2012 International Conference on Radar, Communication and Computing (ICRCC),

SKP Engineering College, Tiruvannamalai, TN., India. 21 - 22 December, 2012. pp.143-147.

143 978-1-4673-2758-9/12/$31.00 ©2012 IEEE

Non Homogeneous Clutter Mitigation using STAP

Ashwini Saket1, Prince K. Dubey

1, Nandan Mishra

1, Sourav Dhar

1 and Debdatta Kandar

2

Abstract— 1The principal objective of this paper is to

encounter the safety related challenges in intelligent

transportation system (ITS). The detection of nearby

vehicle is an important task for the radar that is used

for safety application in ITS. But the received signal,

contains the target information, is corrupted by

clutter. This is an important issue in out door radar

detection. Thus in this paper a novel method, based on

STAP, has been proposed to address this issue. The

mathematical modelling of non-homogeneous clutter is

also presented here. The proposed methodology has

been validated with help of a case study in a utopian

vehicular safety scenario.

Index Terms— ITS, STAP, SCR, non-homogeneous

clutter, grazing angle.

I. INTRODUCTION

One of the major thrust areas of application of

intelligent transportation system (ITS) is the driver and

passenger safety system [6, 7, 10]. Radar based sensing is

an integral part of the vehicular safety system. But the

clutter put major challenge for outdoor Radar operations.

The clutter is radio frequency (RF) echoes reflected back

from targets which are highly undesirable in radar

operation. The type of clutter changes with the application

type and operating environment. The analysis to eliminate

non-homogeneous clutter tends to be very difficult, costly,

and time consuming. This paper introduces a novel signal

processing approach that can simplify analysis of clutter

problem.

The Space-time adaptive processing (STAP)[1,2] is

the signal processing technique which is used for

detecting slow moving targets using airborne radars.

STAP is useful in radar signal processing where

interference, like ground clutter [3,4,5], is a severe

problem. The challenge of ground clutter mitigation can

be addressed at first by applying the STAP to mono-static

radar platforms. The computational load of the STAP

processors further can be reduced by designing the

efficient adaptive methods. Finally, at the third step the

mitigation methods to encounter the jammers are to be

designed. Throughout this process, designers focused

almost solely on uniform linear arrays (ULA), where the

elements are uniformly spaced on a line. STAP combines

1 Sikkim Manipal Institute of Technology, Majitar, Rangpo,

Sikkim, India. Email: sourav.dhar80@gmail.com 2 SKP Engineering College, Tiruvannamalai, Tamil Nadu, India.

spatial and temporal observations using a data-dependent

weighting to mitigate the impact of clutter and

interference, thereby enhancing output signal to

interference and noise ratio (SINR). Hence, the STAP

processor can be considered as a two-dimensional (2-D)

filter which performs both beam forming (spatial filtering)

and Doppler (temporal) filtering to suppress interference

and achieve target detection and parameter estimation.

II. BACKGROUND OF THE WORK

Authors are involved in the development of multi-

channel solution for ITS challenges. Remote sensing is

used in ITS for safety applications. Authors have shown

in [6, 7], how digital radar is effective to avoid collision.

Ubiquitous communication is another major requirement

for both safety and non safety applications. Authors have

taken an initiative to design a robust vertical handover

algorithm to provide seamless connectivity in

heterogeneous radio access network scenario [8, 9, 11].

Convergence of both remote sensing and communication

is presented in [10]. This work is an extension to the

works presented in [6-11]. Here, a novel method is

proposed for clutter mitigation non-homogeneous outdoor

environment.

III. NON HOMOGENEOUS CLUTTER MODEL

Instead of Signal-to-Noise Ratio (SNR) RADAR’s

capability to detect targets in a high clutter background

depends on the Signal-to-Clutter Ratio (SCR). Normally,

clutter signal level is much higher than the receiver noise

level as because of clutter echoes are random and have

thermal noiselike characteristics. Grazing angle [12] (ψg),

surface roughness, and the RADAR wavelengths mainly

affect the amount of clutter in the RADAR operation. On

basis of the target RCS st, and the anticipated clutter RCS

sc (via clutter map) the RADAR can distinguish the target

return from the clutter echoes. Where clutter RCS can be

defined as the equivalent radar cross section attributed to

reflections from a clutter area, Ac.

The average clutter [12] is given as

0

c c.A (1) (1)

Where 0 (m2

/m2

) is the clutter scattering co-

efficient.

Ability to detect target in a high clutter environment

by Radar depends upon Signal to Clutter ratio (SCR)

2012 International Conference on Radar, Communication and Computing (ICRCC)

144

rather than Signal to Noise ratio (SNR) Clutter RCS can

be defined as the equivalent radar cross section attributed

to reflections from a clutter area, Ac.

Fig. 1. Illustration of RADAR return signal

Signal to Clutter ratio can be defined as,

t g

0

B

2 cosSCR (3)

RC

(2)

Where,

3dB 3dB beam width.

PtPeak transmitted power.

G Antenna gain.

Pulse width.

Wavelength

t Target RCS.

g Grazing Angle

Fig. 2. Illustration of Grazing Angle.

In this paper the clutter model used is Non-homogeneous clutter [13]. Non-homogeneous clutter is

basically a non-uniform clutter whose amplitude varies significantly from cell-to-cell. Non homogeneous clutter is generally derived from the non-stationarity of the environment [14]. The motion of the traffic and leaves of tree through which the clutter in produced [15, 16].A

Non-homogeneous area [17] here means that some illuminated patches are statistically different from some other patches. For instance when radar scans an area consisting of grassland and forest, the distribution of the

clutter can be considered as a combination of the clutter of grassland and the clutter of forest. Supposing there are n different types of land cover, and the clutter of each of

them is Rayleigh distributed [15], the combined distribution is

n

c i i c ci

i 1

p( ) k ,p ( , ) (4)

(4)

Where, i c ci

p ( , ) is the probability density function

of Rayleigh with mean ci i

,k is the portion of the ith

type

of land clutter, and 1 2 n

k k ...... k 1 .

The performance of space time adaptive processing (STAP) is greatly affected in non-homogeneous clutter environments. In this paper, the effect of non-homogeneous clutter on STAP is analysed, then non

homogeneous clutter suppression scheme using MTI[11,12], STAP [1,2,3]and D3(Direct Data Domain) STAP using Sparse representation [18] is proposed and the improvement factor is calculated and compared for the

above three methods.

Now the received signal [19] from the target after reflection can be represented as a sum of the target signal and interference terms

S t, tX S( f ) C J N (5) (5)

Where, αs is the target amplitude; S(φt , ft)= a( φt)

b(ft)

T is the space time steering matrix (corresponding to

the look direction φ(t)); and look Doppler of ft . The matrices C, J and N represent clutter, jammer and noise

signals, respectively.

Now for a target direction of φ(t), with the angle referred to broadside, the signal advance from element to element by the phase factor,

s tZ .j .sin( ) (6) (6)

Associated with this direction the spatial steering vector is

ta( ) =[1,

sZ , 2

sZ ,……., N 1

sZ ] T (7)

Where (⋅)T denotes transposition.

Similarly, for a target Doppler frequency of ft, the

phase of the target signal advances from one pulse to the

next by the factor of tt

r

fZ exp( j.2. .( ))

f resulting a

temporal steering vector of

tb(f ) =[1,

tZ , 2

tZ …….., M 1

tZ ] T (8)

Considering a linear array with N antennas uniformly spaced with the half wavelength separation, where M pulses are transmitted with a PRF of fr within a CPI.

IV. PRINCIPLE OF STAP

The block diagram of STAP based signal processing is

depicted in figure 3. The common principle of STAP is as

follows:

Non Homogeneous Clutter Mitigation using STAP

145

1. A train of M coherent pulses are transmitted by the

radar.

2. The echoes from the target, mixed with clutter, are

gathered by ‘N’ individual elements of an antenna

array. Separate Rx chains are connected to each of the

array elements [3].

3. Now, the received signals are sampled at a series of L

range gates i.e., successive ranges or distances.

4. STAP processing is applied to the snapshot matrix

which is nothing but an M × N matrix of samples

collected at each range gate. The collection of

snapshots at all range gates is referred to as a data

cube that contains all the available information for

target detection within a CPI (coherent processing

interval).

Fig. 3. Block diagram for STAP processing.

There is a rising need for STAP processing to enhance

the quality of radar signal processing heterogeneous

environments [20, 21, 22]. This crisis refers to the lack of

stationarity of the Rx signals w.r.t. range. Stationarity

tends to fade away when the terrain becomes rough with

non-uniform reflectivity properties and in the presence of

internal clutter motion due to the movement of the target.

One upcoming method known as knowledge-aided STAP

is capable to encounter the problem of heterogeneity by

using a priori knowledge that is stored in databases [23].

Fig. 4. Data cube for STAP processing.

The increment in the range bin, within a particular PRI

(pulse repetition interval), is called as the fast time

samples whereas increment in the range bin across the

PRI are known as slow time samples. In an antenna array,

if there are N antenna elements and M pulses over the CPI,

then the data cube could be formed by taking one sample

from each of the N antenna element. Thus, one snapshot

is equal to the one column of STAP data cube (figure 4)

and there are (M.N) snapshots for each range bin.

A. Algorithm for stap

Most conventional STAP algorithm consists of the

following steps shown in figure 3.

i) Estimation of the troubling parameters (for example:

interference covariance matrix, target complex

amplitude)

ii) Formation of the weight vector considering the

inverse covariance matrix

iii) Calculation of the inner product of weight vector and

data vector from a cell under test.

iv) Comparison of the squared magnitude of the inner

product obtained in step (iii) with a threshold

determined according to a particular false alarm

probability.

B. Working of stap

In STAP, two types of data are typically processed:

one is training data, which is used to estimate the

interference covariance matrix and adaptive weight vector.

Other is the primary data or the test data on which

detection and parameter estimation are performed.

Succinctly stated, the fully optimal STAP algorithm

consists of the following steps:

1) Starting with a data cube, identify the cell under test

(corresponding to the length-JN data vector x) and

form the target steering vector e for every Doppler bin

of interest.

2) Select K representative training data from both sides

of the cell under test, avoiding guard cells to account

for target leakage and competing targets.

3) Form dR

the estimated interference covariance matrix

using the training data.

4) Calculate a weight vector 1

dW R .e

V. CASE STUDY

A non-homogeneous clutter environment is created by

using the Rayleigh probability density function. Also the

Doppler Frequency Shift due to the Doppler clutter

(occurring due to the motion of the target or the source) is

considered into the system. The Doppler frequency is use

to determine the Peak RCS of the target. The target

considered here is a point size square object and the of

the target is considered accordingly. The target is

considered to be moving in a velocity of approximately

30 kmph towards the fixed source. The clutter

environment is considered to have a radar transmission

frequency of 76 GHz and a Pulse Rate Frequency (PRF)

of 10MHz.

2012 International Conference on Radar, Communication and Computing (ICRCC)

146

The Grazing angle is considered to vary under 150 and

the angle of detection of the target is considered around

200.

For the STAP the array antenna is taken to be a 2 (N)

element antenna with each element to be having 2 (M)

pulses transmitted over each CPI (Coherent Processing

Interval).

VI. RESULT ANALYSIS

Fig. 5. SCR variations w.r.t relative velocity.

Fig. 6. Detection of Two Targets after STAP processing.

Figure 5 shows the variations in signal to clutter ratio

(SCR) in non-homogeneous clutter environment with

respect to the relative velocity between the two vehicles.

It is assumed that the radar under consideration is

operational at 76 GHz and is capable of detecting a target

situated at least 50m distance. The SCR variations of

targets situated at 50m and 200m has been shown here.

The retrieval of two targets, after applying STAP,

from the received signal has been depicted Figure 6. It is

obvious from the results that the application of STAP in

radar signal processing provides encouraging results.

VII. NOVELTY OF THE WORK

The STAP proves to be very effective with the non

homogenous clutter as compared to the the other tradition

methods of such as MTI filters and KALMAN filters. The

estimation of the covariance matrix proves to be a

problem in the non homogeneous scenario however using

the advanced version of STAP these shortcomings can

easily be overcomed. Also the mathematical complexity

of the MxN DOF of the system calls for the need of high

end digital signal processors. The improvement factor of

the system comes out to be very satisfactory and so the

clutter and interference is suppressed well.

VIII. CONCLUSION

In this paper an attempt has been made to mitigate the

non homogeneous clutter with STAP based signal

processing. The system showed satisfactory results with

the non-homogeneous Doppler clutter of a slow moving

target. There is a scope of mitigation of non homogeneous

clutter in future by applying various advanced STAP

methods such as D3SR, hybrid sigma delta and Joint

Domain Localised STAP. The estimation of covariance

matrix needs to be simplified and effective in future

works.

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