CN103217673A - CFAR detecting method under inhomogeneous Weibull clutter background - Google Patents

Abstract

The invention relates to a CFAR detecting method under an inhomogeneous Weibull clutter background. The CFAR detecting method is characterized in that the method is a uniform reference unit selecting method based on an M-N clutter edge binary accumulation detecting method, data obtained through the M-N clutter edge binary accumulation detecting method are classified and numbered according to scale parameters and shape parameters, and after the data are numbered, a distinguishing unit which is equal to a CUT serial number is selected as a reference window to conduct CFAR detection.

Description

CFAR detection method under a kind of non-homogeneous Weibull clutter background

Technical field

The invention belongs to radar signal processing field, relate to the radar self-adaption detection technique, especially self-adaption constant false-alarm (CFAR) detection method.

Background technology

Because the CFAR detection can be kept the radar false alarm probability in the clutter environment that changes constant substantially, and be widely used in Radar Targets'Detection under the strong clutter background.In CFAR inspection system, unit to be detected (CUT) compared with an adaptive threshold, the resolution element data obtained as the reference unit estimation around adaptive threshold utilized CUT, and the data of reference unit and CUT satisfy independent same distribution (IID).Yet in a lot of actual irradiation areas of radar, comprise many dissimilar landforms (as urban area and seashore etc.), caused radar clutter heterogeneous.It is different that these non-homogeneous clutters cause reference unit data and CUT to distribute, and makes the detection performance of target obviously descend, particularly the clutter edge place.

When the low grazing angle radar of high resolving power detects sea or land area, extra large often clutter of the residing clutter background of target or land clutter, the Weibull distributed model is a kind of amplitude distribution model that is widely used in extra large clutter and land clutter modeling, can be that the Weibull of different distributions parameter distributes with terrain modelings different in the radar scanning zone.Therefore, the design that is applicable to the CFAR detection method in the non-homogeneous Weibull clutter background has important research and is worth.

At present, quite ripe to the research of CFAR detection method under the homogeneous background both at home and abroad, also propose some and be applicable to the CFAR detection method of the non-homogeneous clutter background of part, the CFAR detection method of the non-homogeneous background of a kind of part is proposed based on the Bayesian inference such as Italian Syracuse university, can be reduced in the unsettled background environment adaptive threshold to the susceptibility of environment, thereby improve the performance of CFAR detection method.But these CFAR detection methods only are applicable to evenly or the uniform background environment of part, complicated and when changing fast when background environment, the CFAR detection method that has proposed at present can't guarantee: be used to estimate that the resolution element of CUT adaptive threshold and CUT have the IID characteristic, cause CFAR detection method performance to descend rapidly.In sum, the present most of CFAR detection method that proposes has significant limitation in actual applications.

Summary of the invention

Purpose of the present invention provides the CFAR detection method under a kind of non-homogeneous Weibull clutter background, it is characterized in that: this method is based on the even reference unit system of selection under a kind of M-N clutter edge binary accumulation detection method, according to scale parameter and form parameter the data that M-N clutter edge binary accumulation detection method obtains are carried out classifying and numbering, the numbering back selects the resolution element identical with the CUT numbering to carry out the CFAR detection as the reference window, comprises following concrete steps:

S1, to the processing of taking the logarithm of radar return datum plane, and slide window along distance to the reference window that with length is N and handle, each sliding window is carried out clutter edge detects.Testing process is: define the random sample in the sliding window, x=[x ₀, x ₁..., x _N] ^T∈ R ^N, x wherein _nBe n sample, separate between sample, reference window to be carried out clutter edge detect, step is:

Resolution element data in S11, the initialization reference window are x ₁..., x _n, length is N, wherein ξ=2ln (N);

Have a clutter edge between M and M+1 resolution element in S12, the hypothetical reference window, described M satisfies 1≤M≤N-1, and then clutter edge is divided into y with the reference window data _M∈ R ^MAnd z _M∈ R ^N-MTwo parts, wherein y _MPreceding M component, then z comprising X _MN-M component, i.e. x=[y comprising the X back _M, z _M], definition y _MAnd z _MHas independent same distribution, y _MAnd z _MProbability density function be respectively

With

Described

Be y _MUnknown parameter, described

Be Z _MUnknown parameter, wherein

Be different from

Select

Utilize the maximal possibility estimation parameter, realize

Maximum, wherein the upper bound is got in sup () expression, and k is that first is to the individual probability density function of M, therefore

$f\left(m\right)=\left\{\begin{array}{c}N\ln \left(\mathrm{std}\left(x\right)\right)-\mathrm{\ξ},m=1\\ (N-m)\ln \left(\mathrm{std}\left(z\right)\right)+m\ln \left(\mathrm{std}\left(y\right)\right),2\≤m\≤N-2\end{array}\right.\text{,}$ Wherein, std () is a standard deviation function, y=[x ₁X _m], z=[x _M+1X _N];

S13, according to described in the S12, clutter edge is positioned at

When

The time clutter edge do not exist, when

The time clutter edge exist;

S2, utilize the clutter edge detection method that provides among the S1, upwards remove in each orientation that all resolution elements can carry out N clutter edge detection two sliding windows of head and the tail, the number of times of clutter edge appears in each resolution element of accumulative total in this N time is detected, when clutter number of times 〉=M appears in resolution element, think that then this resolution element is a clutter edge;

S3, in the radar return plane, the interior resolution element of adjacent two clutter edges that utilizes S2 to obtain is estimated the scale parameter and the form parameter of the Weibull distribution that these resolution element data are obeyed, respectively to the scale parameter of all estimations in the data plane and form parameter ordering, respectively sorted form parameter is divided into N according to the range of parameter ₁Part, scale parameter is divided into N ₂Part, with ready-portioned form parameter and scale parameter to the data plane from 1 to N ₁* N ₂Be numbered, then datum plane has N ₁* N ₂Plant landform, described scale parameter and form parameter adopt maximal possibility estimation, and expression formula is ${\hat{B}}^{\hat{C}}=1/L\underset{j=1}{\overset{L}{\mathrm{\Σ}}}{x}_j^{\hat{C}},\underset{j=1}{\overset{L}{\mathrm{\Σ}}}{x}_j^{\hat{C}}\ln x_j/\underset{j=1}{\overset{L}{\mathrm{\Σ}}}{x}_j^{\hat{C}}-1/L\underset{j=1}{\overset{L}{\mathrm{\Σ}}}\ln x_j=1/\hat{C},$

Be the estimated value of scale parameter,

Be the estimated value of form parameter, x _j(j=1 ... L) be the adjacent interior resolution element data of two clutter edges, L is a resolution element number between two adjacent clutter edges, the L difference between different adjacent two clutter edges;

S4, the classification of landform numbering of utilizing S3 to obtain select to have the nearest L=[N that identical landform is numbered with CUT on the echo data plane _Min, N _Max] individual resolution element is as the reference window, carries out Log-t-CFAR and handles, wherein the thresholding of CUT is estimated to obtain by reference window, and the resolution element echo data is w=[w in the definition reference window ₁, w ₂W _L], then thresholding is

Wherein,

Be sample average, std () is a sample standard deviation, the thresholding factor-alpha _LRelevant with L, it satisfies required false-alarm probability P _FaSelect the resolution element identical with the CUT numbering to carry out CFAR as the reference window according to the landform numbering and detect, then selected reference unit and CUT have the IID characteristic.

Advantage of the present invention is: introduce a kind of even reference unit system of selection and realize that the self-adaptation CFAR in highly non-homogeneous Weibull clutter Beijing detects, adopt the accumulation of M-N clutter edge binary to detect a plurality of clutter edges that obtain in the non-homogeneous clutter, and adopt a kind of adaptive classification algorithm of landform to obtain non-conterminous but resolution element in the clutter background with identical landform based on the clutter edge of estimating, make detection method under non-homogeneous background in CUT and the reference window resolution element also be independent identically distributed, the detection method performance can not be subjected to the influence of non-homogeneous background.Therefore, the invention solves the violent problem that descends of CFAR detection method performance when radar scans complex region in the practical application.

Description of drawings

Fig. 1 is the block diagram of flow process of the present invention.

Fig. 2 is a clutter edge detection algorithm process flow diagram.

Fig. 3 is that clutter edge M-N clutter edge detects synoptic diagram.

Fig. 4 is the classification of landform algorithm flow chart.

Fig. 5 (a) is the clutter map of magnitudes in radar illumination zone under the rectangular coordinate system for the IPIX radar environmental map under the earth coordinates, Fig. 5 (b).

Fig. 6 accumulates the clutter edge that detects in the radar scanning zone of estimating to obtain for the IPIX measured data being carried out M-N clutter edge binary.

Fig. 7 is IPIX measured data classification of landform result.

Fig. 8 (a) use the Log-t-CFAR testing result for after the IPIX measured data adds target, Fig. 8 (b) for IPIX measured data adding target after, with the CFAR testing result of the present invention's proposition.

Embodiment

Provide the specific embodiment of the present invention below in conjunction with IPIX radar measured data.

IPIX radar measured data is the actual measurement echo data of Canadian McMaster University IPIX radar, radar is positioned in the face of on the Atlantic steep cliff, and coordinate is 44 ° of 36.72 ' N, 63 ° of 25.41 ' W, be higher than 100 feet on sea level, the data of choosing contain 1900 * 160 data points.Selected radar is positioned at the thick black line intersection point place of Fig. 5 (a), and thick black line enclosing region is a search coverage.As Fig. 5 (b) is the clutter map of magnitudes in radar illumination zone under the rectangular coordinate system.

S1, to the processing of taking the logarithm of echo data plane, and slide window along distance to the reference window that with length is N=32 and handle, each sliding window is carried out clutter edge detects, testing process is:

Resolution element data in S11, the initialization reference window are x ₁..., x ₃₂, length is N=32, wherein ξ=2ln (32);

S12, to m=1,2 ... 30, calculate $f\left(m\right)=\left\{\begin{array}{c}N\ln \left(\mathrm{std}\left(x\right)\right)-\mathrm{\ξ},m=1\\ (N-m)\ln \left(\mathrm{std}\left(z\right)\right)+m\ln \left(\mathrm{std}\left(y\right)\right),2\≤m\≤N-2\end{array}\right.\text{,}$ Wherein, std () is a standard deviation function, y=[x ₁X _m] ^T, z=[x _M+1X _N] ^T

S13, be positioned at when clutter edge

When

The time clutter edge do not exist.

S2, the clutter edge detection method of utilizing S1 to provide, 32 clutter edges detections can be carried out in each orientation upwards all resolution elements except that joining end to end, accumulate clutter edge appears in each resolution element in these 32 times are detected number of times, the number of times that clutter edge occurs when this resolution element is during more than or equal to 16 times, think that there is clutter edge in this resolution element, this step has been eliminated a large amount of false clutter edges, obtained in the datum plane clutter edge distribution situation accurately, the result as shown in Figure 6.

S3, in the radar return plane, utilize the resolution element in adjacent two clutter edges to estimate scale parameter and the form parameter that Weibull distributes in the radar return datum plane.To the scale parameter and the form parameter ordering of all estimations in the data plane, described scale parameter and form parameter adopt maximal possibility estimation, and expression formula is ${\hat{B}}^{\hat{C}}=1/L\underset{j=1}{\overset{L}{\mathrm{\Σ}}}{x}_j^{\hat{C}},$ $\underset{j=1}{\overset{L}{\mathrm{\Σ}}}{x}_j^{\hat{C}}\ln x_j/\underset{j=1}{\overset{L}{\mathrm{\Σ}}}{x}_j^{\hat{C}}-1/L\underset{j=1}{\overset{L}{\mathrm{\Σ}}}\ln x_j=1/\hat{C},$

Be the estimated value of scale parameter,

Be the estimated value of form parameter, x _j(j=1 ... L) be the adjacent interior resolution element data of two clutter edges, L is a resolution element number between two adjacent clutter edges, the L difference between different adjacent two clutter edges.Range according to parameter is divided into 3 parts with sorted form parameter respectively, and sorted scale parameter is divided into 2 parts, and ready-portioned form parameter and scale parameter from 1 to 6 are numbered on datum plane, and then datum plane has 6 kinds of landform.The result as shown in Figure 7.

S4, because the restriction of IPIX measured data, adopt all angles to a specific range unit in add a target, signal-clutter is than being SCR=15dB, target amplitude is fixed, have normalized, (π, π] equally distributed Doppler shift at random.Then utilize and obtain the datum plane of classification of landform as foundation, echo data is chosen reference window carry out the Log-t-CFAR processing, the length of reference window changes between [8～32], and with result and the contrast of traditional Log-t-CFAR detection method, the result is as Fig. 8 (a) with (b).Wherein said Log-t-CFAR detection method is a prior art.

Claims (4)

1. the CFAR detection method under the non-homogeneous Weibull clutter background, it is characterized in that: this method is based on the even reference unit system of selection under a kind of M-N clutter edge binary accumulation detection method, according to scale parameter and form parameter the data that M-N clutter edge binary accumulation detection method obtains are carried out classifying and numbering, the numbering back selects the resolution element identical with the CUT numbering to carry out the CFAR detection as the reference window, comprises following concrete steps:

S1, to the processing of taking the logarithm of radar return datum plane, and slide window along distance to the reference window that with length is N and handle, each sliding window is carried out clutter edge detects, testing process is: define the interior random sample of a sliding window, x=[x ₀, x ₁..., x _N] ^T∈ R ^N, x wherein _nBe n sample, separate between sample, reference window to be carried out clutter edge detect, step is:

Resolution element data in S11, the initialization reference window are x ₁..., x _n, length is N, wherein ξ=2ln (N);

Have a clutter edge between M and M+1 resolution element in S12, the hypothetical reference window, described M satisfies 1≤M≤N-1, and then clutter edge is divided into y with the reference window data _M∈ R ^MAnd z _M∈ R ^N-MTwo parts, wherein y _MPreceding M component, then z comprising X _MN-M component, i.e. x=[y comprising the X back _M, z _M], definition y _MAnd z _MHas independent same distribution, y _MAnd z _MProbability density function be respectively $f\left(y_k\right|{\stackrel{\→}{a}}_y)={\mathrm{\Π}}_{k=1}^{M}f\left(y_k\right|{\stackrel{\→}{a}}_y)$ With $f\left(z_k\right|{\stackrel{\→}{a}}_z)={\mathrm{\Π}}_{k=1}^{N-M}f\left(z_k\right|{\stackrel{\→}{a}}_z),$ Described

Be y _MUnknown parameter, described Be Z _MUnknown parameter, wherein

Be different from

Select

Utilize the maximal possibility estimation parameter, realize

Maximum, wherein the upper bound is got in sup () expression, k be first to the individual probability density function of M, therefore:

$f\left(m\right)=\left\{\begin{array}{c}N\ln \left(\mathrm{std}\left(x\right)\right)-\mathrm{\ξ},m=1\\ (N-m)\ln \left(\mathrm{std}\left(z\right)\right)+m\ln \left(\mathrm{std}\left(y\right)\right),2\≤m\≤N-2\end{array}\right.\text{,}$ Wherein, std () is a standard deviation function, y=[x ₁X _m], z=[x _M+1X _N];

S13, according to described in the S12, clutter edge is positioned at When

The time clutter edge do not exist, when

The time clutter edge exist;

S2, utilize the clutter edge detection method that provides among the S1, upwards remove in each orientation that all resolution elements can carry out N clutter edge detection two sliding windows of head and the tail, the number of times of clutter edge appears in each resolution element of accumulative total in this N time is detected, when clutter number of times 〉=M appears in resolution element, think that then this resolution element is a clutter edge;

S3, in the radar return plane, the interior resolution element of adjacent two clutter edges that utilizes S2 to obtain is estimated the scale parameter and the form parameter of the Weibull distribution that these resolution element data are obeyed, respectively to the scale parameter of all estimations in the data plane and form parameter ordering, respectively sorted form parameter is divided into N according to the range of parameter ₁Part, scale parameter is divided into N ₂Part, with ready-portioned form parameter and scale parameter to the data plane from 1 to N ₁* N ₂Be numbered, then datum plane has N ₁* N ₂Plant landform, described scale parameter and form parameter adopt maximal possibility estimation, and expression formula is

Be the estimated value of scale parameter,

Be the estimated value of form parameter, x _j(j=1 ... L) be the adjacent interior resolution element data of two clutter edges, L is a resolution element number between two adjacent clutter edges, the L difference between different adjacent two clutter edges;

S4, the classification of landform numbering of utilizing S3 to obtain select to have the nearest [N that identical landform is numbered with CUT on the echo data plane _Min, N _Max] individual resolution element is as the reference window, carries out Log-t-CFAR and handles, wherein the thresholding of CUT is estimated to obtain by reference window, and the resolution element echo data is w=[w in the definition reference window ₁, w ₂W _L], then thresholding is

Wherein,

Be sample average, std () is a sample standard deviation, the thresholding factor-alpha _LRelevant with L, it satisfies required false-alarm probability P _Fa

2. the CFAR detection method under a kind of non-homogeneous Weibull clutter background according to claim 1, it is characterized in that: the reference window length N described in the S1 is an even number, 100〉N〉30.

3. the CFAR detection method under a kind of non-homogeneous Weibull clutter background according to claim 1 is characterized in that: the accumulation number of times M described in the S2, and according to the complexity of background environment, M=0.5N or M=0.75N.

4. the CFAR detection method under a kind of non-homogeneous Weibull clutter background according to claim 1 is characterized in that: the N described in the S4 _MinAnd N _MaxSliding window length for CFAR detects is generally even number, comes suitable choosing according to the range resolution of data, is the accuracy that the adaptive threshold that guarantees to be used to estimate is estimated, the reference window minimum length must satisfy N _Min〉=8.

Images