CN108535708A - Radar target self-adapting detecting method based on anti-symmetric transformations - Google Patents
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Abstract
The present invention discloses a kind of radar target self-adapting detecting method based on anti-symmetric transformations, and step is：(1) training sample is generated；(2) radar echo signal and training sample for treating detecting distance unit carry out anti-symmetric transformations；(3) steering vector mismatch sensitive detector and the steady type detector of steering vector mismatch are subjected to parameterized treatment, structure parametrization detector；(4) detection threshold of detector is determined using Monte Carlo Experiment；(5) detection statistic of calculating parameter detector；(6) target detection is carried out.The present invention compared with prior art, still has the advantages of preferable detection result under the conditions of small training sample, can be applied to radar scanning pattern and radar tracking pattern simultaneously, applied widely.The method of the present invention is suitable for the radar target self-adapting detecting under the conditions of steering vector mismatch and under the conditions of small training sample.
Description
Technical field
The invention belongs to field of communication technology, the one kind for further relating to Radar Signal Processing Technology field is based on opposing
Claim the radar target self-adapting detecting method of transformation.The present invention can be used under the conditions of steering vector mismatch and small training sample condition
Under radar target self-adapting detecting.
Background technology
Target detection in noise circumstance is a most basic task of radar, in the noise ring that covariance matrix is unknown
Object detection method in border is referred to as self-adapting detecting method, and traditional self-adapting detecting method is using largely without target
The independent identically distributed training sample of signal estimates the covariance matrix of noise, the independent identically distributed trained sample needed
This number should be greater than twice of covariance matrix dimension, and in practical applications, available independent same distribution number of training is
Limited, the detection performance of adaptive detector traditional at this time is decreased obviously.Another limitation of traditional self-adapting detecting method
Property be, all adaptive detectors all assume a nominal steering vector, but the practical guiding arrow in practical application
Amount and nominal steering vector are there are inconsistency, this inconsistent i.e. steering vector mismatch, in different application environments, for
The sensitive requirements of steering vector mismatch are different, and such as under radar tracking pattern, steering vector mismatch responsive type are needed to examine
Device is surveyed, and under radar scanning pattern, then the steady type detector of steering vector mismatch is needed, traditional self-adapting detecting method is only
Suitable for a kind of application environment.
Patent document " radar mesh based on linear fusion of the Naval Aeronautical Engineering Institute PLA in its application
Mark self-adapting detecting method " (application number：201710284871.4 application publication number：CN 106872958A) in a kind of base is disclosed
In the radar target self-adapting detecting method of linear fusion.This method comprises the following steps：Step 1, radar receives echo-signal
And obtain data to be tested vector sum reference data vector；Step 2, sample covariance matrix estimated value is calculated；Step 3, to not
Know that parameter carries out parameter Estimation and obtains test statistics and carry out linear fusion to detection statistic；Step 4, target inspection is carried out
It surveys.Shortcoming existing for this method is：This method is only applicable to the target detection under training sample sufficiency, in small training
Under conditions of sample, the effect of target detection is poor.
Paper " the Persymmetric that Yongchan Gao, Guisheng Liao, Shengqi Zhu et al. is delivered at it
Adaptive Detectors in Homogeneous and Partially Homogeneous Environments”
(IEEE Transactions on Signal Processing, 62 (2), 331-342,2014) propose a kind of antisymmetry thunder
Up to objective self-adapting detection method.The realization step of this method is：Step 1, it obtains echo data and establishes dualism hypothesis model；Step
Rapid 2, parameter Estimation is carried out to unknown parameter；Step 3, detection limit is obtained；Step 4, target detection is carried out.Existing for this method not
It is in place of foot：Detector designed by this method is steering vector mismatch sensitive detector, may not apply to radar scanning mould
Formula, a variety of application environments not being suitable under the conditions of steering vector mismatch.
Invention content
It is an object of the invention to overcome above-mentioned the deficiencies in the prior art, a kind of radar mesh based on anti-symmetric transformations is proposed
Mark self-adapting detecting method.This method can be effectively reduced the demand of independent same distribution training sample, be also applied for simultaneously
A variety of application environments under the conditions of steering vector mismatch, to solve under the conditions of steering vector mismatch and small training sample condition
Under radar target self-adapting detecting problem.
The present invention realization approach be：First, training sample is generated；Then, the radar return of detecting distance unit is treated
Signal and training sample carry out anti-symmetric transformations；Then, steering vector mismatch sensitive detector and steering vector mismatch is steady
Strong type detector carries out parameterized treatment, structure parametrization detector；Then, it is determined using Monte Carlo Experiment described adaptive
The detection threshold of detector；Finally, detection statistic is calculated, target detection is carried out.
The present invention is as follows：
(1) training sample is generated：
(1a) extracts the thunder of a range cell to be detected from all radar echo signals that radar antenna array receives
Up to echo-signal；
(1b) is extracted more around range cell to be detected from all radar echo signals that radar antenna array receives
In a range cell, multiple radar echo signals of the signal containing only noise, composition training sample y are free of0；
(2) anti-symmetric transformations are carried out：
Matrix dimension anti-symmetric transformations matrix identical with the element number of array of radar signal receiving array is arranged in (2a)；
Anti-symmetric transformations matrix is distinguished the radar echo signal and training sample y of premultiplication range cell to be detected by (2b)0,
Obtain the radar echo signal and training sample y of the unit to be detected after anti-symmetric transformations1；
(3) structure parametrization detector：
(3a) utilizes joint probability density formula, calculates separately that there is no under goal condition to be detected and there are mesh to be detected
Under the conditions of mark, the radar echo signal and training sample y of unit to be detected1Joint probability density；
(3b) utilizes generalized likelihood-ratio test criterion, respectively obtains steering vector mismatch sensitive detector and steering vector
The steady type detector of mismatch；
(3c) carries out steering vector mismatch sensitive detector and steering vector mismatch steady type detector at parametrization
Reason obtains parametrization detector；
(4) Monte Carlo Experiment is utilized, determines the detection threshold of detector；
(5) according to the following formula, the detection statistic of calculating parameter detector：
Wherein, the detection statistic of t expression parameters detector, | | indicate that modulo operation, s make difficulties after title transformation
Steering vector, H expressions take conjugate transposition operation, and S indicates that sample covariance matrix, the sample covariance matrix refer to, pass through
All training sample y after anti-symmetric transformations1The matrix that the matrix of composition is multiplied with its conjugate transposition, ()-1Expression takes
Inverse operation, x make difficulties claim transformation after unit to be detected radar echo signal, γ indicate adjustable parameter, the parameter according to
Radar application pattern, the value between range [0,1]；
(6) target detection is carried out：
Compare the size of detection statistic and detection threshold, if the detection statistic is more than the detection threshold, sentences
There are targets in the fixed radar return data；Otherwise, it is determined that target is not present in the radar return data.
The present invention has the following advantages compared with prior art：
First, it is since present invention employs the methods of anti-symmetric transformations, anti-symmetric transformations matrix difference premultiplication is to be detected
The radar echo signal and training sample of range cell, obtain the unit to be detected after anti-symmetric transformations radar echo signal and
Training sample overcomes the prior art under conditions of small training sample, the poor problem of the effect of target detection so that this hair
It is bright to have the advantages that can be applied to small training sample condition.
Second, since present invention employs the method for structure parametrization detector, steering vector mismatch responsive type being detected
Device and the steady type detector of steering vector mismatch carry out parameterized treatment, obtain parametrization detector, overcome the prior art not
The problem of suitable for a variety of application environments under the conditions of steering vector mismatch so that the present invention can be applied to radar scanning simultaneously
Pattern and radar tracking pattern, have the advantages that applied widely.
Description of the drawings
Fig. 1 is the flow chart of the present invention；
Fig. 2 is the result figure of emulation experiment 1 of the present invention；
Fig. 3 is the result figure of emulation experiment 2 of the present invention.
Specific implementation mode
The present invention will be further described below in conjunction with the accompanying drawings.
Referring to Fig.1, specific implementation step of the invention is as follows：
Step 1, training sample is generated.
From all radar echo signals that radar antenna array receives, the radar of one range cell to be detected of extraction returns
Wave signal；From radar antenna array receive all radar echo signals in, extract around range cell to be detected it is multiple away from
From in unit, multiple radar echo signals of the signal containing only noise are free of, form training sample y0。
Step 2, anti-symmetric transformations are carried out.
One matrix dimension anti-symmetric transformations matrix identical with the element number of array of radar signal receiving array, table are set
It is up to formula,
Wherein, T, which makes difficulties, claims transformation matrix,Indicate that sqrt operation, I indicate that unit matrix, N indicate radar
The array element sum of signal receiving array, J indicate that a back-diagonal element is the permutation matrix that 1 other elements are all 0, and j is indicated
Imaginary symbols.
Anti-symmetric transformations matrix is distinguished to the radar echo signal and training sample y of premultiplication range cell to be detected0, obtain
The radar echo signal and training sample y of unit to be detected after anti-symmetric transformations1。
Step 3, structure parametrization detector.
Using joint probability density formula, calculate separately that there is no under goal condition to be detected and there are target items to be detected
Under part, the radar echo signal and training sample y of unit to be detected1Joint probability density, expression formula is,
Wherein, f0It indicates to be not present under conditions of target to be detected, the radar echo signal and training sample of unit to be detected
This y1Joint probability density, π indicate pi, N indicate radar signal receiving array array element sum, det () indicate row
Column symbol, R make difficulties claim transformation after unit to be detected radar echo signal in noise covariance matrix, exp
() indicates using natural constant e as the index operation at bottom ,-indicate that negative symbol, tr () indicate that track taking operation, K make difficulties
Claim the training sample y after transformation1Sum, ()-1Expression takes inverse operation, and x makes difficulties the unit to be detected after claiming transformation
Radar echo signal, H expressions take conjugate transposition operation, S to indicate that sample covariance matrix, the sample covariance matrix refer to,
All training sample y after anti-symmetric transformations1The matrix that the matrix of composition is multiplied with its conjugate transposition, f1Indicate exist
Under goal condition to be detected, the radar echo signal and training sample y of unit to be detected1Joint probability density, α indicate signal
Amplitude, s, which makes difficulties, claims the steering vector after converting.
Using generalized likelihood-ratio test criterion, steering vector mismatch sensitive detector and steering vector mismatch are respectively obtained
Steady type detector, the generalized likelihood-ratio test criterion refer to constituting generalized likelihood test using the ratio between likelihood function
Formula estimates the unknown parameter in detection formula.
Steering vector mismatch sensitive detector and the steady type detector of steering vector mismatch, are calculated separately using lower two formula
Its detection statistic：
Wherein, η indicates the detection statistic of steering vector mismatch sensitive detector, | | indicate modulo operation.
Wherein, ζ indicates the detection statistic of the steady type detector of steering vector mismatch.
Step 4, using Monte Carlo Experiment, the detection threshold of detector is determined.
First, with 100 divided by preset false-alarm probability value, experiment number is obtained, using matlab softwares, is generated every time real
Every group of noise data input detector is obtained an alternative threshold value by the one group of noise data tested, by all alternative thresholdings
Value forms alternative thresholding sequence；The false-alarm probability value refers to, one default according to actual demand in [0,1] range
Value；
Second, descending arrangement is carried out to all elements in alternative thresholding sequence, using the 100th element as detection threshold
Value.
Step 5, according to the following formula, the detection statistic of calculating parameter detector.
Wherein, the detection statistic of t expression parameters detector, | | indicate that modulo operation, s make difficulties after title transformation
Steering vector, H expressions take conjugate transposition operation, and S indicates that sample covariance matrix, the sample covariance matrix refer to, pass through
All training sample y after anti-symmetric transformations1The matrix that the matrix of composition is multiplied with its conjugate transposition, ()-1Expression takes
Inverse operation, x make difficulties claim transformation after unit to be detected radar echo signal, γ indicate adjustable parameter, the parameter according to
Adjustable parameter is set as 1 under radar tracking pattern, is swept in radar by radar application pattern, the value between range [0,1]
It retouches under pattern, adjustable parameter is set as 0, under other application pattern, can as needed set [0,1] adjustable parameter to
Between other numerical value.
Step 6, target detection is carried out.
Compare the size of detection statistic and detection threshold, if the detection statistic is more than the detection threshold, sentences
There are targets in the fixed radar return data；Otherwise, it is determined that target is not present in the radar return data.
The effect of the present invention is further described with reference to emulation experiment.
1. simulated conditions：
In the emulation experiment of the present invention, the array element sum of setting radar signal receiving array is 10；Steering vector form is
[1,ej2πf,...,ej2π(10-1)f]T, f is set as 1000；(i, j) a element that the covariance matrix of noise is arranged is ρ|i-j|,
Wherein ρ indicates single order hysteresis index, is set as ρ=0.9；The false-alarm probability that adaptive detector detection is arranged is 10-2, Meng Teka
Lip river experiment number is set as 10-4；
2. emulation content：
For the present invention there are two emulation experiment, first emulation experiment is the comparison present invention under steering vector matching condition
With the contrast experiment of detection result of two prior arts under small training sample.Second emulation experiment is steering vector mismatch
Under the conditions of, the confirmatory experiment of verification present invention test effect under different γ parameters.
Emulation experiment 1：
10 training samples, the array element sum of the 10 radar signal receiving array less than 2 times are chosen, therefore constitutes small training
Sample conditions.Under the conditions of small training sample, two prior arts (generalized likelihood test device and Adaptive matchings are respectively adopted
Filter) it is emulated, while being emulated under the conditions of γ=0 and γ=1 using the method for the present invention, finally obtain detection
Four curves that probability changes with input signal-to-noise ratio, as shown in Figure 2.
Emulation experiment 2：
It is set as 15dB in input signal-to-noise ratio, γ parameters are set as under conditions of 0,0.5,0.8,1, using the method for the present invention
It is emulated under different steering vector mismatch degree, finally obtains under different γ parameters detection probability with steering vector mismatch journey
Four curves of variation are spent, as shown in Figure 3.
3. analysis of simulation result：
Fig. 2 is the result figure of emulation experiment 1 of the present invention, and the abscissa in Fig. 2 represents input signal-to-noise ratio, and physical unit is
DB, ordinate represent detection probability.The curve indicated with five-pointed star in Fig. 2 represents inspection of the method for the present invention when γ parameters are 0
Probability is surveyed with input signal-to-noise ratio change curve, detection of the method for the present invention when γ parameters are 1 is represented with the curve that circle indicates
Probability represents the detection of prior art generalized likelihood test device with the curve of square mark with input signal-to-noise ratio change curve
For probability with input signal-to-noise ratio change curve, the curve indicated with triangle represents the detection of prior art adaptive matched filter
Probability is with input signal-to-noise ratio change curve.By Fig. 2 it will be evident that within the scope of adjustable parameter, detection probability of the invention with
Input signal-to-noise ratio change curve is all in the detection probability of the prior art with the top of input signal-to-noise ratio change curve, in small trained sample
Detection result using the present invention is better than art methods under the conditions of this, and performance improvement is apparent.
Fig. 3 is the result figure of emulation experiment 2 of the present invention, and the abscissa in Fig. 3 represents steering vector mismatch degree, to be oriented to
The cosine value of vector displacement angle, the value indicate complete mismatch when being 0, indicate to exactly match when being 1, it is general that ordinate represents detection
Rate.The curve indicated with circle in Fig. 3 represents detection probability of the method for the present invention under conditions of γ parameters are 0 with steering vector
Mismatch change curve represents detection of the method for the present invention under conditions of γ parameters are 0.5 with the curve that triangle indicates
Probability represents the method for the present invention in γ parameters as 0.8 with steering vector mismatch degree change curve, with the curve of square mark
Under conditions of detection probability with steering vector mismatch degree change curve, the curve that is indicated with diamond shape is represented the method for the present invention and existed
γ parameters be 1 under conditions of detection probability with steering vector mismatch degree change curve.By Fig. 3 it will be evident that γ parameters
It close to 0, remains to keep higher detection probability under the conditions of steering vector mismatch, i.e., detector shows under steering vector mismatch
Steadily and surely, γ parameters are close to 1, and detection probability is relatively low under the conditions of steering vector mismatch, i.e., the detector table under steering vector mismatch
Existing sensitivity illustrates that the present invention can be suitable for for steering vector mismatch sensitivity and steady environment simultaneously.
The correctness of the above-mentioned simulation results show present invention, validity and reliability.
Claims (6)
1. a kind of radar target self-adapting detecting method based on anti-symmetric transformations, which is characterized in that include the following steps：
(1) training sample is generated：
(1a) from all radar echo signals that radar antenna array receives, the radar of one range cell to be detected of extraction returns
Wave signal；
(1b) extracts multiple distances around range cell to be detected from all radar echo signals that radar antenna array receives
In unit, multiple radar echo signals of the signal containing only noise, composition training sample y are free of0；
(2) anti-symmetric transformations are carried out：
Matrix dimension anti-symmetric transformations matrix identical with the element number of array of radar signal receiving array is arranged in (2a)；
Anti-symmetric transformations matrix is distinguished the radar echo signal and training sample y of premultiplication range cell to be detected by (2b)0, obtain
The radar echo signal and training sample y of unit to be detected after anti-symmetric transformations1；
(3) structure parametrization detector：
(3a) utilizes joint probability density formula, calculates separately that there is no under goal condition to be detected and there are target items to be detected
Under part, the radar echo signal and training sample y of unit to be detected1Joint probability density；
(3b) utilizes generalized likelihood-ratio test criterion, respectively obtains steering vector mismatch sensitive detector and steering vector mismatch
Steady type detector；
Steering vector mismatch sensitive detector and the steady type detector of steering vector mismatch are carried out parameterized treatment by (3c),
Obtain parametrization detector；
(4) Monte Carlo Experiment is utilized, determines the detection threshold of detector；
(5) according to the following formula, the detection statistic of calculating parameter detector：
Wherein, the detection statistic of t expression parameters detector, | | indicate modulo operation, s make difficulties claim transformation after lead
To vector, H expressions take conjugate transposition operation, and S indicates that sample covariance matrix, the sample covariance matrix refer to, through opposing
Claim all training sample y after transformation1The matrix that the matrix of composition is multiplied with its conjugate transposition, ()-1Expression takes inverse behaviour
Make, x make difficulties claim transformation after unit to be detected radar echo signal, γ indicate adjustable parameter, the parameter is according to radar
Application model, the value between range [0,1]；
(6) target detection is carried out：
Compare detection statistic and detection threshold size, if the detection statistic be more than the detection threshold, judgement described in
There are targets in radar return data；Otherwise, it is determined that target is not present in the radar return data.
2. the radar target self-adapting detecting method according to claim 1 based on anti-symmetric transformations, which is characterized in that step
Suddenly the expression formula of the anti-symmetric transformations matrix described in (2a) is as follows：
Wherein, T, which makes difficulties, claims transformation matrix,Indicate that sqrt operation, I indicate that unit matrix, N indicate that radar signal connects
The array element sum of array is received, J indicates that a back-diagonal element is the permutation matrix that 1 other elements are all 0, and j indicates imaginary number symbol
Number.
3. the radar target self-adapting detecting method according to claim 1 based on anti-symmetric transformations, which is characterized in that step
Suddenly the joint probability density formula described in (3a) is as follows：
Wherein, f0It indicates to be not present under conditions of target to be detected, the radar echo signal and training sample y of unit to be detected1's
Joint probability density, π indicate that pi, N indicate that the array element sum of radar signal receiving array, det () indicate determinant symbol
Number, R make difficulties claim transformation after unit to be detected radar echo signal in noise covariance matrix, exp () indicate
Using natural constant e as the index operation at bottom ,-indicate that negative symbol, tr () indicate that track taking operation, K make difficulties after title transformation
Training sample y1Sum, ()-1Expression takes inverse operation, and x makes difficulties the radar return of the unit to be detected after claiming transformation
Signal, H expressions take conjugate transposition operation, S to indicate sample covariance matrix, and the sample covariance matrix refers to, through antisymmetry
All training sample y after transformation1The matrix that the matrix of composition is multiplied with its conjugate transposition, f1There are mesh to be detected for expression
Under the conditions of mark, the radar echo signal and training sample y of unit to be detected1Joint probability density, α indicate signal amplitude, s tables
Show the steering vector after anti-symmetric transformations.
4. the radar target self-adapting detecting method according to claim 1 based on anti-symmetric transformations, which is characterized in that step
Suddenly the generalized likelihood-ratio test criterion described in (3b) refers to constituting generalized likelihood test formula using the ratio between likelihood function, right
Unknown parameter in detection formula is estimated.
5. the radar target self-adapting detecting method according to claim 1 based on anti-symmetric transformations, which is characterized in that step
Suddenly the step of detection threshold value that detector is determined using Monte Carlo Experiment described in (4) is as follows：
First, with 100 divided by preset false-alarm probability value, experiment number is obtained, using matlab softwares, generates and to test every time
Every group of noise data input detector is obtained an alternative threshold value by one group of noise data, by all alternative threshold value groups
At alternative thresholding sequence；The false-alarm probability value refers to, according to the default value of actual demand in [0,1] range；
Second, descending arrangement is carried out to all elements in alternative thresholding sequence, using the 100th element as detection threshold value.
6. the radar target self-adapting detecting method according to claim 1 based on anti-symmetric transformations, which is characterized in that step
Suddenly the adjustable parameter described in (5) is according to radar application pattern, and value refers between range [0,1], under radar tracking pattern,
Adjustable parameter is set as 1, under radar scanning pattern, adjustable parameter is set as 0, under other application pattern, Ke Yigen
According to other numerical value for needing to set adjustable parameter between [0,1].
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| CN109444869A (en) * | 2018-12-19 | 2019-03-08 | 中国人民解放军空军预警学院 | A kind of radar extension target component tunable detector for SLM Signal Label Mismatch |
| CN109444869B (en) * | 2018-12-19 | 2022-07-08 | 中国人民解放军空军预警学院 | Radar extension target parameter adjustable detector for signal mismatch |
| CN110954887A (en) * | 2019-12-16 | 2020-04-03 | 西安电子科技大学 | Phased array MIMO beam forming method based on spherical invariant constraint and antisymmetry |
| CN110954887B (en) * | 2019-12-16 | 2021-05-18 | 西安电子科技大学 | Phased array MIMO beam forming method based on spherical invariant constraint and antisymmetry |
| CN111157956A (en) * | 2019-12-24 | 2020-05-15 | 清华大学 | Radar signal mismatch sensitivity detection method and system under non-Gaussian background |
| CN114089307A (en) * | 2022-01-18 | 2022-02-25 | 中国人民解放军空军预警学院 | Radar detection and classification method and system under target and interference conditions |
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