A satellite internet of things active user detection method, device and storage medium

Abstract

The present invention discloses a satellite Internet of Things active user detection method, device and storage medium, and the method includes three parts: receiving multi-user identity information, identifying user codewords and estimating user angles. Receiving multi-user identity information is the reception of identity sequence information and noise superimposed by multiple active users in the same time slot by a satellite receiving end; identifying user codewords is to obtain the codeword index of the active user using a sparse reconstruction algorithm; estimating the user's angle is to estimate the direction of arrival of the reconstructed estimation signal obtained after code domain filtering to obtain user angle information; finally, the codeword indexes and angles of different active users are identified, and the purpose of user identification is achieved by combining the code domain and the air domain.

Description

Technical Field

[0001] The present invention relates to a satellite Internet of Things active user detection method, device and storage medium, and belongs to the technical field of satellite communications. Background Art

[0002] With the continuous development of society, our requirements for communication technology are getting higher and higher. As the Internet of Things penetrates deeper into "ubiquitous connection", how to connect devices to the communication network in a timely and effective manner will become a major problem at present and in the future. Although my country has vigorously built ground base stations and related equipment, from a national perspective, the coverage of ground base stations is still in densely populated areas, and the coverage rate of ground base stations in uninhabited areas is very low, especially in remote mountainous areas, oceans, deserts and other environments with poor geological conditions. How to connect Internet of Things devices in these areas is a problem to be solved. Therefore, in recent years, the research and construction of "satellite Internet of Things" at home and abroad has been increasing year by year.

[0003] The ground mobile communications we are familiar with have moved from the 4G era to the 5G era. The key technical indicators of 5G, such as the communication frequency band, application requirements and technical standards, have been determined one after another. Among them, the connection-oriented massive machine type communications (mMTC) is an important application scenario of the 5G communication system. With the development of communication technology, it is expected that after 2025, the 3GPP international standards organization will start the development of the international technical standards for the sixth generation of mobile communication technology (6G). The 6G network integrates ground wireless and satellite communications to achieve seamless coverage of air, land, and sea, which will greatly promote the development of the Internet of Things.

[0004] The rapid development of satellite IoT technology has also brought great challenges to satellite communication technology. How to ensure the quality of service for users and how to meet the needs of large-scale user access are urgent problems to be solved. When using a random access method based on authorization in scenarios such as the IoT that require large connections and low power consumption, although the satellite can obtain active user IDs to identify active users during the handshake process, this transmission method will bring a heavy burden to the communication system and reduce the utilization rate of communication system resources. In terms of reducing access delay and improving the efficiency of the communication system, unauthorized transmission technology can obtain obvious advantages. However, an important premise of unauthorized transmission is that the user's data transmission behavior is often unpredictable at the satellite end, and because unauthorized transmission does not go through the radio resource control connection process, the system cannot guarantee orthogonal transmission for each user. For a large number of small data packets, the system's wireless resources are limited. The use of traditional orthogonal multiple access will result in insufficient utilization of system resources and low system capacity. To avoid the limitation of orthogonality, consider using non-orthogonal access to achieve efficient use of resources. Therefore, combining unauthorized access technology with uplink NOMA transmission can make the system more efficient. Due to the existence of massive users, data transmission of different users is often carried out on the same wireless resource block. At this time, the data of different users overlap with each other, and the system cannot be sure that the user is active, which is not conducive to multi-user detection. Therefore, active user detection needs to be performed first. Summary of the invention

[0005] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a method for detecting active users of a satellite Internet of Things.

[0006] In the short burst communication of satellite Internet of Things, within a beam range, if only code domain identification is relied on, whether it is artificial division or erroneous coding that causes different users to use the same codeword, errors will occur when active users are identified at the receiving end, reaching the bottleneck of system performance. In order to overcome the deficiencies in the prior art, the present invention provides a satellite Internet of Things active user detection method. At present, code domain methods are mostly used for active user identification, but there are many users in the Internet of Things, and relying solely on code domain identification will cause the address code dimension to surge, greatly reducing the frame efficiency. Considering that information such as location is important access auxiliary information, the original code domain is extended from the airspace, based on phased array technology, the airspace is introduced, the direction of arrival (DOA) of the signal is estimated, and a multi-domain cascade method is used for active user detection.

[0007] This application studies a satellite Internet of Things active user detection method, which adds user location information to the detection process, expands the user information recognition dimension, improves the communication system capacity, and meets the requirements of massive connections. In order to reduce complexity, conflict judgment can be performed at the codeword recognition stage. In the case of a codeword sequence conflict, the user's wave direction is estimated to obtain angle information, and active user identification is further performed when the code domain recognition method is limited. When the user activity is unknown, the accurate number of active users can be found through data cross-validation to reduce recognition errors.

[0008] Technical solution: To achieve the above purpose, the technical solution adopted by the present invention is:

[0009] In a first aspect, a method for detecting active users of a satellite Internet of Things is provided, comprising:

[0010] Acquire a signal y(t) received by a satellite receiving end, wherein the signal is: a superposition signal of identity sequence information of multiple active users and noise received by the satellite receiving end based on multiple users sending information in the same time slot;

[0011] Based on the observation matrix C of the satellite receiving end, the received signal is used as the initial residual; entering the iteration step to iterate until the iteration stop condition is met; wherein the iteration step includes:

[0012] Calculate the correlation coefficient between the residual and the measurement matrix C, take the largest L columns in the measurement matrix C and add the corresponding column number γ to the updated user index set Ω β ;

[0013] According to the user index set Ω β , get the estimated active user codeword index and user index set Ω β The selected column set of the observation matrix C

[0014] According to the column set The received signal y(t) is reconstructed using the least squares method to obtain an estimated signal And update the residual;

[0015] The iterative step further includes: judging L, and in response to L>1, Based on constructing the array manifold matrix A, the sparse reconstruction algorithm is used to estimate the user's direction of arrival and obtain the user's angle information;

[0016] According to all detected user codeword index information and angle information, the detection results of active users are obtained.

[0017] In some embodiments, the satellite IoT active user detection method, multiple active users send information at the same time, the information x sent by the i-th user in time slot t i (t) is expressed as:

[0018]

[0019] Where: u i 、s i (t), c i are the complex envelope, symbol information and spread spectrum code of the sending user respectively, ω i is the carrier frequency of the sending user, ω i =2πv / λ i , v is the speed of signal propagation in space, λ i is the wavelength, j represents the complex number sign; e is the base of the natural logarithm;

[0020] The signal received by the reference array element in time slot t is expressed as:

[0021]

[0022] Where: c i =[c i,1 …c i,m ] T is the codeword vector of the i-th user, h i =[h i,1 …h i,m ] T is the channel vector between the satellite and the i-th user, T is the transposed symbol of the matrix, diag() is the diagonal matrix representation symbol, N is the total number of potential users within a beam range, and n(t) is the Gaussian noise vector;

[0023] The received signal of the mth array element is:

[0024]

[0025] Where: n m (t) is the noise of the mth array element in time slot t, τ mi is the time difference between the i-th signal reaching the m-th array element and the reference array element, τ mi =(m-1)dsinθ i / v, d is the array element spacing, θ i is the incident angle of the i-th user;

[0026] The signals received by all M array elements in time slot t are arranged into a column vector to form a snapshot of data. Due to the orthogonality of the spatial domain and the code domain, the signal is expressed as:

[0027]

[0028] Written in vector form:

[0029] Y(t)=AP(t)+N(t)

[0030] Among them: Y(t)=[y1(t),y2(t),…,y M (t)] T is the snapshot data vector received by the array, P(t)=[p1(t),p2(t),…,p N (t)] T is the vector of N user signals, and p i (t) = x i (t)·h i ,N(t)=[n1(t),n2(t),…,n M (t)] T is the noise data vector received by the array, the matrix is the set of array steering vectors, A=[a1a2 … a N ] M×N , where a i is the steering vector of the i-th source;

[0031]

[0032] In some embodiments, the column numbers γ corresponding to the largest L columns in the measurement matrix are:

[0033]

[0034] Among them, the observation matrix C is the spread spectrum code matrix, which is known, H is the conjugate matrix representing the symbol, and the initial residual r 0 is the received signal, β is the current iteration number, the initial value is 1, r β-1 is the residual of the βth iteration, and argmax is the index of the maximum value calculated.

[0035] In some embodiments, according to the column set The received signal y(t) is reconstructed using the least squares method to obtain an estimated signal include:

[0036]

[0037] Where y(t) is the received signal, is the pseudo-inverse symbol of the matrix, and T is the transpose symbol of the matrix.

[0038] Further, the residual is updated, including:

[0039]

[0040] Among them, y(t) is the received signal, r β is the updated residual, i.e., the residual of the β+1th iteration.

[0041] In some embodiments, the iteration stopping condition is that the cross-validation error of this iteration is not less than the cross-validation error of the previous iteration.

[0042] In some embodiments, a method for calculating a cross-validation error includes:

[0043]

[0044] Among them, ε (t) is the cross validation error, y(t) is the received signal, is the user index set Ω β The selected column set of the observation matrix C, is the estimated signal obtained by this reconstruction.

[0045] In a second aspect, a satellite Internet of Things active user detection device is provided, including a processor and a storage medium;

[0046] The storage medium is used to store instructions;

[0047] The processor is used to operate according to the instructions to execute the steps of the method according to the first aspect.

[0048] According to a third aspect, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method according to the first aspect are implemented.

[0049] Beneficial effects: The present invention provides an active user detection method based on satellite Internet of Things. The method first uses the code domain method to identify users. If there is a code conflict, the spatial domain arrival direction estimation method is used to estimate the user angle and further identify different users. This process has an operation to determine the number of users using the same number of code words, and the cross-validation error method can be used to estimate the sparsity of active users. This method not only takes into account the use of code domain identification methods when user code words do not conflict, but also can use angle information to identify different users when code words conflict. It has a certain degree of compatibility and meets the scenario of massive connections of satellite Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart of the implementation process of the method of the embodiment of the present invention;

[0051] Figure 2 This is a comparison chart of recognition success rates between the method according to the embodiment of the present invention and the code domain detection method in the absence of codeword conflict;

[0052] Figure 3 The figure is a curve diagram showing the relationship between the signal-to-noise ratio and the recognition success rate under codeword conflict in the method of the embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0054] In the description of the present invention, "several" means more than one, "many" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0055] In the description of the present invention, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0056] Example 1

[0057] A satellite Internet of Things active user detection method, comprising:

[0058] Acquire a signal y(t) received by a satellite receiving end, wherein the signal is: a superposition signal of identity sequence information of multiple active users and noise received by the satellite receiving end based on multiple users sending information in the same time slot;

[0059] Based on the observation matrix C of the satellite receiving end, the received signal is used as the initial residual; entering the iteration step to iterate until the iteration stop condition is met; wherein the iteration step includes:

[0060] Calculate the correlation coefficient between the residual and the measurement matrix C, take the largest L columns in the measurement matrix C and add the corresponding column number γ to the updated user index set Ω β ;

[0061] According to the user index set Ω β , get the estimated active user codeword index and user index set Ω β The selected column set of the observation matrix C

[0062] According to the column set The received signal y(t) is reconstructed using the least squares method to obtain an estimated signal And update the residual;

[0063] The iterative step further includes: judging L, and in response to L>1, Based on constructing the array manifold matrix A, the sparse reconstruction algorithm is used to estimate the user's direction of arrival and obtain the user's angle information;

[0064] According to all detected user codeword index information and angle information, the detection results of active users are obtained.

[0065] In some embodiments, the satellite IoT active user detection method, multiple active users send information at the same time, the information x sent by the i-th user in time slot t i (t) is expressed as:

[0066]

[0067] Where: u i 、s i (t), c i are the complex envelope, symbol information and spread spectrum code of the sending user respectively, ω i is the carrier frequency of the sending user, ω i =2πv / λ i , v is the speed of signal propagation in space, λ i is the wavelength, j represents the complex number sign; e is the base of the natural logarithm;

[0068] The signal received by the reference array element in time slot t is expressed as:

[0069]

[0070] Where: c i =[c i,1 …c i,m ] T is the codeword vector of the i-th user, h i =[h i,1 …h i,m ] T is the channel vector between the satellite and the i-th user, T is the transposed symbol of the matrix, diag() is the diagonal matrix representation symbol, N is the total number of potential users within a beam range, and n(t) is the Gaussian noise vector;

[0071] The received signal of the mth array element is:

[0072]

[0073] Where: n m (t) is the noise of the mth array element in time slot t, τ mi is the time difference between the i-th signal reaching the m-th array element and the reference array element, τ mi =(m-1)dsinθ i / v, d is the array element spacing, θ i is the incident angle of the i-th user;

[0074] The signals received by all M array elements in time slot t are arranged into a column vector to form a snapshot of data. Due to the orthogonality of the spatial domain and the code domain, the signal is expressed as:

[0075]

[0076] Written in vector form:

[0077] Y(t)=AP(t)+N(t)

[0078] Among them: Y(t)=[y1(t),y2(t),…,y M (t)] T is the snapshot data vector received by the array, P(t)=[p1(t),p2(t),…,p N (t)] T is the vector of N user signals, and p i (t) = x i (t)·h i ,N(t)=[n1(t),n2(t),…,n M (t)] T is the noise data vector received by the array, the matrix is the set of array steering vectors, A=[a1a2 … a N ] M×N , where a i is the steering vector of the i-th source;

[0079]

[0080] In some embodiments, the column numbers γ corresponding to the largest L columns in the measurement matrix are:

[0081]

[0082] Among them, the observation matrix C is the spread spectrum code matrix, which is known, H is the conjugate matrix representing the symbol, and the initial residual r 0 is the received signal, β is the current iteration number, the initial value is 1, r β-1is the residual of the βth iteration, and argmax is the index of the maximum value calculated.

[0083] In some embodiments, according to the column set The received signal y(t) is reconstructed using the least squares method to obtain an estimated signal include:

[0084]

[0085] Where y(t) is the received signal, is the pseudo-inverse symbol of the matrix, and T is the transpose symbol of the matrix.

[0086] Further, the residual is updated, including:

[0087]

[0088] Among them, y(t) is the received signal, r β is the updated residual, i.e., the residual of the β+1th iteration.

[0089] In some embodiments, the iteration stopping condition is that the cross-validation error of this iteration is not less than the cross-validation error of the previous iteration.

[0090] In some embodiments, a method for calculating a cross-validation error includes:

[0091]

[0092] Among them, ε (t) is the cross validation error, y(t) is the received signal, is the user index set Ω β The selected column set of the observation matrix C, is the estimated signal obtained by this reconstruction.

[0093] In some embodiments, Figure 1 As shown, a satellite Internet of Things active user detection method includes three parts: multi-user identity information reception, user codeword recognition and user angle estimation.

[0094] Part 1: Receiving multi-user identity information

[0095] Within the same satellite beam range, multiple active users send information at the same time. The signal x sent by the i-th user i (t) can be expressed as:

[0096]

[0097] Where: u i 、si (t), c i are the complex envelope, symbol information and spread spectrum code of the sending user respectively, ω i is the carrier frequency of the sending user, ω i =2πv / λ i , v is the speed of signal propagation in space, λ i is the wavelength, j represents the complex number sign; e is the base of the natural logarithm;

[0098] The signal received by the reference array element in time slot t is expressed as:

[0099]

[0100] Where: c i =[c i,1 …c i,m ] T is the codeword vector of the i-th user, h i =[h i,1 …h i,m ] T is the channel vector between the satellite and the i-th user, T is the transposed symbol of the matrix, diag() is the diagonal matrix representation symbol, N is the total number of potential users within a beam range, and n(t) is the Gaussian noise vector;

[0101] The received signal of the mth array element is:

[0102]

[0103] Where: n m (t) is the noise of the mth array element in time slot t, τ mi is the time difference between the i-th signal reaching the m-th array element and the reference array element, τ mi =(m-1)d sinθ i / v, d is the array element spacing, θ i is the incident angle of the i-th user;

[0104] The signals received by all M array elements in time slot t are arranged into a column vector to form a snapshot of data. Due to the orthogonality of the spatial domain and the code domain, the signal is expressed as:

[0105]

[0106] Written in vector form:

[0107] Y(t)=AP(t)+N(t)

[0108] Among them: Y(t)=[y1(t),y2(t),…,y M (t)] Tis the snapshot data vector received by the array, P(t)=[p1(t),p2(t),…,p N (t)] T is the vector of N user signals, and p i (t) = x i (t)·h i ,N(t)=[n1(t),n2(t),…,n M (t)] T is the noise data vector received by the array, the matrix is the set of array steering vectors, A=[a1 a2 … a N ] M×N , where a i is the steering vector of the i-th source;

[0109]

[0110] Part 2: User codeword recognition

[0111] (2.1) Find the L columns in the measurement matrix that have the greatest correlation with the residual, that is, And record their corresponding column numbers γ in the measurement matrix to update the user index set Ω β , β is the current iteration number, the initial value is 1,; where the measurement matrix is ​​the spread spectrum code matrix C, H is the conjugate matrix representing the symbol, and the initial residual r 0 To receive the signal, r β-1 is the residual of the βth iteration, argmax is the index of the maximum value calculated;

[0112] (2.2) Reconstruct the approximate solution of active users in the current iteration Right now And update the residual, that is in According to the user index set Ω β The selected set of columns of the measurement matrix C;

[0113] (2.3) Judge L and decide whether to continue the user arrival direction estimation. If L>1, proceed to the arrival direction estimation in step (3); if L=1, proceed to the cross-validation error in step (3).

[0114] Part 3: User Angle Estimation

[0115] (3.1) The signal estimated by the code domain in step (2) Obtaining a user signal for which direction of arrival estimation is required;

[0116] (3.2) Construct the array popularity matrix A and use the sparse reconstruction algorithm to estimate the direction of arrival;

[0117] (3.3) Obtain the estimated active user codeword index and angle set;

[0118] (3.4) Calculate the cross validation error If it is less than the error of the previous iteration, the number of iterations is increased by 1, and the process returns to step (2) to continue calculating the L columns in the measurement matrix with the largest correlation with the residual; otherwise, the iteration is terminated, indicating that all active users in the time slot have been detected.

[0119] Figure 2 This is a comparison chart of the recognition success rates of the method of the present invention and the code domain detection method without codeword conflict; Figure 3 The figure is a curve diagram showing the relationship between the signal-to-noise ratio and the recognition success rate under codeword conflicts in the method of the present invention.

[0120] In summary, the satellite Internet of Things active user detection method provided by the present invention first uses the code domain method to identify users. If there is a code word conflict, the spatial domain wave direction estimation method is continued to be used to estimate the user angle to further identify different users. This process has an operation to judge the number of users using the same number of code words, and the cross-validation error method can be used to estimate the sparsity of active users. This method not only takes into account the use of the code domain identification method for identification when the user code words do not conflict, but also can use the angle information to identify different users when the code words conflict. It has a certain compatibility and meets the scenario of massive connections of the satellite Internet of Things.

[0121] Example 2

[0122] In a second aspect, this embodiment provides a satellite Internet of Things active user detection device, including a processor and a storage medium;

[0123] The storage medium is used to store instructions;

[0124] The processor is used to operate according to the instructions to execute the steps of the method according to embodiment 1.

[0125] Example 3

[0126] In a third aspect, this embodiment provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Example 1 are implemented.

[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0130] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0131] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A satellite Internet of Things active user detection method, characterized in that: include: Acquire a signal y(t) received by a satellite receiving end, wherein the signal is: a superposition signal of identity sequence information of multiple active users and noise received by the satellite receiving end based on multiple users sending information in the same time slot; Based on the observation matrix C of the satellite receiving end, the received signal is used as the initial residual; Enter the iteration step to iterate until the iteration stop condition is met; The iterative steps include: Calculate the correlation coefficient between the residual and the measurement matrix C, take the largest L columns in the measurement matrix C and add the corresponding column number γ to the updated user index set Ω β ; According to the user index set Ω β , get the estimated active user codeword index and user index set Ω β The column set C of the selected observation matrix C Ωβ ; According to the column set C Ωβ , the received signal y(t) is reconstructed using the least squares method to obtain an estimated signal And update the residual; The iterative step further includes: judging L, and in response to L>1, Based on constructing the array manifold matrix A, the sparse reconstruction algorithm is used to estimate the user's direction of arrival and obtain the user's angle information; According to all detected user codeword index information and angle information, the detection results of active users are obtained.

2. The satellite Internet of Things active user detection method according to claim 1 is characterized in that: Multiple active users send information at the same time. The information x sent by the i-th user in time slot t is i (t) is expressed as: x i (t)=u i ·s i (t)·c i ·e jωit Where: u i 、s i (t), c i are the complex envelope, symbol information and spread spectrum code of the sending user respectively, ω i is the carrier frequency of the sending user, ω i =2πv / λ i , v is the speed of signal propagation in space, λ i is the wavelength, j represents the complex number sign; e is the base of the natural logarithm; The signal received by the reference array element in time slot t is expressed as: Where: c i =[c i,1 …c i,m ] T is the codeword vector of the i-th user, h i =[h i,1 …h i,m ] T is the channel vector between the satellite and the i-th user, T is the transposed symbol of the matrix, diag() is the diagonal matrix representation symbol, N is the total number of potential users within a beam range, and n(t) is the Gaussian noise vector; The received signal of the mth array element is: Where: n m (t) is the noise of the mth array element in time slot t, τ mi is the time difference between the i-th signal reaching the m-th array element and the reference array element, τ mi =(m-1)d sinθ i / v, d is the array element spacing, θ i is the incident angle of the i-th user; The signals received by all M array elements in time slot t are arranged into a column vector to form a snapshot of data. Due to the orthogonality of the spatial domain and the code domain, the signal is expressed as: Written in vector form: Y(t)=AP(t)+N(t) Among them: Y(t)=[y1(t),y2(t),…,y M (t)] T is the snapshot data vector received by the array, P(t)=[p1(t),p2(t),…,p N (t)] T is the vector of N user signals, and p i (t) = x i (t)·h i ,N(t)=[n1(t),n2(t),…,n M (t)] T is the noise data vector received by the array, the matrix is the set of array steering vectors, A=[a1 a2 …a N ] M×N , where a i is the steering vector of the i-th source; to i =[and -jwiτ1i And -jwiτ2i … And -jwiτMi ] T i=1,2,…,N。 3. The satellite Internet of Things active user detection method according to claim 1 is characterized in that: The column number γ corresponding to the largest L columns in the observation matrix is: Among them, H is the conjugate matrix representation symbol, the initial residual r 0 is the received signal, β is the current iteration number, the initial value is 1, r β-1 is the residual of the βth iteration, and argmax is the index of the maximum value calculated.

4. The satellite Internet of Things active user detection method according to claim 3 is characterized in that: According to the column set C Ωβ , the received signal y(t) is reconstructed using the least squares method to obtain an estimated signal include: Where y(t) is the received signal, is the pseudo-inverse symbol of the matrix, and T is the transpose symbol of the matrix.

5. The satellite Internet of Things active user detection method according to claim 4 is characterized in that: Update residual r β ,include: Among them, y(t) is the received signal, r β is the updated residual.

6. The satellite Internet of Things active user detection method according to claim 1, characterized in that: The iteration stopping condition is that the cross-validation error of this iteration is not less than the cross-validation error of the previous iteration.

7. The satellite Internet of Things active user detection method according to claim 6 is characterized in that: The calculation method of cross-validation error includes: Among them, ε (t) is the cross-validation error, y(t) is the received signal, C Ωβ is the user index set Ω β The selected column set of the observation matrix C, is the estimated signal obtained by this reconstruction.

8. A satellite Internet of Things active user detection device, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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