A method, system, device, and medium for displaying large amounts of radar signal parameters.

Abstract

This invention relates to the field of radar monitoring technology and discloses a method, system, device, and medium for displaying large amounts of radar signal parameters. The method includes: acquiring and buffering radar pulse data; retrieving buffered radar pulse data according to a preset time range; processing the retrieved radar pulse data using a signal sorting algorithm to assign unique identifiers to radar pulses corresponding to different radiation sources; parsing various radar feature parameters from the retrieved radar pulse data; applying an improved downsampling algorithm to downsample these radar feature parameters to obtain feature point sets; the improved downsampling algorithm selects feature points by bucketing, preserving data distribution boundaries and detailed features; and displaying the downsampled feature point sets in a graphically linked rendering process. This invention achieves high fidelity during the downsampling process and supports intelligent multi-view linkage and color-differentiated display.

Description

Technical Field

[0001] This invention relates to the field of radar monitoring technology, and in particular to a method, system, device, and medium for displaying large amounts of radar signal parameters. Background Technology

[0002] In the field of radar monitoring, detecting and analyzing radar signals from the surrounding environment generates a large amount of pulse correlation data containing various key parameters. This data is the core basis for accurately determining the signal source, analyzing target characteristics, and understanding signal change patterns. Traditional signal display solutions typically present this data through multiple independent charts, each corresponding to a key parameter and its relationship with time. All data points are drawn with a single shape and color, lacking effective visual differentiation. Figure 1 As shown.

[0003] With the continuous development of radar technology and the ever-upgrading of monitoring needs, the generation speed and scale of pulse data are growing explosively. The limitations of traditional display solutions are becoming increasingly apparent, making it difficult to meet the needs of actual application scenarios. When faced with a surge in massive amounts of data, traditional software chart controls, due to inherent limitations in computing power and rendering resources, are unable to efficiently complete the processing and drawing of large-scale data points. This often results in problems such as interface lag and refresh delays, seriously affecting the smoothness of operation and the timeliness of judgment for analysts.

[0004] To alleviate this contradiction, traditional display systems typically employ two passive compromises: either drastically compressing the data display time range, presenting only signal data within a short period, preventing analysts from obtaining a complete view of the signal's time series and making it difficult to capture the long-term patterns and potential characteristics of the signal; or using a simple and crude downsampling method, dividing the time axis into several segments and extracting only a portion of the extreme values ​​from each segment for plotting. While this crude downsampling method can reduce the amount of data and improve plotting speed to some extent, it comes at the cost of data integrity, directly leading to the loss of a large amount of internal detail in the pulse stream. For modern complex radar signals, many key signal features are precisely hidden in these neglected details. Once lost, analysts cannot accurately grasp the true patterns of signal change, easily leading to misjudgments or omissions during signal analysis, posing a significant risk to the accuracy of radar monitoring missions.

[0005] Besides the core contradiction between data processing and fidelity, traditional display systems also suffer from significant shortcomings in terms of ease of operation and analytical efficiency. In existing systems, various parameter charts are mostly designed independently, lacking an effective correlation mechanism. When analysts need to delve into the signal characteristics of a specific time segment and zoom or pan one chart, other related charts cannot automatically adjust to the corresponding time range, requiring manual matching. This manual adjustment is not only cumbersome and time-consuming but also prone to errors that cause inconsistencies in the time axes of different charts, thus affecting the accuracy of the analysis. Simultaneously, there is a significant disconnect between the display layer and the signal processing layer, making it impossible to organically combine the information from different radiation sources obtained after signal sorting with the chart display. It is also impossible to distinguish pulse signals from different sources through intuitive visual differences. In densely overlapping pulse streams, various signals are mixed together, requiring analysts to check the parameter information of each data point individually to distinguish different targets. This is difficult, inefficient, and hinders the rapid identification and tracking of specific signals.

[0006] Furthermore, traditional display methods use only a single shape and color to present all data, which further exacerbates the difficulty of signal differentiation. Even experienced analysts need to spend a lot of time and effort to filter target signals, which seriously restricts the real-time performance and effectiveness of radar monitoring.

[0007] In order to improve the above problems, some existing patents have attempted to propose improvement solutions, but these solutions still have many shortcomings and have failed to fully solve the core pain points in practical applications.

[0008] For example, CN116819456A provides a graphical display method for radar signal pulse descriptor parameters. This method includes: using a two-dimensional scatter plot on the user display interface to perform time-domain correlation and target correlation of multiple dimensions of the full pulse parameters; specifically, it correlates and displays the pulse's arrival time, frequency, amplitude, pulse width, repetition interval, and azimuth, and marks parameters belonging to the same target with color, thereby intuitively displaying the target radar's parameter information and the variation characteristics between pulses. It can be seen that this method achieves time-domain correlation and target correlation of multiple pulse parameters through a two-dimensional scatter plot, and uses color to mark related parameters of the same target, improving the intuitiveness of signal display to a certain extent. However, this solution still has significant shortcomings:

[0009] Its downsampling strategy is not specifically optimized for the two-dimensional distribution characteristics of radar signals. When faced with massive pulse data, it is difficult to achieve a precise balance between processing efficiency and data fidelity. It does not capture the core details of complex signals sufficiently, and key information may still be lost. It relies only on color as a single visual dimension for target differentiation, lacking the auxiliary support of other visual dimensions such as shape. In scenarios where multiple target parameters are similar or signals overlap densely, the identification of different targets is insufficient, making it difficult for analysts to quickly locate targets of interest. At the same time, the solution does not realize intelligent linkage between multiple views. During the analysis process, the time range of each view still needs to be manually adjusted, which is not convenient enough and fails to effectively reduce the operational burden and time cost of analysts.

[0010] For example, CN116383452A discloses a method, device, equipment, and storage medium for adaptive display of monitoring data. The method includes: obtaining the time range of time-series monitoring data and the resolution of the front-end display screen; obtaining the time step based on the time range and resolution; dividing the time-series monitoring data into multiple slices based on the time step; obtaining representative data points in each slice based on the LTTB algorithm; and generating a chart using the representative data points of all slices for display on the front end. It is evident that this method, by dividing the data into multiple slices based on the time step and extracting representative data points using traditional algorithms, has certain advantages in adaptability to ordinary time-series data, but exhibits significant adaptation deficiencies in radar signal display scenarios.

[0011] Radar signals are typically displayed as two-dimensional scatter plots, with each data point containing both parameter values ​​and time information, exhibiting complex two-dimensional distribution characteristics. However, the traditional algorithms used in this solution are primarily designed for one-dimensional time-series data, making it difficult to adapt to the two-dimensional distribution characteristics of radar signals. They cannot accurately capture the aggregation characteristics of the data or the core change patterns of complex signals, retaining only some boundary information or a small number of representative data points. This makes it difficult to truly restore the complete distribution of the signal, especially failing to accurately present the core features of signals with special change patterns. Furthermore, the downsampling parameter settings only consider the time range and the front-end display resolution, without taking into account the professional characteristics of radar signals, such as the distribution patterns of different parameters and the differences in radiation sources after signal sorting. Consequently, the displayed results fail to meet the precise requirements of radar signal analysis.

[0012] In summary, due to the inherent defects of these traditional solutions, the display of large amounts of radar signal parameters still faces severe challenges in terms of data processing efficiency, display fidelity, ease of operation, and target distinguishability. There is an urgent need for a more complete display method that better meets the needs of practical applications to overcome the existing technological bottlenecks. Summary of the Invention

[0013] To address the aforementioned issues, this invention proposes a method, system, device, and medium for displaying large amounts of radar signal parameters. This method achieves high fidelity during downsampling, significantly improving display accuracy and analytical value. Furthermore, it supports intelligent multi-view linkage and color-differentiated display, greatly enhancing the efficiency and intuitiveness of signal analysis.

[0014] The technical solution adopted in this invention is as follows:

[0015] A method for displaying large amounts of radar signal parameters, comprising:

[0016] Collect radar pulse data and cache it, then retrieve the cached radar pulse data according to a preset time range;

[0017] The retrieved radar pulse data is processed using a signal sorting algorithm, and a unique identifier is assigned to the radar pulses corresponding to different radiation sources.

[0018] Multiple radar feature parameters are obtained by parsing the retrieved radar pulse data. An improved downsampling algorithm is used to downsample the various radar feature parameters to obtain a feature point set. The improved downsampling algorithm selects feature points by bucketing, preserving the data distribution boundary and detailed features.

[0019] The graphical display of the feature point set obtained after downsampling is rendered in a linked manner.

[0020] Furthermore, the improved downsampling algorithm selects feature points by binning, including: determining the total number of input signal data as X, setting the number of data points retained after downsampling as N, and dividing the signal data into N equal bins, with the number of data points in each bin being M = (X-2) / (N-2); wherein, the signal data includes frequency data, amplitude data, pulse width data, repetition interval data, and data type information.

[0021] Furthermore, the improved downsampling algorithm, which selects feature points by bucketing, also includes:

[0022] The first bucket selects the first data point of the input signal data by default, and the Nth bucket selects the last data point of the input signal data by default.

[0023] For each of the 2nd to N-1th buckets, two feature points are selected. The selection method includes: using the maximum value of the previous bucket and the first data point of the next bucket as two fixed points, constructing triangles with each data point in the current bucket, and selecting the two data points with the largest and smallest areas of the corresponding triangles as the feature points of the current bucket.

[0024] Furthermore, the area of ​​the triangle is calculated using a vector cross product algorithm, which calculates the area of ​​a triangle formed by three points in a two-dimensional coordinate system using only addition and multiplication operations.

[0025] Furthermore, the graphical display of the feature point set obtained through downsampling processing through linked rendering includes:

[0026] The downsampling output data is classified into frequency data, amplitude data, pulse width data, repetition interval data, and data type information.

[0027] Prepare multiple colors and dot plot shapes. Based on the radiation source type corresponding to the assigned unique identifier, establish a one-to-one correspondence between the classified data and the corresponding color and dot plot shape.

[0028] Data from different radiation source types are placed on the same layer, and the layer display parameters are set according to the corresponding colors and dot plot shapes. After image rendering, the chart is displayed on the human-computer interaction interface.

[0029] Furthermore, the graphical display of the feature point set obtained by downsampling and linked rendering also includes: using time as the horizontal axis of each chart, and when any chart is scaled, the other charts are synchronously linked to the same time range to maintain the consistency of the time dimension.

[0030] Furthermore, the graphical display of the feature point set obtained by downsampling processing through linkage rendering also includes: when drawing the chart, associating the horizontal and vertical coordinate values ​​of the scatter points with the assigned unique identifier, and using the unique identifier to reverse index to the corresponding original pulse data.

[0031] A radar signal parameter large-scale data display system, comprising:

[0032] The data caching and retrieval module is configured to collect radar pulse data and cache it, and retrieve the cached radar pulse data according to a preset time range;

[0033] The signal sorting module is configured to process the retrieved radar pulse data using a signal sorting algorithm and assign a unique identifier to the radar pulses corresponding to different radiation sources.

[0034] The downsampling processing module is configured to parse various radar feature parameters from the retrieved radar pulse data, and to perform downsampling processing on the various radar feature parameters respectively using an improved downsampling algorithm to obtain a feature point set; the improved downsampling algorithm selects feature points by bucketing, preserving data distribution boundaries and detailed features;

[0035] The graphical display module is configured to perform linked rendering of the feature point set obtained after downsampling.

[0036] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for displaying large amounts of radar signal parameters.

[0037] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for displaying large amounts of radar signal parameters.

[0038] The beneficial effects of this invention are as follows:

[0039] 1. The downsampling processing method of this invention is specifically designed for the two-dimensional distribution characteristics of radar signals. It is optimized based on the LTTB+ algorithm, selecting feature points by bucketing and simultaneously capturing data boundaries and aggregation points, thus fully preserving the detailed features of complex signals. This avoids the problem of traditional downsampling methods that only retain extreme values ​​and lose key information. This downsampling processing method only requires traversing the data once, has low computational overhead, requires no hardware acceleration, and can quickly process large-scale data using only the CPU. Its adaptability far exceeds existing one-dimensional downsampling solutions.

[0040] 2. This invention uses time as a unified benchmark, enabling intelligent linkage of multiple parameter charts. When any chart is zoomed or panned, other charts automatically synchronize the corresponding time range without manual adjustment. This solves the problems of independent multiple views and cumbersome and error-prone operation in existing systems, allowing analysts to observe the time correlation characteristics of different parameters in a coherent manner and improving signal analysis efficiency.

[0041] 3. This invention assigns unique identifiers to different radiation sources using a signal sorting algorithm, presenting targets using both color and shape dimensions. Compared to existing patents that use single-color markings, this method offers higher distinguishability and is more intuitive. It also supports personalized editing of the display style for individual targets, facilitating the rapid locking of specific signals from dense pulse streams and overcoming the problems of traditional display styles being monotonous and signal differentiation being difficult.

[0042] 4. In the downsampling process, the present invention uniformly numbers the data and establishes the correspondence between the chart scatter points and the original pulse data. It can quickly index the original parameters in reverse, providing support for signal verification and in-depth review. It makes up for the shortcomings of existing solutions in that it is difficult to trace the original data and improves the accuracy of analysis.

[0043] 5. This invention, by leveraging efficient algorithms and a caching retrieval mechanism, significantly extends the stable display time span. At the same time, it optimizes the rendering mechanism, enabling real-time rendering without waiting for data to be fully cached, resulting in rapid refresh. This not only overcomes the limitations of traditional solutions in terms of display duration but also meets the needs of real-time analysis. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the existing pulse descriptor chart representation format.

[0045] Figure 2 This is a flowchart of a method for displaying large amounts of radar signal parameters according to Embodiment 1 of the present invention.

[0046] Figure 3 This is a flowchart of a method for displaying large amounts of radar signal parameters according to Embodiment 2 of the present invention.

[0047] Figure 4 This is one of the large data volume display effect diagrams of radar signal parameters in Embodiment 2 of the present invention.

[0048] Figure 5 This is the second illustration of the large data volume display effect of radar signal parameters in Embodiment 2 of the present invention.

[0049] Figure 6 This is a flowchart of the LTTB+ algorithm downsampling process in Embodiment 2 of the present invention.

[0050] Figure 7 This is a diagram showing the downsampling effect of the LTTB+ algorithm in Embodiment 2 of the present invention.

[0051] Figure 8 This is a diagram showing the effect of downsampling processing using the existing extreme value statistical method.

[0052] Figure 9 This is an original data diagram of Embodiment 2 of the present invention.

[0053] Figure 10 This is a diagram showing the downsampling effect of the existing LTTB algorithm.

[0054] Figure 11 This is a diagram showing the downsampling effect of the LTTB+ algorithm in Embodiment 2 of the present invention.

[0055] Figure 12 This is a flowchart of the graphical display method of Embodiment 2 of the present invention.

[0056] Figure 13 This is one of the effect diagrams of the graphical display method in Embodiment 2 of the present invention.

[0057] Figure 14 This is the second illustration of the graphical display method in Embodiment 2 of the present invention.

[0058] Figure 15 This is the third illustration of the graphical display method of Embodiment 2 of the present invention. Detailed Implementation

[0059] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments are now described. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0060] Example 1

[0061] like Figure 2 As shown, this embodiment provides a method for displaying large amounts of radar signal parameters, including:

[0062] Collect radar pulse data and cache it, then retrieve the cached radar pulse data according to a preset time range;

[0063] The retrieved radar pulse data is processed using a signal sorting algorithm, and a unique identifier is assigned to the radar pulses corresponding to different radiation sources.

[0064] Multiple radar feature parameters are obtained by parsing the retrieved radar pulse data. An improved downsampling algorithm is used to downsample the various radar feature parameters to obtain a feature point set. The improved downsampling algorithm selects feature points by bucketing, thus preserving the data distribution boundary and detailed features.

[0065] The graphical display of the feature point set obtained after downsampling is rendered in a linked manner.

[0066] It should be noted that this method can achieve efficient storage and accurate retrieval of radar pulse data, identify the radiation source through signal sorting, retain key information while reducing the amount of data through the improved downsampling algorithm, and make the data presentation more intuitive through linked rendering of graphical displays, thereby improving the overall efficiency of processing and displaying large amounts of radar signal parameters.

[0067] Preferably, the improved downsampling algorithm selects feature points by binning, including: determining the total number of input signal data, setting the number of data points to be retained after downsampling, dividing the signal data into several bins on an average basis, and calculating the number of data points in each bin; wherein, the signal data includes frequency data, amplitude data, pulse width data, repetition interval data, and data type information.

[0068] Specifically, before performing downsampling processing, the total number of input signal data of various types is first counted. Based on the actual display requirements and data processing efficiency requirements, the number of data points to be retained after downsampling is set. According to the set number of data points to be retained, the signal data is evenly divided into a corresponding number of buckets. By calculating the correlation between the total number and the number of data points to be retained, the number of data points that each bucket should contain is obtained. At the same time, the data types of the signal data participating in downsampling processing are clearly defined, covering frequency, amplitude, pulse width, repetition interval related data and corresponding data type information, to ensure that all kinds of key data can be processed.

[0069] It should be noted that this method makes the downsampling process more standardized and targeted by clearly defining data statistics, binning, and data type ranges. The number of data points in each bin is calculated reasonably, providing a uniform data source foundation for the accurate selection of subsequent feature points and ensuring the representativeness of the downsampled data.

[0070] More preferably, the improved downsampling algorithm selects feature points by binning, and further includes: the first bin by default selects the first data point of the input signal data, and the last bin by default selects the last data point of the input signal data; each of the intermediate bins selects two feature points, and the selection method includes: using the maximum value of the previous bin and the first data point of the next bin as two fixed points, constructing triangles with each data point in the current bin in turn, and selecting the two data points with the largest and smallest areas of the corresponding triangles as the feature points of the current bin.

[0071] Specifically, after binning, the starting data point of the input signal data is directly determined as the feature point of the first bin, and the ending data point of the input signal data is determined as the feature point of the last bin. For all intermediate bins between the first and last bins, the maximum value data point of the previous adjacent bin and the starting data point of the next adjacent bin are first obtained, and these two data points are used as fixed reference points. Then, each data point in the current bin is combined with these two fixed points to construct triangles in a two-dimensional coordinate system. By calculating the area of ​​each triangle, the two data points corresponding to the largest and smallest area values ​​are selected and determined as the feature points of the current bin.

[0072] It should be noted that this method ensures the integrity and continuity of the data by fixing the key data points of the first and last buckets; the middle bucket uses the area of ​​triangles to filter feature points, which can accurately capture the fluctuations and key features of the data, avoid the loss of detailed information during downsampling, and make the downsampled data better reflect the distribution pattern of the original data.

[0073] More preferably, the area of ​​the triangle is calculated using a vector cross product algorithm, which calculates the area of ​​a triangle formed by three points in a two-dimensional coordinate system using only addition and multiplication operations. Specifically, after constructing the triangle, the coordinate values ​​of the three points in the two-dimensional coordinate system are extracted. Based on the fundamental principles of vectors, the three points are transformed into corresponding vector expressions. Through the operational rules of the vector cross product, only addition and multiplication operations are used to calculate the vectors, without introducing complex operations such as division and square root, to obtain the area value of the triangle. The entire calculation process strictly follows the correspondence between the vector cross product and the area of ​​the triangle in the two-dimensional coordinate system, ensuring the accuracy of the calculation results.

[0074] It should be noted that this method uses a vector cross product algorithm that only involves addition and multiplication operations, which simplifies the calculation process of the triangle area, reduces the computational complexity and amount of computation, improves the efficiency of feature point selection, avoids the errors that may be caused by complex calculations, ensures the accuracy of feature point selection, and adapts to the needs of rapid processing of large amounts of radar signal parameters.

[0075] Preferably, the graphical display of the feature point set obtained after downsampling is performed through linked rendering, including: classifying the data output after downsampling according to frequency data, amplitude data, pulse width data, repetition interval data, and data type information; preparing multiple colors and dot plot shapes, and establishing a one-to-one correspondence between the classified data and the corresponding colors and dot plot shapes according to the radiation source type corresponding to the assigned unique identifier; placing the data of different radiation source types on the same layer, setting the layer display parameters according to the corresponding colors and dot plot shapes, and displaying the chart on the human-computer interaction interface after performing image rendering.

[0076] Specifically, the downsampled feature point set data is first classified and organized according to the dimensions of frequency, amplitude, pulse width, repetition interval, and data type information, and then divided into several categories. Multiple different colors and dot plot shapes are prepared in advance, and a correspondence database between colors, dot plot shapes, and radiation source types is established. Based on the unique identifier of each data point, its radiation source type is determined, and then, according to the correspondence database, each category of classified data is bound to a specific color and dot plot shape. Subsequently, all data of different radiation source types are integrated into the same display layer, and the layer's display attributes are set according to the bound colors and dot plot shapes. The image rendering program is then started to process the data, and finally, the rendered chart is displayed on the human-computer interaction interface.

[0077] It should be noted that this method, through data classification and binding of colors and dot plot shapes, enables data of different types and radiation sources to be clearly distinguished in the chart. The integrated display of the same layer facilitates intuitive comparison of various types of data. After image rendering, it is displayed on the human-computer interaction interface, which improves the visualization effect of the data and makes it convenient for operators to quickly identify and analyze the radar signal parameters of different radiation sources.

[0078] More preferably, the graphical display of the feature point set obtained by downsampling and rendering in a linked manner also includes: using time as the horizontal axis of each chart, and when any chart is scaled, the other charts are synchronized to the same time range to maintain the consistency of the time dimension.

[0079] Specifically, when drawing various charts, the time dimension is uniformly set as the horizontal axis of all charts to ensure that the time measurement standard of each chart is consistent. During human-computer interaction, when the operator performs a zoom operation on any chart, the system will capture the time range change information corresponding to the zoom operation in real time and transmit the time range information synchronously to all other charts. After receiving the information, other charts will automatically adjust their display range so that the horizontal axis time range of all charts is completely consistent with the scaled chart.

[0080] It should be noted that this method ensures the synchronization of all charts in the time dimension by unifying the horizontal time axis and the scaling linkage mechanism. Operators do not need to adjust the time range of each chart separately to achieve time alignment comparison across charts, which reduces the complexity of operation and improves the consistency and efficiency of data analysis.

[0081] More preferably, the graphical display of the feature point set obtained by downsampling processing through linkage rendering also includes: when drawing the chart, associating the horizontal and vertical coordinate values ​​of the scatter points with the assigned unique identifier, and using the unique identifier to back-index to the corresponding original pulse data.

[0082] Specifically, during the chart drawing process, for each displayed scatter point, its corresponding horizontal and vertical coordinate values ​​are extracted, and the unique identifier corresponding to the data to which the scatter point belongs is retrieved. Through the system's built-in association mapping mechanism, the horizontal and vertical coordinate values ​​are bound to the unique identifier to form a complete association data record. When it is necessary to query the original pulse data, by inputting or selecting the unique identifier corresponding to the target scatter point, the system retrieves the corresponding original pulse data by reverse retrieval based on the association record.

[0083] It should be noted that this method establishes a correlation between scatter point coordinates and unique identifiers, enabling rapid tracing from visualized charts to raw pulse data. This allows operators to easily verify the original data information when analyzing chart data, improving the convenience and accuracy of data verification and providing support for in-depth analysis of radar signal parameters.

[0084] Accordingly, this embodiment also provides a radar signal parameter large-scale data display system, including:

[0085] The data caching and retrieval module is configured to collect radar pulse data and cache it, and retrieve the cached radar pulse data according to a preset time range;

[0086] The signal sorting module is configured to process the retrieved radar pulse data using a signal sorting algorithm and assign a unique identifier to the radar pulses corresponding to different radiation sources.

[0087] The downsampling processing module is configured to parse various radar feature parameters from the retrieved radar pulse data, and then use an improved downsampling algorithm to downsample the various radar feature parameters to obtain a feature point set. The improved downsampling algorithm selects feature points by bucketing, thus preserving the data distribution boundaries and detailed features.

[0088] The graphical display module is configured to perform linked rendering of the feature point set obtained after downsampling.

[0089] Specifically, the data caching and retrieval module acquires radar pulse data through the data acquisition interface, stores the data in a preset cache medium, and simultaneously receives externally input preset time range instructions. Based on these instructions, it retrieves radar pulse data that meets the conditions from the cache medium and outputs it. The signal sorting module receives the data output by the data caching and retrieval module, activates the built-in signal sorting algorithm to analyze and process the data, classifies the pulse data according to the characteristic differences of the radiation source, assigns a unique identifier to the pulse data corresponding to each type of radiation source, and marks it. The downsampling processing module parses various radar characteristic parameters from the marked pulse data, uses an improved downsampling algorithm for each characteristic parameter, filters feature points by bucketing, preserves the distribution boundaries and detailed features of the data, and generates a feature point set. The graphical display module receives the feature point set output by the downsampling processing module, classifies the feature point set, associates colors and dot plot shapes, sets display parameters, and performs linked rendering to display the chart on the interactive interface.

[0090] It should be noted that, through the division of labor and cooperation among various modules, this system achieves fully automated processing of large amounts of radar signal parameters, from acquisition, caching, retrieval, sorting, downsampling to graphical display. Each module has a highly targeted function, data transmission is smooth, the improved downsampling algorithm ensures the efficiency and quality of data processing, and the graphical display module improves the readability of the data. The overall system can efficiently and accurately process and display large amounts of radar signal parameters.

[0091] Example 2

[0092] This embodiment provides a method for displaying large amounts of radar signal parameters, including: first, caching the collected pulse data; retrieving data according to a preset time range; assigning color labels to pulses from different radiation sources using a signal sorting algorithm; most importantly, using an improved LTTB+ downsampling algorithm to process the layered frequency, amplitude, pulse width, and repetition interval data respectively; and finally, performing linked rendering display on the downsampled feature point set.

[0093] Among them, the LTTB+ downsampling algorithm is based on the classic LTTB algorithm. By simultaneously calculating and selecting the feature points corresponding to the areas of the largest and smallest triangles, it can accurately capture and display detailed features such as the center value of the repetition frequency jitter signal while preserving the data distribution boundary.

[0094] It should be noted that on platforms without GPUs or OpenGL, this method can smoothly display millions of pulse data points lasting more than 20 seconds at a time using only CPU graphics rendering, with a fast refresh rate. The improved LTTB+ algorithm achieves high fidelity during downsampling, significantly improving the accuracy of the display and the value of analysis. At the same time, it supports intelligent linkage of multiple views and color differentiation display, which greatly improves the efficiency and intuitiveness of signal analysis.

[0095] Specifically, the method for displaying large amounts of radar signal parameters is explained in detail below.

[0096] I. Overall Implementation Process

[0097] like Figure 3 As shown, the radar signal parameter large data volume display method of this embodiment can be implemented by the following steps: radar signal acquisition device; host computer software receives data; data parsing (multi-threaded); obtaining frequency data, pulse width data, amplitude data and repetition interval data; downsampling algorithm LTTB+; the downsampled data is numbered according to the sorting results; data with different numbers are set to different colors and shapes; display frequency-time graph, pulse width-time graph, amplitude-time graph and repetition interval time graph.

[0098] It should be noted that this method has the following advantages:

[0099] 1. Real-time requirements. In the past, pulse data was cached, and the next packet of pulse data was retrieved and plotted after the chart was completed. Although the pulses could be plotted on the chart, the efficiency was low. This method can plot the pulse stream data on the interface within 500ms.

[0100] 2. Multi-chart linkage function. This method links charts based on time on the horizontal axis. When any chart is zoomed in or out, the other charts are zoomed in to the same time.

[0101] 3. Association Indexing. This method numbers the data during downsampling, so the plotted chart can be traced back to the original corresponding pulse data, which is relatively rare in existing technologies. Most existing technologies produce scatter plots that are difficult to index to the corresponding pulse data, while this method maps the x and y values ​​of the scatter plots to the original pulse numbers, enabling rapid indexing of pulse data.

[0102] 4. Long display time. This method can display pulse data from the past 30 seconds in the chart. Existing technologies, due to the large size of pulse data, can only support a maximum of 20 seconds of pulse data; this method, through an improved downsampling algorithm, reduces the computational overhead required for plotting and displays data with better fidelity, supporting data display up to 30 seconds in the chart. Figure 4 As shown.

[0103] 5. Pulse data rendering uses a combination of color and shape to represent different radar targets, such as... Figure 5 As shown.

[0104] 6. This method does not rely on hardware acceleration, which can reduce hardware costs.

[0105] II. LTTB+ Algorithm Description

[0106] The LTTB algorithm is an efficient time-series data downsampling algorithm that reduces data size by preserving representative features of the original data, making it suitable for high-frequency data scenarios. For one-dimensional data, the LTTB algorithm can retain local minimum and maximum values, preserving the data's variation characteristics after downsampling. However, if the data is two-dimensional, such as the frequency-time plot, pulse width-time plot, amplitude-time plot, and repetition interval-time plot of radar signals required in this invention (which is a scatter plot), retaining only the maximum and minimum values ​​would lose its accuracy. For example, with jitter signals, the jitter center cannot be seen.

[0107] Based on this, this embodiment proposes the LTTB+ algorithm, which is an efficient two-dimensional data downsampling algorithm for radar signals. It inputs two-dimensional data and, while calculating the maximum and minimum values, also finds the cluster points so that the downsampling algorithm can preserve the authenticity of the two-dimensional data.

[0108] like Figure 6 and Figure 7 As shown, the LTTB+ algorithm in this embodiment includes the following steps:

[0109] The total number of signal data points is X, and after downsampling, only N data points are retained. The signal data is divided into N buckets based on the total input, with each bucket containing M = (X-2) / (N-2) data points. The signal data includes frequency data, amplitude data, pulse width data, repetition interval data, and data type. It should be noted that N is typically a maximum of 2000 points, and the interface size is usually around 1000 pixels. Since most data is rendered in a collapsed manner, a large number of buckets is not necessary.

[0110] The first bucket uses the first data point x0 by default, and the Nth bucket uses the last data point x0 by default. max From the second bucket to the (N-1)th bucket, two points are retained (the maximum value of the previous bucket and the first data of the next bucket, plus a point in the current bucket, to construct the areas of the maximum and minimum triangles). It should be noted that calculating the area of ​​the maximum triangle can represent feature points, and calculating the area of ​​the minimum triangle can represent cluster points. The points used are all actual data and will not be distorted.

[0111] The area of ​​a triangle formed by three points in a two-dimensional coordinate system is calculated using the cross product of vectors. For example, if the three points are A, B, and C, then S... 面积 =((Ax-Cx)*(By-Ay)-(Ax-Bx)*(Cy-Ay)) / 2. It should be noted that the vector cross product is used to calculate the triangle formed by three arbitrary points in a two-dimensional coordinate system. This only requires simple addition and multiplication operations, improving efficiency, and can be implemented in any programming language.

[0112] After downsampling, the number of outputs is 2 + (N-2)*2 = 2N-2; the output data is split into individual data (frequency, pulse, amplitude, repetition interval, and data type) to prepare for drawing charts later, and the color and shape of the display are determined according to the data type.

[0113] Specifically, the LTTB+ algorithm in this embodiment is compared with existing downsampling algorithms as follows:

[0114] 1. Direct extraction method: The downsampling algorithm for two-dimensional data usually takes the first data in each bucket. This method is the fastest, but it has the least fidelity, so it is not used for two-dimensional data.

[0115] 2. Maximum and Minimum Value Statistical Method: The most popular method is to take the maximum and minimum values ​​of each bucket, such as... Figure 8 As shown. Although this method can display the edge cases of the data after downsampling, it cannot represent the cluster points, resulting in poor display fidelity. If the cluster points in the current bucket are recalculated, histogram statistics need to be performed again, and a reasonable tolerance value needs to be set, which will increase the processing overhead.

[0116] 3. Existing LTTB Algorithm: The LTTB algorithm can quickly and effectively downsample one-dimensional data, but its performance on two-dimensional data is unsatisfactory, and its fidelity is poor. We will now compare the results by simulating scatter parameters, where the original data is as follows: Figure 9 As shown, after downsampling using the LTTB algorithm, as Figure 10 As shown, it is evident that the downsampling resulted in severe distortion, with the original data's y-axis focal point at 9450 being lost, leading to unsatisfactory results.

[0117] 4. The LTTB+ algorithm in this embodiment: After downsampling using the LTTB+ algorithm, as shown below... Figure 11 As shown, the results clearly demonstrate a more accurate representation of the original data compared to the downsampling results of the existing LTTB algorithm.

[0118] In summary, the LTTB+ algorithm in this embodiment effectively solves the downsampling problem for two-dimensional data. Since it only requires traversing the data once, the computational overhead is minimal, allowing for fast processing of large datasets (66ms for 500,000 data points, and 146ms for 1,210,000 data points). Furthermore, since the LTTB+ algorithm only involves addition and multiplication operations, it supports programming languages ​​such as C / C++, Python, and PHP. This method can be implemented across platforms without relying on hardware acceleration, which is highly beneficial for domestic development and design.

[0119] III. Graphical Display

[0120] like Figure 12 As shown, in this embodiment, the downsampled feature point set is rendered and displayed in a linked manner: input the downsampled data (frequency, pulse width, amplitude, repetition interval, and data type); prepare multiple colors and dot plot shapes (the number here depends on the actual needs, generally 10 different data types are sufficient to show the display effect); assign colors and dot plot shapes according to the data type; the chart places data of different data types on a layer; each layer is set according to the color and dot plot shape corresponding to the data type; and the CPU image rendering is used to display it on the interface.

[0121] It should be noted that the graphical display method of this embodiment has the following advantages:

[0122] 1. Compared with traditional display methods, this method supports the simultaneous change of color and shape of different radar targets.

[0123] 2. For example Figures 13-15 As shown, this method adopts the design concept of associating the same layer with the target, and displays data with the same color and shape on the same layer. When it is necessary to modify the color of a certain layer, it supports editing the displayed color and shape of a certain target.

[0124] Example 3

[0125] This embodiment is based on embodiment 1:

[0126] This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement a large-scale radar signal parameter display method according to Embodiment 1 or 2. The computer program can be in the form of source code, object code, executable file, or some intermediate form.

[0127] Example 4

[0128] This embodiment is based on embodiment 1:

[0129] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a large-scale radar signal parameter display method of Embodiment 1 or 2. The computer program can be in the form of source code, object code, executable file, or some intermediate form. The storage medium includes any entity or device capable of carrying computer program code, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0130] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

[0131] It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. A method for displaying large amounts of radar signal parameters, characterized in that, include: Collect radar pulse data and cache it, then retrieve the cached radar pulse data according to a preset time range; The retrieved radar pulse data is processed using a signal sorting algorithm, and a unique identifier is assigned to the radar pulses corresponding to different radiation sources. Multiple radar feature parameters are obtained by parsing the retrieved radar pulse data. An improved downsampling algorithm is used to downsample the various radar feature parameters to obtain a feature point set. The improved downsampling algorithm selects feature points by bucketing, preserving the data distribution boundary and detailed features. The graphical display of the feature point set obtained after downsampling is rendered in a linked manner; The improved downsampling algorithm selects feature points by bucketing, including: determining the total number of input signal data as X, setting the number of data points retained after downsampling as N, and dividing the signal data into N buckets on an average basis, with the number of data points in each bucket being M = (X-2) / (N-2); wherein, the signal data includes frequency data, amplitude data, pulse width data, repetition interval data, and data type information; The improved downsampling algorithm selects feature points by bucketing, and also includes: The first bucket selects the first data point of the input signal data by default, and the Nth bucket selects the last data point of the input signal data by default. For each of the 2nd to N-1th buckets, two feature points are selected. The selection method includes: using the maximum value of the previous bucket and the first data point of the next bucket as two fixed points, constructing triangles with each data point in the current bucket, and selecting the two data points with the largest and smallest areas of the corresponding triangles as the feature points of the current bucket.

2. The method for displaying large amounts of radar signal parameters according to claim 1, characterized in that, The area of ​​the triangle is calculated using the cross product algorithm, which calculates the area of ​​a triangle formed by three points in a two-dimensional coordinate system using only addition and multiplication operations.

3. The method for displaying large amounts of radar signal parameters according to claim 1, characterized in that, The graphical display of the feature point set obtained through downsampling and rendering includes: The downsampling output data is classified into frequency data, amplitude data, pulse width data, repetition interval data, and data type information. Prepare multiple colors and dot plot shapes. Based on the radiation source type corresponding to the assigned unique identifier, establish a one-to-one correspondence between the classified data and the corresponding color and dot plot shape. Data from different radiation source types are placed on the same layer, and the layer display parameters are set according to the corresponding colors and dot plot shapes. After image rendering, the chart is displayed on the human-computer interaction interface.

4. The method for displaying large amounts of radar signal parameters according to claim 3, characterized in that, The graphical display of the feature point set obtained by downsampling and rendering in a linked manner also includes: using time as the horizontal axis of each chart, and when any chart is scaled, the other charts are synchronously linked to the same time range to maintain the consistency of the time dimension.

5. The method for displaying large amounts of radar signal parameters according to claim 3, characterized in that, The graphical display of the feature point set obtained by downsampling processing through linkage rendering also includes: when drawing the chart, associating the horizontal and vertical coordinate values ​​of the scattered points with the assigned unique identifier, and using the unique identifier to back-index to the corresponding original pulse data.

6. A radar signal parameter large-scale data display system, characterized in that, include: The data caching and retrieval module is configured to collect radar pulse data and cache it, and retrieve the cached radar pulse data according to a preset time range; The signal sorting module is configured to process the retrieved radar pulse data using a signal sorting algorithm and assign a unique identifier to the radar pulses corresponding to different radiation sources. The downsampling processing module is configured to parse various radar feature parameters from the retrieved radar pulse data, and to perform downsampling processing on the various radar feature parameters respectively using an improved downsampling algorithm to obtain a feature point set; the improved downsampling algorithm selects feature points by bucketing, preserving data distribution boundaries and detailed features; The graphical display module is configured to perform linked rendering of the feature point set obtained by downsampling; The improved downsampling algorithm selects feature points through bucketing, including: Let X be the total number of input signal data, N be the number of data points retained after downsampling, and divide the signal data into N equal buckets, with M = (X-2) / (N-2) for each bucket; where the signal data includes frequency data, amplitude data, pulse width data, repetition interval data, and data type information; The improved downsampling algorithm selects feature points by bucketing, and also includes: The first bucket selects the first data point of the input signal data by default, and the Nth bucket selects the last data point of the input signal data by default. For each of the 2nd to N-1th buckets, two feature points are selected. The selection method includes: using the maximum value of the previous bucket and the first data point of the next bucket as two fixed points, constructing triangles with each data point in the current bucket, and selecting the two data points with the largest and smallest areas of the corresponding triangles as the feature points of the current bucket.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the radar signal parameter large data volume display method according to any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the radar signal parameter large data volume display method according to any one of claims 1-5.

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