A multi-target fast direction finding method and system based on single snapshot data

Abstract

The present invention relates to the technical field of electromagnetic signal processing, and provides a multi-target fast direction finding method and system based on single snapshot data. The multi-target fast direction finding method based on single snapshot data includes: dividing the space where K signals are located at a certain angular grid interval to obtain a rough estimate of the direction angle of each signal; based on the rough estimate of the direction angle of each signal, calculating the amplitudes of the K signals, performing K cycles, and performing one correction to obtain the currently to-be-estimated signal retained only in each cycle; based on the to-be-estimated signal in the current cycle, performing a secondary correction using a closed-form expression based on angle deviation to obtain the signal direction angle; iteratively cycling the processes of one correction and secondary correction until the difference between the signal direction angles of each signal in two adjacent cycles is less than a preset threshold, ending the cycle, and outputting the signal direction angles of all signals. The present invention realizes fast and high-precision direction finding of multiple targets.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic signal processing, and in particular, to a multi-target fast direction finding method and system based on single snapshot data. Background Art

[0002] The statements in this part only provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] The actual electromagnetic environment is complex and changeable. When multiple targets exist simultaneously, how to quickly and accurately determine the directions of multiple targets has become an urgent problem to be solved. The traditional method for target direction finding based on multi-snapshot data requires long-term accumulation of received data and cannot guarantee fast direction finding for multiple targets. In addition, some traditional target direction finding methods divide the spatial angle at a certain angle grid interval and assume that the direction angle of the estimated signal is estimated at discrete sampling grid points, ignoring the deviation caused by the angle grid and severely reducing the estimation accuracy. Summary of the Invention

[0004] In order to solve the technical problems in the above background art, the present invention provides a multi-target fast direction finding method and system based on single snapshot data. Based on the single snapshot received signal, a method combining rough estimation of the target direction angle and multiple cyclic iterative corrections of the angle deviation generated by the rough estimation is adopted to achieve fast and high-precision direction finding of multiple targets.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The first aspect of the present invention provides a multi-target fast direction finding method based on single snapshot data.

[0007] A multi-target fast direction finding method based on single snapshot data includes:

[0008] Performing beamforming on all received single snapshot received signals, and searching for K signals with signal values greater than a set threshold after beamforming; dividing the space where the K signals are located at a certain angle grid interval to obtain a rough estimation of the direction angle of each signal;

[0009] Based on the rough estimation of the direction angle of each signal, calculating the amplitudes of the K signals, performing K cycles, and performing one correction to obtain the current signal to be estimated retained in each cycle;

[0010] Based on the signal to be estimated in the current cycle, performing secondary correction using the closed-form expression based on the angle deviation to obtain the signal direction angle;

[0011] Iterate the processes of primary calibration and secondary calibration once until the difference between the signal direction angles of two adjacent cycles of each signal is less than a preset threshold, then the loop ends and the signal direction angles of all signals are output.

[0012] Further, the space where the K signals are located is divided at certain angular grid intervals to obtain a rough estimate of each signal direction angle; the method includes: dividing the space where the K signals are located at certain angular grid intervals, positioning the angle of each signal at the nearest discrete sampling grid point, and obtaining a rough estimate of each signal direction angle.

[0013] Further, based on the rough estimate of each signal direction angle, calculate the amplitudes of the K signals, perform K loops, and perform primary calibration to obtain the current signal to be estimated retained in each loop; it is represented by the following formula:

[0014]

[0015] where, represents the k th signal, which is the current signal to be estimated; represents the signal received in each loop, represents the i th signal amplitude, represents the i th rough estimate of the signal direction angle.

[0016] Further, based on the signal to be estimated in the current loop, perform secondary calibration using a closed-form expression based on angle deviation to obtain the signal direction angle; the method includes: conjugate multiplying the signals to be estimated in the current loop received by two adjacent array elements, taking the phase after multiplication, and constructing a closed-form expression based on angle deviation based on the phase after multiplication and the covariance matrix of the noise difference vector between two adjacent array elements, and solving to obtain the signal direction angle.

[0017] Even further, the conjugate multiplication of the signals to be estimated in the current loop received by two adjacent array elements is represented by the following formula:

[0018]

[0019] where, represents the new value formed by conjugate multiplying the signal to be estimated in the current loop received by the n +1th array element and the signal to be estimated in the current loop received by the n th array element, represents the k th signal amplitude, is the position difference between two adjacent array elements, $\theta_k$ is the unknown direction-of-arrival angle of the $k$-th signal to be estimated. $\Delta n$ is the noise difference between two adjacent array elements.

[0020] Furthermore, the closed-form expression based on angle deviation is described by the following formula:

[0021]

[0022] where, $\theta$ represents the signal direction angle, $\mathbf{R}_n^{-1}$ represents the inverse matrix of the covariance matrix of the noise difference vector, $\varphi$ is the phase.

[0023] The second aspect of the present invention provides a multi-target fast direction-finding system based on single-snapshot data.

[0024] A multi-target fast direction-finding system based on single-snapshot data includes:

[0025] A coarse estimation module, which is configured to: perform beamforming on all received single-snapshot received signals, search for $K$ signals with signal values greater than a set threshold after beamforming; divide the space where the $K$ signals are located at a certain angular grid interval to obtain a coarse estimation of the direction angle of each signal;

[0026] A primary correction module, which is configured to: calculate the amplitudes of the $K$ signals based on the coarse estimation of the direction angle of each signal, perform $K$ loops and one-time correction to obtain the current signal to be estimated that is only retained in each loop;

[0027] A secondary correction module, which is configured to: perform secondary correction based on the signal to be estimated in the current loop using the closed-form expression based on angle deviation to obtain the signal direction angle;

[0028] An iterative loop and output module, which is configured to: loop and iterate the processes of primary correction and secondary correction until the difference between the signal direction angles of each signal in two adjacent loops is less than a preset threshold, at which point the loop ends and the signal direction angles of all signals are output.

[0029] The third aspect of the present invention provides a computer-readable storage medium.

[0030] A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps in the multi-target fast direction-finding method based on single-snapshot data described in the first aspect above.

[0031] The fourth aspect of the present invention provides a computer device.

[0032] A computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the multi-target fast direction finding method based on single snapshot data as described in the first aspect above.

[0033] The fifth aspect of the present invention provides a computer program product or a computer program.

[0034] The present invention provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to execute the steps in the multi-target fast direction finding method based on single snapshot data as described in the first aspect above.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] A multi-target fast direction finding method and system based on single snapshot data proposed by the present invention can overcome the problem of slow target direction finding speed caused by the need for long-term accumulation of received data, and can also overcome the problem of low target direction finding accuracy caused by angle deviation caused by fixed division of angle grid intervals. By receiving and processing single snapshot data and iteratively correcting the target direction angle, fast and high-precision direction finding of multiple targets is achieved.

[0037] The present invention makes a rough estimate of the target direction angle based on a single snapshot received signal, and can quickly find the direction of the target without the need to accumulate more snapshot data for a long time. Then, based on the closed-form expression of the angle deviation obtained by analysis, the angle deviation generated by the rough estimate is corrected through multiple iterative loops. During the iterative loop correction process, only one signal angle is corrected in each loop, and the remaining signals except the signal to be estimated in the current loop are subtracted from the received signal. By correcting all signal angles through multiple iterative loops, the direction finding accuracy of multiple targets is improved, and high-precision direction finding of the target is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0039] Figure 1 is a flowchart of the multi-target fast direction finding method based on single snapshot data shown in the present invention;

[0040] Figure 2 is a graph showing the variation of the mean square error with the signal-to-noise ratio in the case of a single real signal shown in the present invention;

[0041] Figure 3 It is a graph showing the variation of the mean square error with the signal-to-noise ratio in the case of a single complex signal shown by the present invention;

[0042] Figure 4 It is a graph showing the variation of the mean square error with the signal-to-noise ratio in the case of multiple real signals shown by the present invention;

[0043] Figure 5 It is a graph showing the variation of the mean square error with the signal-to-noise ratio in the case of multiple complex signals shown by the present invention;

[0044] Figure 6 It is a structural diagram of a multi-target fast direction finding system based on single snapshot data shown by the present invention. Detailed implementation manners

[0045] The present invention will be further described below in conjunction with the drawings and embodiments.

[0046] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0047] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0048] It should be noted that the flowcharts and block diagrams in the drawings illustrate the possible architectures, functions, and operations of methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, the program segment, or the part of code may include one or more executable instructions for implementing the logical functions specified in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. Similarly, it should be noted that each block in the flowchart and / or block diagram, and the combinations of blocks in the flowchart and / or block diagram, may be implemented using a dedicated hardware-based system for performing the specified functions or operations, or may be implemented using a combination of dedicated hardware and computer instructions.

[0049] Signal perception and processing in a complex electromagnetic environment can better recognize and understand the surrounding environment. Target direction finding is an important part of signal perception and processing, which determines the source direction by receiving and analyzing radio waves. In a complex environment when there are multiple moving targets, the time and accuracy of signal direction finding become the focus of attention. It is necessary to study a fast and high-precision direction finding method for multiple targets in a complex environment. The present invention proposes a multi-target fast direction finding method and system based on single snapshot data. The following describes the invention in detail through several embodiments:

[0050] Embodiment 1

[0051] As Figure 1 shown, this embodiment provides a multi-target fast direction finding method based on single snapshot data. This embodiment takes the application of this method to a server as an example. It can be understood that this method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, web servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected through wired or wireless communication methods, and this application does not limit this. In this embodiment, the method includes the following steps:

[0052] Perform beamforming on all received single snapshot received signals, and search for K signals whose signal values are greater than a set threshold after beamforming; divide the space where the K signals are located at a certain angular grid interval to obtain a rough estimate of the signal direction angle of each signal;

[0053] Based on the rough estimate of the signal direction angle of each signal, calculate the amplitudes of the K signals, perform K loops, perform one correction, and obtain the currently estimated signal retained only in each loop;

[0054] Based on the currently estimated signal in the current loop, perform a secondary correction using a closed-form expression based on angle deviation to obtain the signal direction angle;

[0055] Loop and iterate the processes of one correction and secondary correction until the difference between the signal direction angles of each signal in two adjacent loops is less than a preset threshold, the loop ends, and the signal direction angles of all signals are output.

[0056] The present invention estimates the target direction angle based on single snapshot received signals and can perform fast direction finding on moving targets.

[0057] The multi-target fast direction finding method based on single snapshot data described in this embodiment will be described in detail below. This method includes:

[0058] Step 1: Assume that the number of signals to be estimated is known as , and the spatial angles of the signals to be estimated are divided according to a certain angular grid interval. The direction angles of the signals to be estimated are not at the discrete sampling grid points. First, beamforming is performed on the single snapshot received signal. By searching for the positions of the maximum values of the signals after beamforming, the angle is located on the grid closest to the true angle, and a rough estimate of the direction angle of the signal to be estimated is obtained.

[0059] Step 2: On the basis of obtaining the rough estimate of the signal direction angle, calculate the amplitudes of the signals . For loops (only one signal is corrected in each loop, and there are loops in total), in each loop, subtract the remaining signals except the signal to be estimated in the current loop from the received signal. The specific formula is as follows:

[0060] .

[0061] Among them, represents the k th signal, represents the signal received in each loop, represents the amplitude of the i th signal, represents the rough estimate of the direction angle of the i th signal. It should be noted that only the angle of the current signal to be estimated is corrected in each loop, that is, the angle of the th signal .

[0062] In each loop of the present invention, only one signal angle is corrected, which minimizes the influence of other signals on the signal to be estimated in the current loop. After multiple loop iterations, all signal angles can be corrected one by one, realizing the direction finding of multiple targets simultaneously.

[0063] Step 3: The rough estimate of the signal direction angle obtained by beamforming is essentially to approximate the estimated direction angle of the signal to the nearest angular grid point, resulting in a certain estimation error. Therefore, for the signal to be estimated in the current loop, a method based on a closed-form expression of the angle deviation is proposed to further correct the angle deviation to obtain a high-precision signal direction angle.

[0064] Regarding Step 3, the detailed steps of the method provided in this solution are as follows:

[0065] Step 301: When solving the closed - form expression based on the angle deviation, first conjugate - multiply the received signal data of two adjacent array elements, and then take the phase of the data obtained after multiplication. The phase contains the position difference between two adjacent array elements, the unknown incident wave direction angle, and the noise difference between two adjacent array elements. The specific formula is as follows:

[0066]

[0067] where, denotes the new value formed by conjugate - multiplying the signal received by the n +(1)th antenna (array element) and the signal received by the n th antenna (array element), denotes the amplitude of the k th signal, is the position difference between two adjacent array elements, is the unknown incident wave direction angle of the k - th signal to be estimated, is the noise difference between two adjacent array elements.

[0068]

[0069] where, is the phase of the data.

[0070] The above formula is obtained by conjugate - multiplying the data of the k - th signal received by two adjacent array elements. Therefore, whether it is a complex or real - number signal, the phase will be eliminated during the conjugate - multiplication process, which does not affect the subsequent processing. Therefore, the present invention is applicable to both real - number signals and complex - number signals for direction finding, and gives the simulation verification results. For single or multiple real - number or complex - number signals, the mean - square error of direction finding can approach the Cramer - Rao bound, realizing high - precision direction finding.

[0071] Step 302: Since the noise - difference vector between two adjacent array elements follows a Gaussian distribution, the covariance matrix of the noise - difference vector can be obtained according to the existing method, which will not be elaborated in the present invention.

[0072] Step 303: Based on the covariance matrix of the noise - difference vector obtained in the above step, the closed - form expression based on the angle deviation can be calculated by the weighted least - squares method, and the true direction angle of the signal can be corrected by the closed - form expression based on the angle deviation for the rough estimation of the signal angle. The specific formula is as follows:

[0073] ,

[0074] .

[0075] where, denotes with respect to The weighted least squares function represents the signal direction angle.

[0076] Step 4: Iteratively loop Steps 2 and 3 until, for each signal, the signal direction angles of two adjacent loops are less than a preset threshold, at which point the loop ends. Finally, method performance simulation evaluation is carried out for different signal direction angles of multiple signals.

[0077] Regarding Step 4, the detailed performance evaluation scheme provided by this solution is as follows:

[0078] The Cramer-Rao bound is an important criterion for measuring the accuracy of signal angle estimation. The Cramer-Rao bound is affected by factors such as different signal direction angles and the number of snapshots. It should be noted that the method proposed in this invention is based on single-snapshot data, so the influence of the number of snapshots on the method performance is not considered in the simulation analysis. This invention has carried out simulation experiments. Figure 2 is the curve of the mean square error versus the signal-to-noise ratio in the case of a single real signal shown in this invention; Figure 3 is the curve of the mean square error versus the signal-to-noise ratio in the case of a single complex signal shown in this invention; Figure 4 is the curve of the mean square error versus the signal-to-noise ratio in the case of multiple real signals shown in this invention; Figure 5 is the curve of the mean square error versus the signal-to-noise ratio in the case of multiple complex signals shown in this invention. In the simulation, 200 Monte Carlo runs are used for statistics, the curve of the mean square error of signal angle estimation versus the signal-to-noise ratio is given, and it is compared with the Cramer-Rao bound of the method proposed in this invention, indicating that the angle estimation error of the multi-target fast direction finding method based on single-snapshot data proposed in this invention can approach the Cramer-Rao bound, achieving high-precision signal direction finding.

[0079] This invention improves the target direction finding accuracy by adopting a method that combines rough estimation of the target direction angle and multiple iterative corrections of the angle deviation generated by the rough estimation. At the same time, taking the Cramer-Rao bound as the criterion for measuring the accuracy of signal angle estimation, the direction finding method proposed in this invention can approach the Cramer-Rao bound in different numbers of signals, real signals, and complex signal cases, achieving high-precision target direction finding.

[0080] Embodiment 2

[0081] This embodiment provides a multi-target fast direction finding system based on single-snapshot data.

[0082] As Figure 6 shown, a multi-target fast direction finding system based on single-snapshot data includes:

[0083] A coarse estimation module, which is configured to: perform beamforming on all received single-snapshot received signals, search for K signals with signal values greater than a set threshold after beamforming; divide the space where the K signals are located at a certain angular grid interval to obtain a coarse estimation of the direction angle of each signal;

[0084] A primary calibration module, which is configured to: calculate the amplitudes of the K signals based on the coarse estimation of the direction angle of each signal, perform K cycles, perform a primary calibration, and obtain the current signal to be estimated that is retained only in each cycle;

[0085] A secondary calibration module, which is configured to: perform secondary calibration based on the signal to be estimated in the current cycle using a closed-form expression based on angular deviation to obtain the signal direction angle;

[0086] An iterative loop and output module, which is configured to: loop and iterate the processes of primary calibration and secondary calibration until the difference between the signal direction angles of each signal in two adjacent cycles is less than a preset threshold, end the loop, and output the signal direction angles of all signals.

[0087] In some embodiments, the coarse estimation module is further configured to: divide the space where the K signals are located at a certain angular grid interval, position the angle of each signal at the nearest discrete sampling grid point, and obtain a coarse estimation of the direction angle of each signal.

[0088] In some embodiments, the method executed by the primary calibration module is represented by the following formula:

[0089]

[0090] Where, represents the k th signal, that is, the current signal to be estimated; represents the signal received in each cycle, represents the amplitude of the i th signal, represents the i th coarse estimation of the signal direction angle.

[0091] In some embodiments, the secondary calibration module is further configured to: conjugate multiply the signals to be estimated in the current cycle received by two adjacent array elements, take the phase after multiplication, and construct a closed-form expression based on angular deviation based on the phase after multiplication and the covariance matrix of the noise difference vector between two adjacent array elements, and solve to obtain the signal direction angle.

[0092] In some embodiments, the conjugate multiplication of the signals to be estimated in the current cycle received by two adjacent array elements is represented by the following formula:

[0093]

[0094] Among them, represents the new value formed by conjugate multiplication of the signal to be estimated in the current cycle received by the n +(1)th array element and the signal to be estimated in the current cycle received by the n th array element. represents the amplitude of the k th signal. is the position difference between two adjacent array elements. is the unknown arrival direction angle of the kth signal to be estimated. is the noise difference between two adjacent array elements.

[0095] In some embodiments, the closed-form expression based on the angle deviation is described by the following formula:

[0096]

[0097] Among them, represents the signal direction angle. represents the inverse matrix of the covariance matrix of the noise difference vector. is the phase.

[0098] Embodiment III

[0099] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps in the multi-target fast direction finding method based on single snapshot data as described in Embodiment I above are implemented.

[0100] Embodiment IV

[0101] This embodiment provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps in the multi-target fast direction finding method based on single snapshot data as described in Embodiment I above are implemented.

[0102] Embodiment V

[0103] This embodiment provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in the multi-target fast direction finding method based on single snapshot data as described in Embodiment I above.

[0104] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) that contain computer-usable program code.

[0105] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0106] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0108] Those of ordinary skill in the art can understand that to implement all or part of the processes in the above-described embodiment methods, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-described method embodiments. Among them, the storage medium can be a magnetic disk, an optical disc, a read-only memory (ROM), or a random access memory (RAM), etc.

[0109] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi-target fast direction finding method based on single snapshot data, characterized in that, Including: Perform beamforming on all received single-snapshot received signals, and search for K signals whose signal values are greater than a set threshold after beamforming; divide the space where the K signals are located at a certain angular grid interval to obtain a rough estimate of the angular direction of each signal; Based on the rough estimate of the angular direction of each signal, calculate the amplitudes of the K signals, perform K loops, and perform one correction to obtain the current signal to be estimated retained in each loop; Based on the signal to be estimated in the current loop, perform secondary correction using a closed-form expression based on angular deviation to obtain the angular direction of the signal. The closed-form expression based on angular deviation is described by the following formula: Among them, represents the signal direction angle, represents the inverse matrix of the covariance matrix of the noise difference vector, is the phase; Iteratively loop the processes of one correction and secondary correction until the difference between the angular directions of the signals in two adjacent loops of each signal is less than a preset threshold, end the loop, and output the angular directions of all signals.

2. The multi-target fast direction finding method based on single snapshot data according to claim 1, characterized in that The method of dividing the space where the K signals are located at a certain angular grid interval to obtain a rough estimate of the angular direction of each signal includes: dividing the space where the K signals are located at a certain angular grid interval, positioning the angle of each signal at the nearest discrete sampling grid point, and obtaining a rough estimate of the angular direction of each signal.

3. The multi-target fast direction finding method based on single snapshot data according to claim 1, characterized in that, Based on the rough estimate of the angular direction of each signal, calculate the amplitudes of the K signals, perform K loops, and perform one correction to obtain the current signal to be estimated retained in each loop; It is represented by the following formula: Among them, represents the k th signal, which is the current signal to be estimated; represents the signal received in each loop, represents the i amplitude of the th signal, i represents the rough estimation of the direction angle of the th signal.

4. The multi-target fast direction finding method based on single snapshot data according to claim 1, characterized in that The method of performing secondary correction using a closed-form expression based on angular deviation based on the signal to be estimated in the current loop to obtain the angular direction of the signal includes: conjugately multiplying the signals to be estimated in the current loop received by two adjacent array elements, taking the phase after multiplication, and constructing a closed-form expression based on angular deviation based on the phase after multiplication and the covariance matrix of the noise difference vector between two adjacent array elements, and solving to obtain the angular direction of the signal.

5. The multi-target fast direction finding method based on single snapshot data according to claim 4, characterized in that, The conjugately multiplying the signals to be estimated in the current loop received by two adjacent array elements is represented by the following formula: Among them, represents the new value formed by conjugate multiplication of the signal to be estimated in the current cycle received by the n +(1)th array element and the signal to be estimated in the current cycle received by the n th array element. represents the amplitude of the k th signal. is the position difference between two adjacent array elements. is the unknown incident direction angle of the kth signal to be estimated. is the noise difference between two adjacent array elements.

6. A multi-target fast direction-finding system based on single snapshot data, characterized in that, Including: A rough estimation module configured to: perform beamforming on all received single-snapshot received signals, search for K signals whose signal values are greater than a set threshold after beamforming; divide the space where the K signals are located at a certain angular grid interval to obtain a rough estimate of the angular direction of each signal; A primary correction module configured to: based on the rough estimate of the angular direction of each signal, calculate the amplitudes of the K signals, perform K loops, and perform one correction to obtain the current signal to be estimated retained in each loop; A secondary correction module configured to: based on the signal to be estimated in the current loop, perform secondary correction using a closed-form expression based on angular deviation to obtain the angular direction of the signal. The closed-form expression based on angular deviation is described by the following formula: Among them, represents the signal direction angle, represents the inverse matrix of the covariance matrix of the noise difference vector, is the phase; An iterative loop and output module configured to: iteratively loop the processes of one correction and secondary correction until the difference between the angular directions of the signals in two adjacent loops of each signal is less than a preset threshold, end the loop, and output the angular directions of all signals.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps in the multi-target fast direction finding method based on single snapshot data according to any one of claims 1-5.

8. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the multi-target fast direction finding method based on single snapshot data according to any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes a computer program which, when executed by a processor, implements the steps in the multi-target fast direction finding method based on single snapshot data according to any one of claims 1-5.

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