Radar signal sorting method and system based on cyclostationary analysis

Abstract

The invention provides a radar signal sorting method and system based on cyclostationary analysis, and the method comprises the steps: collecting superposed pulses of different radar transmitting sources, and obtaining a multi-source aliasing pulse signal sequence; based on a physical acoustic propagation principle, generating a compensated pulse signal sequence for inhibiting complex medium propagation distortion; according to a preset pulse repetition interval feature extraction rule, obtaining a feature data set containing pulse repetition interval features; performing cyclostationary analysis on the compensated pulse signal sequence to obtain a cyclostationary feature set containing periodic component information; according to a preset radar signal sorting rule, performing fusion sorting processing on the feature data set and the cyclostationary feature set to generate a sorting result of the multi-source aliasing radar pulse signal sequence; according to the technical scheme provided by the invention, efficient sorting and feature extraction of the multi-source aliasing radar pulse signals are realized, and the accuracy and stability of radar signal processing in a complex medium environment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, in particular to a radar signal sorting method and system based on cyclic stationary analysis. BACKGROUND

[0002] In urban environments, with the rapid development of intelligent transportation systems, environmental monitoring networks and other applications, radar devices are widely deployed in complex urban spaces. These radar systems will generate a large number of dense and mutually interfering signals during operation. Especially under the conditions of multi-source coexistence and complex propagation paths, how to accurately sort out the pulse sequences of each radar transmitting source from the aliasing signals becomes a key technical problem to ensure system reliability and data integrity. Therefore, there is an urgent need for a technical solution that can achieve efficient and accurate signal sorting in a strong interference background.

[0003] Current research has proposed a radar signal sorting method based on the combination of adaptive filtering and statistical feature extraction. This method performs time-frequency analysis on the received signal, combines short-time Fourier transform to obtain the local frequency characteristics of the signal, and suppresses the background noise. Then, key feature parameters of the signal are extracted using statistical methods such as principal component analysis, and finally, different radar sources are classified and identified through clustering algorithms. However, the existing technical solution has certain limitations, for example, since the influence of physical media on signal propagation path and intensity is not considered, the signal feature extraction stage is easily disturbed by amplitude attenuation and phase distortion caused by non-uniform media, thereby affecting the final classification accuracy; it is difficult to capture the periodic or quasi-periodic structure information existing in the radar signal, leading to misjudgment or missed detection when facing highly overlapping pulse sequences, and the overall sorting performance is limited. SUMMARY

[0004] The present application provides a radar signal sorting method and system based on cyclic stationary analysis to solve the problems of insufficient classification accuracy due to interference in the signal feature extraction stage, misjudgment or missed detection when facing highly overlapping pulse sequences, and limited overall sorting performance in the prior art.

[0005] In a first aspect, the present application provides a radar signal sorting method based on cyclic stationary analysis, comprising:

[0006] Collecting superimposed pulses from different radar transmitting sources to obtain a multi-source aliasing pulse signal sequence;

[0007] Based on the principle of physical acoustic propagation, the multi-source aliasing pulse signal sequence is subjected to medium attenuation compensation processing to generate a compensated pulse signal sequence that suppresses complex medium propagation distortion;

[0008] According to a preset pulse repetition interval feature extraction rule, feature extraction is performed on the compensated pulse signal sequence to obtain a feature data set containing pulse repetition interval features;

[0009] Cyclostationary analysis is performed on the compensated pulse signal sequence to obtain a cyclostationary feature set containing periodic component information;

[0010] According to a preset radar signal sorting rule, the feature data set and the cyclostationary feature set are fused and sorted to generate a sorting result of the multi-source mixed pulse signal sequence.

[0011] Optionally, based on the physical acoustic propagation principle, medium attenuation compensation processing is performed on the multi-source mixed pulse signal sequence to generate a compensated pulse signal sequence that suppresses complex medium propagation distortion, including:

[0012] Based on the physical acoustic propagation principle, the propagation path of the radar signal in the complex medium is analyzed to obtain a path node coordinate set;

[0013] The medium attribute data of each path node in the path node coordinate set is identified to separate a medium density parameter set and a medium propagation speed parameter set from the medium attribute data;

[0014] Based on the medium density parameter set and the propagation speed parameter set, path scattering loss and path phase delay are calculated respectively, and the path scattering loss and the path phase delay are combined to obtain an attenuation compensation coefficient set;

[0015] The propagation path type of each pulse signal in the multi-source mixed pulse signal sequence is identified to obtain an intermediate pulse signal sequence with a propagation path identifier;

[0016] According to the attenuation compensation coefficient set, the intermediate pulse signal sequence is corrected to obtain a corrected pulse signal sequence;

[0017] The corrected pulse signal sequence is reorganized to obtain a compensated pulse signal sequence that suppresses complex medium propagation distortion.

[0018] Optionally, according to a preset pulse repetition interval feature extraction rule, feature extraction is performed on the compensated pulse signal sequence to obtain a feature data set containing pulse repetition interval features, including:

[0019] According to a time mark condition of the preset pulse repetition interval feature extraction rule, the pulse signals in the compensated pulse signal sequence are position marked to obtain a pulse time position set;

[0020] Calculate time intervals between adjacent pulse time positions in the set of pulse time positions to generate a set of original pulse repetition intervals;

[0021] Extract valid pulse repetition intervals from the set of original pulse repetition intervals based on a preset pulse repetition interval range to obtain a set of valid pulse repetition intervals;

[0022] Group the set of valid pulse repetition intervals according to a preset statistical distribution feature to generate a set of pulse signal groups containing pulse repetition interval features;

[0023] Match the set of pulse signal groups with a preset radar source identifier to obtain a set of feature data.

[0024] Optionally, perform cyclic stationary analysis on the compensated pulse signal sequence to obtain a set of cyclic stationary features containing periodic component information, including:

[0025] Segment the compensated pulse signal sequence to obtain a plurality of pulse signal segments;

[0026] Perform time-frequency conversion on each pulse signal segment to generate a set of time-frequency distributions;

[0027] According to a preset periodic stability condition, detect time-frequency regions with periodic fluctuations in energy intensity in the set of time-frequency distributions to determine candidate time-frequency regions that satisfy a preset periodic fluctuation amplitude change threshold;

[0028] Measure the time interval between adjacent energy peaks of each candidate time-frequency region to generate a set of region period values;

[0029] Calculate the difference between the highest frequency and the lowest frequency of each candidate time-frequency region in the set of candidate time-frequency regions to obtain a set of region frequency spans;

[0030] Fuse the set of region frequency spans and the set of region period values to generate a set of periodic component parameters;

[0031] According to a preset radar source periodic feature, classify the set of periodic component parameters by source, containing a set of cyclic stationary features containing periodic component information.

[0032] Optionally, according to a preset periodic stability condition, detect time-frequency regions with periodic fluctuations in energy intensity in the set of time-frequency distributions to determine candidate time-frequency regions that satisfy a preset periodic fluctuation amplitude change threshold, including:

[0033] According to a preset periodic stability condition, detect time-frequency regions with periodic fluctuations in energy intensity in the set of time-frequency distributions to determine a plurality of peak positions;

[0034] computing a time interval variance between the peak positions, and taking the peak positions as candidate peak positions when the time interval variance is less than a preset peak interval stability threshold value;

[0035] performing time feature matching on the candidate peak positions based on a preset time coincidence degree threshold value and a preset peak interval similarity threshold value, to select target peak positions that meet both a time coincidence degree requirement and an interval similarity requirement;

[0036] performing energy amplitude variation verification on a time-frequency region where the target peak positions are located, to take a time-frequency region where a difference between adjacent peak amplitudes is less than a preset periodic fluctuation amplitude variation threshold value as a candidate time-frequency region.

[0037] Optionally, a time interval between adjacent energy peaks of each candidate time-frequency region is measured, and a region period value set is generated, including:

[0038] arranging the target peak positions corresponding to each candidate time-frequency region in ascending order to obtain a corresponding sorted target peak position sequence;

[0039] computing a time interval between adjacent target peak positions in each sorted target peak position sequence to generate a plurality of original time interval sequences;

[0040] computing an absolute difference value of adjacent time intervals in each original time interval sequence, and selecting a stable time interval sequence with an absolute difference value not exceeding a preset interval tolerance from the corresponding original time interval sequence;

[0041] taking an average value of each stable time interval sequence as a region period value of the corresponding candidate time-frequency region, and aggregating the region period values of all candidate time-frequency regions to obtain a region period value set.

[0042] Optionally, according to a preset radar signal sorting rule, the feature data set and the cyclostationary feature set are fused and sorted to generate a sorting result of a multi-source mixed radar pulse signal sequence, including:

[0043] based on a main matching rule of a preset radar signal sorting rule, performing feature correlation matching on a pulse repetition interval feature in the feature data set and periodic component information in the cyclostationary feature set to generate a fusion feature group set;

[0044] based on an auxiliary matching rule of a preset radar signal sorting rule, matching a periodic parameter of each fusion feature group in the fusion feature group set with a preset radar source feature template to generate a fusion feature group set with a radar source identifier;

[0045] identify the pulse signals corresponding to the time characteristic parameters of the fusion feature group set from the post-compensation pulse signal sequence, and generate a feature group pulse set;

[0046] According to the acquisition time sequence of the multi-source mixed radar pulse signal sequence, the pulse signals with the same radar source identifier in the feature group pulse set are reorganized to obtain a sorting result of the multi-source mixed radar pulse signal sequence.

[0047] In a second aspect, the present application provides a radar signal sorting system based on cyclic stationary analysis, comprising:

[0048] An acquisition module is configured to acquire superimposed pulses of different radar transmission sources to obtain a multi-source mixed pulse signal sequence;

[0049] A compensation module is configured to perform medium attenuation compensation processing on the multi-source mixed pulse signal sequence based on the physical acoustic propagation principle to generate a post-compensation pulse signal sequence that suppresses complex medium propagation distortion;

[0050] An extraction module is configured to perform feature extraction on the post-compensation pulse signal sequence according to a preset pulse repetition interval feature extraction rule to obtain a feature data set containing pulse repetition interval features;

[0051] An analysis module is configured to perform cyclic stationary analysis on the post-compensation pulse signal sequence to obtain a cyclic stationary feature set containing periodic component information;

[0052] A sorting module is configured to perform fusion sorting processing on the feature data set and the cyclic stationary feature set according to a preset radar signal sorting rule to generate a sorting result of the multi-source mixed radar pulse signal sequence.

[0053] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a radar signal sorting method based on cyclic stationary analysis as described in the first aspect above.

[0054] In a fourth aspect, the present application provides a computer storage medium storing a computer program, wherein the computer program is executed by a computer to implement a radar signal sorting method based on cyclic stationary analysis as described in the first aspect.

[0055] In the present application, the superimposed pulses of different radar transmitting sources are collected to obtain a multi-source mixed superimposed pulse signal sequence; based on the physical acoustic propagation principle, the multi-source mixed superimposed pulse signal sequence is subjected to medium attenuation compensation processing to generate a compensated pulse signal sequence that suppresses the distortion of complex medium propagation; according to a preset pulse repetition interval feature extraction rule, the compensated pulse signal sequence is subjected to feature extraction to obtain a feature data set containing pulse repetition interval features; the compensated pulse signal sequence is subjected to cyclostationary analysis to obtain a cyclostationary feature set containing periodic component information; according to a preset radar signal sorting rule, the feature data set and the cyclostationary feature set are subjected to fusion sorting processing to generate a sorting result of the multi-source mixed superimposed radar pulse signal sequence. The technical scheme provided in the present application captures mixed signals from multiple radar sources, provides an original data basis for subsequent signal processing, avoids information loss caused by incomplete sampling, simulates and compensates signal amplitude attenuation and phase distortion caused by non-uniform media such as buildings and terrain according to the physical acoustic propagation principle. The technical effect lies in reducing the influence of environmental factors on signal integrity and improving the accuracy of subsequent feature extraction. The features extracted from the compensated signal serve as an important basis for identifying radar transmitting sources and provide structured data support for subsequent fusion analysis. By using cyclostationary analysis, the hidden periodic components in the radar signal are mined, the distinguishability between radar signals is enhanced, and the recognition ability in highly overlapped signals is improved. Combined with multiple feature information, comprehensive judgment and classification are realized to automatically sort radar signals. The present application introduces the physical acoustic propagation principle to solve the feature distortion problem caused by the fact that the existing method does not consider the influence of complex media on signal propagation, thereby improving the fidelity of the signal in the propagation path. At the same time, through the cyclostationary analysis technology, the potential periodic structure information in the radar signal is deeply mined, overcoming the limitation of traditional methods based on static statistical features that are difficult to deal with highly overlapped signals, so that the system has stronger anti-interference ability and higher sorting accuracy when facing the scene of urban radar signals with multiple sources coexisting and serious interference, thereby breaking through the performance bottleneck of the existing technology in practical application.

[0056] These aspects or other aspects of the present application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0058] Figure 1A flow chart of a radar signal sorting method based on cyclic stationary analysis provided by the present application is shown.

[0059] Figure 2 A structural schematic diagram of a radar signal sorting system based on cyclic stationary analysis provided by the present application is shown.

[0060] Figure 3 A structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0061] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the present application embodiment will be clearly and completely described below in combination with the drawings in the present application embodiment.

[0062] In some of the descriptions in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed or in parallel without the order appearing in the text, the serial numbers of the operations such as 101, 102, etc. are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or less operations, and the operations can be executed in sequence or in parallel. It should be noted that the "first", "second" and the like in the text are used to distinguish different messages, devices, modules, etc., and do not represent the order, nor limit that the "first" and the "second" are different types.

[0063] The technical scheme in the present application embodiment will be clearly and completely described below in combination with the drawings in the present application embodiment. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0064] In urban environments, with the widespread deployment of radar equipment, the mutual interference problem of multi-source radar signals in complex propagation paths is becoming increasingly serious. How to accurately sort out the pulse sequences of each radar emission source from highly aliased signals has become a key technical problem. Although the existing technology uses a method of adaptive filtering and time-frequency analysis combined with statistical feature extraction, it does not consider the influence of the physical medium on signal propagation, resulting in the feature extraction process being susceptible to amplitude attenuation and phase distortion caused by non-uniform media. At the same time, it lacks the characterization of the periodic structure in the radar signal, making it difficult to deal with the misjudgment and missed detection problems caused by highly overlapping signals. In response to the above-mentioned defects, this application proposes a radar signal sorting method based on cyclostationary analysis. By introducing the physical acoustic propagation principle, the signal is compensated for medium attenuation and the fidelity of the signal in complex environments is improved. At the same time, combined with cyclostationary analysis technology, the periodic characteristics in the radar signal are deeply explored to enhance the distinguishability between different radar sources. Finally, by fusing the pulse repetition interval feature and the cyclostationary feature, high-precision and high-robustness signal sorting is achieved, thereby breaking through the technical bottleneck of the existing technology that limits its performance in complex urban electromagnetic environments.

[0065] Figure 1 A flow chart of a radar signal sorting method based on cyclostationary analysis is provided for the embodiment of the present application. Figure 1 As shown, the method includes:

[0066] Step 101: Collect superimposed pulses from different radar emission sources to obtain a multi-source aliased pulse signal sequence.

[0067] In this step, the superimposed pulse refers to the composite waveform generated by the time-domain overlap of pulse signals emitted by multiple radar sources. This waveform consists of a mixture of pulses with different frequencies, amplitudes, and arrival times. The multi-source mixed pulse signal sequence is a temporally ordered sequence of superimposed pulses, including signals from multiple radar sources that interfere with each other.

[0068] In an embodiment of the present application, pulse signals overlapping in the time domain from multiple radar emission sources are collected to obtain a superimposed pulse sequence containing signals from different radar sources; the arrival time, amplitude, and frequency parameters of each pulse are recorded using signal synchronization acquisition technology to obtain a multi-source aliasing pulse signal sequence.

[0069] Step 102: Based on the physical acoustic propagation principle, medium attenuation compensation processing is performed on the multi-source aliasing pulse signal sequence to generate a compensated pulse signal sequence that suppresses complex medium propagation distortion.

[0070] In this step, the physical acoustic propagation principle refers to the attenuation model of sound waves propagating in a medium, including the quantitative relationship between path scattering loss and path phase delay. The compensated pulse signal sequence is the pulse sequence after medium distortion is eliminated through reverse gain adjustment, including the corrected amplitude and phase information.

[0071] In the embodiment of the present application, based on the physical acoustic propagation principle, the path scattering loss and path phase delay of the signal in the multi-medium path are calculated, each pulse signal is corrected, the pulse signal distortion is eliminated, and a compensated pulse signal sequence suppressing the propagation distortion of the complex medium is generated.

[0072] In the embodiment of the present application, according to the physical acoustic propagation principle, the scattering loss and phase delay of the signal in the multi-medium path such as buildings and air layers are calculated, the amplitude of each pulse is corrected by reverse gain, the signal distortion caused by the urban environment is eliminated, and a compensated pulse signal sequence after amplitude and phase correction is generated.

[0073] Step 103: According to the preset pulse repetition interval feature extraction rule, feature extraction is performed on the compensated pulse signal sequence to obtain a feature data set containing pulse repetition interval features.

[0074] In this step, the preset pulse repetition interval feature extraction rule refers to the set pulse interval effective range and statistical distribution threshold. The feature data set refers to the pulse repetition interval data set grouped according to the radar source, containing the interval value and its statistical distribution attribute.

[0075] In the embodiment of the present application, based on the preset pulse repetition interval feature extraction rule, the compensated pulse signal sequence is subjected to pulse time marking, adjacent pulse interval calculation and interval clustering processing, the effective pulse repetition interval representing the working mode of the radar source is extracted, and a feature data set containing pulse repetition interval features is generated.

[0076] Step 104: The compensated pulse signal sequence is subjected to cyclic stationary analysis to obtain a cyclic stationary feature set containing periodic component information.

[0077] In this step, the cyclic stationary feature set refers to the parameter set extracted by time-frequency conversion technology analysis.

[0078] In the embodiment of the present application, the compensated pulse signal sequence is decomposed into a time-frequency distribution set by time-frequency conversion technology, the time-frequency region with periodic fluctuation of energy intensity is detected, the region period value and frequency span are measured, the parameters are fused for source classification, and a cyclic stationary feature set of periodic component information is generated.

[0079] Step 105: According to the preset radar signal sorting rule, the feature data set and the cyclic stationary feature set are subjected to fusion sorting processing to generate a sorting result of the multi-source mixed radar pulse signal sequence.

[0080] In this step, the preset radar signal sorting rule refers to the set feature matching and pulse backtracking logic. The sorting result refers to the time sequence pulse sequence classified according to the radar source.

[0081] In the embodiment of the present application, according to the preset radar source feature matching rule, the pulse repetition interval feature is associated and fused with the periodic component information, the fused feature group set is bound to the radar source identifier, and the sorting result of the multi-source aliasing radar pulse signal sequence is integrated and generated.

[0082] The embodiment of the present application solves the problem of source identifier loss caused by multipath distortion and signal aliasing by fusing physical acoustic compensation, cyclostationary analysis and double feature sorting; signal integrity is improved by using medium attenuation compensation processing, and feature discrimination is enhanced by combining time-frequency cycle detection, so as to finally realize high-precision radar signal sorting and improve the sorting robustness in complex scenes.

[0083] The present application provides an embodiment, step 102, based on the principle of physical acoustic propagation, medium attenuation compensation processing is performed on the multi-source aliasing pulse signal sequence to generate a compensated pulse signal sequence that suppresses complex medium propagation distortion, specifically including the following steps:

[0084] Step 201: Based on the principle of physical acoustic propagation, the propagation path of the radar signal in the complex medium is analyzed to obtain a path node coordinate set.

[0085] In this step, the propagation path refers to the physical trajectory of the radar signal propagating in the complex medium. The path node coordinate set refers to a sequence of three-dimensional coordinate points formed by discretizing the propagation path.

[0086] In the embodiment of the present application, based on the principle of physical acoustic propagation, the propagation trajectory of the radar signal in the building group is simulated by ray tracing technology, the path is discretized into line segment nodes and its three-dimensional coordinates are recorded to obtain the path node coordinate set.

[0087] Step 202: Identify the medium attribute data of each path node in the path node coordinate set to separate the medium density parameter set and the medium propagation speed parameter set from the medium attribute data.

[0088] In this step, the medium attribute data refers to the physical characteristic data set of the medium at the path node. The medium density parameter set refers to the set of medium density values of each path node, and the density is equal to the mass divided by the volume. The medium propagation speed parameter set refers to the set of sound wave propagation speed values of each path node.

[0089] In the embodiment of the present application, the material properties at each path node coordinate in the path node coordinate set are queried, and the medium density parameter set and the medium propagation speed parameter set are extracted by an attribute classification algorithm.

[0090] Step 203: based on the medium density parameter set and the propagation speed parameter set, the path scattering loss and the path phase delay are calculated respectively, and the path scattering loss and the path phase delay are combined to obtain an attenuation compensation coefficient set.

[0091] In this step, the attenuation compensation coefficient set refers to a compensation value set that fuses the scattering loss and the phase delay.

[0092] In the embodiment of the present application, the path phase delay = path length ÷ speed, the path scattering loss value and the path phase delay value are weighted and added according to the nodes to obtain the attenuation compensation coefficient, and the specific calculation formula is: attenuation compensation coefficient = path scattering loss value + path phase delay value × 2π × frequency parameter, wherein the frequency parameter is obtained by signal synchronous acquisition technology, and each path node is given a corresponding attenuation compensation coefficient to obtain the attenuation compensation coefficient set.

[0093] Step 204: identifying the propagation path type of each pulse signal in the multi-source mixed pulse signal sequence to obtain an intermediate pulse signal sequence with propagation path identification.

[0094] In this step, the propagation path type refers to the classification of the propagation mode of the pulse signal. The intermediate pulse signal sequence refers to the pulse sequence with the path type identification added.

[0095] In the embodiment of the present application, the three types of propagation paths, i.e. direct, reflection and diffraction, are distinguished by pulse arrival time difference and phase feature matching, the propagation path type identification is added to each pulse signal in the multi-source mixed pulse signal sequence to generate the intermediate pulse signal sequence with the propagation path identification.

[0096] Step 205: correcting the intermediate pulse signal sequence according to the attenuation compensation coefficient set to obtain a corrected pulse signal sequence.

[0097] In this step, the corrected pulse signal sequence refers to the pulse sequence after amplitude compensation.

[0098] In the embodiment of the present application, according to the attenuation compensation coefficient set, the corresponding attenuation compensation coefficient is called to perform a correction operation on the amplitude of each pulse signal in the intermediate pulse signal sequence to generate the corrected pulse signal sequence after amplitude correction, wherein the corrected amplitude = original amplitude × 1 / attenuation compensation coefficient.

[0099] Step 206: recombining the corrected pulse signal sequence to obtain a compensated pulse signal sequence that suppresses the propagation distortion of complex media.

[0100] In the embodiment of the present application, all the corrected pulse signal sequences are integrated in the original time sequence, the signal waveform is reconstructed, and the compensated pulse signal sequence that suppresses the propagation distortion of complex media is generated.

[0101] The embodiment of the application accurately quantifies the medium attenuation effect through physical acoustic modeling, realizes pulse-level directional compensation in combination with path type identification, suppresses signal distortion and energy attenuation, and provides a high-fidelity signal basis for subsequent feature extraction.

[0102] The application provides an embodiment, step 103, performing feature extraction on the compensated pulse signal sequence according to a preset pulse repetition interval feature extraction rule to obtain a feature data set containing pulse repetition interval features, specifically including the following steps:

[0103] Step 301: Position marking of the pulse signals in the compensated pulse signal sequence according to a time marker condition of the preset pulse repetition interval feature extraction rule to obtain a pulse time position set.

[0104] In this step, the time marker condition refers to an amplitude trigger threshold of pulse front edge detection. The pulse time position set refers to an ordered set of all pulse start time stamps in the compensated pulse signal sequence.

[0105] In the embodiment of the application, the pulse front edge detection threshold in the preset pulse repetition interval feature is taken as the time marker condition, the intersection of the rising edge of the pulse amplitude of the pulse signal and the time marker condition is compared to determine the pulse start time point, an accurate time stamp is marked for each pulse in the compensated pulse signal sequence, and a pulse time position set is obtained.

[0106] Step 302: Calculating the time interval between adjacent pulse time positions in the pulse time position set to generate an original pulse repetition interval set.

[0107] In this step, the original pulse repetition interval set refers to an original data set of adjacent pulse time position differences.

[0108] In the embodiment of the application, the time points in the pulse time position set are arranged in time sequence, the difference between adjacent time points is calculated, and an original pulse repetition interval set is generated.

[0109] Step 303: Extracting valid pulse repetition intervals from the original pulse repetition interval set based on a preset pulse repetition interval range to obtain a valid pulse repetition interval set.

[0110] In this step, the preset pulse repetition interval range refers to a valid interval value domain set according to the radar type, for example, 1-100 ms for a city traffic radar. The valid pulse repetition interval set refers to a pulse interval data set that meets the preset range.

[0111] In the embodiment of the present application, according to the preset pulse repetition interval range, interval values falling within the range in the original pulse repetition interval set are screened, and after removing abnormal values, an effective pulse repetition interval set is generated.

[0112] Step 304: According to the preset statistical distribution characteristics, the effective pulse repetition interval set is grouped to generate a pulse signal grouping set containing pulse repetition interval characteristics.

[0113] In this step, the preset statistical distribution characteristics refer to the width threshold of the interval value aggregation interval, which is used to define the maximum allowed deviation of the homologous signal interval. The pulse signal grouping set refers to the pulse group set divided according to the interval value aggregation characteristics, and each group represents a potential homologous radar signal.

[0114] In the embodiment of the present application, based on the density of the statistical interval value distribution of the aggregation interval width of the preset statistical distribution characteristics, the interval values in the density peak interval are grouped together to generate a pulse signal grouping set containing pulse repetition interval characteristics.

[0115] Step 305: Matching the pulse signal grouping set and the preset radar source identifier to obtain a feature data set.

[0116] In this step, the preset radar source identifier refers to a pre-established radar source feature template library.

[0117] In the embodiment of the present application, the interval groups in the pulse grouping set are matched and bound with the preset radar source identifier to generate a feature data set.

[0118] The embodiment of the present application solves the problem of interval characteristic fragmentation caused by signal loss and interference by dynamically statistically grouping and matching radar source identifiers, and improves the radar source identification accuracy in a multi-source aliasing scenario.

[0119] The present application provides a specific embodiment, step 104, cyclically stationary analysis of the compensated pulse signal sequence to obtain a cyclically stationary feature set containing periodic component information, specifically including the following steps:

[0120] Step 401: Segmenting the compensated pulse signal sequence to obtain a plurality of pulse signal segments.

[0121] In this step, the plurality of pulse signal segments refers to discrete signal units formed by time window segmentation.

[0122] In the embodiment of the present application, the compensated pulse signal sequence is cut by a fixed length time window to divide the continuous pulse stream into equal length segments to obtain a plurality of pulse signal segments.

[0123] Step 402: Time-frequency conversion of each pulse signal segment to generate a time-frequency distribution set.

[0124] In this step, the time-frequency distribution set refers to a two-dimensional energy matrix generated by time-frequency conversion of the pulse signal segment, the horizontal axis is the time unit, the vertical axis is the frequency unit, and the unit value is the energy intensity.

[0125] In the embodiments of the present application, the short-time Fourier transform technology is used to decompose each pulse signal segment into time and frequency two-dimensional units, the signal energy intensity in each unit is calculated, and the energy intensities of all units constitute the time-frequency distribution set.

[0126] Step 403: According to the preset periodic stability condition, the time-frequency region with periodic fluctuation of energy intensity in the time-frequency distribution set is detected to determine the candidate time-frequency region that meets the preset periodic fluctuation amplitude change threshold.

[0127] In this step, the preset periodic stability condition refers to the double threshold combination of periodicity determination. The preset periodic fluctuation amplitude change threshold refers to the maximum allowed value of the amplitude difference between adjacent energy peaks. The candidate time-frequency region refers to the rectangular time-frequency block detected by the periodic stability detection.

[0128] In the embodiments of the present application, based on the double conditions that the energy peak interval variance is less than the preset peak interval stability threshold and the adjacent peak amplitude difference is less than the preset periodic fluctuation amplitude change threshold, the periodic region is identified by scanning the time-frequency plane, and the candidate time-frequency region set is generated.

[0129] Step 404: The time interval between adjacent energy peaks of each candidate time-frequency region is measured to generate a region period value set.

[0130] In this step, the adjacent energy peak refers to two local energy maximum points that appear continuously along the time axis in the time-frequency region. The region period value set refers to the set of region period measurement values of each candidate time-frequency region.

[0131] In the embodiments of the present application, the continuous energy peak position in the candidate time-frequency region is extracted, the peak time difference is calculated, and the region period value set is generated.

[0132] Step 405: The difference between the highest frequency and the lowest frequency of each candidate time-frequency region in the candidate time-frequency region set is calculated to obtain a region frequency span set.

[0133] In this step, the region frequency span set refers to the set of frequency band widths of each candidate time-frequency region.

[0134] In the embodiments of the present application, the frequency boundary of the candidate time-frequency region is located, the frequency band width value is obtained by subtracting the highest frequency from the lowest frequency, the frequency band width values of all candidate time-frequency regions are integrated according to the region number, and the region frequency span set is constituted.

[0135] Step 406: performing parameter fusion on the regional frequency span set and the regional period value set to generate a period component parameter set.

[0136] In this step, the period component parameter set refers to a two-dimensional parameter set that integrates the period value and the bandwidth, and each parameter corresponds to a feature vector of a candidate time-frequency region.

[0137] In an embodiment of the present application, the period value and the frequency span value of the same region are combined into a two-dimensional feature vector to obtain a period component parameter set.

[0138] Step 407: According to the preset radar source periodic characteristics, the periodic component parameter set is classified into a source, and a cyclostationary feature set containing periodic component information is obtained.

[0139] In this step, the preset radar source period characteristics refer to a database of period and bandwidth characteristics of known radar sources.

[0140] In an embodiment of the present application, the period and bandwidth feature ranges in the preset radar source periodic features are compared, the matching periodic component parameters are classified into the corresponding radar source category, and a cyclostationary feature set of the periodic component information is generated.

[0141] The embodiment of the present application enhances the ability to capture the cyclic characteristics of frequency hopping signals through time-frequency domain periodic characteristics extraction and dual parameter fusion, solves the problem of period annihilation, and provides high-discrimination features for dense signal sorting.

[0142] This application provides a specific embodiment, step 403, based on a preset periodic stability condition, detecting time-frequency regions in the time-frequency distribution set where energy intensity fluctuates periodically to determine candidate time-frequency regions that meet a preset periodic fluctuation amplitude change threshold, specifically comprising the following steps:

[0143] Step 411: According to a preset periodic stability condition, the time-frequency region in which the energy intensity in the time-frequency distribution set fluctuates periodically is detected to determine a plurality of peak positions.

[0144] In this step, the peak position refers to the coordinate point where the energy intensity is locally maximum on the time-frequency plane.

[0145] In an embodiment of the present application, according to a preset periodic stability condition, each frequency unit of the time-frequency distribution set is scanned through a sliding window, and the maximum points whose energy intensity is greater than that of the adjacent time-frequency units and fluctuates periodically are identified, their time and frequency coordinates are recorded, and multiple peak positions are determined.

[0146] Step 412: Calculate the time interval variance between the peak positions. When the time interval variance is less than a preset peak interval stability threshold, use the peak position as a candidate peak position.

[0147] In this step, the preset peak interval stability threshold refers to an upper limit of allowed peak time interval fluctuation for determining cycle stability. The candidate peak position refers to a reliable peak position set screened by interval variance, reflecting potential signal source positions meeting cycle consistency.

[0148] In the embodiments of the present application, the peak positions of the same frequency unit are sorted by time, the variance of adjacent peak time intervals is calculated, and if the variance is less than the preset peak interval stability threshold, all peak positions of the frequency unit are retained as candidate peak positions.

[0149] Step 413: Based on the preset time coincidence threshold and the preset peak interval similarity threshold, time feature matching is performed on the candidate peak positions to select target peak positions meeting both the time coincidence requirement and the interval similarity requirement.

[0150] In this step, the preset time coincidence threshold refers to the minimum proportion requirement of peak time overlap of different frequency units. The preset peak interval similarity threshold refers to the maximum allowed value of peak interval difference between different frequency units. The time coincidence requirement refers to the condition that the proportion of peak time coordinates overlapping between spatially adjacent frequency units must be greater than the preset threshold, reflecting signal time domain synchronization. The interval similarity requirement refers to the condition that the difference value of peak intervals between spatially adjacent frequency units must be less than the similarity threshold, representing signal cycle consistency. The target peak position refers to the peak position set meeting both the time coincidence and the interval similarity, identifying the accurate distribution of homologous signals in the time-frequency plane.

[0151] In the embodiments of the present application, the candidate peak positions of spatially adjacent frequency units are matched, first, it is verified whether the time coincidence meets the preset time coincidence threshold, and second, the peak interval difference is calculated, and if the interval difference is less than the preset peak interval similarity threshold, the target peak positions meeting both the time coincidence requirement and the interval similarity requirement are screened.

[0152] Step 414: Energy amplitude variation verification is performed on the time-frequency region where the target peak position is located, so as to take the time-frequency region where the adjacent peak amplitude difference is less than the preset cycle fluctuation amplitude variation threshold as a candidate time-frequency region.

[0153] In the embodiments of the present application, continuous energy peaks in the time-frequency region where the target peak position is located are extracted, the absolute values of adjacent peak amplitude differences are calculated, and if all amplitude differences are less than the preset cycle fluctuation amplitude variation threshold, the time-frequency block is marked as a candidate time-frequency region.

[0154] The embodiments of the present application accurately separate homologous cycle signals through the time-space dual verification mechanism, overcome the feature annihilation problem caused by frequency hopping interference and signal fragmentation, and improve the robustness of cycle region detection.

[0155] The application provides an embodiment, step 404, measuring the time interval between adjacent energy peaks of each candidate time-frequency region, generating a set of region period values, specifically including the following steps:

[0156] Step 421: arranging the target peak position corresponding to each candidate time-frequency region in ascending order to obtain a corresponding sorted target peak position sequence;

[0157] In this step, the sorted target peak position sequence refers to a linear sequence formed by arranging the target peak positions in ascending order of time values, and the sequence elements are time coordinate values, reflecting the time sequence relationship of the energy peaks.

[0158] In the embodiment of the application, the time coordinate values in the set of target peak positions are extracted, reordered in ascending order of numerical values, and a sorted target peak position sequence is generated.

[0159] Step 422: calculating the time interval of adjacent target peak positions in each sorted target peak position sequence to generate a plurality of original time interval sequences.

[0160] In this step, the plurality of original time interval sequences refers to the interval value sequences generated by each candidate time-frequency region, and the sequence elements are the time differences of adjacent target peak positions.

[0161] In the embodiment of the application, the time difference between two adjacent target peak positions in the sorted sequence is calculated to generate an original time interval sequence.

[0162] Step 423: calculating the absolute difference value of adjacent time intervals in each original time interval sequence, and selecting a stable time interval sequence with an absolute difference value not exceeding a preset interval tolerance from the corresponding original time interval sequence.

[0163] In this step, the preset interval tolerance refers to the upper limit of the allowed adjacent period interval fluctuation, which is used to determine the period stability. The stable time interval sequence refers to the continuous interval segment selected by the tolerance, reflecting the high stability period characteristics.

[0164] In the embodiment of the application, the absolute difference value of the continuous interval pairs of the original time interval sequence is calculated, and only the continuous interval segment with a difference value less than or equal to the preset interval tolerance is retained to generate a stable time interval sequence that meets the stability requirement.

[0165] Step 424: taking the average value of each stable time interval sequence as the region period value of the corresponding candidate time-frequency region, and aggregating the region period values of all candidate time-frequency regions to obtain a set of region period values.

[0166] In this step, the region period value refers to the arithmetic mean value of the stable time interval sequence.

[0167] In the embodiment of the present application, the arithmetic mean of all interval values in the stable time interval sequence is calculated, and the result is taken as the region period value of the corresponding candidate time-frequency region. Finally, the region period values of all candidate time-frequency regions are summarized to obtain the region period value set.

[0168] The embodiment of the present application accurately extracts the anti-interference period value through the double stability verification mechanism, solves the problem of superposition of Doppler period jitter caused by vehicle motion and instantaneous interference, and improves the reliability of the period feature.

[0169] The present application provides a specific embodiment, step 105, according to the preset radar signal sorting rule, the feature data set and the cyclostationary feature set are fused and sorted, and the sorting result of the multi-source aliasing radar pulse signal sequence is generated, which specifically includes the following steps:

[0170] Step 501: Based on the main matching rule of the preset radar signal sorting rule, the pulse repetition interval feature in the feature data set and the period component information in the cyclostationary feature set are matched, and a fusion feature group set is generated.

[0171] In this step, the main matching rule refers to the association logic of the pulse repetition interval feature and the period component information. The pulse repetition interval feature refers to the pulse interval data set grouped according to the radar source. The period component information refers to the time-frequency period parameter set. The fusion feature group set refers to the feature combination set successfully associated by the main matching rule.

[0172] In the embodiment of the present application, based on the main matching rule of the preset radar signal sorting rule, the association strength of the pulse repetition interval feature and the period component information in the cyclostationary feature set is calculated, and when the interval value of the pulse repetition interval feature and the period value of the period component information satisfy the time ratio constraint, the feature association is established. The matched feature groups are combined to generate a fusion feature group set.

[0173] Step 502: Based on the auxiliary matching rule of the preset radar signal sorting rule, the period parameters of each fusion feature group in the fusion feature group set are matched with the preset radar source feature template to generate a fusion feature group set with radar source identification.

[0174] In this step, the auxiliary matching rule refers to the matching logic of the fusion feature group and the radar source template. The preset radar source feature template refers to the feature parameter database of the known radar source.

[0175] In the embodiment of the present application, based on the auxiliary matching rule of the preset radar signal sorting rule, the period parameters of each fusion feature group in the fusion feature group set are extracted, and the parameters in the radar source feature template library are verified for inclusion. The radar source identification is assigned to the matched feature group to generate a fusion feature group set with radar source identification.

[0176] Step 503: identifying the pulse signals corresponding to the time characteristic parameters of the fusion feature group set from the compensated pulse signal sequence, and generating a feature group pulse set.

[0177] In this step, the feature group pulse set refers to the pulse data set positioned through time window matching.

[0178] In the embodiment of the present application, according to the time characteristic parameters of the fusion feature group set, the pulses in the time window in the compensated pulse signal sequence are scanned to generate the feature group pulse set.

[0179] Step 504: reorganizing the pulse signals with the same radar source identifier in the feature group pulse set according to the acquisition time sequence of the multi-source mixed radar pulse signal sequence, to obtain the sorting result of the multi-source mixed radar pulse signal sequence.

[0180] In this step, the same radar source identifier refers to the radar source unique code assigned by the auxiliary matching rule, which is used to identify the homologous signal pulse.

[0181] In the embodiment of the present application, all the pulse signals with the same radar source identifier are sorted according to the acquisition time sequence of the original multi-source mixed radar pulse signal sequence, and after removing the repeated pulses, a time-sequentially continuous radar source pulse sequence is generated, which constitutes the sorting result of the multi-source mixed radar pulse signal sequence.

[0182] The embodiment of the present application solves the problem of feature and pulse association breakage caused by signal mixing through the double-stage feature matching and time window backtracking mechanism, and realizes high-precision separation and reorganization of multi-source radar pulses.

[0183] Figure 2 A structure diagram of a radar signal sorting system based on cyclic stationary analysis is provided for the embodiment of the present application, as shown in FIG. 1, which includes: Figure 2

[0184] The acquisition module 21 is configured to acquire the superimposed pulses of different radar transmitting sources to obtain a multi-source mixed pulse signal sequence.

[0185] The compensation module 22 is configured to perform medium attenuation compensation processing on the multi-source mixed pulse signal sequence based on the physical acoustic propagation principle to generate a compensated pulse signal sequence that suppresses the distortion of complex medium propagation.

[0186] The extraction module 23 is configured to perform feature extraction on the compensated pulse signal sequence according to a preset pulse repetition interval feature extraction rule to obtain a feature data set containing pulse repetition interval features.

[0187] ​The analysis module 24 is configured to perform cyclic stationary analysis on the compensated pulse signal sequence to obtain a cyclic stationary feature set containing periodic component information.

[0188] The sorting module 25 is configured to perform fusion sorting processing on the feature data set and the cyclic stationary feature set according to a preset radar signal sorting rule to generate a sorting result of the multi-source aliasing radar pulse signal sequence.

[0189] Figure 2 The radar signal sorting system based on cyclic stationary analysis can perform Figure 1 The radar signal sorting method based on cyclic stationary analysis has the implementation principle and technical effects as described above, and will not be described here. The specific operation modes of each module and unit of the radar signal sorting system based on cyclic stationary analysis have been described in detail in the embodiments of the method, and will not be described here.

[0190] In one possible design, Figure 2 The radar signal sorting system based on cyclic stationary analysis can be implemented as a computing device, such as a computer. Figure 3 As shown in the figure, the computing device can include a storage component 31 and a processing component 32.

[0191] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0192] The processing component 32 is configured to perform the above Figure 1 The radar signal sorting method based on cyclic stationary analysis.

[0193] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic elements, for executing the above method.

[0194] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic or optical disk.

[0195] Of course, the computing device can also necessarily include other components, such as an input / output interface, a display component, a communication component, etc.

[0196] The input / output interface provides an interface between the processing component and peripheral interface modules, which can be output devices, input devices, etc.

[0197] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0198] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, at which time the computing device can refer to a cloud server, and the processing component, the storage component, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0199] The embodiment of the application further provides a computer storage medium, which stores a computer program, and the computer program can realize the above-mentioned Figure 1 A radar signal sorting method based on cyclic stationary analysis is provided in the embodiment.

[0200] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0201] The device embodiment described above is only schematic, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment. Those skilled in the art can understand and implement without creative labor.

[0202] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and the necessary general hardware platform from the above description of the embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that contributes to the technical solutions can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the methods.

[0203] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.​

Claims

1. A radar signal sorting method based on cyclostationary analysis, characterized in that: include: Collect the superimposed pulses of different radar emission sources to obtain a multi-source aliased pulse signal sequence; Based on the physical acoustic propagation principle, medium attenuation compensation processing is performed on the multi-source aliased pulse signal sequence to generate a compensated pulse signal sequence that suppresses complex medium propagation distortion; Extracting features from the compensated pulse signal sequence according to a preset pulse repetition interval feature extraction rule to obtain a feature data set containing pulse repetition interval features; Performing cyclostationary analysis on the compensated pulse signal sequence to obtain a cyclostationary feature set containing periodic component information; According to a preset radar signal sorting rule, the feature data set and the cyclostationary feature set are fused and sorted to generate a sorting result of a multi-source aliasing radar pulse signal sequence.

2. The method according to claim 1, characterized in that Based on the physical acoustic propagation principle, medium attenuation compensation processing is performed on the multi-source aliased pulse signal sequence to generate a compensated pulse signal sequence that suppresses complex medium propagation distortion, including: Based on the physical acoustic propagation principle, the propagation path of the radar signal in the complex medium is analyzed to obtain the coordinate set of the path nodes; Identifying medium attribute data of each path node in the path node coordinate set to separate a medium density parameter set and a medium propagation speed parameter set from the medium attribute data; Calculating path scattering loss and path phase delay based on the medium density parameter set and the propagation speed parameter set, respectively, and combining the path scattering loss and the path phase delay to obtain an attenuation compensation coefficient set; Identifying the propagation path type of each pulse signal in the multi-source aliased pulse signal sequence to obtain an intermediate pulse signal sequence with a propagation path identifier; Correcting the intermediate pulse signal sequence according to the attenuation compensation coefficient set to obtain a corrected pulse signal sequence; The corrected pulse signal sequence is recombined to obtain a compensated pulse signal sequence that suppresses propagation distortion in complex media.

3. The method according to claim 1, characterized in that According to a preset pulse repetition interval feature extraction rule, feature extraction is performed on the compensated pulse signal sequence to obtain a feature data set containing pulse repetition interval features, including: Marking the positions of the pulse signals in the compensated pulse signal sequence according to the time marking conditions of the preset pulse repetition interval feature extraction rule to obtain a pulse time position set; Calculating the time intervals between adjacent pulse time positions in the pulse time position set to generate an original pulse repetition interval set; Extracting effective pulse repetition intervals from the original pulse repetition interval set based on a preset pulse repetition interval range to obtain an effective pulse repetition interval set; Grouping the effective pulse repetition interval set according to a preset statistical distribution feature to generate a pulse signal group set containing a pulse repetition interval feature; The pulse signal group set is matched with a preset radar source identifier to obtain a feature data set.

4. The method according to claim 1, wherein Performing cyclostationary analysis on the compensated pulse signal sequence to obtain a cyclostationary feature set containing periodic component information, including: Segment-processing the compensated pulse signal sequence to obtain a plurality of pulse signal segments; Perform time-frequency conversion on each pulse signal segment to generate a time-frequency distribution set; According to a preset periodic stability condition, time-frequency regions in the time-frequency distribution set whose energy intensity fluctuates periodically are detected to determine candidate time-frequency regions that meet a preset periodic fluctuation amplitude change threshold; Measuring the time intervals between adjacent energy peaks in each candidate time-frequency region to generate a set of region period values; Calculating the difference between the highest frequency and the lowest frequency of each candidate time-frequency region in the candidate time-frequency region set to obtain a region frequency span set; Performing parameter fusion on the regional frequency span set and the regional period value set to generate a period component parameter set; According to the preset radar source periodic characteristics, the periodic component parameter set is source-classified to obtain a cyclostationary feature set containing periodic component information.

5. The method according to claim 4, characterized in that According to a preset periodic stability condition, time-frequency regions in the time-frequency distribution set whose energy intensity fluctuates periodically are detected to determine candidate time-frequency regions that meet a preset periodic fluctuation amplitude change threshold, including: According to a preset cyclic stability condition, detecting a time-frequency region in which energy intensity in the time-frequency distribution set fluctuates periodically to determine a plurality of peak positions; Calculating the time interval variance between the peak positions, and when the time interval variance is less than a preset peak interval stability threshold, taking the peak position as a candidate peak position; Based on a preset time overlap threshold and a preset peak interval similarity threshold, time feature matching is performed on the candidate peak positions to select a target peak position that meets both the time overlap requirement and the interval similarity requirement; The energy amplitude change verification is performed on the time-frequency region where the target peak position is located, so as to select the time-frequency region where the adjacent peak amplitude difference is less than the preset period fluctuation amplitude change threshold as the candidate time-frequency region.

6. The method according to claim 4, characterized in that Measure the time intervals between adjacent energy peaks in each candidate time-frequency region and generate a set of region period values, including: Arrange the target peak positions corresponding to each candidate time-frequency region in ascending order to obtain the corresponding sorted target peak position sequence; calculating the time intervals between adjacent target peak positions in each sorted target peak position sequence to generate a plurality of original time interval sequences; Calculating the absolute difference between adjacent time intervals in each original time interval sequence, and selecting a stable time interval sequence whose absolute difference does not exceed a preset interval tolerance from the corresponding original time interval sequence; The average value of each stable time interval series is used as the regional period value of the corresponding candidate time-frequency region, and the regional period values ​​of all candidate time-frequency regions are aggregated to obtain a set of regional period values.

7. The method according to claim 1, characterized in that According to a preset radar signal sorting rule, the feature data set and the cyclostationary feature set are fused and sorted to generate a sorting result of a multi-source aliased radar pulse signal sequence, including: Based on the main matching rule of the preset radar signal sorting rule, the pulse repetition interval feature in the feature data set and the periodic component information in the cyclostationary feature set are subjected to feature association matching to generate a fusion feature group set; Based on the auxiliary matching rule of the preset radar signal sorting rule, the period parameter of each fused feature group in the fused feature group set is matched with the preset radar source feature template to generate a fused feature group set with a radar source identifier; Identifying a pulse signal corresponding to a time characteristic parameter of the fused feature group set from the compensated pulse signal sequence to generate a feature group pulse set; According to the acquisition time sequence of the multi-source aliasing radar pulse signal sequence, the pulse signals with the same radar source identifier in the characteristic group pulse set are recombined to obtain a sorting result of the multi-source aliasing radar pulse signal sequence.

8. A radar signal sorting system based on cyclostationary analysis, characterized in that: include: The acquisition module is used to collect the superimposed pulses of different radar emission sources to obtain a multi-source aliased pulse signal sequence; A compensation module is used to perform medium attenuation compensation processing on the multi-source aliased pulse signal sequence based on the physical acoustic propagation principle to generate a compensated pulse signal sequence that suppresses propagation distortion in complex media; An extraction module is used to extract features from the compensated pulse signal sequence according to a preset pulse repetition interval feature extraction rule to obtain a feature data set containing pulse repetition interval features; An analysis module, configured to perform cyclostationary analysis on the compensated pulse signal sequence to obtain a cyclostationary feature set containing periodic component information; The sorting module is used to fuse and sort the feature data set and the cyclostationary feature set according to a preset radar signal sorting rule to generate a sorting result of a multi-source aliasing radar pulse signal sequence.

9. A computing device, characterized in that The invention comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a radar signal sorting method based on cyclostationary analysis as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the radar signal sorting method based on cyclostationary analysis according to any one of claims 1 to 7 is implemented.

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