CN105954739A - Knowledge-aided nonparametric constant false alarm detection method - Google Patents
Knowledge-aided nonparametric constant false alarm detection method Download PDFInfo
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- CN105954739A CN105954739A CN201610248683.1A CN201610248683A CN105954739A CN 105954739 A CN105954739 A CN 105954739A CN 201610248683 A CN201610248683 A CN 201610248683A CN 105954739 A CN105954739 A CN 105954739A
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/04—Systems determining presence of a target
Abstract
The invention discloses a knowledge-aided nonparametric constant false alarm detection method to solve detection problems which may occur in the non-uniform environment with unknown distribution, and especially relates to the knowledge assisting, nonparametric estimation and constant false alarm detection technologies, belonging to the technical field of radar knowledge assisting. The method mainly comprises four steps: the first step is to acquire a reference unit which is as independently and identically distributed as possible with a to-be-detected unit by using priori supplementary knowledge; the second step is to obtain an amplitude probability density function of the to-be-detected unit by using a nonparametric PDF estimation method; the third step is to calculate a detection threshold based on the PDF obtained in the previous step and a set false alarm probability; the last step is to compare the echo of the to-be-detected unit and the threshold obtained in the third step, and determining whether there is a target. In the invention, a uniform reference unit is obtained according to the priori information, and then background distribution is accurately estimated and the detection threshold is calculated, thereby improving the CFAR detection performance when the distribution is unknown in the non-uniform environment.
Description
Technical field
The invention belongs to the field of radar knowledge assistance technology, relate to knowledge assistance, nonparametric estimation and CFAR detection technology.
Background technology
Conventional CFAR detection method first assumes background distributions it is known that then utilize the reference unit closed on to estimate distributed constant, enters
And estimate detection threshold.But along with the lifting of radar coverage, the complication day by day (time-varying space-variant) of detection environment, clutter
Statistical property become increasingly complex, clutter distribution character and radar angle of incidence, polarization mode, topography profile, man-made structures, locate in advance
The conditions such as reason mode (correlative accumulation, amplitude detection etc.) are closely related.An any of the above described condition changes, the statistics of clutter
Characteristic is likely to change therewith, causes the clutter distributed model assumed and actual clutter distributed model mismatch or is difficult to obtain clutter
Statistical distribution characteristic.Now, traditional parameter estimation CFAR detection method exists for the biggest defect: the distribution first assumed that
Model is difficult to the distribution that simulation is complicated, secondly can not be accurately reflected unit clutter to be detected by the non-homogeneous reference unit closed on caused
Power situation.And then causing serious performance loss: actual false-alarm probability deviation expected value, detection probability declines.
The problem existed for traditional method, a kind of method is design nonparametric CFAR detector, and this method need not known distribution mould
Type, it also avoid the Detectability loss that model mismatch brings.Varshney et al. describes symbol detector and Wilcoxon detector two
Plant nonparametric detector.They can realize effective detection of radar target under unknown clutter distributed model, but sweeps at single
False-alarm probability under the conditions of retouching is the highest, needs to take multiple scan accumulation to reduce false-alarm probability, and this limits this to a certain extent
The application of class CFAR detector.The Chen Jianjun of the National University of Defense technology proposes and utilizes the data in reference window to simulate background clutter data
Functional relationship between right truncation probability and upper quantile, thus the method obtaining detection threshold.But this approximating method needs big
The data of amount support, and detect poor-performing under small data quantity.The Li Jun of the National University of Defense technology utilizes fractional order moment estimation method to go
Obtain the maximum entropy PDF estimation of clutter, and then determine detection threshold, it is achieved CFAR detects.These methods utilize to be estimated
The probability density function (PDF) obtained reduces to a certain extent assumes to be distributed the detection performance loss that this defect is brought, but
Being in non-homogeneous environment, the reference unit itself that they utilize can not represent unit to be detected, the most still cannot solve another
Defect.
Knowledge assistance (Knowledge-based KB) signal processing always improves one of key technology of conventional radar detection performance,
And knowledge assistance CFAR detection algorithm the most also has research.The core of algorithm be utilize priori supplementary knowledge choose as far as possible with
Unit to be detected independent identically distributed homogeneous reference unit.2007, Italy A.De Maio et al. utilized GIS information to IPIX
Radar illumination region divides, and obtaining the dividing elements of Nonuniform Domain Simulation of Reservoir is several more uniform regions, then utilizes tradition
CFAR carries out last detection, improves detection performance.2013, the order of University of Electronic Science and Technology hole was said et al. and to be assumed that region to be detected is divided
Cloth obtains homogeneous area it is known that then find out clutter edge, then utilizes tradition CFAR to carry out last detection.Above-mentioned aspect is not
Be in place of foot, when in the face of the environment of complex distributions, it is assumed that distribution often forbidden, and then have impact on last detection performance.
To sum up, for the tradition CFAR detection algorithm two detection defects under complicated non-homogeneous environment, still lack one perfect
Solution.
Summary of the invention
It is an object of the invention to the defect existed for background technology, the nonparametric CFAR detection side of a kind of knowledge assistance of research design
Method, thus under complicated non-homogeneous background, design detector, reach to improve detection performance, the purpose of enhancing detector robustness.
The present invention proposes a kind of nonparametric CFAR detection method of knowledge assistance.The method mainly includes four steps: the first step, profit
Obtain and the most independent identically distributed reference unit of unit to be detected with priori supplementary knowledge；Second step, estimates unit to be detected
The probability density function of clutter amplitude；3rd step, calculates inspection according to the false-alarm probability of probability density function obtained in the previous step and setting
Survey thresholding；Finally, the thresholding that relatively elementary echo to be detected and the 3rd step obtain, it may be judged whether with the presence of target.Thus the present invention
A kind of nonparametric CFAR detection method of knowledge assistance, the method includes:
Step 1: pretreatment；
1.1 initialize systematic parameter includes: closes on and treats that number of reference is K, and a length of N of reference window of selection, false-alarm probability sets
For Pfa, span r of bandwidth；
1.2 read n-th frame data, Z (n)={ z from radar receivern(m)},1<m<Nr, wherein NrFor total distance unit
Number, znM () represents the measuring value in the measurement unit m of n-th frame echo data；
Step 2: choose each reference unit in reference window:
For the m-th unit to be detected of n-th frame datum plane, each K/2 the resolution cell in unit both sides to be detected will be closed on and make
For treating reference unit, utilize knowledge assistance prior information, from treating of closing on of K, reference unit is chosen N number of homogeneous reference unit；
2.1 determine reference unit independent identically distributed with unit to be detected according to prior information, are expressed as:
Wherein zn(CUT) quantity of units measured value to be detected is represented；
If 2.2Just choose that close on and the N number of of Sign (i)=1 and treat that reference unit is reference unit；
If 2.3Just choose all Sign (i)=1 treat reference unit andIndividual close on CUT's and
Sign (i)=0 unit to be detected is total reference unit；
Step 3: calculate probability density function and the distribution function of the clutter amplitude of unit to be detected:
3.1: make kernel function K () be:
3.2: bandwidth h of calculating kernel function weighting:
The reference unit chosen is divided into two parts wherein XiAnd XjRespectively representing a portion, r has model in initialization procedure
Enclose, it addition, K*(x)=K2(x)-2K(x)；
3.3: utilize the probability density function of each unit data estimation unit to be detected in reference windowAnd distribution function Wherein, Φ is standard normal
The distribution function of distribution；
Step 4: calculate detection threshold；
4.1: ask for Preliminary detection thresholding T, RepresentInverse function；
4.2: in order to keep detector to be in the false-alarm probability of setting, in given false-alarm probability PfaLower employing monte carlo method is asked for
Thresholding modifying factor κ；
4.3: calculate accurate detection threshold T*=κ T.
Step 5: judgement has driftlessness；
Amplitude x of unit to be checkedcutCompare with detection threshold, if xcut>T*, then show this unit with the presence of target, at radar
Display screen display point mark, otherwise this unit does not has target, does not show a mark.
The method that make use of knowledge assistance in described step 2 assists chooses uniform as far as possible reference unit, the knowledge information of utilization
Including: geomorphology information, clutter size subregion, clutter amplitude distributed model.
In described step 2, we have introduced the choosing method of a kind of fixed reference window size, can also fit according to environment in practical situation
Degree adjusts reference window size.
Described step 4 sets false-alarm probability PfaUnder, set the excursion of threshold factor as κc=1:0.1:20, passes through
1000/PfaSecondary matlab emulates, at κcIn the range of find out and make actual false-alarm probability P 'faEqual to setting false-alarm probability PfaSuitable
Thresholding modifying factor subvalue κ.
The nonparametric CFAR detection method of knowledge assistance of the present invention, in the non-homogeneous environment of Unknown Background distribution, it is possible to utilize first
Test knowledge acquisition homogeneous reference unit, and then accurately estimate background clutter amplitude PDF, estimate detection threshold, improve detection performance,
There is the effect strong to environmental suitability.
Accompanying drawing explanation
Fig. 1 is the nonparametric constant false alarm detector structural representation of knowledge assistance of the present invention；
Fig. 2 is the nonparametric CFAR detection flow chart of knowledge assistance；
Fig. 3 is the detection performance simulation comparison diagram that Rayleigh mixes several detectors under background with Weibull distribution:
(1) the detection performance of the nonparametric constant false alarm detector (KB-NP-CFAR) of Fig. 3 (a) explicit knowledge auxiliary is already close to
Excellent CFAR detector (KB-CA-CFAR) performance；
(2) Fig. 3 (b) is KB-NP-CFAR detector and the detection performance comparison of traditional log-t-CFAR detector, and figure shows
At detection probability (Pd) when being 0.5, KB-NP-CFAR improves about 10dB than log-t-CFAR detection signal to noise ratio；
(3) Fig. 3 (c) is KB-NP-CFAR detector and the detection performance comparison of knowledge assistance detector (KB-CFAR), aobvious on figure
Show at PdWhen=0.5, KB-NP-CFAR improves about 9.5dB than KB-CFAR detection signal to noise ratio；
(4) Fig. 3 (d) is KB-NP-CFAR detector and the detection performance comparison of nonparametric detector (K-CFAR), and figure shows
At PdWhen=0.5, KB-NP-CFAR improves about 3dB than K-CFAR detection signal to noise ratio.
To sum up, the detection performance of the nonparametric constant false alarm detector of knowledge assistance, already close to optimum CFAR detector performance, is better than
Tradition parameter constant false alarm detector, nonparametric constant false alarm detector and the performance of knowledge assistance parametric detector.So, knowledge is auxiliary
Two problems of model mismatch that the nonparametric CFAR detection method helped solves non-homogeneous environment well and complex environment causes, exhibition
Show good detection performance.
Detailed description of the invention
The present invention mainly produces one group of independent same distribution by Computer Simulation but the data of clutter distribution pattern the unknown, it is assumed that these numbers
Become according to by the plural play staff of Follow Weibull Distribution, but we do not have any prior information to its distribution character.By with several
Tradition CFAR detection algorithm is made comparisons, and verifies the effectiveness of the inventive method.Institute is in steps, conclusion all exists
Confirmation is verified on MATLAB-R2012b.It is embodied as step as follows:
Pretreatment:
(1) initialize systematic parameter to include: treat number of reference K=22, the long N=16 of reference window, false-alarm probability Pfa=10-3,
Span r=0:0.01:10 of bandwidth.
(2) from radar receiver, a frame data plane is read, it is assumed here that Nr=23, unit m=13 to be detected, setting
Scenario parameters is: treat reference unit 1～8,16～23 and unit to be detected 12 meet the rayleigh distributed that distributed constant is 1, treat reference
Unit 9～11 and 13～15 meet scale parameter be 3, form parameter be 2 Wei Buer distribution.
Step 1 chooses reference unit:
For the 13rd unit to be detected of datum plane, each 11 resolution cells in unit both sides to be detected will be closed on as treating reference
Unit, utilize knowledge assistance prior information from 22 close on treat reference unit is chosen 16 homogeneous reference unit, use here
Reference unit choose mode:
(1) determine and the reference unit of unit independent same distribution (the most uniform) to be detected according to prior information, be expressed as:
(2) ifTreat that reference unit is reference unit for 16 that just choose that close on and Sign (i)=1.
(3) ifJust choose all Sign (i)=1 treat reference unit andIndividual close on CUT
And Sign (i)=0 unit to be detected be total reference unit.
Step 2 calculates PDF:
(1) kernel function K () uses the probability density function of standard normal distribution, for:
(2) bandwidth h that kernel function weights is calculated:
Wherein XiAnd XjThe two parts divided equally for the reference unit data chosen, it addition, K*(x)=K2(x)-2K(x)。
(3) probability density function of each unit data estimation unit to be detected in reference window is utilizedAnd distribution function Wherein, Φ is standard normal
The distribution function of distribution.
Step 3 calculates detection threshold:
(1) Preliminary detection thresholding T is asked for.
Detection threshold RepresentInverse function.
(2) thresholding modifying factor κ is asked for
Setting false-alarm probability Pfa=10-3Under, use 1 × 106Secondary DSMC asks for threshold factor κ.
(3) the acquisition T of accurate detection threshold*=κ T.
Step 4 is adjudicated driftlessness:
Amplitude x of unit to be checkedcutCompare with detection threshold, show test point mark.If xcut>Texact, then show that this unit has mesh
Mark exists, at radar asorbing paint screen display point mark.Otherwise this unit does not has target, does not show a mark.
Being embodied as it can be seen that the present invention is by utilizing priori and nonparametric to estimate to construct a knowledge by the present invention
The nonparametric constant false alarm detector of auxiliary, has reached the complicated adaptable effect of non-homogeneous environment.
Those of ordinary skill in the art it will be appreciated that embodiment described here is to aid in the principle of the reader understanding present invention,
Should be understood that protection scope of the present invention is not limited to such special statement and embodiment.Those of ordinary skill in the art can
To make various other various concrete deformation and combination without departing from essence of the present invention according to these technology disclosed by the invention enlightenment, this
A little deformation and combination are the most within the scope of the present invention.
Claims (4)
1. a nonparametric CFAR detection method for knowledge assistance, the method includes:
Step 1: pretreatment；
1.1 initialize systematic parameter includes: closes on and treats that number of reference is K, and a length of N of reference window of selection, false-alarm probability sets
For Pfa, span r of bandwidth；
1.2 read n-th frame data, Z (n)={ z from radar receivern(m)},1<m<Nr, wherein NrFor total distance unit
Number, znM () represents the measuring value in the measurement unit m of n-th frame echo data；
Step 2: choose each reference unit in reference window:
For the m-th unit to be detected of n-th frame datum plane, each K/2 the resolution cell in unit both sides to be detected will be closed on and make
For treating reference unit, utilize knowledge assistance prior information, from treating of closing on of K, reference unit is chosen N number of homogeneous reference unit；
2.1 determine reference unit independent identically distributed with unit to be detected according to prior information, are expressed as:
Wherein zn(CUT) quantity of units measured value to be detected is represented；
If 2.2Just choose that close on and the N number of of Sign (i)=1 and treat that reference unit is reference unit；
If 2.3Just choose all Sign (i)=1 treat reference unit andIndividual close on CUT's and
Sign (i)=0 unit to be detected is total reference unit；
Step 3: calculate probability density function and the distribution function of the clutter amplitude of unit to be detected:
3.1: make kernel function K () be:
3.2: bandwidth h of calculating kernel function weighting:
The reference unit chosen is divided into two parts wherein XiAnd XjRespectively representing a portion, r has model in initialization procedure
Enclose, it addition, K*(x)=K2(x)-2K(x)；
3.3: utilize the probability density function of each unit data estimation unit to be detected in reference windowAnd distribution function Wherein, Φ is standard normal
The distribution function of distribution；
Step 4: calculate detection threshold；
4.1: ask for Preliminary detection thresholding T, RepresentInverse function；
4.2: in order to keep detector to be in the false-alarm probability of setting, in given false-alarm probability PfaLower employing monte carlo method is asked for
Thresholding modifying factor κ；
4.3: calculate accurate detection threshold T*=κ T.
Step 5: judgement has driftlessness；
Amplitude x of unit to be checkedcutCompare with detection threshold, if xcut>T*, then show this unit with the presence of target, at radar
Display screen display point mark, otherwise this unit does not has target, does not show a mark.
The nonparametric CFAR detection method of a kind of knowledge assistance the most as claimed in claim 1, it is characterised in that described step 2
In make use of the method for knowledge assistance to assist to choose as far as possible uniform reference unit, the knowledge information of utilization includes: geomorphology information,
Clutter size subregion, clutter amplitude distributed model.
The nonparametric CFAR detection method of a kind of knowledge assistance the most as claimed in claim 1, it is characterised in that described step 2
In we have introduced the choosing method of a kind of fixed reference window size, in practical situation can also according to environmental fitness adjust reference window big
Little.
The nonparametric CFAR detection method of a kind of knowledge assistance the most as claimed in claim 1, it is characterised in that described step 4
Middle setting false-alarm probability PfaUnder, set the excursion of threshold factor as κc=1:0.1:20, passes through 1000/PfaSecondary matlab imitates
Very, at κcIn the range of find out and make actual false-alarm probability P 'faEqual to setting false-alarm probability PfaSuitable thresholding modifying factor subvalue κ.
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| CN107462886A (en) * | 2017-07-26 | 2017-12-12 | 南京信息工程大学 | A kind of moving-target CFAR detection method based on comparison of wave shape degree optimal algorithm |
| CN110441766A (en) * | 2019-07-02 | 2019-11-12 | 中国航空工业集团公司雷华电子技术研究所 | A kind of airfield pavement FOD detection radar change Threshold detection method |
| CN113376591A (en) * | 2021-06-11 | 2021-09-10 | 电子科技大学 | Clutter knowledge-based radar target constant false alarm detection method |
| CN113504521A (en) * | 2021-07-08 | 2021-10-15 | 哈尔滨工业大学 | Mixed model-based constant false alarm detection method used in multi-target environment |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106707273A (en) * | 2017-01-23 | 2017-05-24 | 西安电子科技大学 | Method for detecting multi-station radar signal fusion based on Neyman-Pearson rule digitalizing |
| CN106707273B (en) * | 2017-01-23 | 2019-05-21 | 西安电子科技大学 | Based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method |
| CN107462886A (en) * | 2017-07-26 | 2017-12-12 | 南京信息工程大学 | A kind of moving-target CFAR detection method based on comparison of wave shape degree optimal algorithm |
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| CN110441766A (en) * | 2019-07-02 | 2019-11-12 | 中国航空工业集团公司雷华电子技术研究所 | A kind of airfield pavement FOD detection radar change Threshold detection method |
| CN110441766B (en) * | 2019-07-02 | 2023-02-17 | 中国航空工业集团公司雷华电子技术研究所 | Airport pavement FOD detection radar variable threshold detection method |
| CN113376591A (en) * | 2021-06-11 | 2021-09-10 | 电子科技大学 | Clutter knowledge-based radar target constant false alarm detection method |
| CN113504521A (en) * | 2021-07-08 | 2021-10-15 | 哈尔滨工业大学 | Mixed model-based constant false alarm detection method used in multi-target environment |
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