A full-pulse amplitude sliding clustering method with controllable time-gap granularity.

Abstract

This invention provides a full-pulse amplitude sliding clustering processing method based on controllable time-gap granularity, comprising the following steps: Step 1: Receiving full pulses of radar radiation signals and extracting the amplitude of the full pulses according to the full pulse flow; Step 2: Setting a variable time window size based on the repetition period of the radar radiation source signal and the coherent processing time; Step 3: Performing a sliding average processing on the amplitude of the full pulses within the time window interval to obtain the amplitude sliding average value; Step 4: Using the time window as the smallest time unit interval, performing sliding clustering on the amplitude sliding average value within each time window; Step 5: Sorting the full pulses after sliding clustering in Step 4. This invention can suppress the influence of amplitude fluctuations on inaccurate sorting and identification, improve the sorting and identification accuracy under amplitude fluctuation conditions, and also improve the accuracy of radar status judgment.

Description

Technical Field

[0001] This invention relates to the field of electronic countermeasures and reconnaissance technology, and more specifically, to a full-pulse amplitude sliding clustering processing method based on controllable time-gap granularity. Background Technology

[0002] Radar source signal sorting is a crucial step in electronic warfare. It involves clustering the entire pulse pipeline in the preceding stage across dimensions such as frequency, amplitude, pulse width, and azimuth to extract signal features for identification. However, due to the scanning and rotation of the radar source signal beam in the azimuth direction, and the amplitude fluctuations and undulations of the receiving antenna relative to the main lobe position during relative motion, the intercepted signal exhibits envelope fluctuations in the amplitude dimension, resulting in strong discreteness and posing significant challenges to signal sorting and feature extraction. Summary of the Invention

[0003] The present invention aims to provide a full-pulse amplitude sliding clustering processing method based on controllable time interval granularity to solve the impact of signal amplitude fluctuations on signal sorting.

[0004] This invention provides a full-pulse amplitude sliding clustering method based on controllable time-gap granularity, comprising the following steps:

[0005] Step 1: Receive the full pulse of the radar radiation signal and extract the amplitude of the full pulse based on the full pulse flow.

[0006] Step 2: Set the size of the variable time window based on the repetition period of the radar radiation source signal and the coherent processing time.

[0007] Step 3: Within the time window interval, perform a moving average on the amplitude of the full pulse to obtain the amplitude moving average.

[0008] Step 4: Using time windows as the smallest time unit interval, perform sliding clustering on the moving average of the amplitude within each time window;

[0009] Step 5: Sorting the full pulses after sliding clustering in Step 4.

[0010] Furthermore, in step two, the time window length needs to be greater than the coherent processing time.

[0011] Furthermore, in step three, before performing the moving average processing, non-point and singular point identification must be performed. Specifically:

[0012] The non-point judgment is used to eliminate full pulses with an amplitude detection of 0.

[0013] The singularity determination refers to whether the difference between the full pulse amplitude and the average full pulse amplitude stored in the moving average buffer meets the tolerance range.

[0014] Furthermore, step three specifically involves:

[0015] When a full pulse enters the time window, a non-point judgment is performed: if the full pulse meets the non-point condition, the full pulse is discarded; if the full pulse does not meet the non-point condition, the full pulse is used as the current full pulse moving average buffer.

[0016] When the next non-point full pulse arrives, a singularity check is performed: if the difference between the full pulse amplitude and the average full pulse amplitude stored in the moving average buffer does not meet the tolerance range, the full pulse is discarded; otherwise, the full pulse enters the moving average buffer for amplitude moving average calculation.

[0017] When the current time window ends, the amplitude moving average value in the moving average cache at this time is used as the output to the moving average clustering stage. At the same time, the moving average cache is updated, the time window is reopened, and this process is repeated.

[0018] Furthermore, step four specifically involves:

[0019] When the first amplitude moving average is received, a tolerance range is set with the current amplitude moving average as the center, and a clustering cache space is opened;

[0020] When the moving average of the amplitude of the incoming full pulse is within the tolerance range of the cluster buffer space, the full pulse is buffered in the cluster buffer space, and the amplitude of all full pulses in the cluster buffer space is averaged again to obtain the moving average of the amplitude cluster outside the time window gap, and the moving average of the amplitude cluster is used as the amplitude center value of the cluster buffer space.

[0021] If the moving average amplitude of the incoming full pulse is outside the tolerance range of the clustering cache space, then the clustering cache space is reopened with the moving average amplitude of the full pulse as the center.

[0022] Furthermore, in step five, after sorting the full pulses after sliding clustering in step four, it is necessary to release and clean up the clustering cache space.

[0023] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0024] This invention targets the amplitude of the full-pulse stream of detected radar radiation source signals, and performs moving average and moving clustering between minimum time units within a controllable time interval. This not only suppresses the impact of amplitude fluctuations of the alarm radar on sorting and converges the pulse amplitude, reducing the processing pressure on pulse amplitude in subsequent sorting and improving processing efficiency and speed, but also enables targeted optimal clustering processing based on the CPI (coherent processing time) characteristics of the radar radiation source signal, while ensuring the integrity of the target amplitude characteristics and optimizing the judgment of the radiation target status. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a full-pulse amplitude sliding clustering processing method based on controllable time-gap granularity in an embodiment of the present invention.

[0027] Figure 2a This is a simulation result of the amplitude sliding average value within a 1ms time interval, as shown in this embodiment of the invention.

[0028] Figure 2b This is a simulation result of the amplitude sliding average value within a 10ms time interval, as shown in this embodiment of the invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0030] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0031] Example

[0032] like Figure 1As shown, this embodiment proposes a full-pulse amplitude sliding clustering method based on controllable time-gap granularity, including the following steps:

[0033] Step 1: Receive the full pulse (PDW) of the radar radiation signal and extract the amplitude (PA) of the full pulse based on the full pulse flow.

[0034] Step 2: Based on the repetition period (PRI) of the radar radiation source signal and the coherent processing time (CPI), set the size of the variable time window; the length of the time window needs to be greater than the coherent processing time.

[0035] Step 3: Within the time window interval, after performing non-point and singular point checks on the full pulse, perform a moving average on the amplitude of the full pulse to obtain the amplitude moving average; specifically:

[0036] When a full pulse enters the time window, a non-point judgment is performed: if the full pulse meets the non-point condition, the full pulse is discarded; if the full pulse does not meet the non-point condition, the full pulse is used as the current full pulse moving average buffer.

[0037] When the next non-point full pulse arrives, a singularity check is performed: if the difference between the full pulse amplitude and the average full pulse amplitude stored in the moving average buffer does not meet the tolerance range, the full pulse is discarded; otherwise, the full pulse enters the moving average buffer for amplitude moving average calculation.

[0038] When the current time window ends, the amplitude moving average value in the moving average cache at this time is used as the output to the moving average clustering stage. At the same time, the moving average cache is updated, the time window is reopened, and this process is repeated.

[0039] like Figure 2a , 2b The figure shows the simulation results of the amplitude moving average within time intervals of 1ms and 10ms when PRI is 20us and CPI is 300us. Figure 2a It can be seen that the amplitude value after moving average significantly converges the fluctuations, which is more conducive to subsequent clustering and sorting feature extraction. For Figure 2b Because the time window is too long, it spans too much of the CPI within 10ms, causing the amplitude moving average to converge too much, which may result in the loss of features in the amplitude dimension. Therefore, the sliding time should not be too large than the CPI.

[0040] Step 4: Using time windows as the smallest time unit interval, perform moving average clustering on the moving average amplitude within each time window; specifically:

[0041] When the first amplitude moving average is received, a tolerance range is set with the current amplitude moving average as the center, and a clustering cache space is opened;

[0042] When the moving average of the amplitude of the incoming full pulse is within the tolerance range of the cluster buffer space, the full pulse is buffered in the cluster buffer space, and the amplitude of all full pulses in the cluster buffer space is averaged again to obtain the moving average of the amplitude cluster outside the time window gap, and the moving average of the amplitude cluster is used as the amplitude center value of the cluster buffer space.

[0043] If the moving average amplitude of the incoming full pulse is outside the tolerance range of the clustering cache space, then the clustering cache space is reopened with the moving average amplitude of the full pulse as the center.

[0044] Step 5: Sorting is performed on the full pulses after sliding clustering in Step 4. After the sorting is completed, the clustering cache space needs to be released and cleaned up.

[0045] Pulse signal sorting verification based on a certain system: Comparison of the effectiveness of the method of this invention and conventional methods:

[0046] (1) Use a certain system to receive the signal of a certain radar radiation source, change the antenna attitude and azimuth accordingly, load the conventional method program, and test the sorting accuracy.

[0047] (2) Using a certain system to receive a radar radiation source signal, change the antenna attitude and azimuth accordingly, load the program of the present invention, and test the sorting accuracy.

[0048] (3) Receive the search signal and intercept signal of a radar radiation source, fix the antenna attitude and azimuth, load the conventional method program, and test the sorting accuracy and status judgment results.

[0049] (4) Receive the search signal and interception signal of a radar radiation source, fix the antenna attitude and azimuth, load the program of the present invention, and test the sorting accuracy and status judgment results.

[0050] Experimental results demonstrate that the clustering processing of the amplitude of the full-pulse stream of the detected radar radiation source signal in this invention, compared with conventional methods, can suppress the impact of amplitude fluctuations on the inaccuracy of sorting and identification, improve the sorting and identification accuracy under amplitude fluctuation conditions, and also improve the accuracy of radar status judgment.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A full-pulse amplitude sliding clustering method based on controllable time-gap granularity, characterized in that, Includes the following steps: Step 1: Receive the full pulse of the radar radiation signal and extract the amplitude of the full pulse based on the full pulse flow. Step 2: Set the size of the variable time window based on the repetition period of the radar radiation source signal and the coherent processing time. Step 3: Within the time window interval, perform a moving average on the amplitude of the full pulse to obtain the amplitude moving average. Step 4: Using time windows as the smallest time unit interval, perform sliding clustering on the moving average of the amplitude within each time window; Step 5: Sorting the full pulses after sliding clustering in Step 4; In step three, before performing the moving average processing, non-point judgment and singular point judgment must be performed first; the non-point judgment is used to remove full pulses with an amplitude detection of 0; the singular point judgment refers to whether the difference between the full pulse amplitude and the average full pulse amplitude stored in the moving average buffer meets the tolerance range. Step three specifically involves: When a full pulse enters the time window, a non-point judgment is performed: if the full pulse meets the non-point condition, the full pulse is discarded; if the full pulse does not meet the non-point condition, the full pulse is used as the current full pulse moving average buffer. When the next non-point full pulse arrives, a singularity check is performed: if the difference between the full pulse amplitude and the average full pulse amplitude stored in the moving average buffer does not meet the tolerance range, the full pulse is discarded; otherwise, the full pulse enters the moving average buffer for amplitude moving average calculation. When the current time window ends, the amplitude moving average in the moving average cache at this time is used as the output to the moving average clustering stage. At the same time, the moving average cache is updated, the time window is reopened, and this process is repeated. Step four is as follows: When the first amplitude moving average is received, a tolerance range is set with the current amplitude moving average as the center, and a clustering cache space is opened; When the moving average of the amplitude of the incoming full pulse is within the tolerance range of the cluster buffer space, the full pulse is buffered in the cluster buffer space, and the amplitude of all full pulses in the cluster buffer space is averaged again to obtain the moving average of the amplitude cluster outside the time window gap, and the moving average of the amplitude cluster is used as the amplitude center value of the cluster buffer space. If the moving average amplitude of the incoming full pulse is outside the tolerance range of the clustering cache space, then the clustering cache space is reopened with the moving average amplitude of the full pulse as the center.

2. The full-pulse amplitude sliding clustering processing method based on controllable time-gap granularity according to claim 1, characterized in that, In step five, after sorting the full pulses after sliding clustering in step four, it is necessary to release and clean up the clustering cache space.

3. The full-pulse amplitude sliding clustering processing method based on controllable time-gap granularity according to any one of claims 1-2, characterized in that, In step two, the time window length needs to be greater than the coherent processing time.

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