CN113569695B - Sea surface target detection method and system based on bispectrum three characteristics - Google Patents
Sea surface target detection method and system based on bispectrum three characteristics Download PDFInfo
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Abstract
The invention provides a sea surface target detection method and system based on bispectrum three characteristics, wherein the method comprises the following steps: obtaining a signal to be detected, and constructing a diagonal integral bispectrum of the signal to be detected; extracting spectrum features from the diagonally integrated dual spectrum and accumulating to obtain dual-spectrum three features, and constructing a three-feature space to obtain feature points of a signal to be detected; wherein the bispectrum three features comprise an accumulated peak value, an accumulated spectrum width and a total variation; and detecting and classifying the characteristic points of the signals to be detected to obtain a target detection result. According to the sea surface target detection method and system based on the dual-spectrum three features, the diagonal integral dual spectrum of the signal to be detected is constructed, three features of an accumulated peak value, an accumulated spectrum width and total variation are conveniently extracted, and target detection is carried out by utilizing the feature points fusing the three features, so that a target unit and a clutter unit can be better distinguished, and an obtained target detection result is more accurate and reliable.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a sea surface target detection method and system based on bispectrum three features.
Background
In the radar detection field, the detection of a sea surface slow small target is generally realized by two modes of an energy detection method and a characteristic detection method. The energy detection method mainly constructs likelihood ratio according to the local amplitude or power level information of sea clutter, forms a detection threshold according to a threshold factor and makes a decision on the existence of a target. The method has the advantages that the required radar residence time is short, the signal-to-noise ratio requirement on the target is high, and a large number of false alarms are easily caused in a sea spike dense scene.
The feature detection method mainly comprises the steps of excavating difference features between sea clutter and a target, converting the sea clutter from a high-overlap observation space to a low-overlap feature space, and realizing target detection in the feature space. The radar residence time required by the feature detection method is long, long-time observation accumulation is required for the sea surface, and the time can reach hundreds of milliseconds or even seconds. However, in the scanning observation mode of radar, the target residence time is often difficult to reach the above magnitude, and the feature detection method performance is severely degraded with decreasing accumulation time.
It is difficult to find that the target detection results obtained in the two modes are not accurate enough, and the actual application requirements are difficult to meet.
Thus, there is a need for a sea surface target detection method that solves the above problems.
Disclosure of Invention
The invention provides a sea surface target detection method and system based on bispectrum three characteristics, which are used for solving the technical problem of inaccurate sea surface target detection results in the prior art.
In a first aspect, the present invention provides a sea surface target detection method based on bispectrum three features, including:
obtaining a signal to be detected, and constructing a diagonal integral bispectrum of the signal to be detected;
Extracting spectral features from the diagonally integrated bispectrum and accumulating to obtain bispectrum three features, and constructing a three-feature space to obtain feature points of the signal to be detected; wherein the bispectrum tri-feature comprises an accumulated peak value, an accumulated spectrum width and a total variation;
and detecting and classifying the characteristic points of the signal to be detected to obtain a target detection result.
According to the sea surface target detection method based on the three characteristics of the bispectrum, the construction of the diagonal integral bispectrum of the signal to be detected comprises the following steps:
Calculating a two-dimensional bispectrum of the signal to be detected;
and extracting a diagonal integral bispectrum from the two-dimensional bispectrum.
According to the sea surface target detection method based on the bispectrum three characteristics, the two-dimensional bispectrum is obtained by performing two-dimensional Fourier transform on the third-order accumulation of the signal to be detected.
According to the sea surface target detection method based on the bispectrum three features, the method for extracting the spectrum features from the diagonal integral bispectrum and accumulating to obtain the bispectrum three features, constructing a three-feature space, and obtaining feature points of the signal to be detected comprises the following steps:
extracting peak value, centroid frequency and spectrum width from the diagonally integrated bispectrum;
Accumulating the peak value and the spectrum width along the frame time dimension to obtain an accumulated peak value and an accumulated spectrum width;
Extracting a total variation for measuring the stability of the centroid frequency characteristic in the frame time dimension;
And constructing three feature spaces by taking the accumulated peak value feature as an X axis, the accumulated spectrum width feature as a Y axis and the total variation feature as a Z axis to obtain feature points of the signal to be detected.
According to the sea surface target detection method based on the bispectrum three features, the feature points of the signal to be detected are detected and classified to obtain a target detection result, and the method comprises the following steps:
Sea clutter data are collected, and feature training is carried out on the sea clutter data through a convex hull algorithm to obtain a decision area;
Judging whether the characteristic points of the signal to be detected fall into the decision area, and if the characteristic points of the signal to be detected fall into the decision area, determining the signal to be detected as a clutter unit; if the characteristic points of the signal to be detected do not fall into the decision area, the signal to be detected is a target unit.
According to the sea surface target detection method based on the bispectrum three features, provided by the invention, the feature points of the signal to be detected are detected and classified to obtain a target detection result, and the method further comprises the following steps:
when the characteristics of the sea clutter data change due to changes in sea state or radar perspective, the sea clutter data are re-acquired to determine a new decision region.
In a second aspect, the present invention further provides a sea surface target detection system based on dual-spectrum three features, including:
The diagonal integral bispectrum construction module is used for acquiring a signal to be detected and constructing a diagonal integral bispectrum of the signal to be detected;
The three-feature extraction module is used for extracting spectrum features from the diagonal integral double spectrums and accumulating the spectrum features to obtain double-spectrum three features, and constructing a three-feature space to obtain feature points of the signal to be detected; wherein the bispectrum tri-feature comprises an accumulated peak value, an accumulated spectrum width and a total variation;
And the target detection module is used for detecting and classifying the characteristic points of the signal to be detected to obtain a target detection result.
According to the sea surface target detection system based on the dual-spectrum three features, the three feature extraction module comprises:
a spectrum feature extraction unit for extracting peak value, centroid frequency and spectrum width from the diagonally integrated bispectrum;
The first feature extraction unit is used for respectively accumulating the peak value and the spectrum width along the frame time dimension to obtain an accumulated peak value and an accumulated spectrum width;
The second feature extraction unit is used for extracting the total variation for measuring the stability of the centroid frequency features in the frame time dimension;
the characteristic point acquisition unit is used for constructing and obtaining three characteristic spaces by taking the accumulated peak value characteristic as an X axis, the accumulated spectrum width characteristic as a Y axis and the total variation characteristic as a Z axis to obtain the characteristic points of the signal to be detected.
In a third aspect, the present invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the above sea surface target detection methods based on the bispectral tri-features when the program is executed.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a sea surface target detection method based on the bispectrum tri-feature as described in any one of the above.
According to the sea surface target detection method and system based on the dual-spectrum three features, the diagonal integral dual spectrum of the signal to be detected is constructed, three features of an accumulated peak value, an accumulated spectrum width and total variation are conveniently extracted, and target detection is carried out by utilizing the feature points fusing the three features, so that a target unit and a clutter unit can be better distinguished, and an obtained target detection result is more accurate and reliable.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow diagram of a sea surface target detection method based on dual-spectrum three features;
Fig. 2 (a) is a schematic diagram of a detection result obtained by performing target detection on measured data by applying the sea surface target detection method based on the bispectrum three features;
FIG. 2 (b) is a schematic diagram of a detection result obtained by performing target detection on measured data by using a detector based on three characteristics of time and frequency;
FIG. 2 (c) is a schematic diagram of a detection result obtained by performing target detection on measured data by using an amplitude and Doppler three-feature detector;
FIG. 2 (d) is a schematic diagram of a detection result obtained by performing target detection on measured data by using a fractal detector;
FIG. 3 is a schematic diagram of the structure architecture of the sea surface target detection system based on the dual-spectrum three features;
FIG. 4 is a schematic diagram of the structure architecture of a three-feature extraction module in a sea surface target detection system based on dual-spectrum three features;
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 shows a sea surface target detection method based on dual-spectrum three features, which is provided by the embodiment of the invention, and comprises the following steps:
S110: and obtaining a signal to be detected, and constructing a diagonal integral bispectrum of the signal to be detected.
Before performing object detection, the embodiment of the invention assumes that a series of coherent pulses are transmitted in one beam for the sea radar and an echo time sequence x (K) of length K is received at each range bin. If the echo information is not affected by the target, the unit to be detected should only contain sea clutter and noise signals c (k), otherwise, the target signals s (k) should be mixed. Based on this, the target detection in sea clutter can be attributed to a binary hypothesis test problem, which in turn can be expressed as:
it will be appreciated that the signal to be detected referred to in this step is essentially a series of finite time sequences.
First, a two-dimensional bispectrum B (ω 1,ω2) of the signal { x (T), t=1, 2, …, T } to be detected is calculated, which is defined as a two-dimensional fourier transform of the third-order cumulative quantity c 3(τ1,τ2) of the signal x (T), expressed as follows:
wherein c 3(τ1,τ2) is required to satisfy the absolute sum condition, and there is:
c3(τ1,τ2)＝E[xH(t)x(t+τ1)x(t+τ2)] (3)
further, a diagonally integrated bispectrum DIB is extracted therefrom, whose expression is as follows:
In the formula, the coordinate system (omega 1′,ω2') is obtained by rotating the original bispectral coordinate system (omega 1,ω2) by 45 degrees anticlockwise.
S120: extracting spectrum features from the diagonally integrated dual spectrum and accumulating to obtain dual-spectrum three features, and constructing a three-feature space to obtain feature points of a signal to be detected; wherein the bispectrum three features comprise cumulative peak value, cumulative spectral width and total variation.
Firstly, according to the nonlinear coupling difference between a sea clutter unit and a target unit, three characteristics of a peak value PV, a centroid frequency FC and a spectrum width SW are extracted from a diagonally integrated dual spectrum, and the expressions of the three spectrum characteristics are as follows:
PV＝max{DIB(ω)} (5)
FC＝∫ω·DIB(ω)dω/∫DIB(ω)dω (6)
Considering that the radar observation time is short, the separability between the sea clutter and the target is insufficient. Therefore, the invention accumulates the two characteristics of peak value and spectrum width along the frame time dimension through the comprehensive application of multi-frame scanning historical data and current frame data to obtain two characteristics of accumulated peak value CPV and accumulated spectrum width CSW, and extracts the characteristic of total variation TV at the same time, and is used for measuring the stability of the centroid frequency characteristic in the frame time dimension, and the expressions of the three accumulated characteristics of accumulated peak value CPV, accumulated spectrum width CSW and total variation TV are as follows:
where s represents the accumulated starting point of the spectral features and L represents the accumulated number of frames of the spectral features.
Accordingly, a three-feature space can be constructed in which the X-axis is the cumulative peak feature CPV, the Y-axis is the cumulative spectral width feature CSW, and the Z-axis is the total variation feature TV. Thus, the characteristic points of the signal to be detected are obtained, the characteristic points comprise three difference characteristics between the target unit and the sea clutter unit, and the target unit and the sea clutter unit can be distinguished more accurately through the characteristic points. Meanwhile, the method provided by the invention carries out fusion detection in the three-feature space, and can avoid the limitation of single-feature detection.
S130: and detecting and classifying the characteristic points of the signals to be detected to obtain a target detection result.
In this embodiment, sea clutter data needs to be collected in the early stage, and feature training is performed on the sea clutter data through a convex hull algorithm to obtain a decision region.
The process of constructing the decision region by adopting the convex hull algorithm is specifically as follows:
(1) Initializing: the characteristic training set of the sea clutter data is set as H, wherein W data are contained, the calculated false alarm number is L=wp F, and p F is the set false alarm rate. Let the starting variable l=0.
(2) A convex hull CH (H) for the current data point is computed, wherein the vertex of convex hull CH (H) is { v 1,v2,…,vr }. The number of data points falling into convex hull CH (H) is then counted and set to n all.
(3) A cyclic variable q is set, from 1 to r, to calculate the convex hull CH (H- { v q }), i.e. the vertex v q is removed from the set H, and then the number of feature points in the new convex hull CH (H- { v q }) is calculated, set to n q.
(4) And comparing the set { n all-n1,…,nall-nr }, and removing the vertex v i corresponding to the maximum value in the set.
(5) Let H- { v i } = H, l+1 = l.
(6) If L is less than L, returning to the step (2) to continue the removal of the next vertex. Otherwise, the removal process is terminated, outputting the final decision region Ω=ch (H).
The decision area is obtained by gradually narrowing the convex hull demarcation range by the convex hull algorithm on the premise of meeting the false alarm rate requirement, so that the current characteristic point is a clutter unit or a target unit can be better distinguished, and the target detection is more accurately realized.
Then judging whether the characteristic points of the signal to be detected fall into the decision area omega, and if the characteristic points of the signal to be detected fall into the decision area omega, the signal to be detected is a clutter unit; if the feature point of the signal to be detected does not fall into the decision area omega, the signal to be detected is a target unit.
In the step, the acquired sea clutter data needs to obtain corresponding feature points through three feature spaces, and a feature training set is constructed by the feature points corresponding to the sea clutter data.
It will be appreciated that when the sea clutter characteristics change significantly due to changes in sea state or radar perspective, sea clutter data needs to be re-acquired to determine a new decision region.
Considering Gao Jiepu as an effective tool for signal analysis, the frequency and phase coupling phenomenon between harmonic components can be visually displayed, and the method has been widely used in the fields of image and object recognition in recent years. The dual spectrum is used as the high-order spectrum with the lowest order, has excellent properties such as time shift invariance, phase maintainability, symmetry and the like, and can still keep all the characteristics of the high-order spectrum, so that the dual spectrum is usually selected as a study object during Gao Jiepu analysis. The method starts from the bispectral characteristics of the signal to be detected, analyzes the difference characteristics between the target unit and the sea clutter unit, and extracts three characteristics from the difference characteristics for detecting the small target on the sea surface, so that the limitation of single characteristic detection is avoided, and the accuracy of target detection is effectively improved.
Since there are time-frequency three-feature-based detectors known in the prior art, the detector fusion is obtained by applying the time-frequency ridge accumulation, the number of connected regions and the maximum connected region size information in the normalized smoothed pseudo-wiener-wili distribution. Meanwhile, there is an amplitude and Doppler three-feature detector, which extracts three features of relative average amplitude, relative Doppler peak height and relative Doppler entropy from radar echo, and realizes detection classification in three-dimensional space by using convex hull algorithm.
In order to demonstrate the advantages of the target detection method provided by the invention, the embodiment adopts measured data for analysis and verification, and specifically, the method provided by the invention, the time-frequency three-feature-based detector, the amplitude, the Doppler three-feature detector and the known fractal detector respectively carry out target detection on the same measured data, and the false alarm rate is set to be 10 -3. The detection results obtained by the embodiment of the invention are shown in fig. 2 (a), and fig. 2 (b), fig. 2 (c) and fig. 2 (d) respectively show detection results based on a time-frequency three-feature detector, an amplitude, a Doppler three-feature detector and a fractal detector in the existing method, and as can be intuitively seen from respective detection result graphs, compared with the existing feature detectors, the detection method provided by the embodiment of the invention has obvious advantages in the aspects of actual false alarm rate control and detection accuracy improvement.
Therefore, according to the sea surface target detection method based on the dual-spectrum three features, the three difference features of the accumulated peak value, the accumulated spectrum width and the total variation are conveniently obtained by constructing the diagonal integral dual spectrum of the signal to be detected, and then the target detection is performed by utilizing the feature points fusing the three features, so that the target unit and the clutter unit can be better distinguished, and the obtained target detection result is more accurate and reliable.
Fig. 3 shows a sea surface target detection system based on dual spectrum three features according to an embodiment of the present invention, including:
The diagonal integral bispectrum construction module 310 is configured to acquire a signal to be detected and construct a diagonal integral bispectrum of the signal to be detected;
The three-feature extraction module 320 is configured to extract spectral features from the diagonally integrated bispectrum and accumulate the extracted spectral features to obtain bispectrum three features, and construct a three-feature space to obtain feature points of the signal to be detected; wherein the bispectrum three features comprise an accumulated peak value, an accumulated spectrum width and a total variation;
the target detection module 330 is configured to detect and classify the feature points of the signal to be detected, and obtain a target detection result.
The diagonal integral bispectrum construction module 310 in this embodiment includes an acquisition unit, a two-dimensional bispectrum calculation unit, and a diagonal integral bispectrum extraction unit, specifically, the diagonal integral bispectrum construction module acquires a signal to be detected through the acquisition unit, and further calculates a two-dimensional bispectrum of the signal to be detected through the two-dimensional bispectrum calculation unit after acquiring the signal to be detected; and finally, extracting the diagonal integral bispectrum from the two-dimensional bispectrum through a diagonal integral bispectrum extracting unit.
Specifically, referring to fig. 4, the three-feature extraction module 320 includes:
a spectrum feature extraction unit 321 for extracting peak value, centroid frequency and spectrum width from the diagonally integrated bispectrum;
A first feature extraction unit 322, configured to accumulate the peak value and the spectrum width along the frame time dimension, respectively, to obtain an accumulated peak value and an accumulated spectrum width;
a second feature extraction unit 323 for extracting a total variation for measuring stability of the centroid frequency feature in the frame time dimension;
the feature point obtaining unit 324 is configured to construct and obtain three feature spaces by taking the cumulative peak feature as the X axis, the cumulative spectrum width feature as the Y axis, and the total variation feature as the Z axis, so as to obtain feature points of the signal to be detected.
In this embodiment, the target detection module 330 includes a decision region generating unit and a judging unit, specifically, the target detection module firstly collects sea clutter data through the decision region generating unit, and performs feature training on the sea clutter data by using a convex hull algorithm to obtain a decision region; judging whether the characteristic points of the signal to be detected fall into a decision area or not through a judging unit, and if the characteristic points of the signal to be detected fall into the decision area, judging that the signal to be detected is a clutter unit; if the feature points of the signal to be detected do not fall into the decision area, the signal to be detected is a target unit.
Preferably, in order to ensure the real-time performance and reliability of the system data processing, the target detection module 330 may further include a decision area updating unit, through which the sea clutter data can be re-collected to determine a new decision area when the characteristics of the sea clutter data change due to the change of sea state or radar view angle.
Therefore, the sea surface target detection system based on the dual-spectrum three features provided by the invention is convenient for extracting the three features of the accumulated peak value, the accumulated spectrum width and the total variation by constructing the diagonal integral dual spectrum of the signal to be detected, and the target detection module is used for carrying out target detection by utilizing the feature points fusing the three features, so that a target unit and a clutter unit can be distinguished more accurately, and the obtained target detection result is more accurate and reliable.
Fig. 5 illustrates a physical schematic diagram of an electronic device, as shown in fig. 5, which may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform a bispectral tri-feature based sea surface target detection method comprising: obtaining a signal to be detected, and constructing a diagonal integral bispectrum of the signal to be detected; extracting spectrum features from the diagonally integrated dual spectrum and accumulating to obtain dual-spectrum three features, and constructing a three-feature space to obtain feature points of a signal to be detected; wherein the bispectrum three features comprise an accumulated peak value, an accumulated spectrum width and a total variation; and detecting and classifying the characteristic points of the signals to be detected to obtain a target detection result.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the execution of a sea surface target detection method based on the bispectral tri-feature provided by the above methods, the method comprising: obtaining a signal to be detected, and constructing a diagonal integral bispectrum of the signal to be detected; extracting spectrum features from the diagonally integrated dual spectrum and accumulating to obtain dual-spectrum three features, and constructing a three-feature space to obtain feature points of a signal to be detected; wherein the bispectrum three features comprise an accumulated peak value, an accumulated spectrum width and a total variation; and detecting and classifying the characteristic points of the signals to be detected to obtain a target detection result.
In yet another aspect, the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the above-provided bispectral tri-feature-based sea surface target detection method, the method comprising: obtaining a signal to be detected, and constructing a diagonal integral bispectrum of the signal to be detected; extracting spectrum features from the diagonally integrated dual spectrum and accumulating to obtain dual-spectrum three features, and constructing a three-feature space to obtain feature points of a signal to be detected; wherein the bispectrum three features comprise an accumulated peak value, an accumulated spectrum width and a total variation; and detecting and classifying the characteristic points of the signals to be detected to obtain a target detection result.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. The sea surface target detection method based on the dual-spectrum three characteristics is characterized by comprising the following steps of:
obtaining a signal to be detected, and constructing a diagonal integral bispectrum of the signal to be detected;
Extracting spectral features from the diagonally integrated bispectrum and accumulating to obtain bispectrum three features, and constructing a three-feature space to obtain feature points of the signal to be detected; wherein the bispectrum tri-feature comprises an accumulated peak value, an accumulated spectrum width and a total variation;
detecting and classifying the characteristic points of the signal to be detected to obtain a target detection result;
Said constructing a diagonally integrated bispectrum of said signal to be detected, comprising:
Calculating a two-dimensional bispectrum of the signal to be detected, wherein the two-dimensional bispectrum is obtained by performing two-dimensional Fourier transform on a third-order cumulant of the signal to be detected;
Extracting a diagonal integral bispectrum from the two-dimensional bispectrum;
The step of extracting spectrum features from the diagonal integral dual spectrum and accumulating to obtain dual-spectrum three features, and constructing a three-feature space to obtain feature points of the signal to be detected comprises the following steps:
extracting peak value, centroid frequency and spectrum width from the diagonally integrated bispectrum;
Accumulating the peak value and the spectrum width along the frame time dimension to obtain an accumulated peak value and an accumulated spectrum width;
Extracting a total variation for measuring the stability of the centroid frequency characteristic in the frame time dimension;
constructing three feature spaces by taking the accumulated peak value feature as an X axis, the accumulated spectrum width feature as a Y axis and the total variation feature as a Z axis to obtain feature points of the signal to be detected;
the step of detecting and classifying the characteristic points of the signal to be detected to obtain a target detection result comprises the following steps:
Sea clutter data are collected, and feature training is carried out on the sea clutter data through a convex hull algorithm to obtain a decision area;
Judging whether the characteristic points of the signal to be detected fall into the decision area, and if the characteristic points of the signal to be detected fall into the decision area, determining the signal to be detected as a clutter unit; if the characteristic points of the signal to be detected do not fall into the decision area, the signal to be detected is a target unit.
2. The sea surface target detection method based on the bispectrum three features according to claim 1, wherein the feature points of the signal to be detected are detected and classified to obtain a target detection result, and further comprising:
when the characteristics of the sea clutter data change due to changes in sea state or radar perspective, the sea clutter data are re-acquired to determine a new decision region.
3. A sea surface target detection system based on dual-spectrum three features, comprising:
The diagonal integral bispectrum construction module is used for acquiring a signal to be detected and constructing a diagonal integral bispectrum of the signal to be detected; said constructing a diagonally integrated bispectrum of said signal to be detected, comprising:
Calculating a two-dimensional bispectrum of the signal to be detected;
Extracting a diagonal integral bispectrum from the two-dimensional bispectrum;
the two-dimensional bispectrum is obtained by performing two-dimensional Fourier transform on the third-order cumulative quantity of the signal to be detected;
The three-feature extraction module is used for extracting spectrum features from the diagonal integral double spectrums and accumulating the spectrum features to obtain double-spectrum three features, and constructing a three-feature space to obtain feature points of the signal to be detected; wherein the bispectrum tri-feature comprises an accumulated peak value, an accumulated spectrum width and a total variation;
The target detection module is used for detecting and classifying the characteristic points of the signal to be detected to obtain a target detection result; the step of detecting and classifying the characteristic points of the signal to be detected to obtain a target detection result comprises the following steps:
Sea clutter data are collected, and feature training is carried out on the sea clutter data through a convex hull algorithm to obtain a decision area;
Judging whether the characteristic points of the signal to be detected fall into the decision area, and if the characteristic points of the signal to be detected fall into the decision area, determining the signal to be detected as a clutter unit; if the characteristic points of the signal to be detected do not fall into the decision area, the signal to be detected is a target unit;
The three-feature extraction module comprises:
a spectrum feature extraction unit for extracting peak value, centroid frequency and spectrum width from the diagonally integrated bispectrum;
The first feature extraction unit is used for respectively accumulating the peak value and the spectrum width along the frame time dimension to obtain an accumulated peak value and an accumulated spectrum width;
The second feature extraction unit is used for extracting the total variation for measuring the stability of the centroid frequency features in the frame time dimension;
the characteristic point acquisition unit is used for constructing and obtaining three characteristic spaces by taking the accumulated peak value characteristic as an X axis, the accumulated spectrum width characteristic as a Y axis and the total variation characteristic as a Z axis to obtain the characteristic points of the signal to be detected.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the sea surface target detection method based on the bispectral three features as claimed in any one of claims 1 to 2 when the program is executed.
5. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the bispectral tri-feature based sea surface target detection method according to any one of claims 1 to 2.
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