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Modelling of Radar Targets and Radar Cross Section For Air Traffic Control
Radars
Article · July 2021
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QAYSAR Salih Mahdy
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Efflatounia
ISSN: 1110-8703
Pages: 664 – 674
Volume: 5 Issue 2
Modelling of Radar Targets and Radar Cross Section For Air
Traffic Control Radars
1Dr.Qaysar
Salih Mahdi, 2Mr.Ganesh Babu Loganathan
ITServices Department/Rectorate,Tishk International University,Erbil,Kurdistan,Iraq
2
Assistant Professor, Mechatronics Engineering, Tishk International University,Erbil, Kurdistan,
Iraq
Email ID:qaysar.mahdy@tiu.edu.iq, ganesh.babu@tiu.edu.iq
1
Abstract—This paper studies the effect of target fluctuating models on ATC radar coverage such as
swerling`s models 1 and 2, and swerling`s models 3 and 5. The results in this work show that swerlings
case 5 (non-fluctuating target) has the highest range and swerling case 2 (fluctuating target) has higher
range than swerling case 1 (fluctuating target). The signal to noise ratio obtained are claimed to be
accurate to within 1dB for values of Hs >100. The deviation from the exact values of the signal to noise
ratio for Hs <100 is rather large. It may be reduced, however, to acceptable values by using certain
correction of the swerling models and acceptable improvements in the SNR is obtained. The applications
of this work sound widely espacially for ATC radar systems and militery applications for target
classification and recognition. This work is performed by using C++ and object oriented programming
and it could be used as a prediction package in the ATCR sitting.
Keywords; ATC radar; fluctuating targets; swerling`s models; SNR
I.
INTRODUCTION
The differentiation between fluctuating and non-fluctuating targets is of essential target in the
processing of radar target classification [1-3,15-24]. The non-fluctuating target would yield target echoes
of constant amplitude which is valid only for a very limited range of time [4, 7-14]. Since real targets e.g.,
aircraft and ships, consist of a large complex structure with small features which will cause multiple
reflections of the impinging electromagnetic radiation [6, 25-41]. Therefore, if a target in motion, the echo
signal is never constant [1,5, 42-59].
II.
THEORETICAL PRINCIPLES
A. SNR Signal To Noise Ratio
The signal to noise ratio (SNR), requested to guarantee a certain value of probability of detection,
differentiate between fluctuating and non-fluctuating targets [6,4]. The non-fluctuating target would yield
target echoes of constant amplitude which is valid only for a very limited range of targets while fluctuating
targets yield targets of variable amplitude [60-67]. A popular method for representing the fluctuations of
targets is the four statistical models described by Peter Swerling [1]. For each of these he calculated the
signal to noise ratio (SNR) as required, as a function of the probability of detection, probability of false
alarm and the number of pulses integrated.
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B. Swerling`s Models Improvements
Five swerling`s models are studied and improved in this study and are introduced to classify the
radar targets
types, these models and their corrections are discussed below.
1) Swerling`s fluctuating case 1
The SNR is given by;
 (P
SNR =
−1
Hs

ln 


fa
)
1
P




.
(1)
Similar to the non-fluctuating target case 5, a correction factor is introduced and is given by;
d
CSW1= 0.06245*ln2(Hs)–0.572*ln(Hs) + 2.435.
(2)
2) Swerling`s fluctuating case 2
The SNR is given by;
SNR =
2[ −1 ( P fa) −  −1 ( P d )]
Hs
.
and similarly a correction factor is introduced and is given by;
CSW2=0.03681*ln2(Hs)–0.40362*ln(Hs)+ 2.25712.
(3)
(4)
3) Swerling`s fluctuating case 3
The SNR is given by;
SNR =
4 −1 ( P fa)
Hs M
.
(5)
Where M is given by;
M = 3.11573–3.48772*Pd+0.26522*Pd2 + 0.11477/Pd
(6)
and similarly a correction factor is introduced and is given by;
CSW3= 0.00052*ln2(Hs) – 0.16429*ln( Hs) + 1.93131.
(7)
4) Swerling`s fluctuating case 4
The SNR is given by;
SNR =
2[ −1 ( P fa) −  −1 ( P d )]
Hs
(8)
and similarly a correction factor is introduced and is given by;
CSW4=0.05981*ln2(Hs)–0.63253*ln(Hs)+2.80934.
(9)
5) Swerling`s non-fluctuating case 5
This model needs no correction because the amplitude of the echo signal is constant.
C. Radar Target Direction
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Once a target is detected, the next step is to pinpoint the precise location of a target. This position
provides the userwith the distance(range) to the target and its direction.
• Range: The distance to a target is determined by measuring the round trip transit time of the signals
between the radar and the target.
• Direction: For most radars, the direction to a target is measured in terms of the angle between the
line of sight to the target and some reference coordinate system. Most of the time, this angle is divided into
its horizontal (azimuth) and vertical (elevation) components and are measured based on the direction where
the antenna is pointed see Fig. (1). By knowing the range and direction of a target, a radar system can use
this information to track the target location as needed.
Figure 1. Target position determination
III.
MODELING AND SIMULATTION
A. Radar Coverage
In the present work the effects of the fluctuating and non fluctuating targets models and their
corrections on the radar coverage are studied in addition to the RCS and PRF. The algorithm steps for the
radar coverage was explained by [ 3,7 ].
B. ATCR Radar Parameters
The ATCR
radar systam which its coverage is modeled using computer simulation, has
the following parameters presented
in Table 1.
TABLE 1. ATCR RADAR PARAMETERS
Frequency
Horizontal Beam Width
Vertical Beam Width
Tilt
Revolution Per Minute (RPM)
Transmitter Peak Power
Receiver noise figure
Receiver Bandwidth
Intermediate frequency of the receiver
1300 MHz
1
6
0
5 r.p.m.
2200 kW
4.5 dB
0.6 MHz
30MHz
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Pulse Repetition Frequency (PRF)
Probability of False Alarm, Pfa
Probability of Detection, Pd
400 Hz
10-6
80%
Plumbing and transmission line losses
12 dB
Aerial height
19 m
Radar Cross Section , RCS ()
IV.
A.
2
m2
RESULTS AND DISCUSSION
The effects of target fluctuating models on radar coverage
The effects of target fluctuating models on
the
radar coverage are shown in Fig. 2
(sweling case 1 and 5 ) and Fig. 3 (swerling case 1 and 2). It can be noticed
that
swerling
case 5 (non-fluctuating target) has the highest range. Swerling case 2 (fluctuating target) has higher
range than swerling case 1 (fluctuating target).
Figure 2. Effects of swerling cases 1 & 5
Figure 3. Effects of swerling cases 1 & 2
B.
The effect of RCS on radar coverage
The
effect of radar cross section is shown in
Fig. 4 where
2
sections are taken 10 and 15 m . The higher cross section has higher range.
two radar cross
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Figure 4. Effects of radar cross section
C.
The effects of PRF on radar coverage
The effects of changing the PRF on radar coverage are presented (two PRFs 100Hz and 1000Hz) in
Fig. 5, where it can be noticed that the higher PRF has higher range, because of the increase in the
number of transmitted pulses and hence increasing the number of hits per scan[8].
Figure 5. Effects of PRF
V. CONCLUSIONS
Classification of radar targets into four models such as swerling 1,2,3, and 4 are studied in order
to simulate
their improvement on
the probability of detection and the
radar range and it
is concluded that swerling case 5 (non-fluctuating target) has the highest range.
Swerling case 2
(fluctuating target) has higher range than swerling case 1
(fluctuating target). Also it is concluded
that the fluctuating targets having lower detection ranges than the non-fluctuating targets which is
noteced in Figs. 2 and 3, because manouvering of
the fighters in speed which affects the
signal to noise
ratio. Also it can be concluded that large targets such as transportation aeroplain
requires higher
signal to noise ratio than small targets such as fighters . Also the other factor which
affecting the signal to noise ratio is
the PRF parameter which increasing the radar range when it is
increased from 100Hz to 1000Hz and acceptable improvement
in the
SNR is obtained.
The applications of this work sounds widely especialy for air trafic control radar systems and militery
applications for target classification and recognition.
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