CN110221265A - Range extension target detection method based on the point self-adapted estimation of strong scattering - Google Patents

Range extension target detection method based on the point self-adapted estimation of strong scattering Download PDF

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CN110221265A
CN110221265A CN201910476624.3A CN201910476624A CN110221265A CN 110221265 A CN110221265 A CN 110221265A CN 201910476624 A CN201910476624 A CN 201910476624A CN 110221265 A CN110221265 A CN 110221265A
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thresholding
strong scattering
value
target
point
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CN110221265B (en
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郭鹏程
戴巧娜
付学斌
罗丁利
任泽宇
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Xi'an Changyuan Electron Engineering Co ltd
Xian Electronic Engineering Research Institute
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering

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Abstract

For conventional Extension object detector when target scattering point information is unknown the unstable problem of detection performance, the invention proposes a kind of range extension target detection methods based on the point self-adapted estimation of strong scattering, the process employs double threshold thoughts, first with kmeans clustering algorithm ART network strong scattering point quantity and the first thresholding, then the second thresholding is determined according to false alarm rate, the first thresholding and scattering point quantity, completes target detection finally by threshold judgement twice.Algorithm proposed by the present invention has higher robustness relative to traditional algorithm, and algorithm does not need any information of priori.

Description

Range extension target detection method based on the point self-adapted estimation of strong scattering
Technical field
The present invention relates to Radar Targets'Detection fields, in particular to range extension target detection, propose a kind of new base In the range extension target detection method of the point self-adapted estimation of strong scattering, extension target detection method proposed by the invention has Superperformance does not need the strong scattering point information of priori target, and detection performance is steady under various scattering point distributional environments.
Background technique
High resolution range radar can reduce the clutter power of each distance unit, and the structure for more accurately portraying target is special Property, therefore wholesome effect is produced to target detection and Classification and Identification etc., therefore have been widely used.Work as Range resolution When rate is much smaller than target, target energy is distributed to multiple Range resolution units, range extension target is formed, using traditional point mesh CFAR detection algorithm is marked, greater loss will be had to detection performance, therefore the constant false alarm for studying range extension target examines detection Algorithm is of great significance.
Two kinds of classical extension target detection algorithms that last century the eighties propose, respectively energy accumulation detector (Integrater) all scattering points in window to be detected are accumulated with binary detection device (M/N), energy accumulation detector, when It is to have more excellent detection performance that signal capabilities are uniformly distributed in detection window, has larger detection to damage when target scattering point is sparse It loses；Binary detection device is the extension of narrowband constant false alarm detector, should be readily appreciated that and realizes, but there are two disadvantages, first is that needing The strong scattering centric quantity of priori target, this priori have larger limitation in practical applications, second is that only when detecting for second Scattering center quantity information is utilized without detection performance can be reduced using energy information.
Optimum detector when thering are some scholars to study scattering dot density priori.There is document to propose scattering dot density priori Maximal possibility estimation detector (Scattering Density Dependent generalized likelihood ratio Test, SDD-GLRT), can the extension target different to degree of rarefication difference/scattering dot density effectively detected, be a kind of system The optimal detection in meaning is counted, scattering point information is underused.The density and amplitude information for having document utilization scattering point propose A kind of double threshold constant false alarm detector (Doule Threshold Constant False Alarm Rate, DT-CFAR), It is detected twice using double threshold completion, and detection uses constant false alarm every time, has larger improvement to the detection performance of sparse target, but Also need the density information of priori scattering point.
In recent years, many scholars attempt the extension target detection device that design does not depend on scattering point prior information.There is scholar to mention A kind of range extension target detection device (Order Statistic-Range Spread based on order statistic is gone out Target, OS-RSTD), descending arrangement is carried out to the scattering point ability in detection window first, the scattering after being then based on sequence Point, all possible scattering point number of exhaustion carries out integration detection and successively carries out integration detection, until making judgement, solves Dependence of the conventional method to target scattering point information.But in order to keep constant false alarm, the false alarm rate of each sense channel is higher than The false alarm rate of detector, when scattering point negligible amounts, opposite single-point detector has greater loss, and due to using exhaustion Formula detection, operand is larger, is unfavorable for Project Realization.There is scholar to propose a kind of improved double threshold maximal possibility estimation detection Device (improved double threshold detector generalized likelihood ratio test, DT- GLRT), do not utilize scattering point prior information to estimate the first thresholding, but utilize maximal possibility estimation and AIC criterion, adopt Use noise power as the first thresholding, the second thresholding is calculated according to false alarm rate and strong scattering points amount.This side Performance of the method in different scattering point distributional environments has a distinct increment compared with conventional method, but due to not having in the calculating of the first thresholding Using target scattering point information, therefore the false alarm rate of detection for the first time is higher, and false-alarm point, which participates in second of detection, reduces system Detection performance.
Shown in sum up, the main problem of conventional Extension object detection method is, first is that need priori scattering point information, this Many applications are not able to satisfy；Second is that when prior information is unknown, detection of the conventional method when different scattering points are distributed It can be unstable.
Summary of the invention
Technical problems to be solved
For conventional Extension object detection method or scattering point prior information is needed, or is detected in the distribution of different scattering points Not steady enough the problem of performance, the present invention propose a kind of range extension target detection side based on the point self-adapted estimation of strong scattering Method.
Technical solution
A kind of range extension target detection method based on the point self-adapted estimation of strong scattering, it is characterised in that steps are as follows:
Step 1: inputting the echo-signal after square law detection to detector, be denoted as Y={ y1,y2,...yJ}；
Step 2: the k-means that cluster is 2 being carried out to Y and is clustered, two gatherings is obtained and closes, the big cluster of mean value is denoted as C1, in addition One cluster is denoted as C2；
Step 3: the value of estimation strong scattering centric quantity K: K=card (C1), wherein card represents the number of element in matrix Amount；
Step 4: the value of the first thresholding γ is determined, for set C2The maximum value of middle element, i.e.,；Wherein max (*) is to be maximized；
Step 5: determine the value of the second thresholding η:
Strong scattering point quantity K, a thresholding γ, the relationship between the second thresholding η:
Wherein, PfaFor the false-alarm probability of system, σ2For noise power, J is input test point number；
By Pfa、σ2, J, K as known quantity, a γ value can be calculated for each η, by the relationship between γ and η It is saved as table, is used for tabling look-up；Using the method for look-up table, corresponding η is inquired according to the γ that step 4 obtains, if in table There is no accurate γ with regard to the corresponding η of γ immediate in inquiry table as the second thresholding；
Step 6: non-inherent accumulation being carried out to strong scattering point, and obtains measurement to be checked
Step 7: being to have when D > η then adjudicates target, otherwise adjudicating target is nothing.
Beneficial effect
A kind of range extension target detection method based on the point self-adapted estimation of strong scattering proposed by the present invention, this method is not Need any prior information that can adaptively estimate the strong scattering point quantity of target, solving conventional Extension goal approach needs The problem of wanting priori scattering point information；The accuracy rate of this method strong scattering point estimation is high, therefore in the distribution of different scattering points Detection performance is steady, solves the problems, such as that detection performance of the conventional method when different scattering points are distributed is unstable.
The present invention has the beneficial effect that compared with traditional Group of Targets Recognition Method
1) noise information is not only utilized in threshold scheme and also utilizes target information, it can be point self-adapted to strong scattering Estimation, to improve the detection performance under different scattering point distributional environments；
2) scattering point prior information is not depended on, its application range is had greatly expanded；
3) using kmeans clustering algorithm estimation thresholding and strong scattering point quantity, algorithm operation quantity is small and is easy to engineering reality It is existing.
Detailed description of the invention
Fig. 1 extension target detection method flow diagram proposed by the present invention
Specific embodiment
Now in conjunction with embodiment, attached drawing, the invention will be further described:
Present invention employs double threshold thought, first with kmeans clustering algorithm ART network strong scattering point quantity with And first thresholding；Then the second thresholding is determined according to the first thresholding and scattering point quantity, to ensure that constant false alarm performance；Most Detection is completed by judgement twice afterwards.Specific step is as follows:
1) input of detector is the echo-signal after square law detection, is denoted as Y={ y1,y2,...yJ}；
2) k-means cluster (cluster 2) is carried out to Y, obtains two gatherings and close, the big cluster of mean value is denoted as C1, another Cluster is denoted as C2；
3) estimated value of strong scattering centric quantity K, K=card (C1), card represents the quantity of element in matrix；
4) value of the first thresholding γ, for set C2The maximum value of middle element, i.e.,；Wherein max (*) is to take Maximum value；
5) formula (1) be strong scattering point quantity K, a thresholding γ, the relationship between the second thresholding η:
In formula, PfaFor the false-alarm probability of system, it is previously set.σ2It can be obtained for noise power with real-time estimation, J is Test point number is inputted, K is the strong scattering point number estimated in step 3), and γ is the value for the first thresholding estimated in step 4), Therefore the variable in formula (1) only has secondary thresholding η.But contain known quantity P since η can not be usedfa、σ2, J, K, γ analytic expression It gives expression to, the method for using look-up table in the present invention, by Pfa、σ2, J, K as known quantity, each η can be calculated Relationship between γ and η is saved as table by one γ value, is used for tabling look-up.During tabling look-up, for given Pfa、σ2, J, Several tables should be made according to different K values, are used for the different K values estimated in step 3).When which can not accurately be corresponded to Corresponding to a η when γ value, with the first thresholding estimatedIt is immediate with γ value in tableAs the second thresholding η；
6) non-inherent accumulation is carried out to strong scattering point, and obtains measurement to be checked
7) detection is completed, it is to have that D > η, which then adjudicates target, and otherwise adjudicating target is nothing.

Claims (1)

1. a kind of range extension target detection method based on the point self-adapted estimation of strong scattering, it is characterised in that steps are as follows:
Step 1: inputting the echo-signal after square law detection to detector, be denoted as Y={ y1,y2,...yJ}；
Step 2: the k-means that cluster is 2 being carried out to Y and is clustered, two gatherings is obtained and closes, the big cluster of mean value is denoted as C1, another cluster It is denoted as C2；
Step 3: the value of estimation strong scattering centric quantity K: K=card (C1), wherein card represents the quantity of element in matrix；
Step 4: the value of the first thresholding γ is determined, for set C2The maximum value of middle element, i.e.,；Wherein max (*) is It is maximized；
Step 5: determine the value of the second thresholding η:
Strong scattering point quantity K, a thresholding γ, the relationship between the second thresholding η:
Wherein, PfaFor the false-alarm probability of system, σ2For noise power, J is input test point number；
By Pfa、σ2, J, K as known quantity, a γ value can be calculated for each η, the relationship between γ and η is saved as Table is used for tabling look-up；Using the method for look-up table, corresponding η is inquired according to the γ that step 4 obtains, if do not had in table Accurate γ is with regard to the corresponding η of γ immediate in inquiry table as the second thresholding；
Step 6: non-inherent accumulation being carried out to strong scattering point, and obtains measurement to be checked
Step 7: being to have when D > η then adjudicates target, otherwise adjudicating target is nothing.
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