Reliable extraction and utilization method of complex pulse flow sequence features, equipment and medium

Abstract

The invention relates to the field of signal sorting, and provides a reliable extraction and utilization method of complex pulse stream sequence features, and the whole process is divided into three stages of feature sequence preprocessing, feature extraction and feature utilization. For feature sequence preprocessing, PA sequence coarse segmentation, outlier removal, smooth noise reduction, sequence characterization and approximate equal interval processing are carried out; for extraction of complex pulse flow sequence features, from the perspective of a time sequence, possible complex pulse flow sequence features in a radar sample database are mined through similarity measurement and complete sequence search of the sequences; for the utilization of complex pulse stream sequence features, the features have certain randomness, are rough and are difficult to be finely applied to signal sorting, simple fuzzy matching is adopted to extract target pulse data, and signal sorting is completed. According to the method, reliable extraction and utilization of complex pulse sequence features are realized, and the signal sorting accuracy can be further improved.

Description

Technical Field

[0001] The present invention relates to the field of signal sorting, and in particular to a method, device and medium for reliably extracting and utilizing complex pulse flow sequence characteristics. Background Art

[0002] Utilizing pulse stream sequence features can further improve signal sorting accuracy. However, with the increasing complexity of electromagnetic environments and the diversification of radar systems, conventional pulse stream sequence features lack sufficient discrimination, while complex pulse stream sequence features are diverse in form and low in explicitness, making them difficult to extract and utilize, making it difficult to effectively promote signal sorting. Summary of the Invention

[0003] The present invention aims to provide a method, device and medium for reliably extracting and utilizing the characteristics of complex pulse stream sequences, so as to solve the problem that the extraction and utilization of complex pulse stream sequence characteristics are difficult, resulting in the difficulty in effectively promoting the signal sorting effect.

[0004] The present invention provides a reliable method for extracting and utilizing complex pulse flow sequence features, comprising: The PA feature sequence representation database TB is established through feature sequence preprocessing; Extract complex pulse flow sequence features from the database TB based on sequence similarity; Based on the extracted complex pulse stream sequence features, fuzzy matching is used to complete signal sorting.

[0005] In some embodiments, the feature sequence preprocessing includes PA sequence coarse segmentation, outlier removal, smoothing and noise reduction, sequence characterization, and approximately equal interval processing.

[0006] In some embodiments, extracting complex pulse stream sequence features from the database TB based on sequence similarity includes: Select query sequences from database TB based on PA value ; Using query sequence , perform full sequence search based on DTW distance metric.

[0007] In some embodiments, the use of query sequences , perform full sequence search based on DTW distance metric, including: Calculate the expression of all PA sequence features in database TB Value, based on The values ​​are reordered from large to small to sort the sequences in the database TB. of The value is the largest, then the query sequence ; Traverse all sequences according to the index of database TB and calculate DTW distance, and determine whether it satisfies DTW The threshold condition of the distance. If the threshold condition is met, the number is counted with c and the index of the corresponding sequence is recorded: When the statistical number c is greater than the statistical threshold, the remaining sequences are no longer traversed and the loop ends. At this time, the query sequence is considered The PA sequence characteristic represented is the beam scanning characteristic; When the statistical number c is less than the statistical threshold, but all sequences have been traversed, the loop ends and the query sequence is determined. The PA sequence characteristics represented indicate that there is no beam scanning feature.

[0008] In some embodiments, the signal sorting is performed using fuzzy matching based on the extracted complex pulse stream sequence features, including: Use structural points to characterize the structure of complex pulse flow sequences; Calculate the jitter tolerance of the matching template based on the structure points; Perform matching search based on jitter tolerance of matching template.

[0009] In some embodiments, the structure point is a query sequence One peak point and two median points in the represented PA sequence feature representation.

[0010] In some embodiments, the step of calculating the jitter tolerance of the matching template based on the structure points includes: The jitter tolerance of the matching template is calculated based on the two median points in the structure point, which is expressed as:

[0011]

[0012] Where, To match the TOA jitter interval of the template, To match the PA jitter interval of the template, c +1 means the query sequence is included In it, there are statistical values ​​of all beam scanning feature sequences; is the TOA jitter interval of a complex pulse stream sequence characteristic, and is the average of the TOA jitter intervals of the left median and right median of the complex pulse stream sequence characteristic; is the PA jitter interval of a complex pulse stream sequence characteristic, and is the average of the PA jitter intervals of the left median and right median values ​​of the complex pulse stream sequence characteristic.

[0013] In some embodiments, performing a matching search based on a jitter tolerance of a matching template includes: For the test data consisting of M pulses , search for the existence of a point in the test data Match with the peak point of the matching template; Based on this point Search for two points from the test data 、 Match the left median and right median points of the matching template; in accordance with 、 、 The three points locate the matching template in the test data, thereby completing the extraction of the target pulse in this data based on the matching template data domain and jitter tolerance.

[0014] In a second aspect, the present invention provides an electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the above method by executing the instructions stored in the memory.

[0015] In a third aspect, the present invention provides a computer-readable storage medium for storing instructions, which implement the above method when the instructions are executed.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The embodiment of the present invention provides a reliable extraction and utilization method for complex pulse stream sequence features. The entire process is divided into three stages: feature sequence preprocessing, feature extraction, and feature utilization. For feature sequence preprocessing, PA sequence coarse segmentation, outlier removal, smoothing and noise reduction, sequence characterization, and approximate equal interval processing are performed; for the extraction of complex pulse stream sequence features, from the perspective of time series, through sequence similarity measurement and full sequence search, possible complex pulse stream sequence features in the radar sample database are mined; for the utilization of complex pulse stream sequence features, considering that such features have a certain degree of randomness and are relatively rough, and are difficult to be applied to signal sorting in a refined manner, simple fuzzy matching is used to extract target pulse data to complete signal sorting. This method realizes the reliable extraction and utilization of complex pulse sequence features, and can further improve the accuracy of signal sorting. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for reliably extracting and utilizing complex pulse stream sequence features provided by an embodiment of the present invention.

[0018] Figure 2Schematic diagram of PA sequence segmentation in an embodiment of the present invention.

[0019] Figure 3 Schematic diagram of the sliding average method in an embodiment of the present invention.

[0020] Figure 4 This is a comparison diagram before and after smoothing of the PA sequence in an embodiment of the present invention.

[0021] Figure 5 Schematic diagram showing the PA characteristic sequence in an embodiment of the present invention.

[0022] Figure 6 Schematic diagram showing the fitting of the PA characteristic sequence in an embodiment of the present invention.

[0023] Figure 7 Schematic diagram of approximately equal interval processing of PA feature sequence representation in an embodiment of the present invention.

[0024] Figure 8 Flowchart of full sequence search in an embodiment of the present invention.

[0025] Figure 9 Schematic diagram of beam scanning characteristic structure points in an embodiment of the present invention.

[0026] Figure 10 Schematic diagram of jitter tolerance at a beam scanning characteristic structure point in an embodiment of the present invention.

[0027] Figure 11a This is a test data distribution diagram in an embodiment of the present invention.

[0028] Figure 11b In the embodiment of the present invention Figure 11a A local enlarged view of the TOA at 14s~18s.

[0029] Figure 12 This is a distribution diagram of matching template data in an embodiment of the present invention.

[0030] Figure 13 This is a diagram of target pulse data extraction results in an embodiment of the present invention.

[0031] Figure 14 This is a graph showing the proportion of various types of data in the extraction results in an embodiment of the present invention.

[0032] Figure 15 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0034] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0035] Example like Figure 1 As shown, an embodiment of the present invention provides a reliable extraction and utilization method for complex pulse stream sequence features. The entire process is divided into three stages: feature sequence preprocessing, feature extraction, and feature utilization. For feature sequence preprocessing, PA sequence coarse segmentation, outlier removal, smoothing and noise reduction, sequence characterization, and approximate equal interval processing are performed; for the extraction of complex pulse stream sequence features, from the perspective of time series, through sequence similarity measurement and full sequence search, possible complex pulse stream sequence features in the radar sample database are mined; for the utilization of complex pulse stream sequence features, considering that such features have a certain degree of randomness and are relatively rough, making them difficult to be applied to signal sorting in a refined manner, simple fuzzy matching is used to extract target pulse data to complete signal sorting.

[0036] Taking the beam scanning feature as an example, the specific steps of the method for reliably extracting and utilizing the complex pulse stream sequence feature are as follows: Step 1: Establish a PA feature sequence representation database TB through feature sequence preprocessing. In some embodiments, the feature sequence preprocessing includes PA sequence coarse segmentation, outlier removal, smoothing and noise reduction, sequence characterization, and approximately equal interval processing.

[0037] Step 1.1: Implement coarse segmentation of PA sequence based on TOA like Figure 2 As shown, a suitable segmentation threshold is determined according to prior information, and then segmentation processing of the PA sequence is performed on the TOA axis based on the segmentation threshold.

[0038] Step 1.2: Based on 5 Detection method to remove outliers The outliers caused by accidental factors will affect the beam scanning characteristics of the entire PA sequence to a certain extent. The probability of such accidental factors is very small. Therefore, this embodiment uses 5 The detection method is used to remove outliers to facilitate reliable feature extraction. This method is derived from the Chebyshev inequality. For any ,have:

[0039] in, For expectations, is the standard deviation.

[0040] when When It reflects the degree of dispersion of the data from the mean value, that is:

[0041] Therefore, among all the data, 96% of the data are within 5 standard deviations of the mean, and 4% of the data are outside the 5 standard deviations of the mean. These data are outliers and need to be removed.

[0042] Step 1.3: Noise reduction based on sliding average method The sliding average method can reduce the influence of random noise to a certain extent. Assuming the sliding window length is 3, its expression is:

[0043] Where, represents the data smoothed by the sliding average method, For the original data.

[0044] Figure 3 The schematic diagram of the sliding average method is shown. By continuously sliding the window and averaging, the PA sequence can be effectively smoothed and the noise can be reduced. Figure 4 The comparison diagram of the PA sequence before and after smoothing is shown, and it can be found that the expected effect is met through observation.

[0045] Step 1.4: PA feature sequence representation based on regional peak points The PA feature sequence is represented by using the regional peak point to replace all points in the region, such as Figure 5 As shown in FIG, the line segments connected by a few key points can approximately represent the change trend of the original PA sequence. This PA change trend is the beam scanning feature.

[0046] Step 1.5: Approximately equal interval processing of PA feature sequence representation First, the least square method is used to fit the PA feature sequence representation. Assume that the PA feature sequence is represented as And its corresponding pulse index is , L Indicates the length of the sequence represented by the PA feature sequence. L Sample points To fit the polynomial, the polynomial expression is as follows:

[0047] Figure 6 The fitting curve of the sample points represented by the PA feature sequence is shown, which basically retains the changing trend characteristics of PA and meets the requirements of subsequent algorithms.

[0048] Then with The fitting curve is sampled at intervals, and N refers to the total length of the original PA sequence. is the rounding function, and the processing result is as follows Figure 7 shown.

[0049] Step 2: Extract complex pulse stream sequence features from the database TB based on sequence similarity, that is, extract beam scanning features in this embodiment.

[0050] Step 2.1: Select query sequences from database TB based on PA value

[0051] Considering the physical meaning of the PA sequence itself, it represents the pulse signal power. The larger the PA value, the higher the corresponding signal-to-noise ratio. The higher the signal-to-noise ratio, the more accurate and reliable the PA sequence data. Therefore, this embodiment selects the PA feature sequence with the largest PA value from the database TB as the query sequence. , let PA feature sequence represent database TB= ,by To characterize the size of the PA value represented by each PA feature sequence in each database TB, its expression is:

[0052]

[0053] Where, is the median function, which means finding the median of a sequence. Representation sequence The part of the sequence that is greater than the median; is the mean function, Representation sequence The mean of the sequence that is greater than the median. Select from the PA feature sequence database TB The PA feature sequence with the largest value is represented as the query sequence .definition Function, which can be integrated into the above formula:

[0054] Then, the query sequence The expression is:

[0055] Step 2.2: Using the query sequence , based on DTW Full sequence search using distance metrics Since the PA sequence represents the pulse signal power, the larger the PA value, the higher the corresponding signal-to-noise ratio, and the more accurate and reliable the PA sequence data. The value size is used to establish the search structure and improve the retrieval efficiency. If there are multiple sequences in the database that meet the DTW distance condition, the query sequence can be considered The corresponding PA feature sequence representation is the extracted beam scanning feature.

[0056] Specific as Figure 8 As shown: For the input original PA sequence, it is converted into a PA feature sequence representation database TB= through feature sequence preprocessing. , calculate the features of all PA sequences in database TB Value, based on The values ​​are reordered from large to small to sort the sequences in the database TB. of The value is the largest, then the query sequence ; Traverse all sequences according to the index of database TB and calculate DTW Distance, expressed as , and determine whether the threshold is met If the threshold condition is met, the number of counts is c and the index of the corresponding sequence is recorded. When , the remaining sequences are no longer traversed and the loop ends. At this time, the query sequence is considered The PA sequence feature represented by it is the beam scanning feature; when the statistical quantity c is less than , but when all sequences have been traversed, the loop ends and the query sequence is determined The PA sequence characteristics represented indicate that there is no beam scanning feature.

[0057] in, express DTW The distance threshold between the two sequences DTW Distance less than When , the two sequences can be considered similar, according to the prior information setting. Indicates the statistical threshold. When the database TB contains the same sequence as the query sequence satisfy DTW The number of sequences of the distance condition is greater than , which can be considered as the query sequence The PA sequence represented is the beam scanning characteristic. is the threshold coefficient, between 0 and 1, and generally less than 0.5. is the total number of sequences in the database TB.

[0058] Step 3: Based on the extracted complex pulse stream sequence features, which are also beam scanning features in this embodiment, fuzzy matching is used to complete signal sorting.

[0059] Step 3.1: Use structure points to represent the structure of complex pulse stream sequences In this embodiment, the query sequence The PA sequence features represented by the peak point and two median points are used to characterize the beam scanning features, such as Figure 9 As shown, the black circles represent the query sequence The PA sequence features represented by the red asterisks indicate structural points consisting of a peak point and two median points. These three points can well characterize the structure of the beam scanning features.

[0060] Step 3.2: Calculate the jitter tolerance of the matching template based on the structure points In this embodiment, the jitter tolerance of the matching template is calculated based on the two median points of the structure points, such as Figure 10 As shown, the length and width of the green box represent Figure 10 The TOA jitter range and PA jitter range of the beam scanning characteristics are as follows: 、 and 、 Define the TOA jitter interval and PA jitter interval of the left median and right median respectively, then Figure 10 The TOA jitter range of the medium beam scanning characteristic is: , the PA jitter range is: .

[0061] The jitter tolerance is not limited to the jitter range of a certain beam scanning feature, but needs to take all beam scanning features into consideration. The TOA jitter range of a beam scanning feature is defined as , PA jitter range is , then the jitter tolerance of the matching template is:

[0062]

[0063] Where, To match the TOA jitter interval of the template, To match the PA jitter interval of the template, c +1 means the query sequence is included Within, there are statistical values ​​of all beam scanning feature sequences.

[0064] Step 3.3: Search for a match based on the jitter tolerance of the matching template Assume that the PA peak coordinates of the matching template are , then the coordinates of the corresponding left median and right median are 、 For the test data consisting of M pulses , first search for the point in the test data Match the peak point of the matching template, that is:

[0065] Then based on this point Search for two points from the test data 、 Match the left and right median points of the matching template, that is:

[0066]

[0067] Finally, based on 、 、 The three points locate the matching template in the test data, thereby completing the extraction of the target pulse in this data based on the matching template data domain and jitter tolerance.

[0068] Here is a brief description of this process. Take any point in this test pulse data. , search for such a point from the matching template , satisfying the following formula:

[0069] Then, the point can be determined to be the target pulse. By traversing all points of this pulse data, the target pulse in this data can be extracted. Then, the next point that matches the peak point of the matching template is found again. The above steps are repeated until all the data are traversed.

[0070] The simulation environment is as follows: Two radar sources of the same model, technical parameters, and mode, continuously emitting pulses, are located in the same direction as an electronic reconnaissance receiver. These radars differ only in their distance from the receiver, which is large enough to allow for differences in the PAs used to intercept the pulse data. Furthermore, some unexpected electromagnetic devices are present in this scenario, causing interference pulses to the intercepted data. The parameters of these radar sources are shown in Table 1.

[0071] Table 1, radar parameters:

[0072] Figure 11a 、 Figure 11b This is the distribution diagram of test data intercepted by the electronic reconnaissance receiver. Through observation, it can be found that there are two radars in search state; the detection threshold power of the receiver is around -38dB; and there are a large number of interference pulses.

[0073] After extracting the matching template from the sample, the target pulse data can be extracted according to the proposed algorithm. The structural points of the matching template are: (0, -24.5), (-0.9017, -29.37), (0.9556, -29.44), and the jitter tolerance is =0.08, =1.00, the data distribution of the matching template is as follows Figure 12 shown.

[0074] The extraction results of target pulse data based on the reliable extraction and utilization method of complex pulse stream sequence characteristics are as follows: Figure 13 As shown, it can be observed that the target pulse is basically extracted successfully.

[0075] Figure 14 The data extraction results show the percentage of each type of data, with target radar pulses accounting for 87%, non-target radar pulses for 11%, and interference pulses for 2%. This indicates that the extraction and utilization of beam scanning features is effective.

[0076] Based on the same technical concept, an embodiment of the present invention further provides an electronic device that can implement the reliable extraction and utilization method of complex pulse stream sequence features provided in the above embodiment of the present invention. In one embodiment, the electronic device can be a server, or a terminal device or other electronic device. Figure 15 As shown, the electronic device may include: At least one processor, and a memory connected to the at least one processor. The embodiment of the present invention does not limit the specific connection medium between the processor and the memory. Figure 15 The example in this article is that the processor and memory are connected via a bus. Figure 15 The connections between the other components are shown in bold lines, which are only for illustration and not limiting. The bus can be divided into address bus, data bus, control bus, etc. Figure 15 The processor is represented by a single thick line, but this does not mean that there is only one bus or only one type of bus. Alternatively, the processor can also be called a controller, without any limitation on the name.

[0077] In an embodiment of the present invention, the memory stores instructions that can be executed by at least one processor. By executing the instructions stored in the memory, the at least one processor can execute the method for reliably extracting and utilizing complex pulse stream sequence features discussed above.

[0078] Among them, the processor is the control center of the device, which can use various interfaces and lines to connect the various parts of the entire control device, and monitor the device as a whole by running or executing instructions stored in the memory and calling data stored in the memory, the various functions of the device and processing data.

[0079] In an optional design, the processor may include one or more processing units, and the processor may integrate an application processor and a modem processor, wherein the application processor primarily processes the operating system, user interface, and application programs, and the modem processor primarily processes wireless communications. It is understood that the modem processor may not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip, or in some embodiments, they may be implemented on separate chips.

[0080] The processor can be a general-purpose processor, such as a CPU, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method for reliably extracting and utilizing complex pulse stream sequence features disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware processor, or by a combination of hardware and software modules in the processor.

[0081] As a non-volatile computer-readable storage medium, memory can be used to store non-volatile software programs, non-volatile computer executable programs and modules. Memory can include at least one type of storage medium, for example, can include flash memory, hard disk, multimedia card, card-type memory, random access memory (Random Access Memory, RAM), static random access memory (Static Random Access Memory, SRAM), programmable read-only memory (Programmable Read Only Memory, PROM), read-only memory (Read Only Memory, ROM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), magnetic memory, disk, optical disk, etc. Memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in the embodiment of the present invention can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0082] By designing and programming a processor, the code corresponding to the method for reliably extracting and utilizing complex pulse stream sequence features described in the aforementioned embodiment can be embedded within the chip, enabling the chip to execute the steps of the method described in the aforementioned embodiment during operation. Designing and programming a processor is well known to those skilled in the art and will not be further described here.

[0083] Based on the same inventive concept, an embodiment of the present invention further provides a storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer executes the reliable extraction and utilization method of complex pulse stream sequence characteristics discussed above.

[0084] In some optional embodiments, the present invention also provides various aspects of a method for reliably extracting and utilizing complex pulse stream sequence features, which can also be implemented in the form of a program product, which includes program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of a method for reliably extracting and utilizing complex pulse stream sequence features according to various exemplary embodiments of the present invention described above in this specification.

[0085] It should be noted that although several units or subunits of the device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to an embodiment 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 a unit described above can be further divided into multiple units to be embodied. In addition, 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 this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0086] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a server, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process 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.

[0088] Program code for performing the operations of the present invention may be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0089] Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0090] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0092] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A reliable method for extracting and utilizing complex pulse flow sequence features, characterized in that: include: The PA feature sequence representation database TB is established through feature sequence preprocessing; Extract complex pulse flow sequence features from the database TB based on sequence similarity; Based on the extracted complex pulse stream sequence features, fuzzy matching is used to complete signal sorting.

2. The method for reliably extracting and utilizing complex pulse flow sequence features according to claim 1, characterized in that: The feature sequence preprocessing includes PA sequence coarse segmentation, outlier removal, smoothing and noise reduction, sequence characterization and approximately equal interval processing.

3. The method for reliably extracting and utilizing complex pulse flow sequence features according to claim 1, characterized in that: The complex pulse stream sequence features are extracted from the database TB based on sequence similarity, including: Select query sequences from database TB based on PA value ; Using query sequence , perform full sequence search based on DTW distance metric.

4. The method for reliably extracting and utilizing complex pulse flow sequence features according to claim 3, characterized in that: The query sequence , perform full sequence search based on DTW distance metric, including: Calculate the expression of all PA sequence features in database TB Value, based on The values ​​are reordered from large to small to sort the sequences in the database TB. of The value is the largest, then the query sequence ; Traverse all sequences according to the index of database TB and calculate DTW distance, and determine whether it satisfies DTW The threshold condition of the distance. If the threshold condition is met, the number is counted with c and the index of the corresponding sequence is recorded: When the statistical number c is greater than the statistical threshold, the remaining sequences are no longer traversed and the loop ends. At this time, the query sequence is considered The PA sequence characteristic represented is the beam scanning characteristic; When the statistical number c is less than the statistical threshold, but all sequences have been traversed, the loop ends and the query sequence is determined. The PA sequence characteristics represented indicate that there is no beam scanning feature.

5. The method for reliably extracting and utilizing complex pulse flow sequence features according to claim 1, characterized in that: The signal sorting is completed by using fuzzy matching based on the extracted complex pulse stream sequence features, including: Use structural points to characterize the structure of complex pulse flow sequences; Calculate the jitter tolerance of the matching template based on the structure points; Perform matching search based on jitter tolerance of matching template.

6. The method for reliably extracting and utilizing complex pulse stream sequence features according to claim 5, characterized in that: The structural point is the query sequence One peak point and two median points in the represented PA sequence feature representation.

7. The method for reliably extracting and utilizing complex pulse stream sequence features according to claim 6, characterized in that: The jitter tolerance calculation of the matching template based on the structure points includes: The jitter tolerance of the matching template is calculated based on the two median points in the structure point, which is expressed as: Where, To match the TOA jitter interval of the template, To match the PA jitter interval of the template, c +1 means the query sequence is included In it, there are statistical values ​​of all beam scanning feature sequences; is the TOA jitter interval of a complex pulse stream sequence characteristic, and is the average of the TOA jitter intervals of the left median and right median of the complex pulse stream sequence characteristic; is the PA jitter interval of a complex pulse stream sequence characteristic, and is the average of the PA jitter intervals of the left median and right median values ​​of the complex pulse stream sequence characteristic.

8. The method for reliably extracting and utilizing complex pulse stream sequence features according to claim 7, characterized in that: The matching search based on the jitter tolerance of the matching template includes: For the test data consisting of M pulses , search for the existence of a point in the test data Match with the peak point of the matching template; Based on this point Search for two points from the test data 、 Match the left median and right median points of the matching template; in accordance with 、 、 The three points locate the matching template in the test data, thereby completing the extraction of the target pulse in this data based on the matching template data domain and jitter tolerance.

9. An electronic device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the method according to any one of claims 1 to 8 by executing the instructions stored in the memory.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store instructions, and when the instructions are executed, the method according to any one of claims 1 to 8 is implemented.

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