A pulse signal detection method and system

Abstract

The present disclosure provides a pulse signal detection method and system, relating to the technical field of signal analysis and identification, the method comprising: acquiring a signal to be detected, converting the signal to be detected into time-domain energy data and performing smoothing processing; dividing the time-domain energy data after smoothing processing into data segments of a set length, performing preliminary detection of pulse signals on each data segment, and dividing the data segment into a pulse signal segment and a noise segment; and performing secondary detection on the pulse signal segment based on a sliding window step length to obtain a detection result of the pulse signal segment. The present disclosure can improve the efficiency of pulse signal detection.

Description

Technical Field

[0001] This disclosure relates to the field of signal analysis and recognition technology, specifically to a pulse signal detection method and system. Background Technology

[0002] Pulse signal screening is widely used in the analysis and identification of complex electromagnetic signals. By screening pulse signals of interest, it is possible to locate these signals and estimate their parameters. However, there are many types of pulse signals, including linear frequency modulation (LFM), nonlinear frequency modulation (NLFM), and phase-coded signals (PCS), each with numerous parameters such as peak power, average power, start time, pulse width, pulse repetition interval, rise time, fall time, pulse frequency, and duty cycle. In practical applications, the in-phase and quadrature (IQ) data files generated by high-speed acquisition are often large and lack prior information, posing a significant challenge to the accurate location and parameter estimation of pulse signals.

[0003] Research on pulse signal screening and parameter estimation techniques consists of two steps: signal detection and parameter estimation. In the field of signal detection, the Energy Detection Algorithm (EDA) is one of the most widely used methods in wireless communication and signal processing. It determines the presence of a target signal by calculating the time-domain or frequency-domain energy within a window and comparing it to a detection threshold. This method is simple to implement and requires no prior knowledge, but it requires sliding processing of the energy window. Existing methods use the same calculation method for signals within all windows, resulting in low screening efficiency when processing large data files. Summary of the Invention

[0004] To address the problems existing in the prior art, this disclosure provides a pulse signal detection method and system to improve the efficiency of pulse signal detection. The technical solution is as follows: Firstly, a pulse signal detection method is provided, including: Acquire the signal to be detected, convert the signal to be detected into time-domain energy data and perform smoothing processing; The smoothed time-domain energy data is divided into data segments of a set length. Pulse signals are initially detected in each data segment, and the data segments are divided into pulse signal segments and noise segments. The pulse signal segment is then detected a second time based on the sliding window step size to obtain the detection result of the pulse signal segment.

[0005] Secondly, a pulse signal detection system is provided, comprising: The preprocessing module is configured to acquire the signal to be detected, convert the signal to be detected into time-domain energy data, and perform smoothing processing. The preliminary detection module is configured to divide the smoothed time-domain energy data into data segments of a set length, perform preliminary pulse signal detection on each data segment, and divide the data segment into a pulse signal segment and a noise segment. The secondary detection module is configured to perform secondary detection on the pulse signal segment based on the sliding window step size to obtain the detection result of the pulse signal segment.

[0006] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to complete the steps of the pulse signal detection method described above.

[0007] Fourthly, a computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, complete the steps of the aforementioned pulse signal detection method.

[0008] Fifthly, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of the pulse signal detection method described above.

[0009] The beneficial effects of the technical solution provided in this disclosure are as follows: In this disclosure, data is first read in segments, and pulse signals are treated as abrupt changes. Z-scores are used for detection to distinguish between pulse signal segments and noise segments. In subsequent secondary detection, only the data of pulse signal segments is processed, thereby significantly improving processing efficiency. By processing large data files in segments, large amounts of data are avoided from being loaded at once, reducing memory requirements and thus improving system stability and efficiency. For pulse signal segments that may contain pulse signals, an energy detection method based on an adaptive sliding window step size is used. The sliding window step size is adaptively determined according to the characteristics of pulse signals and noise, which significantly improves detection performance. This not only achieves rapid screening of pulse signals but also reduces the error of time parameter estimation by improving time resolution.

[0010] Advantages of this disclosure in additional aspects will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a pulse signal detection method provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the overall process of the method; Figure 3 A flowchart illustrating the specific steps; Figure 4 A schematic diagram of the fixed sliding window step size; Figure 5 This is a schematic diagram of the adaptive sliding window step size; Figure 6 This diagram illustrates the comparison of the number of processing steps for the three methods. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0014] This disclosure provides a pulse signal detection method (see [link]). Figures 1-3 The method includes: S101. Acquire the signal to be detected, convert the signal to be detected into time-domain energy data and perform smoothing processing; S102. Divide the smoothed time-domain energy data into data segments of a set length, perform preliminary pulse signal detection on each data segment, and divide the data segment into pulse signal segment and noise segment. S103. Perform secondary detection on the pulse signal segment based on the sliding window step size to obtain the detection result of the pulse signal segment.

[0015] This disclosure provides an accurate and rapid screening method for specific pulse signals contained in massive amounts of data. Considering the characteristics of short pulse signal occurrence times and long periods without pulse signals in complex electromagnetic environments, and addressing the problem of low screening efficiency caused by traditional energy detection methods that use a fixed energy window step size regardless of whether the captured signal contains pulses, this method uses a mutation detection and adaptive sliding window step size for pulse signal detection. By detecting mutation values ​​in segmented signals, it initially determines whether the signal may contain pulses. For signal segments that may contain pulses, an energy detection method based on an adaptive sliding window step size is used to detect the pulse signals and further estimate their parameters, thus achieving rapid screening and parameter estimation of pulse signals.

[0016] In step S101, for the IQ signal to be detected, the signal has the characteristics that its amplitude, phase, and frequency change with time in the time domain. Converting the IQ signal into a square wave form during signal detection and parameter estimation has advantages such as simplified calculation, easier pulse parameter estimation, easier detection, and energy concentration. Furthermore, the square wave signal only has two states, high and low, resulting in low processing complexity. Therefore, the signal to be detected is converted into time-domain energy data and smoothed. The time-domain energy value is calculated using the following formula, thus converting the signal to be detected into time-domain energy data: ; Where I and Q are the I-channel data and Q-channel data of the IQ data file, respectively.

[0017] When converting the energy waveform to power form, processing the energy value using a logarithmic function results in rapid attenuation and sharp peaks for energy values ​​less than 1. Since median filtering effectively preserves pulse edges while removing sharp peaks, it is chosen for smoothing. For the signal sample value at the center of the window... The output value of median filtering It can be obtained from the following formula: ; in, Represents signal sample values The median.

[0018] After converting the IQ data into energy data, the resulting data mainly consists of floor noise and square wave pulse signals. Given that the periods without pulse signals are typically long, this embodiment employs a two-stage pulse signal detection method. First, it determines whether the read data segment contains a pulse signal; then, for data segments containing pulse signals, an energy detection method based on an adaptive sliding window step size is applied to detect the pulse signal, thereby significantly improving signal processing efficiency.

[0019] Specifically, in step S102, the method for determining whether the read data segment contains a pulse signal is as follows: each time a data segment of length L is read, the signal in it is considered as a sudden change value. The data segment is analyzed using the Z-score method to distinguish between pulse signal segments and noise segments.

[0020] Z-score is a standardized method that measures the difference between an individual data point and the mean of a dataset. By calculating the mean and standard deviation of a data segment, the Z-score can standardize the determination of whether a data point deviates from the mean, thus effectively distinguishing between signal and noise segments. This method provides an accurate and reliable way to identify and handle anomalous signals. For points in the data... The corresponding Z-score is obtained by the following formula: ; in, The average value of the data segment. This represents the standard deviation of the data segment.

[0021] The Z-value of each point is calculated sequentially and compared. When the Z-value of a point is greater than a set threshold, the calculation stops and it is determined that a pulse signal exists in that data segment; this data segment is then considered a pulse signal segment. Otherwise, the data segment is considered a noise segment. The number N of pulse signal segments read is recorded for subsequent estimation of time parameters.

[0022] In step S103, the pulse signal segment is subjected to secondary detection based on adaptive sliding window step size energy detection. The pulse signal segment determined in S102 not only contains the pulse signal but may also contain some noise. However, the duration of the pulse signal is relatively long, so an energy detection method based on adaptive sliding window step size is adopted here. By dynamically adjusting the sliding window step size, the signal detection process can be optimized according to different signal characteristics, thereby improving the detection accuracy and efficiency.

[0023] First, based on the file's sampling frequency and bandwidth The resolution bandwidth (RBW) is calculated, and then the window length for energy detection is calculated. Resolution bandwidth (RBW) and window length. It is calculated by the following formula: ; .

[0024] To improve temporal resolution, an overlapping window method is typically used, with an initial sliding window step size. Set as The lowest energy value of the signal of interest is set as the detection threshold. The maximum value within the window is the peak power of the pulse signal. With detection threshold The difference is used as the criterion. If there are data points with a difference greater than 0 within the window, it indicates the presence of a pulse signal; if all differences are less than 0, it indicates that the data within the window is base noise. The sliding window step size is adaptively adjusted for the (n+1)th sliding step by determining whether the data within the window is a pulse signal. Using the nth sliding window step size Multiply by adjustment factor Calculations show that It is an integer. The specific expression is: ; Due to the initial sliding window step size , Unable to adapt. If the sliding window step size exceeds the window length, data loss will occur; therefore, here we take... .

[0025] like Figures 4-5 As can be seen from the comparison, compared with the fixed sliding window step size method, this embodiment can adaptively determine the sliding window step size according to the characteristics of pulse signal and noise, thereby significantly improving the detection performance.

[0026] In some implementations, step S104 is also included: estimating the parameters of the detected pulse signal.

[0027] Since signals typically appear consecutively across multiple windows, when a complete pulse signal is detected, that single pulse is extracted for pulse parameter estimation. This process is repeated until the entire IQ data file is processed, generating a pulse signal parameter table, enabling rapid screening and parameter estimation of pulse signals within large data files.

[0028] Pulse parameters are divided into intra-pulse parameters and inter-pulse parameters. Pulse signal parameter estimation includes intra-pulse parameter estimation and inter-pulse parameter estimation. Intra-pulse parameters include peak power. Start time Pulse width Ascent time descent time and pulse frequency Inter-pulse parameters include average power. Pulse repetition interval and duty cycle Among them, peak power It is the value obtained by subtracting the detection threshold from the energy detection value, which has been estimated during the signal detection process.

[0029] For the estimation of intrapulse parameters, start time It refers to the time point from low to high level, and the pulse width is the duration of the high level. In the time-domain energy data to be detected... Record the sliding window step size for each step during the adaptive sliding window step size and energy detection process. And treats signals appearing in multiple consecutive windows as a single pulse, storing them in a temporary pulse. Middle. Record the number of signal start windows. and the number of end windows Signal start time and pulse width It can be obtained from the following formula: ; .

[0030] Ascent Time It is the time it takes for the pulse signal to rise from 10% to 90% of its maximum value, and the fall time. It is the time it takes for the pulse signal to drop from 90% of its maximum value to 10%. (In calculation...) and At that time, the complete pulse signal had already been saved in First, we need to calculate... peak value and average power within the pulse width This allows us to determine the 10% threshold. and 90% threshold .

[0031] Peak-to-peak value It is the peak power and noise power of the pulse signal. The difference between them, because It is not an ideal square wave form, but rather exhibits fluctuations, therefore it is used... This is used to characterize the maximum value of the pulse. and The calculation expression is as follows: ; .

[0032] and The intersection points correspond to the end of the rise time and the beginning of the fall time, respectively. The intersection points correspond to the start of the rising time and the end of the falling time, respectively. Since it's a discrete digital signal, to save computation time, it's not necessary to traverse all data points. Instead, a point-by-point comparison method is used, starting from the peak power point and moving outwards. The comparison proceeds from the peak outwards, with the first comparison on each side being less than... The points represent the start of the rise time and the end of the fall time, respectively. Traversing inwards from the start and end points of the rise and fall times, the first time less than... The points represent the end of the rising time and the beginning of the falling time, respectively. and The expression is as follows: ; .

[0033] in, , , , These are the rising time end index, rising time start index, falling time end index, and falling time end index, respectively.

[0034] Intra-pulse frequency refers to the instantaneous frequency change of a single pulse signal on the time axis, describing the frequency variation characteristics of the pulse signal within one cycle. Because pulse signals are diverse and have significant spectral differences, the center frequency of the pulse signal is used to represent its pulse frequency. .

[0035] For the estimation of interpulse parameters, the interpulse parameters have average power. Pulse repetition interval and duty cycle The core parameter of inter-pulse parameters is the pulse repetition interval. As long as the calculation is By taking the value of and combining it with the calculated intra-pulse parameters, other inter-pulse parameters can be derived.

[0036] Calculating the pulse repetition interval requires adding a variable saver. q represents the number of pulse signals. The calculation formula is as follows: .

[0037] Duty cycle According to and calculate, According to and The calculation is as follows: ; .

[0038] The effectiveness of the method provided in this disclosure will be illustrated below using specific signals as examples.

[0039] LFM signals are one of the most common signal types in pulse signal processing. To verify the effectiveness of the method disclosed herein, five linear frequency modulated (LFM) pulse signals with different parameters were selected for simulation experiments, and the simulated pulse signal data were compared with the results of this method. The sampling frequency was 2.5e9, the number of data points was 5e6, and the resolution bandwidth (RBW) was 1MHz. The pulse parameters in the 100~200us time period were as follows: =20us, =4us, =0.2, =0.4GHz, period of 5; pulse parameters in the 230~260us time period are: =10us, =3.5us, =0.1, =0.2GHz, period of 3; pulse parameters in the time period of 336~396us are: =12us, =8us, =2 / 3, =0.3GHz, period of 5; pulse parameters in the time period of 1245~1365us are: =30us, =7.5us, =0.25, =0.15GHz, period of 4; pulse parameters in the time period of 1396~1546us are: =50us, =5.5us, =0.11, =1GHz, with a period of 3.

[0040] The window length is set by the sampling frequency and resolution bandwidth. While keeping the window length constant, the number of processing steps for the following three energy detection methods is compared: the classic energy detection method, the energy detection method using an adaptive sliding window step size, and the energy detection method that first reads data in segments to determine the pulse signal segment and then uses the adaptive sliding window step size. The comparison results are as follows: Figure 6 As shown.

[0041] Figure 6 The horizontal axis represents the proportion of pulse signals in the IQ data file, while the vertical axis displays the total number of processing iterations, i.e., the number of energy windows. In classic energy detection methods, both the window length and step size are fixed values, and the number of processing iterations in the simulation test file is fixed at 3999. For energy detection methods using adaptive sliding window step sizes, the number of processed windows is negatively correlated with the signal proportion: the higher the signal proportion, the more windows are processed; the lower the proportion, the fewer windows are processed. In the method that first determines the presence of a signal and then applies the adaptive sliding window step size energy detection method, the number of windows is also negatively correlated with the signal proportion. Furthermore, the method that determines the signal first does not perform energy detection on noise segments without signal, thus minimizing the number of windows and significantly improving processing efficiency, thereby accelerating the screening speed of pulse signals.

[0042] After identifying the pulse signals, parameter estimation is performed, and the results are shown in Table 1.

[0043] Table 1. Results of pulse signal screening and parameter estimation

[0044] Analyzing the data in the table, the average error in start time is 0.23 μs; the average error in pulse width is 0.001 μs; the average error in repetition frequency is 0.1875 μs (the repetition frequency is the difference between the start times of adjacent pulses; alternating pulse sequences of different periods can cause significant changes in the repetition interval of the last pulse of the previous sequence, which is not included in the error calculation); and the average error in pulse frequency is 2.05 kHz. For the time-frequency resolution of high-frequency transient signals, the errors in the resulting parameters are relatively small. It is evident that the method provided in this disclosure not only improves processing efficiency but also ensures a high degree of accuracy in parameter estimation.

[0045] This disclosure further provides a pulse signal detection system, the system comprising: The preprocessing module is configured to acquire the signal to be detected, convert the signal to be detected into time-domain energy data, and perform smoothing processing. The preliminary detection module is configured to divide the smoothed time-domain energy data into data segments of a set length, perform preliminary pulse signal detection on each data segment, and divide the data segment into a pulse signal segment and a noise segment. The secondary detection module is configured to perform secondary detection on the pulse signal segment based on the sliding window step size to obtain the detection result of the pulse signal segment.

[0046] The pulse signal detection system provided in the above embodiments is illustrated by the division of the aforementioned hardware modules. In practical applications, these hardware modules can be replaced with other hardware modules with similar or identical functions, or the function of a certain hardware module can be assigned to different hardware modules. Furthermore, the implementation method of the above embodiments is the same as that of a pulse signal detection method, and will not be repeated here.

[0047] This disclosure further provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to perform the steps of the method provided above.

[0048] The processor may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or LA (Programmable Logic Array). The processor may also include a main processor and coprocessors. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0049] The memory may include one or more computer-readable media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable media in the memory is used to store at least one computer program, which is executed by a processor to implement a pulse signal detection method provided in embodiments of this disclosure.

[0050] This disclosure also provides a computer-readable storage medium for storing computer instructions that, when executed by a processor, can complete the steps of a pulse signal detection method provided in this disclosure.

[0051] This disclosure also provides a computer program product, including a computer program / instructions, characterized in that, when the computer program / instructions are executed by a processor, they implement the steps of a pulse signal detection method provided in this disclosure.

[0052] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A pulse signal detection method, characterized in that, include: Acquire the signal to be detected, convert the signal to be detected into time-domain energy data and perform smoothing processing; The smoothed time-domain energy data is divided into data segments of a set length. Pulse signals are initially detected in each data segment, and the data segments are divided into pulse signal segments and noise segments. The pulse signal segment is detected twice based on the sliding window step size to obtain the detection result of the pulse signal segment; The preliminary detection of pulse signals in each data segment, dividing the data segment into pulse signal segments and noise segments, includes: based on the Z-score, sequentially calculating the Z-value of each point in each data segment; if the Z-value of any point is greater than a set threshold, the data segment containing that point is divided into a pulse signal segment; if the Z-value of each point in the data segment is not greater than the set threshold, the data segment is divided into a noise segment. The secondary detection of pulse signal segments based on sliding window step size includes: calculating the resolution bandwidth and window length according to the sampling frequency and bandwidth; setting the initial sliding window step size based on the window length; determining the sliding window step size of the next window based on the comparison result of the peak power of the pulse signal in the previous window and the detection threshold; determining the next window based on the sliding window step size of the next window, and detecting whether the data in each window is a pulse signal.

2. The pulse signal detection method as described in claim 1, characterized in that, The smoothing process includes median filtering of the time-domain energy data.

3. The pulse signal detection method as described in claim 1, characterized in that, The sliding window step size of the next window is calculated based on the comparison result of the peak power of the pulse signal and the detection threshold in the previous window, the sliding window step size of the previous window, and the adjustment factor.

4. The pulse signal detection method as described in claim 1, characterized in that, It also includes parameter estimation of the detected pulse signal, including intra-pulse parameter estimation and inter-pulse parameter estimation.

5. A pulse signal detection system, characterized in that, The pulse signal detection method according to any one of claims 1-4 includes: The preprocessing module is configured to acquire the signal to be detected, convert the signal to be detected into time-domain energy data, and perform smoothing processing. The preliminary detection module is configured to divide the smoothed time-domain energy data into data segments of a set length, perform preliminary pulse signal detection on each data segment, and divide the data segment into a pulse signal segment and a noise segment. The secondary detection module is configured to perform secondary detection on the pulse signal segment based on the sliding window step size to obtain the detection result of the pulse signal segment.

6. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to perform the steps of the method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the steps of the method according to any one of claims 1-4.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-4.

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