CN108387880A - Detection method of small target under a kind of sea clutter background based on multiple dimensioned oriented Lyapunov indexes - Google Patents
Detection method of small target under a kind of sea clutter background based on multiple dimensioned oriented Lyapunov indexes Download PDFInfo
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- CN108387880A CN108387880A CN201810046357.1A CN201810046357A CN108387880A CN 108387880 A CN108387880 A CN 108387880A CN 201810046357 A CN201810046357 A CN 201810046357A CN 108387880 A CN108387880 A CN 108387880A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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Abstract
Radar return data are carried out Hilbert transform, acquire its instantaneous phase angle θ, calculate one, second-order differential by detection method of small target under sea clutter background of the one kind based on " multiple dimensioned oriented Lyapunov indexes "Structural regime space matrixBy some scale by state space partition at mutually disjoint submatrix, seeks the covariance matrix of each submatrix and carry out Eigenvalues Decomposition, extraction dominant eigenvalue σipWith feature vector vip, calculate the angle ψ between the adjacent main feature vector of covariance matrixi, define (σip,ψi) be radar return data oriented Lyapunov indexes.(the σ obtained according to different segmentation lengthip,ψi) multiple dimensioned oriented Lyapunov indexes are constituted, the fluctuation characteristic of the index can accurately detect the Weak target under sea clutter background.
Description
Technical field
The invention belongs to target detection technique fields under sea clutter background, are a kind of based on state space reconstruction and non-linear
The object detection method of signal processing, more particularly to it is weak under a kind of sea clutter background based on multiple dimensioned oriented Lyapunov indexes
Small target detecting method.
Background technology
The research of detection method of small target is long-standing under sea clutter background, be used to detect sea in civil field
Floating material (floating ice, spitkit and aircraft remains etc.) provides technical support for ship for civil use navigation and maritime search and rescue etc..
In military field, this method be used to detect the marine naval vessel with Stealth, submarine periscope and fly close to sea
Aircraft etc., traditional way are that sea clutter is considered as a kind of stationary random process of complexity, are built by largely observing data
Probabilistic model on vertical statistical significance, if Weibull is distributed, log-normal distributions and compound K distributed models etc., Jin Erli
Target detection is realized with ripe detection method.However, being built upon short observation time basis to the hypothesis of sea clutter stationarity
On, to improve the detection probability of Weak target, radar has to increase the observation time to target area, to improve by phase
Target echo energy after dry accumulation, but will no longer be stationary random process with the growth sea clutter of observation time, especially exist
In the case of high sea situation, sea clutter, which is set as stationary random process, can cause larger measurement error.
To solve the above problems, educational circles has carried out a large amount of research work, have a certain number of achievements in research.Classical
Solution is tracked into Mobile state to the statistical properties of radar return, is carried out at segmentation to it by the principle of characteristic close
Reason, solves subproblem, but with the raising of sea situation, the segmentation of radar return data is shorter and shorter, most in engineering level
Eventually with the difference of the situation observed in short-term without essence, there are still higher false-alarm probabilities, relatively low when causing to dim targets detection
Probability of detection the needs of engineering have therefore been unsatisfactory under high sea conditions based on the signal processing method of the statistical properties,
It must look for another way, this problem is solved from theoretical method level.
Invention content
In order to overcome the disadvantages of the above prior art, the purpose of the present invention is to provide one kind based on multiple dimensioned oriented
Detection method of small target under the sea clutter background of Lyapunov indexes will be under sea clutter background according to nonlinear system theory
Dim targets detection be transformed into the signal processing problems of higher dimensional space, the method for adoption status Space Reconstruction reproduces sea clutter
Non-linear and non-stationary property, proposes the concept and algorithm of " multiple dimensioned oriented Lyapunov indexes ", and applies it to strong sea
Dim targets detection under clutter cover.
To achieve the goals above, the technical solution adopted by the present invention is：
Detection method of small target under a kind of sea clutter background based on multiple dimensioned oriented Lyapunov indexes, including it is as follows
Step：
S1) for incoherent radar echo, S2 is executed in order), otherwise turn S3)；
S2 Hilbert transform) is carried out to radar return data, generates I, Q data sequence；
S3 echo data instantaneous phase angle θ) is calculated；
S4 the first differential of instantaneous phase angle θ) is calculatedSecond-order differentialConstruct the state space square of radar return data
Battle arrayWherein, the first differential of θIt is the instantaneous frequency of radar return, second-order differentialIt is the change rate of instantaneous frequency,
State space matricesFor Complete Orthogonal matrix, non-linear, the non-height of sea clutter in radar return is reflected without distortion
This and non-stationary property；
S5 certain segmentation scale L) is pressed, by the state space matrices of radar return dataIt is divided into a series of mutual
Disjoint submatrix xi；
S6 the covariance matrix of each submatrix) is calculatedEigenvalues Decomposition is carried out to each covariance matrix, is carried
Take dominant eigenvalue σipWith corresponding main feature vector vip, calculate the adjacent main feature vector v of covariance matrixip,vip+1Between folder
AngleDefine (σip,ψi) be radar return data oriented Lyapunov indexes；
S7) if to state space matricesDecomposition scale meet target detection precision, then enter S8)；It is no
Then, new segmentation scale is provided, S5 is returned)；
S8 each oriented Lyapunov indexes (σ of scale) is calculatedip,ψi) undulate quantity RMS (m)；
S9) Lyapunov indexes (σ oriented to each scale respectivelyip,ψi) undulate quantity be arranged target detection thresholding, exceed
The undulate quantity of thresholding be the target that detects, corresponding distance and instantaneous velocity by residing for radar return range cell and
Its echo data state space matricesIt determines.
The step S4) in, for coherent radar, the echo data for being output to radar terminal has been done at orthogonalization
Reason includes I, Q data stream；The echo data of incoherent radar output is then needed to do Hilbert transform, generates I, Q data
Stream, then extracts instantaneous phase, establishes the state space matrices of radar return data
The step S5)~S7) in, the setting of segmentation scale L is according to the length N selections of echo data, L=2k, k=1,
2,…P,2P≤ (N-2)/2, k is decomposition scale index, and P is the maximum of k, and definition Lyapunov indexes are radar return data
The dominant eigenvalue σ of the covariance matrix of each submatrix after state space matrices segmentationip, evolution direction is adjacent covariance matrix
Main feature vector vip,vip+1Between angle ψi, (σip,ψi) it is that radar return data are oriented in the case where dividing scale L
Lyapunov indexes, the result that different segmentation scale L is calculated are multiple dimensioned oriented Lyapunov indexes.
The step S5)~S6) in, when the sea clutter data that length is N are converted into state space matricesAfterwards, segmentation scale L is divided into mutually disjoint submatrix x (j)L×3, j=1,2 ... M, M
=floor [(N-2)/L]；
The covariance matrix of each submatrix
Covariance matrix C (j) is L × L square formations, and Eigenvalues Decomposition is carried out to it, obtains L characteristic value σiWith corresponding spy
Levy vector vi, extract dominant eigenvalue σ thereinipWith corresponding main feature vector vip。
The step S7) in, scales are divided when continuous 2 when same distance unit detects target, it is believed that reach
Target detection precision stops the segmentation to state space.
The step S8) in, steps are as follows for calculating：
S8.1) to dominant eigenvalue σipRemove inclined Y (i)=∑ [σip-<σip>], wherein<σip>For dominant eigenvalue σipMean value；
S8.2) the trend of extraction Y (i)Wherein, Cn=polyfit (L, Y (i), m), L
To divide scale, m is the top step number of polynomial fitting, n polynomial orders；
S8.3 undulate quantity) is calculated
With angle ψiY (i) is substituted, S8.1~S8.3 is repeated, obtains angle ψiUndulate quantity.
Due to the non-linear of sea clutter, non-gaussian, non-stationary property so that the multiple dimensioned oriented Lyapunov being calculated
Index has complicated fluctuation characteristic, and during calculating undulate quantity, extraction trend term is concerning the important of target detection precision
Link extracts trend term using the extracting method of higher order polynomial-fitting, and polynomial exponent number is by tracking undulate quantity with exponent number
Variation obtains, in general, with the increase of multinomial top step number m, undulate quantity RMS (m) monotonic decreasings, but m increases to certain number
When value, RMS (m) will turn into rising, and the m corresponding to inflection point is best polynomial order.
The step S9) in, target detection uses double threshold method, i.e., respectively to dominant eigenvalue σipUndulate quantity and main feature
Vector vip,vip+1Between angle ψiUndulate quantity be arranged thresholding, the former is quick in the difference of kinetic characteristics with target to sea clutter
Sense, the latter is sensitive with the difference of electromagnetic scattering characteristic of target to sea, the pure sea clutter that the size of thresholding is surveyed by radar
Data obtain.Detection threshold value is determined using the standard deviation σ of undulate quantity RMS (m).
Compared with prior art, under given sea clutter background, the present invention can detect that conventional method is not detectable
Weak target has apparent raising in accuracy of detection.
Description of the drawings
Fig. 1 is flow chart of the present invention.
Fig. 2 is IPIX radars actual measurement sea clutter, wherein (a) is low sea situation sea clutter, it is (b) high sea situation sea clutter.
Fig. 3 be low sea situation under sea clutter state space, wherein (a) be 3 dimension spaces, (b) beWithPlane
Projection.
Fig. 4 be high sea situation under sea clutter state space, wherein (a) be 3 dimension spaces, (b) beWithPlane
Projection.
Fig. 5 is main characteristic quantity σ1Multiple dimensioned undulate quantity.
Fig. 6 is angle ψ1Multiple dimensioned undulate quantity.
Fig. 7 is main characteristic quantity σ2Multiple dimensioned undulate quantity.
Fig. 8 is angle ψ2Multiple dimensioned undulate quantity.
Specific implementation mode
The embodiment that the present invention will be described in detail with reference to the accompanying drawings and examples.
Detection method of small target under a kind of sea clutter background based on multiple dimensioned oriented Lyapunov indexes of the present invention, such as
Shown in Fig. 1, mainly comprise the following steps：
1, the state space reconstruction method of sea clutter
If the sea clutter signal that radar receives results from a non-linear, non-stationary systemx∈Rn=X
By to state variable x sample quantizations, the One-dimension Time Series obtained：{xi, i=1,2 ... N.
If radar is non-coherent receivers, need to carry out Hilbert transform to above-mentioned time series：
If radar is coherent receivers, the echo-signal received has been converted into I, Q sequence in radar terminal, need not be into
Row Hilbert transform.
Extract the instantaneous phase angle of radar return：
Seek the first differential of phase angle θSecond-order differentialConstruct the state space matrices of echo dataIt is just
The property handed over and complete demonstration are as follows：
…
It can be seen that three rank differential of phase angle θ are parallel with first differential, quadravalence differential is parallel with second-order differential ...,
Therefore, only matrixIt is Complete Orthogonal space, dynamic characteristics more than three ranks is implied in the 3 dimension state space.
As shown in Fig. 2, it is the sea clutter data (http of McMaster universities of utilization Canada IPIX radar surveyings://
Soma.ece.mcmaster.ca/ipix/dartmouth/datasets.html), Fig. 3, Fig. 4 be respectively low, high sea situation when weight
The sea clutter state space of structure.Shown macrostructure can be seen that the motion feature of wave, microstructure are shown from figure
The motion state of wave surface complexity.
2, multiple dimensioned oriented Lyapunov indexes and its undulate quantity computational methods
When the sea clutter data that length is N are converted into state spaceAfterwards, a scale is selected
L, by state space partition at mutually disjoint submatrix x (j)L×3(j=1,2 ... M), M=floor [(N-2)/L] are calculated each
The covariance matrix of submatrix
Matrix C (j) is L × L square formations, and Eigenvalues Decomposition is carried out to it, can obtain L characteristic value σiWith corresponding feature
Vector vi, extract dominant eigenvalue σ thereinipWith corresponding main feature vector vip, calculate the adjacent main feature vector of covariance matrix
vip,vip+1Between angleDefine (σip,ψi) it is the oriented of radar return data
Lyapunov indexes.Change segmentation scale L and repeat the above steps, the multiple dimensioned oriented Lyapunov indexes of sea clutter can be obtained.
Since sea clutter has non-linear, non-gaussian and non-stationary property, multiple dimensioned oriented Lyapunov indexed performances
Go out apparent fluctuation, when in sea clutter including Weak target, will occur apparent difference at target, therefore, extraction is more
The fluctuation of the oriented Lyapunov indexes of scale can realize the detection of Weak target.
Undulate quantity calculating has the following steps：
(1) to dominant eigenvalue σipRemove inclined Y (i)=∑ [σip-<σip>], wherein<σip>For the mean value of dominant eigenvalue；
(2) trend of extraction Y (i)Wherein, Cn=polyfit (L, Y (i), m), L is
Divide scale, m is polynomial fitting top step number.
(3) undulate quantity is calculatedAnd using above-mentioned steps to angle ψiIt does same
Processing, calculate ψiUndulate quantity.
Attached drawing 4,5 is shown in the data processed result comprising sea Weak target of IPIX radar surveyings, sea clutter shares 2
Dominant eigenvalue, dominant eigenvalue σipUndulate quantity it is sensitive in the difference of kinetic characteristics with target to sea clutter, angle ψiFluctuation
Amount is sensitive with the difference of electromagnetic scattering characteristic of target to sea.
3, the object detection method based on double threshold
From Fig. 5, Fig. 6, Fig. 7 and 8 as it can be seen that dominant eigenvalue σ1Undulate quantity reflect wave macroscopic motion feature, in mesh
The residing range cell (Range bin10) of mark has apparent recess, which originates in Range bin10 and terminate at Range
Bin11 shows that target is the floating material of less stationary, fits like a glove with the target acquisition scene designed by IPIX radars.Main spy
Sign amount σ2With angle ψ2Multiple dimensioned undulate quantity reflect the motion feature that sea is tiny, complicated, with main characteristic quantity σ1Undulate quantity
It compares, σ2It is less than σ in amplitude1Undulate quantity, but curve of cyclical fluctuations ratio σ1It is more complicated, between range cell 8 to 12
(Range bin8~Range bin12) has significant depressions, indicates region existing for target；Angle ψ2Undulate quantity compared with angle
ψ1With higher amplitude, occur apparent peak value at target, shows that angle of scattering of the electromagnetic wave at target has occurred
It is apparent to change, with statistical method respectively to σiWith angle ψiThresholding (i=1,2) is set, the ginsengs such as the position of target are can detect
Number.
In the present invention, proposed with instantaneous phase angle and one, two for non-linear, non-gaussian and nonstationary time series
The state space that rank differential is constituted, remains the dynamic characteristic and non-stationary property of sea clutter signal without distortion.
In the present invention, state space is subjected to multi-scale division, by covariance matrix by sea clutter sequence transformation to height
Dimension space obtains the dynamic characteristic and scattering signatures of sea clutter by means of Eigenvalues Decomposition, relative to traditional based on echo
The object detection method of signal energy has apparent theoretical, method advance.
In the present invention, using multiple dimensioned oriented Lyapunov indexes undulate quantity as detection clarification of objective amount, use is two-door
Inspection policies are limited, detect target in 2 incoherent domains of kinetic characteristics and electromagnetic scattering Characteristics Detection respectively, are had bright
Aobvious advantage.
Claims (9)
1. detection method of small target under a kind of sea clutter background based on multiple dimensioned oriented Lyapunov indexes, feature exist
In including the following steps：
S1) for incoherent radar echo, S2 is executed in order), otherwise turn S3)；
S2 Hilbert transform) is carried out to radar return data, generates I, Q data sequence；
S3 echo data instantaneous phase angle θ) is calculated；
S4 the first differential of instantaneous phase angle θ) is calculatedSecond-order differentialConstruct the state space matrices of radar return dataWherein, the first differential of θIt is the instantaneous frequency of radar return, second-order differentialIt is the change rate of instantaneous frequency, shape
State space matrixFor Complete Orthogonal matrix, non-linear, the non-gaussian of sea clutter in radar return are reflected without distortion
And non-stationary property；
S5 certain segmentation scale L) is pressed, by the state space matrices of radar return dataIt is divided into a series of mutual not phases
The submatrix x of friendshipi；
S6 the covariance matrix of each submatrix) is calculatedEigenvalues Decomposition, extraction master are carried out to each covariance matrix
Characteristic value σipWith corresponding main feature vector vip, calculate the adjacent main feature vector v of covariance matrixip,vip+1Between angleDefine (σip,ψi) be radar return data oriented Lyapunov indexes；
S7) if to state space matricesDecomposition scale meet target detection precision, then enter S8)；Otherwise, it gives
Go out new segmentation scale, return to S5)；
S8 each oriented Lyapunov indexes (σ of scale) is calculatedip,ψi) undulate quantity RMS (m)；
S9) Lyapunov indexes (σ oriented to each scale respectivelyip,ψi) undulate quantity be arranged target detection thresholding, exceed thresholding
Undulate quantity be the target that detects, corresponding distance and instantaneous velocity by residing for radar return range cell and its return
Wave number is according to state space matricesIt determines.
2. Dim targets detection side under the sea clutter background according to claim 1 based on multiple dimensioned oriented Lyapunov indexes
Method, which is characterized in that the step S4) in, for coherent radar, the echo data for being output to radar terminal has been done just
Friendshipization processing includes I, Q data stream；The echo data of incoherent radar output is then needed to do Hilbert transform, is generated
I, Q data stream then extracts instantaneous phase, establishes the state space matrices of radar return data
3. Dim targets detection side under the sea clutter background according to claim 1 based on multiple dimensioned oriented Lyapunov indexes
Method, which is characterized in that the step S5)~S7) in, the setting of segmentation scale L is according to the length N selections of echo data, L=
2k, k=1,2 ... P, 2P≤ (N-2)/2, k is decomposition scale index, and P is the maximum of k, and definition Lyapunov indexes are radar
The dominant eigenvalue σ of the covariance matrix of each submatrix after the segmentation of echo data state space matricesip, evolution direction is adjacent association
The main feature vector v of variance matrixip,vip+1Between angle ψi, (σip,ψi) be radar return data in the case where dividing scale L
Oriented Lyapunov indexes, the result that different segmentation scale L is calculated are multiple dimensioned oriented Lyapunov indexes.
4. Dim targets detection side under the sea clutter background according to claim 1 based on multiple dimensioned oriented Lyapunov indexes
Method, which is characterized in that the step S5)~S6) in, when the sea clutter data that length is N are converted into state space matricesAfterwards, segmentation scale L is divided into mutually disjoint submatrix x (j)L×3, j=1,2 ... M, M
=floor [(N-2)/L]；
The covariance matrix of each submatrix
Covariance matrix C (j) is L × L square formations, and Eigenvalues Decomposition is carried out to it, obtains L characteristic value σiWith corresponding feature to
Measure vi, extract dominant eigenvalue σ thereinipWith corresponding main feature vector vip。
5. Dim targets detection side under the sea clutter background according to claim 1 based on multiple dimensioned oriented Lyapunov indexes
Method, which is characterized in that the step S7) in, scales are divided when continuous 2 when same distance unit detects target, are recognized
To have reached target detection precision, stop the segmentation to state space.
6. Dim targets detection side under the sea clutter background according to claim 1 based on multiple dimensioned oriented Lyapunov indexes
Method, which is characterized in that the step S8) in, steps are as follows for calculating：
S8.1) to dominant eigenvalue σipRemove inclined Y (i)=∑ [σip-<σip>], wherein<σip>For dominant eigenvalue σipMean value；
S8.2) the trend of extraction Y (i)Wherein, Cn=polyfit (L, Y (i), m), L are point
Scale is cut, m is the top step number of polynomial fitting, n polynomial orders；
S8.3 undulate quantity) is calculated
With angle ψiY (i) is substituted, S8.1~S8.3 is repeated, obtains angle ψiUndulate quantity.
7. Dim targets detection side under the sea clutter background according to claim 6 based on multiple dimensioned oriented Lyapunov indexes
Method, which is characterized in that trend term is extracted using the extracting method of higher order polynomial-fitting, polynomial exponent number is fluctuated by tracking
Amount changes to obtain with exponent number, and with the increase of multinomial top step number m, undulate quantity RMS (m) monotonic decreasings, but m increases to some
When numerical value, RMS (m) will turn into rising, and the m corresponding to inflection point is best polynomial order.
8. Dim targets detection side under the sea clutter background according to claim 1 based on multiple dimensioned oriented Lyapunov indexes
Method, which is characterized in that the step S9) in, target detection uses double threshold method, i.e., respectively to dominant eigenvalue σipUndulate quantity and
Main feature vector vip,vip+1Between angle ψiUndulate quantity thresholding is set, the former is to sea clutter and target in kinetic characteristics
Difference is sensitive, and the latter is sensitive with the difference of electromagnetic scattering characteristic of target to sea, and the size of thresholding is surveyed pure by radar
Sea clutter data obtain.
9. Dim targets detection side under the sea clutter background according to claim 8 based on multiple dimensioned oriented Lyapunov indexes
Method, which is characterized in that detection threshold value is determined using the standard deviation σ of undulate quantity RMS (m).
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| CN112230218A (en) * | 2020-09-29 | 2021-01-15 | 西安交通大学 | Method and system for removing static background and trend item of life detection radar echo signal and electronic equipment |
| CN112230218B (en) * | 2020-09-29 | 2023-06-16 | 西安交通大学 | Method, system and electronic equipment for removing static background and trend items of life detection radar echo signals |
| CN113191372A (en) * | 2021-04-29 | 2021-07-30 | 华中科技大学 | Construction method and application of ship target directional detection model |
| CN113191372B (en) * | 2021-04-29 | 2022-05-20 | 华中科技大学 | Construction method and application of ship target directional detection model |
| CN113820680A (en) * | 2021-08-13 | 2021-12-21 | 西安电子科技大学 | Multi-frame sea-land radar echo segmentation method based on covariance |
| CN113820680B (en) * | 2021-08-13 | 2023-06-23 | 西安电子科技大学 | Covariance-based multi-frame sea-land radar echo segmentation method |
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