Radar waveform analysis method based on feature extraction algorithm

Abstract

The invention discloses a radar waveform analysis method based on a feature extraction algorithm, and relates to the technical field of radar waveform analysis, and the radar waveform analysis method comprises the step of collecting real-time radar waveform data by using multiple sets of radar equipment. The radar waveform data processing method is suitable for being widely applied to multiple fields, the complexity of data processing is reduced and the processing speed is improved through screening multiple groups of algorithms, algorithm optimization and feature selection, the radar database is subjected to three times of encryption processing through the database encryption unit, and the data processing efficiency is improved. The overall safety is improved, radar data information updated in real time is recorded through the database center, radar waveform analysis data and corresponding analysis results are managed, visualized and stored, radar waveform analysis management can be achieved through internet cloud management and control, and the intelligent level of radar waveform analysis management is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar waveform analysis, and in particular to a radar waveform analysis method based on a feature extraction algorithm. Background Art

[0002] With the development of radar technology, the demand for testing of various radar modules is increasing. The radar waveform generation module belongs to the radar system, which completes the generation of radar waveform signals and the control and management of the RF module. To complete the test of the waveform generation module, many instruments and equipment are required, including spectrum analyzers, oscilloscopes, signal sources, phase noise testers, etc. Since the test requires many instruments and large size, it is not easy to build an experimental test environment.

[0003] The data measured by various instruments in the prior art are independent of each other, and no correlation analysis is performed through a single algorithm or multiple groups of algorithms. If the data needs to be analyzed, a lot of effort must be spent on integrating the data. The processing complexity of the integrated data is high, the overall processing speed is slow, and the overall security cannot be guaranteed. When a problem occurs in a certain link, it is necessary to check and verify them one by one, which affects the normal operation cycle. Therefore, the present invention needs to design a radar waveform analysis method based on a feature extraction algorithm to solve the above-mentioned problems. Summary of the invention

[0004] The purpose of the present invention is to provide a radar waveform analysis method based on a feature extraction algorithm in order to solve the above problems, thereby solving the problems mentioned in the background technology.

[0005] In order to solve the above problems, the present invention provides a technical solution:

[0006] A radar waveform analysis method based on a feature extraction algorithm, the radar waveform analysis method comprising:

[0007] S1. Use multiple groups of radar equipment to collect real-time radar waveform data, perform denoising and normalization on the collected raw waveform data, and record multiple raw data while acquiring the waveform data;

[0008] S2, generate a database center, continuously update the storage information of the database center through Internet connection, and encrypt the radar database three times;

[0009] S3. Retrieve relevant algorithms for the key features that may be involved in this analysis, match the retrieved algorithms one by one, further screen the most appropriate algorithms, and promptly connect the single algorithm or multiple groups of algorithms that have completed the screening to the next link;

[0010] S4. Perform auxiliary operations such as repairing and enhancing the images in one or more sets of collected radar waveform data, use the extracted features to classify and identify the waveforms, combine pattern recognition technology to determine the type, location and state of the target object, and generate a training model to identify and mark different types of radar waveforms.

[0011] As a preferred technical solution, in step 1, a radar waveform data analysis system is constructed before installing multiple sets of radar data.

[0012] As a preferred technical solution, the triple encryption in step S2 includes one encryption of the database center, identity authentication of the staff accessing the database center to achieve a secondary encryption effect, and three encryptions to determine whether the staff has the authority to retrieve the corresponding entry.

[0013] As a preferred technical solution, the radar waveform data in step S1 includes parameters such as amplitude, phase and frequency.

[0014] As a preferred technical solution, the original data in step S1 includes acquisition time, acquisition source, acquisition processing result, and acquisition staff.

[0015] As a preferred technical solution, the radar waveform analysis system includes a radar database, an algorithm access module, a waveform analysis module and a data acquisition module. The output end of the data acquisition module is communicatively connected to the input end of the radar database, the output end of the radar database is communicatively connected to the input end of the algorithm access module, and the output end of the algorithm access module is communicatively connected to the input end of the waveform analysis module.

[0016] As a preferred technical solution, the data acquisition module includes a waveform data acquisition unit, a preprocessing unit and an acquisition data information recording unit, the output end of the waveform data acquisition unit is communicatively connected to the input end of the preprocessing unit, and the waveform data acquisition unit and the acquisition data information recording unit are bidirectionally communicatively connected;

[0017] The waveform data acquisition unit is used to collect real-time radar waveform data using multiple groups of radar equipment;

[0018] The preprocessing unit is used to perform denoising and normalization processing on the collected original waveform data, and use signal filtering technology to improve signal quality;

[0019] The acquired data information recording unit is used to record multiple pieces of original data while acquiring waveform data.

[0020] As a preferred technical solution, the radar database includes a real-time update unit, a database encryption unit and a database center, and the real-time update unit is connected to the database encryption unit and the database center in a two-way communication manner;

[0021] The real-time updating unit is used to continuously update the stored information in the database center by means of Internet connection;

[0022] The database encryption unit is used to perform three encryption processes on the radar database;

[0023] The database center is used to record the real-time updated radar data information, and is also used to record the entire process of each radar waveform data analysis, and to mark and explain the problems therein.

[0024] As a preferred technical solution, the algorithm access module includes an information retrieval unit, an algorithm screening unit and an algorithm access unit, the output end of the information retrieval unit is communicatively connected to the input end of the algorithm screening unit, and the output end of the algorithm screening unit is communicatively connected to the input end of the algorithm access unit;

[0025] The information retrieval unit is used to retrieve algorithms related to key features that may be involved in this analysis, and multiple searches can be performed by replacing secondary features, and the algorithm with the highest occurrence rate is retained;

[0026] The algorithm screening unit is used to match the retrieved algorithms one by one, and further screen the most appropriate algorithm, and can generate an online form for staff to join the screening process, and test the algorithms appearing in the form one by one;

[0027] The algorithm access unit is used to access the screened algorithm to the radar waveform analysis system for subsequent waveform analysis and training operations;

[0028] The algorithm includes but is not limited to:

[0029] Fourier transform algorithm, which converts time domain signals into frequency domain and analyzes the spectrum characteristics of radar waveform signals;

[0030] Target detection algorithms, including Kalman filtering and minimum mean square error, for real-time tracking and estimation of radar target states;

[0031] Deep learning model algorithms that use convolutional neural networks for complex pattern recognition and waveform classification;

[0032] Principal component analysis algorithm, used for dimensionality reduction and feature extraction, extracting the most important radar waveform signal features;

[0033] Adaptive filtering algorithm to eliminate noise and interference and improve the signal-to-noise ratio of radar waveform signals;

[0034] Data fusion technology algorithms integrate data from multiple sensors to improve the accuracy of multi-group radar target detection and recognition.

[0035] As a preferred technical solution, the waveform analysis module includes a waveform image recognition unit, a waveform marking unit and a waveform analysis training unit, the output end of the waveform image recognition unit is communicatively connected to the input end of the waveform analysis training unit, and the waveform marking unit is integrated inside the waveform analysis training unit;

[0036] The waveform image recognition unit is used to perform auxiliary operations such as repairing and enhancing the images in one or more sets of collected radar waveform data, and mark the repaired parts accordingly;

[0037] The waveform marking unit is used to identify and mark different types of radar waveforms, including pulse waveforms and continuous waveforms;

[0038] The waveform analysis training unit is used to classify and identify waveforms using the extracted features, and to determine the type, location and state of the target object in combination with pattern recognition technology, while generating a training model to facilitate continuous analysis and training.

[0039] The beneficial effects of the present invention are as follows: by setting up a radar waveform analysis system, the present invention, when in use, performs auxiliary operations such as repairing and enhancing images in one or more groups of collected radar waveform data, uses extracted features to classify and identify waveforms, combines pattern recognition technology to judge the type, position and state of the target object, and generates a training model at the same time to identify and mark different types of radar waveforms. The system can efficiently identify and classify targets in complex environments and improve analysis accuracy. It not only improves the efficiency and accuracy of radar signal processing, but is also suitable for wide application in multiple fields. By screening multiple groups of algorithms, algorithm optimization and feature selection, the complexity of data processing is reduced and the processing speed is improved. The radar database is encrypted three times by a database encryption unit, thereby improving the overall security. The real-time updated radar data information is recorded by the database center, and the radar waveform analysis data and the corresponding analysis results are managed, visualized and stored, which helps to realize radar waveform analysis management through Internet cloud management and control, and improve the intelligence level of radar waveform analysis management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] For ease of explanation, the present invention is described in detail with reference to the following specific implementations and the accompanying drawings.

[0041] Figure 1 The present invention is a flowchart of the overall process of a radar waveform analysis method based on a feature extraction algorithm. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0043] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0044] It should be understood that the term "and / or" used in this article is only a description of 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 at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0045] It should be understood that although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0046] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0047] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0048] It should be particularly noted that any symbols and / or numbers in the specification that are not marked in the accompanying drawings are not drawing marks.

[0049] The optional embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0050] like Figure 1 As shown, this specific implementation adopts the following technical solutions:

[0051] A radar waveform analysis method based on a feature extraction algorithm, the radar waveform analysis method comprising:

[0052] S1. Use multiple groups of radar equipment to collect real-time radar waveform data, perform denoising and normalization on the collected raw waveform data, and record multiple raw data while acquiring the waveform data. The raw data includes acquisition time, acquisition source, acquisition processing results, and acquisition staff. The radar waveform data includes parameters such as amplitude, phase, and frequency;

[0053] S2. Generate a database center, continuously update the stored information of the database center through Internet connection, and encrypt the radar database three times. The three encryptions include one encryption of the database center, identity authentication of the staff who access the database center to achieve the effect of secondary encryption, and three encryptions of whether the staff has the authority to retrieve the corresponding entry;

[0054] S3. Retrieve relevant algorithms for the key features that may be involved in this analysis, match the retrieved algorithms one by one, further screen the most appropriate algorithms, and promptly connect the single algorithm or multiple groups of algorithms that have completed the screening to the next link;

[0055] S4. Perform auxiliary operations such as repairing and enhancing the images in one or more sets of collected radar waveform data, use the extracted features to classify and identify the waveforms, combine pattern recognition technology to determine the type, location and state of the target object, and generate a training model to identify and mark different types of radar waveforms.

[0056] The radar waveform analysis system includes a radar database, an algorithm access module, a waveform analysis module and a data acquisition module. The output end of the data acquisition module is communicatively connected to the input end of the radar database, the output end of the radar database is communicatively connected to the input end of the algorithm access module, and the output end of the algorithm access module is communicatively connected to the input end of the waveform analysis module.

[0057] The data acquisition module includes a waveform data acquisition unit, a preprocessing unit and an acquisition data information recording unit. The output end of the waveform data acquisition unit is communicatively connected to the input end of the preprocessing unit, and the waveform data acquisition unit and the acquisition data information recording unit are bidirectionally communicatively connected; the waveform data acquisition unit is used to use multiple groups of radar equipment to collect real-time radar waveform data; the preprocessing unit is used to denoise and normalize the collected original waveform data, and use signal filtering technology to improve signal quality; the acquisition data information recording unit is used to record multiple original data while acquiring waveform data.

[0058] The radar database includes a real-time update unit, a database encryption unit and a database center. The real-time update unit is connected to the database encryption unit and the database center for two-way communication respectively; the real-time update unit is used to continuously update the storage information of the database center through an Internet connection; the database encryption unit is used to encrypt the radar database three times; the database center is used to record the real-time updated radar data information, and is also used to record the entire process of each radar waveform data analysis, and to mark and explain the problems therein.

[0059] The algorithm access module includes an information retrieval unit, an algorithm screening unit and an algorithm access unit, wherein the output end of the information retrieval unit is communicatively connected to the input end of the algorithm screening unit, and the output end of the algorithm screening unit is communicatively connected to the input end of the algorithm access unit; the information retrieval unit is used to retrieve algorithms related to key features that may be involved in this analysis, and multiple retrievals can be performed by replacing secondary features to retain the algorithms with the highest occurrence rate; the algorithm screening unit is used to match the retrieved algorithms one by one, and further screen the most appropriate algorithms, and can generate an online table for staff to join the screening process, and test the algorithms appearing in the table one by one; the algorithm access unit is used to access the screened algorithms to the radar waveform analysis system for subsequent waveform analysis and training operations;

[0060] The algorithm includes but is not limited to:

[0061] Fourier transform algorithm, which converts time domain signals into frequency domain and analyzes the spectrum characteristics of radar waveform signals;

[0062] Target detection algorithms, including Kalman filtering and minimum mean square error, for real-time tracking and estimation of radar target states;

[0063] Deep learning model algorithms that use convolutional neural networks for complex pattern recognition and waveform classification;

[0064] Adaptive filtering algorithm to eliminate noise and interference and improve the signal-to-noise ratio of radar waveform signals;

[0065] Data fusion technology algorithms that integrate data from multiple sensors to improve the accuracy of multi-group radar target detection and recognition;

[0066] The feature extraction algorithm is the principal component analysis algorithm, which is used for dimensionality reduction and feature extraction. It extracts the most important radar waveform signal features and reduces high-dimensional data to 2 or 3 dimensions to facilitate visualization and exploration of data distribution. It retains the main components of the radar waveform and removes low-variance noise components to extract important features and reduce information redundancy.

[0067] The waveform analysis module includes a waveform image recognition unit, a waveform marking unit and a waveform analysis training unit. The output end of the waveform image recognition unit is communicatively connected to the input end of the waveform analysis training unit, and the waveform marking unit is integrated inside the waveform analysis training unit; the waveform image recognition unit is used to perform auxiliary operations such as repairing and enhancing images in one or more groups of collected radar waveform data, and to mark the repaired parts accordingly; the waveform marking unit is used to identify and mark different types of radar waveforms, including pulse waveforms and continuous waveforms; the waveform analysis training unit is used to classify and identify waveforms using extracted features, and to determine the type, position and state of the target object in combination with pattern recognition technology, and to generate a training model to facilitate continuous analysis and training.

[0068] Example

[0069] When multiple groups of algorithms are connected to the radar waveform analysis manager:

[0070] S1. The staff shall inspect the equipment involved in the on-site and remote work sites one by one. After all the inspections are correct and can be started normally, the corresponding inspection time, location, and participants shall be recorded, and the radar waveform analysis system shall be started. The waveform data acquisition unit uses multiple groups of radar equipment to collect real-time radar waveform data. The pre-processing unit performs denoising and normalization processing on the collected original waveform data, and uses signal filtering technology to improve signal quality. The acquisition data information recording unit is used to record multiple original data while acquiring waveform data. The original data includes acquisition time, acquisition source, acquisition processing results, and acquisition staff. The radar waveform data includes parameters such as amplitude, phase, and frequency;

[0071] S2. The real-time update unit continuously updates the storage information of the database center through the Internet connection. The database encryption unit is used to perform three encryption processes on the radar database, including one encryption of the database center, identity authentication of the staff accessing the database center to achieve a secondary encryption effect, and three encryption of the staff's authority when searching for the corresponding entry. The database center records the real-time updated radar data information, and also records the entire process of each radar waveform data analysis, and marks and explains the problems therein;

[0072] S3. The information retrieval unit searches for algorithms related to the key features that may be involved in this analysis. Multiple searches can be performed by replacing secondary features, and the algorithm with the highest appearance rate is retained. The algorithm screening unit is used to match the retrieved algorithms one by one, and further screen the most appropriate algorithms. An online table can be generated for staff to join the screening process, and the algorithms appearing in the table can be tested one by one. The algorithm access unit connects the screened algorithms and the pre-connected feature extraction algorithms to the radar waveform analysis system for subsequent waveform analysis and training operations.

[0073] The feature extraction algorithm is the principal component analysis algorithm, which is used for dimensionality reduction and feature extraction. It extracts the most important radar waveform signal features and reduces high-dimensional data to 2 or 3 dimensions to facilitate visualization and exploration of data distribution. It retains the main components of the radar waveform and removes low-variance noise components to extract important features and reduce information redundancy.

[0074] S4. The waveform image recognition unit performs auxiliary operations such as repairing and enhancing the images in one or more groups of collected radar waveform data, and marks the repaired parts accordingly. The waveform marking unit recognizes and marks different types of radar waveforms, including pulse waveforms and continuous waveforms. The waveform analysis and training unit is used to classify and recognize waveforms using the extracted features, and combines pattern recognition technology to determine the type, position and state of the target object, and generate a training model to facilitate continuous analysis and training.

[0075] Specifically: In actual applications, there are multiple data acquisition modules, which are used in conjunction with the radar database, the algorithm access module, and the waveform analysis module respectively. The multiple data acquisition modules are located in different geographical locations. The present invention sets a radar waveform analysis system. When in use, the images in one or more groups of radar waveform data collected are repaired, enhanced, and other auxiliary operations are performed, the extracted features are used to classify and identify the waveforms, and the type, position, and state of the target object are determined in combination with pattern recognition technology. At the same time, a training model is generated to identify and mark different types of radar waveforms. The system can efficiently identify and classify targets in complex environments and improve the analysis accuracy. It not only improves the efficiency and accuracy of radar signal processing, but is also suitable for wide application in multiple fields. By screening multiple groups of algorithms, the complexity of data processing is reduced and the processing speed is improved through algorithm optimization and feature selection. The radar database is encrypted three times by a database encryption unit, which improves the overall security. The real-time updated radar data information is recorded by the database center, and the radar waveform analysis data and the corresponding analysis results are managed, visualized, and stored, which is helpful to realize radar waveform analysis management through Internet cloud management and control, and improve the intelligent level of radar waveform analysis management.

[0076] Those of ordinary skill in the art will appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, equipment and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] In the several embodiments provided in the present application, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or equipment, which can be electrical, mechanical or other forms.

[0079] The modules for radar database, algorithm access, waveform analysis and data acquisition may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0081] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program instructions, such as a USB flash drive, a mobile hard disk, a read-only storage server, a random access storage server, a magnetic disk or an optical disk.

[0082] In addition, it should be noted that the combination of the various technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way unless there is a contradiction between them.

[0083] It should be noted that the above examples are only specific embodiments of the present invention, and the present invention is obviously not limited to the above examples, and there are many similar variations. All variations directly derived or associated from the contents disclosed by the technicians in this field should fall within the protection scope of the present invention.

[0084] The above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A radar waveform analysis method based on a feature extraction algorithm, characterized in that: The radar waveform analysis method comprises: S1. Use multiple groups of radar equipment to collect real-time radar waveform data, perform denoising and normalization on the collected raw waveform data, and record multiple raw data while acquiring the waveform data; S2, generate a database center, continuously update the storage information of the database center through Internet connection, and encrypt the radar database three times; S3. Retrieve relevant algorithms for the key features that may be involved in this analysis, match the retrieved algorithms one by one, further screen the most appropriate algorithms, and promptly connect the single algorithm or multiple groups of algorithms that have completed the screening to the next link; S4. Perform auxiliary operations such as repairing and enhancing the images in one or more sets of collected radar waveform data, use the extracted features to classify and identify the waveforms, combine pattern recognition technology to determine the type, location and state of the target object, and generate a training model to identify and mark different types of radar waveforms.

2. The radar waveform analysis method based on feature extraction algorithm according to claim 1, characterized in that: In the step 1, a radar waveform data analysis system is constructed before installing multiple sets of radar data.

3. The radar waveform analysis method based on feature extraction algorithm according to claim 1, characterized in that: The triple encryption in step S2 includes one encryption of the database center, identity authentication of the staff accessing the database center to achieve a secondary encryption effect, and three encryptions to determine whether the staff has the authority to retrieve the corresponding entry.

4. The radar waveform analysis method based on feature extraction algorithm according to claim 3 is characterized in that: The radar waveform data in step S1 includes parameters such as amplitude, phase and frequency.

5. The radar waveform analysis method based on feature extraction algorithm according to claim 1, characterized in that: The original data in step S1 includes acquisition time, acquisition source, acquisition processing result, and acquisition staff.

6. The radar waveform analysis method based on feature extraction algorithm according to claim 2, characterized in that: The radar waveform analysis system includes a radar database, an algorithm access module, a waveform analysis module and a data acquisition module. The output end of the data acquisition module is communicatively connected to the input end of the radar database, the output end of the radar database is communicatively connected to the input end of the algorithm access module, and the output end of the algorithm access module is communicatively connected to the input end of the waveform analysis module.

7. The radar waveform analysis method based on feature extraction algorithm according to claim 6 is characterized in that: The data acquisition module includes a waveform data acquisition unit, a preprocessing unit and an acquisition data information recording unit, wherein the output end of the waveform data acquisition unit is communicatively connected to the input end of the preprocessing unit, and the waveform data acquisition unit and the acquisition data information recording unit are bidirectionally communicatively connected; The waveform data acquisition unit is used to collect real-time radar waveform data using multiple groups of radar equipment; The preprocessing unit is used to perform denoising and normalization processing on the collected original waveform data; The acquired data information recording unit is used to record multiple pieces of original data while acquiring waveform data.

8. The radar waveform analysis method based on feature extraction algorithm according to claim 6, characterized in that: The radar database includes a real-time update unit, a database encryption unit and a database center, and the real-time update unit is connected to the database encryption unit and the database center for two-way communication respectively; The real-time updating unit is used to continuously update the stored information in the database center by means of Internet connection; The database encryption unit is used to perform three encryption processes on the radar database; The database center is used to record radar data information updated in real time.

9. The radar waveform analysis method based on feature extraction algorithm according to claim 6, characterized in that: The algorithm access module includes an information retrieval unit, an algorithm screening unit and an algorithm access unit, wherein the output end of the information retrieval unit is communicatively connected to the input end of the algorithm screening unit, and the output end of the algorithm screening unit is communicatively connected to the input end of the algorithm access unit; The information retrieval unit is used to perform a search for algorithms related to key features that may be involved in the analysis; The algorithm screening unit is used to match the retrieved algorithms one by one and further screen the most appropriate algorithm; The algorithm access unit is used to access the screened algorithm to the radar waveform analysis system.

10. The radar waveform analysis method based on feature extraction algorithm according to claim 6, characterized in that: The waveform analysis module includes a waveform image recognition unit, a waveform marking unit and a waveform analysis training unit, wherein the output end of the waveform image recognition unit is communicatively connected to the input end of the waveform analysis training unit, and the waveform marking unit is integrated inside the waveform analysis training unit; The waveform image recognition unit is used to perform auxiliary operations such as repairing and enhancing images in one or more sets of collected radar waveform data; The waveform marking unit is used to identify and mark different types of radar waveforms; The waveform analysis training unit is used to classify and identify waveforms using the extracted features, and to determine the type, location and state of the target object in combination with pattern recognition technology, while generating a training model to facilitate continuous analysis and training.

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