CN111505598A - Three-feature joint detection device and method based on FRFT domain - Google Patents
Three-feature joint detection device and method based on FRFT domain Download PDFInfo
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Abstract
The invention discloses a device and a method for three-feature joint detection based on FRFT domain, wherein the method comprises the following steps: collecting radar echo data, and carrying out FRFT (fractional Fourier transform) to obtain a fractional order domain data vector; carrying out feature extraction on the fractional order domain data vector; and combining the extracted features, and realizing target detection comparative analysis by using a rapid convex hull detection algorithm. The invention discloses a fractional Fourier transform (FRFT) domain three-feature combined sea surface target detection device, which is used for proving the feasibility of the FRFT domain three-feature combined detection algorithm by comparing the detection performance curve experiments of FRFT domain three-feature combined detection and single-feature detection on actually measured sea clutter data.
Description
Technical Field
The invention particularly relates to a three-feature combined detection device and method based on an FRFT domain, and belongs to the technical field of radar target detection.
Background
The sea surface environment is very complicated to be influenced by sea conditions, sea conditions and wind speeds, sea clutter presents non-Gaussian, non-uniform and non-stationary three-NOT characteristics, the traditional sea surface target detection algorithm based on energy accumulation is a statistical optimal algorithm under the linear, stationary and Gaussian sea clutter conditions, the radar target detection technology in the uniform and stationary clutter environment is mature day by day, but the clutter background presents the non-uniform and non-stationary characteristics due to the actual complex geographical position, the clutter is modeled into single Gaussian distribution which is not reasonable, the selected reference unit is not compliant with the same distribution characteristics, and the complexity of the sea conditions causes the detection performance of the classical radar target optimal algorithm to be reduced.
Aiming at the detection problem under the complex environment, scholars at home and abroad make a great deal of research and aim at improving the detection performance and the false suppression capability of the algorithm. The conventional constant false alarm detector has the following problems: the traditional detector assumes that a clutter background obeys Rayleigh distribution, and exponential distribution is obtained after square-law detection, but a detection strategy under Rayleigh distribution is still adopted to cause false alarm to rise in the actual clutter environment with non-Gaussian distribution; due to the increasingly complex environment, the actual radar monitoring environment is not uniformly distributed any more, non-uniform scenes such as interference targets and clutter edges exist, and the performance of the optimal detector under the uniform background is sharply reduced.
The Nanjing information engineering university scholanzi's article "joint feature difference detection of targets under sea clutter background" adopts a trend-elimination fluctuation analysis method to extract fractal parameters of sea clutter, analyzes power spectrum features of the sea clutter, and utilizes a convex hull training algorithm to obtain a sea clutter discrimination region. However, due to the dispersive energy distribution of the time domain signal, the effect of obtaining the fractal feature by performing the analysis of the fluctuation of the elimination trend in the time domain is not good. Although there are a number of detector algorithms that improve performance under multiple targets and clutter edges, there is no single detector that can solve the detection problem in all contexts.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a three-feature combined detection device and method based on an FRFT domain, which improve the detection performance in the face of different targets in an actual measurement sea clutter data experiment.
In a first aspect, the invention provides a FRFT domain-based three-feature joint detection method, which includes the following steps:
collecting radar echo data, and carrying out FRFT (fractional Fourier transform) to obtain a fractional order domain data vector;
carrying out feature extraction on the fractional order domain data vector;
and combining the extracted features, and realizing target detection comparative analysis by using a rapid convex hull detection algorithm.
With reference to the first aspect, further, the fractional order domain data vector is obtained by the following formula:
F(u)＝FrFt(R(t),P)
wherein FrFt is fractional Fourier transform, F (u) is a fractional domain data vector after FRFT transform of radar echo data R (t), u is the scale of an FRFT domain, t is the time scale, and P is the order of the FRFT transform; f (u) { F1,F2,F3,…,FNWhere the scale u of the FRFT domain ranges from u to 1,2,3, …, N being the total length of the fractional order domain data vector f (u).
Further, the feature extraction includes:
step A: the amplitude values of the fractional order domain data vector f (u) are sorted,
F(u)＝Sort({F1,F2,F3,…,FN})＝{F(1),F(2),F(3),…,F(N)}
where Sort is the ascending permutation of vector magnitude values, { F }(1),F(2),F(3),…,F(N)The data vector is a fractional order domain data vector with ascending amplitude values, and N is 1,2,3, …, N;
and B: fractional order field data vector { F) with ascending amplitude arrangement(1),F(2),F(3),…,F(N)Dividing the amplitude value into K independent segments with equal length,
{F(1),F(2),F(3),…,F(N)}＝{F1,F2,…,Fk,…,FK}
wherein, FkRepresents a short vector of fractional order amplitude values of the kth independent segment, the range of the segment scale K is 1,2,3, …, K is K, K is the short vector F of fractional order amplitude valueskIs given as each FkThe length of the vector being NkFractional order magnitude short vector FkProbability P of falling into the kth segment scalekThe calculation is as follows:
and C: based on three groups of characteristics of the transformation domain information entropy, the transformation domain peak-to-average ratio and the transformation domain peak standard deviation of the fractional order domain data vector F (u) extracted in the second step,
the transform domain information entropy FIE is expressed as:
the transform domain peak-to-average ratio FPA is expressed as:
wherein max represents the maximum value of the vector;
the transform domain peak standard deviation FPS is expressed as:
where std denotes the standard deviation of the vectors.
Further, the fast convex hull detection algorithm includes:
step a: calculating a convex hull CH (a) of a feature set a of the current pure sea clutter data, wherein the minimum set of a is defined by the convex hull as:
where CH (-) represents the convex set of feature set a, η is element ηiαiI represents the number of elements in the set S,
a real three-dimensional space;
L＝K*Pfak represents the total sample number of a characteristic vector set of pure sea clutter data, PfaIs a false alarmRate, L, is the number of samples to be deleted under this false alarm probability;
step b, removing L sample convex hulls CH (a- { v) from the set of the step aq}) in which { vqIs a set of points of L samples, passed through CH (a- { v)qH) is compared with the relative position of the guide area omega, the overlapping area of the two is V,
V≡CH(a-{vq}))∩Ω；
step c: and c, comparing the relative positions of the convex set and the target region omega in the step b to obtain an overlapping region V of the convex set and the target region omega,
V≡CH(a-{vq}))∩Ω；
step d: according to the step c, the detection probability P under the current false alarm probability is calculatedd
Where # (-) represents the number of elements in the set,
the characteristic set of the pure sea clutter signal extraction is a ═ FIE; FPA; FPS ].
Further, the method comprises performing a resolvable analysis on the feature combinations in a three-dimensional space, wherein
A convex hull in space is a convex polyhedron made up of Q triangular faces, represented as:
wherein SP represents a convex polyhedron composed of triangular faces, Q is the number of the triangular faces,
three vertices representing the triangular faces constituting the convex hull, the symbol triangle ·, consisting of
Triangle composed of three vertexesAnd if the characteristic point of the data to be detected falls in the convex hull, judging the characteristic point to be H0Indicating that no target exists, otherwise, judging as H1Indicating the presence of a target.
Further, the comparative analysis of the detection result comprises: and comparing the feature joint detection with the single feature detection to obtain an optimal performance curve, wherein the single feature detection comprises the following steps:
wherein, η1、η2And η3Respectively, detection statistics determined based on clutter features.
In a second aspect, the present invention provides a three-feature joint detection apparatus based on FRFT domain, which is characterized by comprising
A training module: acquiring pure clutter data, and performing FRFT (fractional Fourier transform) to obtain a fractional order domain data vector; carrying out feature extraction on the fractional order domain data vector; combining the extracted features, and realizing target detection comparative analysis by using a rapid convex hull detection algorithm;
a detection module: acquiring data to be detected, and carrying out FRFT (fractional Fourier transform) to obtain a fractional order domain data vector; carrying out feature extraction on the fractional order domain data vector; and combining the extracted features through guide region comparison, and realizing target detection comparative analysis by using a rapid convex hull detection algorithm.
In a third aspect, the invention provides a FRFT domain-based three-feature joint detection apparatus, including a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of any of the preceding methods.
In a fourth aspect, the invention also provides a computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the steps of any of the methods described above.
Compared with the prior art, the invention has the following beneficial effects:
compared with other detectors, the FRFT domain three-feature combined detector provided by the invention can obtain better detection performance in the face of different targets in an actual measurement sea clutter data experiment. The rotation characteristic and order continuity of FRFT are utilized to find a transform domain with the most concentrated energy, so that the optimality of the extracted features is ensured, and the method has the advantages of small calculated amount and stable performance; the fast convex hull detection algorithm utilizes clutter data characteristics to construct abnormal detection of a detection judgment area, controls the size of a convex hull through the change of a false alarm rate, and has good detection performance.
Drawings
FIG. 1 is a flow chart of a FRFT domain three feature target detector in an embodiment of the invention;
FIG. 2 is a three-dimensional distribution diagram of three groups of features FIE, FPA and FPS extracted according to the embodiment of the present invention;
FIG. 3 is a comparison graph of detection performance curves of a single feature detector and a FRFT domain three feature combined detector in accordance with an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention relates to a three-feature combined target detector based on FRFT domain, which comprises the following technical measures: firstly, FRFT transformation is carried out on a received echo signal by utilizing the property of fractional Fourier transformation, and the optimality of information entropy of three groups of extracted characteristic transformation domains, peak-to-average ratio of the transformation domains and standard deviation of peak values of the transformation domains is ensured; then, a rapid convex hull detection algorithm is applied to obtain a three-feature combined detector; finally, the single-feature detection statistic is compared with respective thresholds to obtain a detection performance curve which is compared with the detection performance curve of the three-feature combined detector.
As shown in fig. 1, the FRFT domain-based three-feature joint detector of the present invention specifically includes the following steps:
example 1: performing FRFT transformation on radar echo data, and performing feature extraction on data of an FRFT transformation domain;
performing FRFT on the radar echo data R (t),
F(u)＝FrFt(R(t),P) (1)
wherein FrFt is fractional Fourier transform, F (u) is a fractional domain data vector after FRFT transform of radar echo data R (t), u is the scale of FRFT domain, t is the time scale, and P is the order of FRFT transform. F (u) { F1,F2,F3,…,FNIn which FuIs an element of the fractional order domain data vector f (u), the scale u of the FRFT domain ranges from u to 1,2,3, …, N being the total length of the fractional order domain data vector f (u).
The amplitude values of the fractional order domain data vector f (u) are subjected to a sorting process,
F(u)＝Sort({F1,F2,F3,…,FN})＝{F(1),F(2),F(3),…,F(N)} (2)
where Sort is the ascending permutation of vector magnitude values, { F }(1),F(2),F(3),…,F(N)The (N) is a subscript of the ascending element, and the range of the (N) is 1,2,3, …, N.
Fractional order field data vector { F) with ascending amplitude arrangement(1),F(2),F(3),…,F(N)Divide it into equal-length K independent segments according to the magnitude of amplitude value
{F(1),F(2),F(3),…,F(N)}＝{F1,F2,…,Fk,…,FK} (3)
Wherein, FkRepresents a short vector of fractional order amplitude values of the kth independent segment, the range of the segment scale K is 1,2,3, …, K is K, K is the short vector F of fractional order amplitude valueskThe total number of (c). Let each FkThe length of the vector being Nk. Fractional order range amplitude value short vector FkProbability P of falling into the kth segment scalekIs calculated as
The transform domain information entropy (FIE) of the fractional order domain data vector F (u) is represented as
Clutter is potentially distinguishable from target amplitude and can be used for detection because the former has a spectrum that is predominantly distributed over wavy and rough surfaces, while the latter is more concentrated near a peak. To quantify this difference, the transform domain peak-to-average ratio (FPA) of the fractional domain data vector F (u) is expressed as
Where max represents the maximum value of the vector.
Considering that the FRFT domain clutter and the discrete degree of each characteristic point of the target have difference, the target is more discrete than the clutter, and in order to quantify the difference, the transformation domain peak standard deviation (FPS) of the fractional order domain data vector F (u) is expressed as
Where std denotes the standard deviation of the vectors.
Example 2: target detection is realized based on a three-feature combined fast convex hull detection algorithm;
by analyzing the graph in FIG. 2, it is found that the feature points of the sea clutter are generally relatively concentrated, and the feature points of the target echo are relatively dispersed, and by utilizing the phenomenon, the detection problem of the target is converted into the judgment of a convex hull formed by the sea clutter feature data, if the judgment is to be performedThe feature point of the detected data falls in the convex hull and is judged as H0Indicating that no target exists, otherwise, judging as H1Indicating the presence of a target.
Assuming a three-dimensional finite set of
Wherein, ηiRefers to the elements in the set S, I represents the number of the elements in the set S,
is a real three-dimensional space. The minimum set of S defined by the convex hull is:
where CH (-) represents the convex set of set S, η is element ηiαiAre the combining coefficients.
In that
A convex hull in space is a convex polyhedron made up of Q triangular faces, represented as:
wherein SP represents a convex polyhedron composed of triangular faces, Q is the number of the triangular faces,
three vertices representing the triangular faces constituting the convex hull (clockwise observation), the symbol triangle (a.) being represented by
A triangular surface formed by three vertexes.
The characteristic vector extracted from the received pure sea clutter signal is assumed to be a ═ FIE; FPA; FPS]The guiding region is omega, and the false alarm probability is PfaThe specific process is as follows:
a: extracting a characteristic vector a of the pure sea clutter data as [ FIE; FPA; FPS ];
b, calculating a convex hull CH (a) of the feature set a of the current pure sea clutter data, wherein L is K PfaK represents the total sample number of a characteristic vector set of pure sea clutter data, PfaL is the number of samples to be deleted under the false alarm probability;
c, calculating a convex hull CH (a- { v) after L samples are removed from the set aq}) in which { vqIs a set of points of L samples, passed through CH (a- { v)qH) is compared with the relative position of the guide area omega, the overlapping area of the two is V,
V≡CH(a-{vq}))∩Ω (10)
d: calculating the detection probability P under the current false alarm probabilityd，
Where # (-) represents the number of elements in the set.
e: and (4) converting the false alarm probability from 0.001 to 1 according to a logarithmic scale, and calculating the detection probability under each false alarm probability to obtain a detection performance curve.
Example 3: and comparing the performance curves of the single-feature detection and the three-feature combined detection.
The single-feature detectors are respectively:
wherein, η1、η2And η3Respectively, detection statistics determined based on clutter features.
FIG. 3 is a comparison of performance curves based on single feature detection and proposed FRFT domain three feature combined detection. The effectiveness of a FRFT domain three-feature-based joint detector proposed by the patent is verified.
The measured data is named 19931107_135603_ starea17, and the experimental data is used by professor s.haykin at Memaser university in real marine environment using IPIX radar, HH polarization pattern. The data consists of 14 adjacent distance units, the sampling point number of each distance unit is 131072 (about 131s), the 9 th distance unit where the target is located is called a unit to be detected, 8, 10 and 11 are auxiliary units, and the rest are clutter units.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (9)
1. A three-feature joint detection method based on FRFT domain is characterized by comprising the following steps:
collecting radar echo data, and carrying out FRFT (fractional Fourier transform) to obtain a fractional order domain data vector;
carrying out feature extraction on the fractional order domain data vector;
and combining the extracted features, and realizing target detection comparative analysis by using a rapid convex hull detection algorithm.
2. The FRFT domain-based three-feature joint detection method of claim 1, wherein the fractional order domain data vector is obtained by the following formula:
F(u)＝FrFt(R(t),P)
wherein FrFt is fractional Fourier transform, F (u) is a fractional domain data vector after FRFT transform of radar echo data R (t), u is the scale of an FRFT domain, t is the time scale, and P is the order of the FRFT transform; f (u) { F1,F2,F3,…,FNTherein, scale of FRFT domainu ranges from 1,2,3, …, N being the total length of the fractional domain data vector f (u).
3. The FRFT domain-based three-feature joint detection method of claim 2, wherein the feature extraction comprises:
step A: the amplitude values of the fractional order domain data vector f (u) are sorted,
F(u)＝Sort({F1,F2,F3,…,FN})＝{F(1),F(2),F(3),…,F(N)}
where Sort is the ascending permutation of vector magnitude values, { F }(1),F(2),F(3),…,F(N)The data vector is a fractional order domain data vector with ascending amplitude values, and N is 1,2,3, …, N;
and B: fractional order field data vector { F) with ascending amplitude arrangement(1),F(2),F(3),…,F(N)Dividing the amplitude value into K independent segments with equal length,
{F(1),F(2),F(3),…,F(N)}＝{F1,F2,…,Fk,…,FK}
wherein, FkRepresents a short vector of fractional order amplitude values of the kth independent segment, the range of the segment scale K is 1,2,3, …, K is K, K is the short vector F of fractional order amplitude valueskIs given as each FkThe length of the vector being NkFractional order magnitude short vector FkProbability P of falling into the kth segment scalekThe calculation is as follows:
and C: based on three groups of characteristics of the transformation domain information entropy, the transformation domain peak-to-average ratio and the transformation domain peak standard deviation of the fractional order domain data vector F (u) extracted in the second step,
the transform domain information entropy FIE is expressed as:
the transform domain peak-to-average ratio FPA is expressed as:
wherein max represents the maximum value of the vector;
the transform domain peak standard deviation FPS is expressed as:
where std denotes the standard deviation of the vectors.
4. The FRFT domain based three feature joint detection method of claim 3, wherein the fast convex hull detection algorithm comprises:
step a: calculating a convex hull CH (a) of a feature set a of the current pure sea clutter data, wherein the minimum set of a is defined by the convex hull as:
where CH (-) represents the convex set of feature set a, η is element ηiαiI represents the number of elements in the set S,
a real three-dimensional space;
L＝K*Pfak represents the total sample number of a characteristic vector set of pure sea clutter data, PfaL is the number of samples to be deleted under the false alarm probability;
step b, removing L sample convex hulls CH (a- { v) from the set of the step aq}) in which { vqIs a set of points of L samples,by CH (a- { v)qH) is compared with the relative position of the guide area omega, the overlapping area of the two is V,
V≡CH(a-{vq}))∩Ω；
step c: and c, comparing the relative positions of the convex set and the target region omega in the step b to obtain an overlapping region V of the convex set and the target region omega,
V≡CH(a-{vq}))∩Ω；
step d: according to the step c, the detection probability P under the current false alarm probability is calculatedd
Where # (-) represents the number of elements in the set;
the characteristic set of the pure sea clutter signal extraction is a ═ FIE; FPA; FPS ].
5. The FRFT domain-based three feature joint detection method of claim 4, further comprising performing a resolvable analysis on the feature joint in a three-dimensional space
A convex hull in space is a convex polyhedron made up of Q triangular faces, represented as:
wherein SP represents a convex polyhedron composed of triangular faces, Q is the number of the triangular faces,
three vertices representing the triangular faces constituting the convex hull, the symbol triangle ·, consisting of
If the characteristic point of the data to be detected falls on the convex hull, the triangular surface consisting of three vertexesInterior syndrome is judged to be H0Indicating that no target exists, otherwise, judging as H1Indicating the presence of a target.
6. The FRFT domain-based three-feature joint detection method of claim 1, wherein the comparison analysis of the detection results comprises: and comparing the feature joint detection with the single feature detection to obtain an optimal performance curve, wherein the single feature detection comprises the following steps:
wherein, η1、η2And η3Respectively, detection statistics determined based on clutter features.
7. A three-feature joint detection device based on FRFT domain is characterized by comprising
A training module: acquiring pure clutter data, and performing FRFT (fractional Fourier transform) to obtain a fractional order domain data vector; carrying out feature extraction on the fractional order domain data vector; combining the extracted features, and realizing target detection comparative analysis by using a rapid convex hull detection algorithm;
a detection module: acquiring data to be detected, and carrying out FRFT (fractional Fourier transform) to obtain a fractional order domain data vector; carrying out feature extraction on the fractional order domain data vector; and combining the extracted features through guide region comparison, and realizing target detection comparative analysis by using a rapid convex hull detection algorithm.
8. A three-feature combined detection device based on FRFT domain is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 6.
9. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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