CN107942308A - Adaptive Rao detection methods based on gamma texture under complex Gaussian environment - Google Patents
Adaptive Rao detection methods based on gamma texture under complex Gaussian environment Download PDFInfo
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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
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- 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
Abstract
The adaptive Rao detection methods based on gamma texture, implementation step are under a kind of complex Gaussian environment：Obtain radar data；Choose range cell to be detected and training sample；With selected training sample and unit to be detected estimation clutter covariance matrix；Based on gamma grain distribution, Rao detection statistics are designed.Calculate the detection statistic of range cell to be detected；Determine the corresponding detection threshold value of range cell to be detected；Judge whether the detection statistic of range cell to be detected is more than detection threshold value, if so, illustrating there is target in unit to be detected, otherwise, illustrate in unit to be detected without target.The present invention is detected available for the target under complex Gaussian environment using Rao criterions design Rao detection algorithms, is improved the robustness of the target detection in the case of steering vector mismatch.
Description
Technical field
The invention belongs to field of communication technology, further relates to a kind of compound height in Radar Targets'Detection technical field
Adaptive Rao detection methods based on gamma texture under this environment.The present invention can be used for the target under complex Gaussian environment into
Row detection, improves the robustness of the target detection in the case of steering vector mismatch.
Background technology
In recent years, increasingly mature with Radar Technology, radar has been widely used for military forecasting, missile guidance, civil aviaton
The various fields such as control, topographic survey, navigation.The task first of radar is that target interested is detected in noise background,
Only possesses such function, radar could provide the letter such as effective target bearing, distance and movement locus to operating personnel
Breath.
The core concept of signal detecting method traditional at present is to assume noise covariance matrix in the first step it is known that obtaining
To the statistic mixed-state amount for other unknown parameters；In second step the estimation of noise covariance matrix is obtained using auxiliary data
This estimate, is then replaced the noise covariance matrix in detection statistic obtained by the first step by value.
Zhang Xiaoli et al. its paper delivered " the Rao detections of distributed object in K Distribution Clutters " (《Electronics and information
Journal》Volume 32 page 2496~2500 of 10th phase in 2010) in propose a kind of distribution under Distribution Clutter environment based on K
The Rao detection methods of target.This method uses K fitting of distribution clutter amplitudes, and distributed object is modeled as subspace signal,
Two-step method inspection policies are used during construction detector, it is adaptive to devise a kind of distributed object based on Rao detections
Detection algorithm.This method is effectively reduced calculation amount and complexity, improves detection probability, and still, this method still has
Shortcoming be do not solve for detector steering vector and actually lead the mismatch problems of vector, cause in radar system
Uniting, there are Studies of Radar Detection performance under systematic error to decline.
Patent document " the radar signal self-adapting detecting based on autoregression model that Xian Electronics Science and Technology University applies at it
Method " (application number：201610616198.5 application publication number：CN106019256A one kind is disclosed in) and is based on autoregression mould
The radar signal self-adapting detecting method of type.This method represents radar target detection problems with dualism hypothesis, and radar is done
The autoregression model that echo is expressed as low order is disturbed, then designs the radar signal based on autoregression model using Rao detection methods
Self-adapting detecting method.The invention can effectively improve the detection performance of target in the case where training data lacks, still, should
The shortcoming that method still has is to fail accurately to describe Compound-Gaussian Clutter structure, causes the mesh under complex Gaussian environment
Detection performance is marked to decline.
The content of the invention
It is an object of the invention to, propose to be based on gamma under a kind of complex Gaussian environment in view of the above shortcomings of the prior art
Texture self-adaption Rao detection methods, by accurately portraying clutter amplitude characteristic, Rao detection algorithms are designed using Rao criterions, can
For being detected to the target under complex Gaussian environment, the robustness of the target detection in the case of steering vector mismatch is improved.
Realize comprising the following steps that for the object of the invention：
(1) radar data is obtained：
A receiving channel is arbitrarily chosen from radar system, each coherent pulse received to selected receiving channel
What one N × L of sampled data formation in processing interval was tieed up includes clutter and target or the reception signal square for only including clutter
Battle array, wherein, N represents the pulse accumulation sum of fast time dimension sampling, N >=1, and L dimension tables show that the range cell of slow time dimension sampling is total
Number, L >=1；
(2) range cell to be detected and training sample are chosen：
(2a) optional row from the receipt signal matrix that N × L is tieed up, as range cell to be detected；
(2b) respectively removes an adjacent cells from the both sides of range cell to be detected；
(2c) is each from the remaining range cell in the both sides of range cell to be detected continuously to choose W adjacent distance list
Member, as the training sample of range cell to be detected, W/2 >=N；
(3) fixed point covariance estimation formulas is utilized, clutter covariance matrix is estimated with selected training sample；
(4) according to the following formula, the Rao detection statistics based on gamma grain distribution of range cell to be detected are calculated：
Wherein, ξ represents the Rao detection statistics based on gamma grain distribution of range cell to be detected, and β represents to be detected
The scale parameter of contained clutter amplitude in range cell, β >=0, v represent Doppler's steering vector of target, and H represents conjugate transposition
Operation,Representing clutter covariance matrix, -1 representing matrix inversion operation, z represents range cell to be detected, | |2Expression takes
Modulus value square operation, q represent the form parameter of contained clutter amplitude in range cell to be detected, q >=0,Represent numerical value evolution
Operation, ()2Represent magnitude square operation；
(5) monte carlo method is utilized, determines the corresponding detection threshold value of range cell to be detected；
(6) judge whether the Rao detection statistics based on gamma grain distribution of range cell to be detected are more than detection door
Limit value, if so, then confirming there is target in unit to be detected, otherwise, without target in unit to be detected.
Compared with prior art, the present invention has the following advantages：
First, clutter statistical characteristics are more accurately featured since present invention utilizes gamma texture model, are overcome
The mismatch of clutter statistical model in the prior art so that the present invention improves detection probability under complex Gaussian.
Second, since the present invention is using Rao criterions design Rao detection algorithms, calculate the Rao detections of range cell to be detected
Statistic, overcoming radar system in the prior art, there are the decline of Studies of Radar Detection performance under systematic error so that the present invention exists
Robustness is had more in the case of there are steering vector mismatch.
Brief description of the drawings
Fig. 1 is the flow chart of the present invention；
Fig. 2 is analogous diagram of of the invention and generalized likelihood test device the detection probability with signal-to-noise ratio change curve.
Fig. 3 is the analogous diagram of the present invention and generalized likelihood test device detector detection probability with displacement angle's change curve.
Embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings.
Referring to the drawings 1, of the invention comprises the following steps that.
Step 1, radar data is obtained：
A receiving channel is arbitrarily chosen from radar system, each coherent pulse received to selected receiving channel
What one N × L of sampled data formation in processing interval was tieed up includes clutter and target or the reception signal square for only including clutter
Battle array, wherein, N represents the pulse accumulation sum of fast time dimension sampling, N >=1, and L dimension tables show that the range cell of slow time dimension sampling is total
Number, L >=1；
Step 2, range cell to be detected and training sample are chosen：
An optional row from the receipt signal matrix of N × L dimensions, as range cell to be detected；From range cell to be detected
Both sides respectively remove an adjacent cells；From the remaining range cell in the both sides of range cell to be detected it is each continuously choose it is adjacent
W range cell, as the training sample of range cell to be detected, W/2 >=N；
Step 3, clutter covariance matrix is estimated with selected training sample and unit to be detected：
Target detection can be according to the following formula in Compound-Gaussian Clutter binary hypothesis test represents：
Wherein, H0Represent only have the situation that target may be not present in clutter, H1Indicate clutter and there is a situation where target, z tables
Show range cell to be detected, n represents the Compound-Gaussian Clutter vector tieed up N × 1, and n can be expressed as according to the following formula：Its
In, τ represents the texture component of contained clutter amplitude in range cell to be detected, is positive stochastic variable, obeys Gamma distribution, τ's
Probability density function (PDF) is：Wherein, β represents contained clutter in range cell to be detected
The scale parameter of amplitude, β >=0, q represent the form parameter of contained clutter amplitude in range cell to be detected, q >=0, Γ () table
Show gamma function, g represents the speckle component of contained clutter amplitude in range cell to be detected, and it is zero that it, which obeys average, variance R
Multiple Gauss distribution, R represent N × N-dimensional unknown positive definite covariance matrix, zkRepresent and range cell independent same distribution to be detected
One group of training sample, for estimating clutter covariance matrix, K represents training sample sum, and α represents definite unknown scalar,
Being reflected by echo signal and being propagated by passage is influenced, and v represents the Doppler's steering vector of N × 1 of target, α v represent target letter
Number expression formula.
Using fixed point covariance estimation formulas, clutter covariance matrix is estimated with training sample
Wherein,Represent clutter covariance matrix, N represents that the pulse accumulation of fast time dimension sampling is total, selected by K expressions
Training sample sum, ∑ represent sum operation, zkTraining sample selected by expression, k=1 ..., K.
Step 4, the Rao detection statistics based on gamma grain distribution of range cell to be detected are calculated：
Assuming that clutter covariance matrix R is it is known that Rao detection algorithms can generate according to the following formula：
Wherein ξ1Represent the detection statistic of unit to be detected,Represent derivation operations, ln () represents that numerical value is natural
Log operations, and f (z | θ) represent H1The PDF of unit to be detected under assuming that,Represent a 3-dimensional vector, θr=[αR,
αI]TRepresent 2 n dimensional vector ns, wherein, αRRepresent the real part of α, αIRepresent the imaginary part of α, θs=τ represents a stochastic variable, T tables
Show that transposition operates,Represent H0Assuming that the maximal possibility estimation of lower θ；Expression is treated
Estimate the fischer information matrix of parameter θ, and have
H1The PDF of unit to be detected is under assuming thatTo mesh
The two-dimentional column vector θ of mark amplituderDerivation can obtain,
Wherein, Re () represents to take real part to operate, and Im () represents to take imaginary part to operate, therefore tries to achieve to be estimated
The piecemeal of the fischer information matrix of parameter θ,
Wherein, diag () represents the square formation of given diagonal entry, inverts to the fischer information matrix of θ
According toDefinition, according to the following formula, calculate
WhereinRepresent parameter θsF when being maximized (z | θ) f (θs) value, eventually pass through simple
Derive, obtain：
Wherein, ξ2Represent original detection statistic ξ1Detection statistic after suitably modified；
WillBring above formula into instead of R, obtain the present invention detection statistic expression formula range cell to be detected based on gal
The Rao detection statistics of agate grain distribution
Wherein, ξ represents statistic ξ2Detection statistic after suitably modified, represent range cell to be detected based on
The Rao detection statistics of gamma grain distribution；
(5) monte carlo method is utilized, determines the corresponding detection threshold value of range cell to be detected：According to progress target inspection
The receipt signal matrix in C coherent pulse processing interval that radar system is received before survey, C >=1, utilizes claim 1
The step of (2) the method, choose and C range cell of range cell same position to be detected and its corresponding trained sample
This, calculates C detection statistic of C range cell, and C detection statistic is carried out descending arrangement, after descending arrangement
In detection statistic, is takenA element value as detection threshold value,Represent the downward floor operation of numerical value, f represents setting
False-alarm probability, 0 ＜ f ＜ 1.
(6) judge whether the detection statistic of range cell to be detected is more than detection threshold value, if so, illustrating list to be detected
There is target in member, otherwise, illustrate in unit to be detected without target.
Verification explanation is carried out to the above-mentioned beneficial effect of the present invention below by way of emulation experiment.
1st, simulation parameter is set：
The receiving channel number of radar system is arranged to 1, the pulse accumulation sum N of fast time dimension sampling is arranged to 8, training
Total sample number is arranged to 16, and the form parameter q of Gamma distribution is arranged to 4, and scale parameter β is arranged to 3；Signal-to-noise ratio is defined asRepresent the ratio between signal power and clutter power, false-alarm probability PfaIt is arranged to 10-3。
2. emulation content：
Emulation experiment 1：
Use document [Yongchan Gao, Guisheng Liao, Shengqi Zhu, and Dong Yang " A
persymmetric GLRT for adaptive detection in compound-Gaussian clutter with
Random texture, " IEEE Signal ProcessingLetters, vol.20, No.6, pp.615~618,2013.]
Disclosed in the prior art carried out pair based on the generalized likelihood test method under Gamma distribution complex Gaussian environment and the present invention
Than carrying out 105Secondary experiment, the value range of signal-to-noise ratio are arranged to -10dB to 30dB, and the value of signal-to-noise ratio is set to 2dB,
The detection probability under different signal-to-noise ratio value is counted, and draws the curve map that detection probability changes with signal-to-noise ratio.
As shown in Figure 2, abscissa is signal-to-noise ratio (unit dB) to the result of emulation experiment 1 in Fig. 2, and ordinate is detection
Probability, the curve indicated with chain-dotted line in Fig. 2, represents the detection probability curve obtained using the method for the present invention, with solid line mark
The curve shown, represents to obtain based on the generalized likelihood test method under Gamma distribution complex Gaussian environment using the prior art
The detection probability curve arrived.
Emulation experiment 2：
Signal-to-noise ratio is arranged to 12dB, the present invention and the prior art is respectively adopted based on Gamma distribution complex Gaussian environment
Under generalized likelihood test method carry out 105Secondary experiment carries out adaptive signal detection, and displacement angle θ is theoretical guide vector v
With actual steering vector vmBetween angle, by formulaObtain, cos2The value range of θ
It is arranged to 0 to 1, cos2θ values are set to 0.05, count different cos2Detection probability under θ values, and draw detection
Probability is with cos2The curve map of θ changes.
As shown in Figure 3, abscissa is cos to the result of emulation experiment 2 in Fig. 32θ, ordinate are detection probability, in Fig. 3
The curve indicated with chain-dotted line, represent the detection probability curve drawn using the method for the present invention, the curve indicated with solid line, table
Show using the prior art based on the generalized likelihood test method under Gamma distribution complex Gaussian environment, obtained detection probability
Curve.
3. analysis of simulation result：
By the present invention in Fig. 2 with the prior art based on the generalized likelihood test side under Gamma distribution complex Gaussian environment
The curve of method can be seen that when detection probability is higher than 0.818, and detection performance of the invention is preferable under the conditions of equal signal-to-noise ratio.
By the present invention in Fig. 3 with based on the corresponding song of generalized likelihood test method under Gamma distribution complex Gaussian environment
Line can be seen that in the case of there are steering vector mismatch, and robustness of the invention is more preferable.
Claims (3)
1. the adaptive Rao detection methods based on gamma texture under a kind of complex Gaussian environment, it is characterised in that including following step
Suddenly：
(1) radar data is obtained：
A receiving channel is arbitrarily chosen from radar system, each coherent pulse that selected receiving channel receives is handled
What one N × L of sampled data formation in interval was tieed up includes clutter and target or the receipt signal matrix for only including clutter, its
In, N represents the pulse accumulation sum of fast time dimension sampling, N >=1, and L dimension tables show that the range cell of slow time dimension sampling is total, L >=
1；
(2) range cell to be detected and training sample are chosen：
(2a) optional row from the receipt signal matrix that N × L is tieed up, as range cell to be detected；
(2b) respectively removes an adjacent cells from the both sides of range cell to be detected；
(2c) is each from the remaining range cell in the both sides of range cell to be detected continuously to choose W adjacent range cell, makees
For the training sample of range cell to be detected, W/2 >=N；
(3) fixed point covariance estimation formulas is utilized, clutter covariance matrix is estimated with selected training sample；
(4) according to the following formula, the Rao detection statistics based on gamma grain distribution of range cell to be detected are calculated：
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Wherein, ξ represents the Rao detection statistics based on gamma grain distribution of range cell to be detected, and β represents distance to be detected
The scale parameter of contained clutter amplitude in unit, β >=0, v represent Doppler's steering vector of target, and H represents conjugate transposition behaviour
Make,Representing clutter covariance matrix, -1 representing matrix inversion operation, z represents range cell to be detected, |2Represent modulus
It is worth square operation, q represents the form parameter of contained clutter amplitude in range cell to be detected, q >=0,Represent numerical value evolution behaviour
Make, ()2Represent magnitude square operation；
(5) monte carlo method is utilized, determines the corresponding detection threshold value of range cell to be detected；
(6) judge whether the Rao detection statistics based on gamma grain distribution of range cell to be detected are more than detection threshold value,
If so, then confirm there is target in unit to be detected, otherwise, without target in unit to be detected.
2. the adaptive Rao detection methods based on gamma texture, its feature under complex Gaussian environment according to claim 1
It is, fixed point covariance estimation formulas is as follows described in step (3)：
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Wherein,Represent the clutter covariance matrix estimated with selected training sample, N represents the pulse product of fast time dimension sampling
Tired sum, training sample sum, Σ represent sum operation, z selected by K expressionskTraining sample selected by expression, k=1 ..., K.
3. the adaptive Rao detection methods based on gamma texture, its feature under complex Gaussian environment according to claim 1
It is, utilizes monte carlo method described in step (5), determine the specific of the corresponding detection threshold value of range cell to be detected
Step is, according to the reception signal square in the C coherent pulse processing interval that radar system is received before carrying out target detection
Battle array, C >=1, using (2) the method the step of claim 1, chooses the C distance with range cell same position to be detected
Unit training sample corresponding with its, calculates C detection statistic of C range cell, and C detection statistic is carried out descending
Arrangement, in the detection statistic after descending arrangement, takes theA element value as detection threshold value,Represent numerical value to
Lower floor operation, f represent the false-alarm probability of setting, 0 ＜ f ＜ 1.
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