A method and apparatus for identifying multiple targets of frequency-hopping radiation sources

Abstract

This invention discloses a method and apparatus for identifying multiple targets of frequency-hopping radiation sources. The method includes: performing time-frequency analysis on the aliased signals of each receiving station to obtain a time-spectrum map; reducing noise and binarizing the time-spectrum map; optimizing the binarized time-spectrum map; performing connected region analysis on the optimized binarized time-spectrum map using an eight-connected region labeling method, and matching signals between different receiving stations based on the characteristic parameters of each hop signal; separating single-hop signals from the mixed signals using a narrowband filter with a time window; calculating the arrival time difference of the single-hop signals received by the master station and each slave station, and combining them into a time difference vector; clustering the time difference vector to form clusters, removing noise points from each cluster to obtain time difference measurements; and jointly calculating the time difference measurements corresponding to each target to calculate the target location. This method achieves the simultaneous location of multiple frequency-hopping radiation sources without signal sorting.

Description

Technical Field

[0001] This invention relates to the field of radio signal processing technology, and in particular to a method and apparatus for identifying multiple targets of frequency-hopping radiation sources. Background Technology

[0002] Frequency-hopping signals, with their constantly shifting carrier frequencies, have gained widespread application in both civilian and military communications due to their superior anti-interference capabilities, low probability of interception, excellent anti-fading characteristics, and high level of security. In civilian applications, the Global System for Mobile Communications (GSM), Bluetooth devices, and drone remote control signals all utilize frequency-hopping technology. In the military field, frequency-hopping communication has become one of the core communication methods for militaries worldwide. Precisely locating frequency-hopping radiation sources is of significant value in target detection and threat identification.

[0003] Passive positioning, also known as passive location, is a technology that uses one or more receiving stations to passively receive radiation signals from a target to achieve positioning. Among them, time difference positioning technology has become the mainstream solution in the current passive positioning field due to its advantages such as high positioning accuracy and strong resistance to multipath interference.

[0004] However, when multiple frequency-hopping radiation sources communicate in a network, the receiving station receives mixed signals from multiple sources, which overlap in the time and frequency domains, making traditional time difference estimation methods difficult to apply. On the one hand, single-hop signals have short residence times, resulting in low accuracy in time difference estimation using single-hop signals; on the other hand, directly estimating the time difference from mixed signals faces the problem of correlation ambiguity, leading to positioning failures or a large number of false targets. Existing time difference estimation methods mainly target single-source signals and are difficult to solve the positioning problem in scenarios with overlapping signals from multiple frequency-hopping radiation sources. Some studies have proposed signal sorting methods, but the sorting process is complex, and its performance in complex electromagnetic environments is difficult to guarantee. Summary of the Invention

[0005] This invention provides a method and apparatus for identifying multiple targets of frequency-hopping radiation sources, in order to solve the problems of difficult sorting caused by time-frequency aliasing of receiver signals and fuzzy correlation of traditional time difference estimation methods in multi-frequency-hopping radiation source localization scenarios under complex networking conditions.

[0006] Firstly, this invention provides a method for identifying multiple targets of frequency-hopping radiation sources, which is executed by a computing device. The computing device can be understood as a computer or similar device, but is not limited thereto in this invention. The method includes:

[0007] Time-frequency analysis was performed on the aliased signals at each receiving station to obtain time-frequency spectra. This analysis included short-time Fourier transform, modulus taking, and squaring of the aliased signals at each station. Adaptive two-dimensional Wiener filtering was applied to the time-frequency spectra for noise reduction, followed by binarization to obtain a binarized time-frequency spectra. The binarized time-frequency spectra were then optimized to obtain an optimized binarized time-frequency spectra. An eight-connected-region labeling method was used to perform connected-region analysis on the optimized binarized time-frequency spectra, extracting characteristic parameters for each hop signal. Signal matching between different receiving stations was then performed based on these characteristic parameters. Finally, a time-windowed... Narrowband filters separate single-hop signals from the aliased signals at each receiving station; the generalized cross-correlation method is used to calculate the arrival time difference of the single-hop signals received by the master station and each slave station, and these differences are combined into a time difference vector; the time difference vector is clustered into at least one cluster using a density-based spatial clustering algorithm, and noise points are removed from each cluster to obtain the time difference measurement value for each cluster; the time difference measurements corresponding to each target are jointly solved using the Chan-Taylor algorithm to calculate the target position corresponding to each time difference measurement value; a Monte Carlo experiment is used to determine the accuracy of the calculated target position for each time difference measurement value.

[0008] Through the above methods, this invention first performs time-frequency analysis on the aliased signals from multiple receiving stations to obtain a time-spectrum map. Then, it applies adaptive two-dimensional Wiener filtering to reduce noise in the time-spectrum map and binarizes the denoised time-spectrum map to obtain a binarized time-spectrum map, thereby reducing the amount of data and highlighting the main data features. Furthermore, it uses an eight-connected region labeling method to perform connected region analysis, extracts the feature parameters of each hop signal, and performs signal matching between different receiving stations, eliminating the need for complex signal sorting of the mixed signals and simplifying the positioning process. By using a narrowband filter with a time window, it separates single-hop signals from the mixed signals and uses the generalized cross-correlation method to calculate the arrival time difference of the single-hop signals received by the master station and each slave station, combining them into a time difference vector. This solves the time difference correlation ambiguity problem in multi-target signal aliasing scenarios and enables synchronous positioning of multiple targets. Density clustering effectively removes isolated noise points, improving the accuracy of time difference measurement. The time difference measurements corresponding to each target are jointly solved using the Chan-Taylor algorithm to calculate the target position, showing good application potential in complex electromagnetic environments. Sudden interference and environmental noise are effectively suppressed through image processing.

[0009] In the aforementioned method for identifying multiple targets of frequency-hopping radiation sources, the time-frequency analysis of the aliasing signals of each receiving station is performed using short-time Fourier transform.

[0010] Using the above method, short-time Fourier transform is employed for time-frequency analysis of aliased signals, which has strong diagnostic capabilities and can clearly distinguish between true low-frequency and high-frequency aliasing.

[0011] In the aforementioned method for identifying multiple targets of frequency-hopping radiation sources, the time-spectrum image after noise reduction is binarized, including: the noise reduction method uses adaptive Wiener filtering, and the binarization process uses Otsu's method.

[0012] The above methods, using adaptive Wiener filtering for noise reduction, can effectively restore the signal and suppress the noise; using Otsu's method for binarization processing, can determine the image binarization segmentation threshold without being affected by image brightness and contrast.

[0013] In the aforementioned method for identifying multiple targets of frequency-hopping radiation sources, the binarized time-spectrum image is optimized by filling gaps, eliminating noise and sudden interference through morphological filtering, thereby optimizing the image.

[0014] Optimizing the time-spectrum diagram using the above methods can significantly improve the accuracy and efficiency of signal analysis, making the key parts of the signal clearer and more prominent, thus providing a more reliable input for subsequent processing.

[0015] In the aforementioned method for identifying multiple targets of frequency-hopping radiation sources, the characteristic parameters of each hop signal include at least one of the following: frequency hopping period, bandwidth, and center frequency.

[0016] By using the above method, the characteristic parameters of each hop signal include at least one of the following: hopping period, bandwidth, and center frequency, which improves data accuracy.

[0017] In the aforementioned method for identifying multiple targets of frequency-hopping radiation sources, the time difference measurement value is the centroid of the core point of each cluster.

[0018] Using the above method, the time difference measurement value is the centroid of the core point of each cluster, ensuring that the selection of the time difference measurement value is more in line with the characteristics of each cluster. This not only improves data accuracy, but also effectively suppresses sudden interference and environmental noise through image processing.

[0019] In a second aspect, the present invention provides a multi-target identification device for frequency-hopping radiation sources, comprising: an acquisition module, a processing module, an optimization module, an analysis module, a separation module, a combination module, a clustering module, a calculation module, and an experimental module;

[0020] The system comprises the following modules: an acquisition module for performing time-frequency analysis on the aliased signals from each receiving station to obtain a time-frequency spectrum; a processing module for performing adaptive two-dimensional Wiener filtering to reduce noise on the time-frequency spectrum and binarizing the denoised time-frequency spectrum to obtain a binarized time-frequency spectrum; an optimization module for optimizing the binarized time-frequency spectrum to obtain an optimized binarized time-frequency spectrum; the time-frequency analysis includes performing short-time Fourier transform, modulus taking, and squaring on the aliased signals from each receiving station; an analysis module for performing connected component analysis on the optimized binarized time-frequency spectrum using an eight-connected region labeling method, extracting feature parameters of each hop signal, and performing signal matching between different receiving stations based on these feature parameters; and a separation module. The system is used to separate single-hop signals from aliased signals from each receiving station using a narrowband filter with a time window; a combination module is used to calculate the arrival time difference of the single-hop signals received by the master station and each slave station using the generalized cross-correlation method, and combine them into a time difference vector; a clustering module is used to cluster the time difference vector to form at least one cluster using a density-based spatial clustering algorithm, and remove noise points from the clustering results to obtain the time difference measurement value for each target; a calculation module is used to jointly solve each time difference measurement value using the Chan-Taylor algorithm to calculate the target position corresponding to each time difference measurement value; and an experimental module is used to perform a Monte Carlo experiment on the target position corresponding to each time difference measurement value to determine the accuracy of the calculated target position.

[0021] Thirdly, the present invention also provides a computing device, comprising: a memory for storing program instructions; and a processor for calling the program instructions stored in the memory and executing the method described in the first aspect according to the obtained program instructions.

[0022] Fourthly, the present invention also provides a computer-readable storage medium storing computer-readable instructions, which, when read and executed by a computer, implement the method of the first aspect described above.

[0023] Fifthly, the present invention provides a computer program product comprising a computer program executable by a computer device, wherein when the program is run on the computer device, the computer device performs the method described in the first aspect.

[0024] Beneficial effects:

[0025] Through the above methods, this invention first performs time-frequency analysis on the aliased signals from multiple receiving stations to obtain a time-spectrum map. Then, it applies adaptive two-dimensional Wiener filtering to reduce noise in the time-spectrum map and binarizes the denoised time-spectrum map to obtain a binarized time-spectrum map, thereby reducing the amount of data and highlighting the main data features. Furthermore, it uses an eight-connected region labeling method to perform connected region analysis, extracts the feature parameters of each hop signal, and performs signal matching between different receiving stations, eliminating the need for complex signal sorting of the mixed signals and simplifying the positioning process. By using a narrowband filter with a time window, it separates single-hop signals from the mixed signals and uses the generalized cross-correlation method to calculate the arrival time difference of the single-hop signals received by the master station and each slave station, combining them into a time difference vector. This solves the time difference correlation ambiguity problem in multi-target signal aliasing scenarios and enables synchronous positioning of multiple targets. Density clustering effectively removes isolated noise points, improving the accuracy of time difference measurement. The time difference measurements corresponding to each target are jointly solved using the Chan-Taylor algorithm to calculate the target position, showing good application potential in complex electromagnetic environments. Sudden interference and environmental noise are effectively suppressed through image processing. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart illustrating a method for identifying multiple targets of frequency-hopping radiation sources according to Embodiment 1 of the present invention.

[0028] Figure 2 This is a flowchart illustrating a method for identifying multiple targets of frequency-hopping radiation sources according to Embodiment 2 of the present invention.

[0029] Figure 3 This is a schematic diagram of a localization scenario for a multi-target identification method for frequency-hopping radiation sources provided in Embodiment 2 of the present invention;

[0030] Figure 4 This is a schematic diagram of the localization result of a multi-target identification method for frequency-hopping radiation sources provided in Embodiment 2 of the present invention;

[0031] Figure 5 This is a schematic diagram of a frequency-hopping radiation source multi-target identification device provided in Embodiment 3 of the present invention;

[0032] Figure 6 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0034] In the following embodiments of the present invention, "and / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (item) below" or similar expressions refer to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple. The singular forms "a", "one kind", "the", "above-mentioned", "this", and "this one" are also intended to include expressions such as "one or more" unless there is a clear contrary indication in the context. Also, unless otherwise stated, the ordinal numbers "first", "second", etc. mentioned in the embodiments of the present invention are used to distinguish multiple objects and are not used to limit the order, time sequence, priority, or importance degree of multiple objects.

[0035] Referring to "one embodiment" or "some embodiments" described in the specification of the present invention means that specific features, structures, or characteristics described in combination with that embodiment are included in one or more embodiments of the present invention. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear at different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments" unless otherwise specifically emphasized in another way. The terms "comprise", "include", "have" and their variants all mean "including but not limited to" unless otherwise specifically emphasized in another way.

[0036] Embodiment 1

[0037] Embodiment 1 of the present invention provides a method for multi-target identification of a frequency-hopping radiation source, as Figure 1 shown, to achieve positioning of multiple frequency-hopping radiation sources simultaneously without signal sorting. This method is executed by a computing device, which can be understood as a device such as a computer or a server, and is not specifically limited herein in the present invention, including:

[0038] Step 101: Perform time-frequency analysis on the aliased signals of each receiving station to obtain a time-frequency spectrogram.

[0039] Among them, the time-frequency analysis of the aliasing signals of each receiving station is performed using short-time Fourier transform. Using short-time Fourier transform for time-frequency analysis of aliasing signals has strong diagnostic capabilities and can clearly distinguish between true low-frequency and high-frequency aliasing.

[0040] Step 102: Perform adaptive two-dimensional Wiener filtering to reduce noise on the time-spectrum graph, and then binarize the denoised time-spectrum graph to obtain the binarized time-spectrum graph.

[0041] The noise reduction method employs adaptive Wiener filtering, and the binarization process employs Otsu's method.

[0042] An adaptive two-dimensional Wiener filter is applied to the time-spectrum image for noise reduction, and the Otsu method is used to binarize the denoised image. The adaptive Wiener filter can effectively restore the signal and suppress the noise. The Otsu method can determine the image binarization segmentation threshold without being affected by the image brightness and contrast.

[0043] Step 103: Optimize the binarized time-spectrum to obtain the optimized binarized time-spectrum.

[0044] Optimization includes filling gaps, eliminating noise and sudden interference through morphological filtering to improve the image. Optimizing the time-spectrum graph can significantly improve the accuracy and efficiency of signal analysis, making key parts of the signal clearer and more prominent, thus providing a more reliable input for subsequent processing.

[0045] Step 104: The eight-connected region labeling method is used to perform connected region analysis on the optimized binarized time-frequency spectrum, extract the characteristic parameters of each hop signal, and perform signal matching between different receiving stations based on the characteristic parameters.

[0046] The characteristic parameters of each hop signal include at least one of the following: hopping period, bandwidth, and center frequency.

[0047] Step 105: Separate the single-hop signal from the mixed signal using a narrowband filter with a time window.

[0048] Step 106: The arrival time difference of the single-hop signals received by the master station and each slave station is calculated using the generalized cross-correlation method and combined into a time difference vector.

[0049] Generalized Cross-Correlation (GCC) is a method for estimating the time delay between two signals by calculating the cross-correlation function between them. The basic idea is to utilize the phase and amplitude information of the signals, and use a weighting function to enhance the peak value of the cross-correlation function, thereby improving the accuracy of the time delay estimation.

[0050] Step 107: Cluster the time difference vectors using a density-based spatial clustering algorithm to form clusters, remove noise points from each cluster, and obtain the time difference measurement value for each cluster.

[0051] The time difference measurement is the centroid of the core point of each cluster.

[0052] The time difference measurement is the centroid of the core point of each cluster. This ensures that the selection of the time difference measurement is more in line with the characteristics of each cluster, which not only improves data accuracy, but also effectively suppresses sudden interference and environmental noise through image processing.

[0053] Step 108: Use the Chan-Taylor algorithm to jointly calculate the time difference measurements of each target to determine the target position.

[0054] Monte Carlo experiments were conducted on the calculation results, and the positioning accuracy was improved after multiple Monte Carlo experiments.

[0055] Example 2

[0056] Embodiment 2 of the present invention provides a specific implementation method for the multi-target identification method of frequency hopping radiation sources based on Embodiment 1. The method flow is as follows: Figure 2 As shown. This method is executed by a computing device, which can be understood as a computer or server, etc. This invention is not specifically limited to this, but includes:

[0057] Four receiving stations are set up in a two-dimensional plane, with coordinates as follows:

[0058] , , , ;

[0059] Five frequency-hopping radiation sources were set up, with units in kilometers and coordinates as follows:

[0060] , , , , .

[0061] The frequency-hopping radiation source network is synchronous and non-orthogonal. The frequency-hopping signal uses BFSK modulation, the frequency-hopping sequence is a different pseudo-random sequence, the noise is additive white Gaussian noise, the signal-to-noise ratio is 10dB, and the other parameters are the same, as shown in Table 1 below:

[0062] Table 1 Frequency Hopping Signal Parameters

[0063] Sampling rate (MHz) Symbol rate (kbps) Frequency hopping period (ms) Number of frequency points 100 10 0.2 64 Signal duration (ms) BFSK frequency offset (kHz) Starting frequency (MHz) Frequency hopping bandwidth (MHz) 5 50 30 50

[0064] Step 201, Preprocessing.

[0065] First, short-time Fourier transforms were performed on the aliased signals received from the four stations, and the modulus was taken and squared to obtain the time spectrum. Then, adaptive two-dimensional Wiener filtering was applied to the time spectrum for noise reduction. The Otsu method was used to binarize the denoised image. At the same time, morphological filtering was used to fill gaps, eliminate noise and sudden interference, and optimize the image.

[0066] Step 202, Single-hop signal matching and extraction.

[0067] An eight-connected region labeling method is used to perform connected component analysis on the binarized image to extract the feature parameters of each hop signal. The feature parameters include at least one of the following: hopping period, bandwidth, center frequency, etc. Single-hop signals with similar center time and center frequency are matched as the same hop signal to form signal pairs. Based on the time range and frequency range of each pair of signals, a narrowband filter with a time window is used to separate the single-hop signals.

[0068] Step 203, Time Difference Vector Construction.

[0069] For the signals separated in step 202, the generalized cross-correlation method is used to calculate the arrival time difference between the signals received by the master station and each slave station, and the results are combined into a time difference vector. In this case, the dimension of the time difference vector is 3. It should be noted that the dimension of the time difference vector mentioned above is only a preferred embodiment of the present invention, and the present invention does not limit it.

[0070] Step 204, clustering solution.

[0071] The DBSCAN algorithm was used to cluster the time difference vectors, forming 5 clusters. Noise points were removed, and the average coordinates of the core points of the 5 clusters were taken as the time difference measurement value of each target.

[0072] The time difference measurements corresponding to the five targets are used to jointly calculate the target positions using the Chan-Taylor algorithm. (See below.) Figure 3 The image shown is a positioning scene diagram provided in Embodiment 2 of the present invention.

[0073] as follows Figure 4 The image shown is a schematic diagram of the positioning results provided in an embodiment of the present invention. Figure 4 As can be seen, after 200 Monte Carlo experiments, the final positioning accuracy of the five targets reached 0.24%, 0.84%, 0.42%, 3%, and 2.9%, respectively. The positioning results were accurate and the positioning effect was good.

[0074] Example 3

[0075] After introducing the multi-target identification method for frequency-hopping radiation sources in Embodiments 1 and 2 of the present invention, Embodiment 3 of the present invention provides a multi-target identification device for frequency-hopping radiation sources, used to implement the content of the multi-target identification method for frequency-hopping radiation sources provided by the present invention. A schematic diagram of the device is shown below. Figure 5 As shown, it includes: an acquisition module, a processing module, an optimization module, an analysis module, a separation module, a combination module, a clustering module, and a calculation module.

[0076] The acquisition module is used to perform time-frequency analysis on the aliased signals of each receiving station to obtain a time-spectrum diagram.

[0077] The processing module is used to perform adaptive two-dimensional Wiener filtering for noise reduction on the time-spectrum graph, and to perform binarization processing on the noise-reduced time-spectrum graph to obtain the binarized time-spectrum graph.

[0078] The optimization module is used to optimize the binarized time-spectrum image to obtain an optimized binarized time-spectrum image.

[0079] The analysis module is used to perform connected region analysis on the optimized binarized time-frequency spectrum using the eight-connected region labeling method, extract the characteristic parameters of each hop signal, and perform signal matching between different receiving stations based on the characteristic parameters;

[0080] A separation module is used to separate single-hop signals from mixed signals using a narrowband filter with a time window;

[0081] The combination module is used to calculate the arrival time difference of the single-hop signals received by the master station and each slave station using the generalized cross-correlation method, and combine them into a time difference vector;

[0082] The clustering module is used to cluster time difference vectors using a density-based spatial clustering algorithm, remove noise points from the clustering results, and obtain the time difference measurement value for each target.

[0083] The calculation module is used to jointly solve the time difference measurements corresponding to each target using the Chan-Taylor algorithm to calculate the target position.

[0084] Example 4

[0085] After introducing a method and apparatus for identifying multiple targets of frequency-hopping radiation sources in an exemplary embodiment of the present invention, the following describes a computing device in another exemplary embodiment of the present invention.

[0086] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”

[0087] In some possible implementations, the computing device according to the invention may include at least one processor and at least one memory. The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps in the frequency-hopping radiation source multi-target identification method according to various exemplary embodiments of the invention described above.

[0088] The following reference Figure 6 To describe a computing device 130 according to this embodiment of the invention. Figure 6 The computing device 130 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. Figure 6 As shown, the computing device 130 is presented in the form of a general-purpose smart terminal (or Bluetooth headset). The components of the computing device 130 may include, but are not limited to: at least one processor 131, at least one memory 132, and a bus 133 connecting different system components (including memory 132 and processor 131).

[0089] Bus 133 represents one or more of several bus architectures, including a memory bus or memory controller, peripheral bus, processor, or local bus using any of the various bus architectures. Memory 132 may include readable media in the form of volatile memory, such as random access memory (RAM) 1321 and / or cache memory 1322, and may further include read-only memory (ROM) 1323. Memory 132 may also include a program / utility 1325 having a set (at least one) of program modules 1324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0090] The computing device 130 can also communicate with one or more external devices 134 (e.g., keyboard, pointing device, etc.), and / or with any device that enables the computing device 130 to communicate with one or more other smart terminals (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 135. Furthermore, the computing device 130 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 136. As shown, network adapter 136 communicates with other modules used in the computing device 130 via bus 133. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the computing device 130, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0091] In some possible implementations, various aspects of the frequency-hopping radiation source multi-target identification method provided by the present invention can also be implemented in the form of a program product, which includes a computer program. When the program product is run on a computer device, the computer program is used to cause the computer device to perform the steps in the frequency-hopping radiation source multi-target identification method according to various exemplary embodiments of the present invention as described above.

[0092] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0093] The program product for time-domain noise processing according to embodiments of the present invention may employ a portable compact disc read-only memory (CD-ROM) and include a computer program, and may run on a smart terminal. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0094] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a readable computer program. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0095] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0096] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0097] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable access frequency prediction device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable access frequency prediction device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable access predictive device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable access predictive device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0101] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for identifying multiple targets of frequency-hopping radiation sources, characterized in that, include: Time-frequency analysis is performed on the aliased signals of each receiving station to obtain a time-frequency spectrum; wherein, the time-frequency analysis includes performing short-time Fourier transform, modulus taking, and squaring on the aliased signals of each receiving station; Adaptive two-dimensional Wiener filtering is applied to the time-spectrum graph for noise reduction, and the noise-reduced time-spectrum graph is then binarized to obtain a binarized time-spectrum graph. The binarized time-spectrum image is optimized to obtain an optimized binarized time-spectrum image; The optimized binarized time-spectrum map is analyzed for connected regions using the eight-connected region labeling method. Feature parameters of each hop signal are extracted, and signal matching between different receiving stations is performed based on the feature parameters. Single-hop signals are separated from the aliased signals of each receiving station using a narrowband filter with a time window; The arrival time difference of the single-hop signal received by the master station and each slave station is calculated using the generalized cross-correlation method and combined into a time difference vector; The time difference vectors are clustered using a density-based spatial clustering algorithm to form at least one cluster. Noise points are removed from each cluster to obtain the time difference measurement value for each cluster. Each time difference measurement is jointly solved using the Chan-Taylor algorithm to calculate the target location corresponding to each time difference measurement. A Monte Carlo experiment was conducted on the target location corresponding to each time difference measurement to determine the accuracy of the calculated target location.

2. The method according to claim 1, characterized in that, The time-frequency analysis of the aliased signals at each receiving station was performed using short-time Fourier transform.

3. The method according to claim 1, characterized in that, The binarization process of the denoised time-spectrum image includes: The noise reduction method employs adaptive Wiener filtering, and the binarization process employs Otsu's method.

4. The method according to claim 1, characterized in that, The optimization of the binarized time-spectrum image includes filling gaps, eliminating noise and sudden interference through morphological filtering, thereby optimizing the image.

5. The method according to claim 1, characterized in that, The characteristic parameters of each hop signal include at least one of the following: hopping period, bandwidth, and center frequency.

6. The method according to claim 1, characterized in that, The time difference measurement is the centroid of the core point of each cluster.

7. A multi-target identification device for frequency-hopping radiation sources, characterized in that, include: The acquisition module is used to perform time-frequency analysis on the aliased signals of each receiving station to obtain a time-frequency spectrum; wherein, the time-frequency analysis includes performing short-time Fourier transform, modulus taking, and squaring on the aliased signals of each receiving station; The processing module is used to perform adaptive two-dimensional Wiener filtering noise reduction on the time-spectrum graph, and to perform binarization processing on the noise-reduced time-spectrum graph to obtain a binarized time-spectrum graph. The optimization module is used to optimize the binarized time-spectrum map to obtain an optimized binarized time-spectrum map. The analysis module is used to perform connected region analysis on the optimized binarized time-frequency spectrum using the eight-connected region labeling method, extract the feature parameters of each hop signal, and perform signal matching between different receiving stations based on the feature parameters. The separation module is used to separate single-hop signals from the aliased signals of each receiving station using a narrowband filter with a time window; The combination module is used to calculate the arrival time difference between the single-hop signals received by the master station and each slave station using the generalized cross-correlation method, and combine them into a time difference vector; The clustering module is used to cluster the time difference vectors using a density-based spatial clustering algorithm to form at least one cluster, remove noise points from the clustering results, and obtain the time difference measurement value for each target. The calculation module is used to jointly solve each time difference measurement value using the Chan-Taylor algorithm to calculate the target location corresponding to each time difference measurement value; The experimental module is used to perform a Monte Carlo experiment on the target position corresponding to each time difference measurement to determine the accuracy of the calculated target position.

8. A computing device, characterized in that, Its features include: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method as described in any one of claims 1-6 according to the obtained program instructions.

9. A computer-readable storage medium, characterized in that, Includes computer-readable instructions that, when read and executed by a computer, cause the method as described in any one of claims 1 to 6 to be implemented.

10. A computer program product, characterized in that, It includes a computer program executable by a computer device, which, when run on the computer device, causes the computer device to perform the steps of the method according to any one of claims 1 to 6.

Images