Proceedings of the 2003 IEEE

Changsha, China - October 2003 Intemational Conference on Robotics,Intelligent Systems and Signal Processing

A New Method of Radar Clutter Recognition Based on

Multi- a Truncation Set

Fang Xueli Liang Diannong Zou Kun Dong zhen Huang Xiaotao

School of Electronic Science and Technology, National University of Defense Technology,

Changsha,Hunan,P.R.China, 410073

Email: fangxueli@l63.com

Abstract Radar clutter recognition is veiy useful for modern radar

signal processing system. a truncation set feature

extraction b&ed method needs short samples and has very

high recognition rate for Log-Normal distribution clutter: but

suffers shortcomings of high error recognition rate to

discriminate Weibull j?om K-distribution when the samples is

short. A modified radar clutter recognition method based on

a truncation set feature extraction - multi- & truncation

set feature extraction based radar clutter recognition method

is presented in this papel: we point out that this method is a

generalized form of rectangular figure-based method. From

the simulation results and analyzing of real radar clutter test,

it is seen that this method is more valid, practical and

prospective.

Keywords: & truncation set Radar clutter Recognition

1. Introduction

Radar works in varieties of clutter backgrounds. With the

development of new radar system and the complexity of radar

backgrounds, the conventional assumption of clutter

amplitude’s Rayleigh distribution becomes more and more

difficult to be satisfied, and some other clutter distribution

models are presented. Among them, Log-Normal, Weibull and

K-Distribution are the most popular ones. In order to

implement the optimum radar signal processing, the

processing strategy should be changed according to the

different clutter backgrounds [l1. This may be called the

adaptation between the signal processor and the clutter

0-7 803-7925-x/03/$17.00 02003 IEEE

background, in which the radar clutter recognition is naturally

a needed step.

At present, the methods of radar clutter recognition

include mainly the spectrum analysis, auto-regressive

simulation, amplitude distribution analysis, parametric and

non-parametric statistics, and neural network mkthod[*- 41 etc,

among which the rectangular figure-based amplitude

distribution analysis method is a typical one 14]. A method

called higher-order statistics (HOS) feature extraction is also

presented by the authorsr5], it overcomes the drawbacks of

long samples required by the rectangular method and obtains

a good recognition result. Because the HOS of clutter,

especially of Log-Normal clutter is not stable, the recognition

rate is limited. To overcome this limitation, the author had

proposed the radar clutter recognition method based on

truncation setL6]. The presentation of the a truncation set

method is mainly due to the consideration of that the most

serious influence of clutter to radar signal processing is (e.g

CFAR processing) its tail. With the use of a truncation set

as feature extraction for radar clutter recognition, we enrich

the feature extraction of radar clutter recognition. When use a

single a! truncation set as the feature, it can only reflect the

feature of radar clutter in an isolated point and lost the

information. In this paper, we improve on a truncation set

method by proposing radar clutter recognition method based

on multi-& truncation set. Some parts of content in this

paper had been reported by the authors in reference [7] to

keep this paper’s integrality.

As the single a! truncation set method, this method

determine the parameters of possible distributions with

moment or maximum likelihood methods fiom sampling data;

then we can get the thresholds of their 0 truncation set by

distributions with determined parameters; and at the same

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Sample data of

radar clutter

possible distribution-2

Calculation the pdf of Calculation of multi- a truncation - samples set of samples (No.0)

Calculation of multi- a Calculation of multi-

possible distribution-M

. . . Calculation of multi- a!

truncation set -M

Calculation of distance- 1 Calculation of distance-2 Calculation of distance-M

ComDarison and Classification

Fig1 Processing flow of multi- a! truncation set based radar clutter

Table 1 PDF and Parameter Formula of Typical Radar Clutter Distributions

Parameters determining

Parameter 1 Parameter 2 Probability Density Function f ( x )

m Weibull f ( x ) = - x m - ' e u ( x )

time we can obtain the threshold of sample's a truncation

set &om fhe estimated probability density hction@df)

with transformed kemel estimation ( TKE 1 method[*] ; at last

we calculate the distances of the thresholds of a!

truncation set from samples to the possible distributions

respectively, and the clutter class is determined according to

the criterion of minimum distance. To be pointed that, the a value of multi- a truncation set method is not a single

decimal fraction with the value between 0 and 1 any more, but

a vector of multi decimal fraction with the value between 0

and 1 .

The paper is organized as follows. In section 2, the

processing flow of multi- a truncation set based clutter

recognition and the related algorithms method are described.

The simulation results and comparisons between a truncation set based method and multi- a truncation set

based method, between multi- a truncation set based method

and rectangular figure-based method are given in section 3 . In

section 4, we analyze a group of real clutter data with the

proposed method, the conclusions are given in section 5.

2. Processing Flow and the

Algorithm

The signal processing flow of the designed radar clutter

recognition system is shown in figure 1, in which the clutter

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sample data is denoted as x = (Xl,X2,***,XN), the

various possible distributions compose the classification

class c = {1,2, * - e , M } .

The typical distribution for description the statistical

property of radar clutter is Weibull(with Rayleigh distribution

included), LogNorma1 and K-Distribution, their

corresponding pdfs (probability density function) are

summarized in table 1 [9-121. See Fig. 1, the beginning of radar

clutter recognition is the calculation of parameters fiom a

group of samples, so, the parameters calculating formulas of

possible distributions are also given in table lr91[111, in which,

U(-) is the unit step function, r(.) is the Gamma function,

K , (.) is the second modified Bessel function of order y

and Y is Euler constants.

In table 1,some more detailed formulas would be:

(1)

(4)

Estimate the pdf f, based on a group of given

clutter data to be recognized with TKE method:

Calculate the parameters of every kind distribution

in set C fiom the clutter data with maximum

likely-hood method or moments method and get

the possible pdfs which can be noted as :

fl,f2,.**,fM ;

Giving a , Calculate the thresholds of a

truncation set of samples x,, and every kind

distributions, noted as: X1 , X2, * a, XM ;

Obtain the recognized results with the criterion of

minimum distance, that is, the solution i of

equation min [xo - x, I) = Ix, - xi I is the

kind of recognition result.

lSkSM

Note: xi,, (i = 0,1,2, * * , Ad) are vectors with the same

dimension as a.

3. Simulation, Comparison

Analyzing

and

/

The recognition rate of simulation results will be

statistically obtained through Monte Carlo method. 500

groups of random data for every possible distribution and for : ‘ i

!

different deterministic length are generated[”]. For

convenience of comparison, table 2 gives the recognition

results by a truncation set methodL6] ( The column of

a = 0.1 ) and the recognition results by multi- a truncation set method with a combination of multi a values

(The column of a = [0 .1,0.2]) is given in table 2 too.

From the simulation results it is found that multi- a truncation set method has the same virtues as a truncation

set, the sample length limitation is short and the recognition

rate for Log-Normal distribution is high, fbthermore,

multi- a truncation set method has better recognition results

between Weibull and K-Distribution than a truncation set

Assume the possible distributions of clutter compose a

set C: (fl,f,,-.-,fM), for f, (1 I i I M ) , giving a

constant a (0 2 a 5 I ) , its threshold of a! truncation

set X, isdefinedas:

jx; f ( t ) d t = a (5)

The calculation algorithm of x, is offered in the

following:

860

method. From the error recognition rate offered in table 2 we

can found that multi- a truncation set method improve on

Table 2 Simulation -and Comparison Results

Probability of Classifying to Every Probability of Classifying to Every

Clutter Model Distribution(%) ( a = [0.1,0.2])

the recognition rate of Weibull and K-Distribution is

because of the error classification between the two

distribution is decreased. a truncation set has high error

recognition rate between the two kinds of distribution show

their tails are very similar, and the feature of truncation

set is not sufficient for radar clutter recognition, so we

proposed multi- 0 truncation set method in this paper.

Assuming a = [0.1,0.2,0.3,... ,0 .9] ,or a equals the uniformity samples between 0 and 1 with an

appropriate interval, the proposed method will equal to

rectangular figure-based method, so the rectangular

figure-based method is a particular form of multi- a based

method and the method proposed in these paper for radar

clutter recognition is a generalized form of rectangular

figure-based method.

4.Analyzing for Real Clutter Data

To test the validity of the proposed method, we’ll analyze

real clutter data. A block of clutter image is shown in figure 2,

Fig2 A Block of Real Clutter Image

its pdf and the three kinds theoretical pdf curves are given in

figure 3.

Figure 3 shows that the clutter’s pdf is most similar

to the theoretical pdf of IC-Distribution easily. With the

method we proposed in this paper, the recognition result is

agreement.

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5. Conclusions

The thresholds of truncation set can be taken as the

feature of radar clutter for recognition. multi-a truncation

set method has more virtues than a truncation set, it is not

only the modification of a truncation set method, but also

the generalized form of rectangular figure-based radar clutter

recognition method. Because of the requirement of the short

data length, the higher recognition ability for Log-Normal

distribution and the low error recognition rate to discriminate

Weibull from K distribution when the samples is short,

multi- a truncation set feature extraction based radar clutter

recognition method is more valid, practical and prospective.

It is very important to determine the values of Multi- a , and an adaptive algorithm for calculating Multi- Ct 's values

based on radar clutter samples should be advanced. This work

will be reported in another paper by the authors soon.

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0 . j \- L 0

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I' * "denotes the calcuiatedpdf of clutter image, rea! line &norestthe theoretsealpdfufthe three kinds of distriubions

fig 3 The shape comparison between clutter &age and the three kinds of clutter models .--

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