] N×M ,W=[W1,W2…W M ] H×M ; Construct objective function Q2: Where U is the signal type and transmit power indicator matrix to be solved; Since Q2 satisfies the KL condition, it is solved by the proximal alternating linear minimization algorithm to obtain the update rule for U: The U matrix obtained by solving the problem also represents the position and transmission power of the unknown signal. The nth row of U represents the position and transmission power of the nth signal, where the subscript corresponding to the maximum element of the row is the grid number where the nth signal is located, and the maximum element is the transmission power of the nth signal.

6. The method for monitoring unmanned aerial vehicles for spectrum aliasing wireless signal identification according to claim 1, characterized in that: The flight strategy of the drone is as follows: First, according to the estimated grid number of the unknown signal Calculate the corresponding two-dimensional coordinates The two-dimensional coordinates of the grid number g are expressed as: Where X max With Y max The farthest horizontal and vertical coordinates after the monitoring range is gridded, g is the number of grids, d0 is the grid spacing; the centroid coordinates (x c ,y c ): Construct a c ,y c ) is the center of the circle and the radius is r c The circle is divided into M equal parts, and the mth dividing point is (x m ,y m ): Each drone moves to the nearest equally divided point. If there is a drone at that equally divided point, it moves to the next equally divided point closest to itself. For M drones, the distance matrix from the drone to the M equally divided points is calculated respectively, where the distance matrix of the mth drone is D m =[d m,1 ,d m,2 …d m,M ]; If there is no drone at position m', the mth drone moves to position m', where m' is: If there is a drone at position m', then m Delete d from m′,M ; After all M drones move to the corresponding equally divided points, the grid numbers of the unknown signals are estimated by monitoring the signals. This moving method is performed repeatedly until the estimated result is close to stable.

7. A server, comprising a processor and a memory, wherein at least one program is stored in the memory, wherein: The program is loaded and executed by the processor to implement the spectrum aliasing wireless signal identification method based on a drone as described in any one of claims 1 to 6.

8. A computer-readable storage medium, wherein at least one program is stored in the storage medium, characterized in that: The program is loaded and executed by a processor to implement the spectrum aliasing wireless signal identification method based on a drone as described in any one of claims 1 to 6.

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