Original Research Article
Year: 2016  Month: JanuaryMarch  Volume: 1  Issue: 01  Pages: 5465
Performance Analysis of Radar for Effective Air Traffic Control System
Biebuma J. J, Peter Ndubuisi Asiagwu
College of Engineering, Department of Electrical/Electronics, University of Portharcourt, Choba, Portharcourt Nigeria
Corresponding Author: Peter Ndubuisi Asiagwu
ABSTRACT
Air Traffic Control (ATC) radar propagation with a short pulse duration lacks the capability of long range detection of targets. Thus, pulse compression techniques can be used in the system to provide the beneﬁts of large range detection capability of long duration pulse and high range resolution capability of short duration pulse especially for solid state radar transmitter with low peak power. In these techniques, a long duration pulse is used which is phase modulated before transmission and the received signal is passed through a matched ﬁlter to compress the energy into a short pulse in other to achieve high signaltonoise ratio (SNR). In this work, pulse compression matched ﬁlter model was developed in which the output is the autocorrelation function (ACF) of a modulated signal associated with range side lobes along with the main lobe which is used to maximise the signaltonoise ratio as shown in the simulation result using MATLAB software. With the implementation of this model, radar system in Port Harcourt can be made to detect targets beyond 256nm.
Keywords: Air traffic control, Pulse compression, Signaltonoise ratio, Matched filter, Range detection, Radar system performance, MATLAB.
1.0 INTRODUCTION
The daytoday problems faced by the air traffic controllers are primarily related to weather and the volume of air traffic demand placed on the radar system. Each landing aircraft must touch down, slow, and exit the runwaybefore the next crosses the approach end of the runway. Air traffic control errors generally occur when the separation (either vertical or horizontal) between airborne aircraft falls below the minimum prescribed separation set or during period of intense activity, when controllers tend to relax and overlook the presence of traffic and conditions that lead to loss of minimum separation. Beyond runway capacity issues, weather is a major factor in traffic capacity. Rain, ice, snow on the runway cause landing aircraft to take longer to slow and exit, thus reducing the safe arrival rate and requiring more space between landing aircraft (Meril I. Skolnik, 2001).
One of the greatest challenges being faced today by pilots in Nigeria airspace is effective communication with the air traffic controllers on ground especially at long range owing to the short range of the radar system of about 256nm together with its inability to detect reply signals at heavy precipitation as a result of narrow pulse width of 0.8µs.
This research work was carried out to present a mathematical model of pulse compression matched filter capable of accumulating the energy of large pulse into a short pulse without sacrificing its range resolution in other to increase its sensitivity, maximize the output signaltonoise ratio and improve radar target detection of aircraft farther than 256nm. The paper also included the analysis and the simulated result of the matched filter as an autocorrelation of the modulated input signal.
Figure 1.0 Radar system schematic
Radar systems are very complex electronic, electromagnetic and mechanical systems. They are composed of many different subsystems, which themselves are composed of many different components. There is a great diversity in the design of radar systems based on purpose, but the fundamental operation and main set of subsystems is the same. Some of the subsystems and important components that are found in typical portable monostatic pulsed ground surveillance radar systems are shown in the figure 1.0 (Varshney Lar, 2002).
1.1 THE RADAR EQUATION
Consider radar with an omnidirectional antenna (one that radiates energy equally in all directions).Equation 1
Since these kinds of antennas have a spherical radiation pattern which can be defined as the peak power density, Pd (Power per unit area) at any point in space as:
The power density at range R away from the radar (assuming a lossless propagation medium) is:
P_{d} =  P_{t} 
4πR^{2} 
(2)
Where Pt is the peak transmitted power and 4πR2 is the surface area of a sphere of radius R. Radar systems utilize directional antennas in order to increase the power density in a certain direction. Directional antenna gain G and the antenna effective aperture Ae are related by:
A_{e} =  Gλ^{2} 
4π 
 (3)
Where λ is the wavelength of the signal. The relationship between the antenna’s effective apertures Ae and the physical aperture A is.
Ae = ρ A (4)
0 < ρ < 1
ρ is referred to as the aperture efficiency and good antennas require ρ → 1. In this work, A and Ae are assumed to be the same including antennas gain in the transmitting and receiving modes. In practice ρ → 0.7 is widely accepted.
The power density at a distance R away from radar using a directive antenna of gain G is then given by:
P_{d} =  P_{t}G 
4πR^{2} 
(5)
When the radar radiated energy impinges on a target, the induced surface currents on that target radiate electromagnetic energy in all directions. The amount of the radiated energy is proportional to the target size, orientation, physical shape, and material, which are all lumped together in one targetspecific parameter called the radar cross section (RCS) and is denoted by σ.
The radar cross section is defined as the ratio of the power reflected back to the radar to the power density incident on the target,
σ =  P_{r}(m^{2}) 
P_{d} 
 (6)
Where Pr is the power reflected from the target. Thus, the total power delivered to the radar signal processor by the antenna is:
P_{dr} =  P_{t}GσA_{e} 
(4πR^{2})^{2} 
 (7)
Substituting the value of Ae from Eq (3) into Eq (7) yields
P_{dr} =  P_{t}G^{2}λ^{2}σ 
(4π)^{2}R^{4} 
 (8)
Let S_{min} denote the minimum detectable signal power. It follows that the maximum radar range R_{max} is
Eq (9) suggests that in order to double the radar maximum range, one must increase the peak transmitted power (P_{t}) Sixteen times; or equivalently increase the effective aperture four times.
In practice situations, the returned signals received by the radar will be corrupted with noise, which introduces unwanted voltages at all radar frequencies. Noise is random in nature and can be described by its Power Spectral Density (PSD) function. The noise power N is a function of the radar operating bandwidth B. More precisely
N = Noise (PSD) x B (10)
The input noise power to a lossless antenna is
Ni = K T_{e} B  (11)
Where K = 1.38 x 10^{23} joule per degree kelvin is boltzman’s constant.
T_{e} = 290 degree kelvin is the effective noise temperature.
It is always desirable that the minimum detectable signal (S_{min}) be greater than the noise power. The fidelity of a radar receiver is normally described by a figure of merit called the noise figure F. The noise figure is defined :
Equation 12
(SNR)_{i} and (SNR)_{o} are respectively the signal to Noise ratios (SNR) at the input and output of the receiver. S_{i} is the input signal power, N_{i} is the input noise power, S_{o} and N_{o} are respectively the output signal and noise power (Barton, 1988).
Substituting Eq. (11) into Eq. (12) and rearranging terms yield:
S_{i} = KT_{e}BF(SNR)_{o}  (13)
Thus, the minimum detectable signal power can be written as:
S_{i}=KT_{e}BF(SNR)_{omin}  (14)
The radar detection threshold is set equal to the minimum output SNR, (SNR)_{omin}.
Substituting Eq (14) into Eq (9) gives:
Or equivalently
(SNR)_{omin} =  P_{t}G^{2}λ^{2}σ 
(4π)^{3}KT_{e}BFR^{4} 
 (16)
Radar losses denoted as L reduces the overall (SNR) and hence
(SNR)_{omin} =  P_{t}G^{2}λ^{2}σ 
(4π)^{3}KT_{e}BFLR^{4} 
 (17)
Though it may take different forms, Eq. (17) is what is widely known as the radar equation. It is a common practice to perform calculations associated with the radar equation using decibels (dB) arithmetic (Bassem, R. and Mahafza, 2000).
1.2 DETECTION RANGE OF RADAR SYSTEM
The radar equation can be modified to compute the maximum detection range required to achieve a certain (SNR)_{omin} for a given pulse width. If B = 1/ τWhere:
B is the bandwidth
τ – pulse width of the transmitted signal
Thus:
Equation 18
1.3 DESIGN SPECIFICATIONS FOR THE RADAR SYSTEM
The design parameters of the Lband radar system is as presented below:
Band: L
Frequency band (F): 1 ~ 2GHz
Wavelength range (λ): 15 ~ 30cm
Transmitting peak power (P_{t}): 2570W
Transmitting average power (P_{avg}): 25.70W
Pulse repetition time (PRT): 1000µs
Pulse repetition frequency (PRF): 1000pulses per second
Pulse width (P_{w}): 10µs
Duty cycle (DC): 0.01
Radar target cross section (σ): 1.0m^{2}
Table 1.0: Antenna and Receiver Parameter Values
Antenna Parameters 
Value 
Radar Receiver Parameters 
Value 
Width: 
7.3m 
Noise figure (F): 
3dB 
Height: 
4.3m 
Boltzman’s constant (K): 
1.38 x 1023J/0K 
Rotating clearance: 
8.7m 
Effective noise temperature (K): 
2900K 
Horizontal beamwidth (ϴ): 
2.40 
Probability of detection (P_{d}): 
80% and above 
Vertical beamwidth (Φ): 
4.00 
Probability of false alarm rate (P_{f}): 
1.0 x 106 
Directional gain (G_{dir}): 
36.4dB 
Minimum signaltonoise ratio (SNR)_{omin} 
13dB 
Scan rate: 
6rpm 


The distance beyond which a target can no longer be detected and correctly processed can be determined with the use of maximum range (R_{max}) radar equation. The criterion for detection is simply that the received power, P_{r} must exceed the minimum, S_{min}. Since the received power decreases with range, the maximum detection range will occur when the received power is equal to the minimum detectable signal of the radar receiver i.e. P_{r} = S_{min} (Blake, L. M., 1991)
Table 2.0: MDS vs pulse width at a specified noise figure and SNR.
From the radar equation, it can be observed that the ability of radar to detect targets can be improved when there is an increase in pulse width. Figure 1.1 shows a typical plot generated by using MATLAB program listed in Appendix A with 2.0µs, 5.0µs and 10.0µs input values of the pulse width.
Figure 1.1: Graph of Range against SNR with three different pulse width
The required signaltonoise ratio (SNR) at the receiver is determined by the design goal of P_{d} and P_{fa}, as well as the detection scheme implemented at the receiver. The relation between P_{d}, P_{fa} and SNR can be best represented by a receiver operating characteristics (ROC) curve shown in figure 1.2.
Figure 1.2: Pd vs Pfa varying pulse SNR (Masanoni Shinriki and Hironori Susaki, 2008)
Wider pulses can effectively increase radar’s sensitivity to weak atmospheric events and enhance its ability to penetrate heavy precipitation. Sensitivity in a radar receiver is normally taken as the minimum input signal (S_{min}) required to produce a desired output signal having a specified signaltonoise ratio (SNR) which is usually stated in dBm.
Table 3.0: Values of Sensitivity in Accordance With Increase in Range and Pulse Width
From the MATLAB simulation in figure 1.3, it is obvious that the sensitivity of radar is reduced when there is an increase in the transmitted pulse width which limits its performance. Consequently, if the radar transmitter can increase its PRF and its receiver performs integration over time via pulse compression technique, an increase in PRF or pulse width can permit the receiver to "pull" coherent signals out of the noise thus reducing S/N_{min} thereby increasing the detection range.
Figure 1.3: Graph of Sensitivity against Range
1.4 PULSE COMPRESSION IN RADAR SYSTEM
The maximum detection range depends upon the strength of the received echo. To get high strength reﬂected echo, the transmitted pulse should have more energy for long distance transmission since it gets attenuated during the course of transmission. The energy content in the pulse is proportional to the duration as well as the peak power of the pulse. The product of peak power and duration of the pulse gives an estimate of the energy of the signal (E_{t}). Where
Et = Pt x τ  (19)
A low peak power pulse with long pulse duration provides the same energy as achieved in case of high peak power and short duration pulse. Shorter duration pulses achieve better range resolution.
The range resolution,
S_{x} =  C_{o} 
2B 
 (20)
Where B is the bandwidth of the pulse and C_{o} is the speed of electromagnetic waves.
Figure 1.4 illustrates two pulses having same energy with different pulse width and peak power
In pulse compression technique, a pulse having long duration and low peak power is modulated either in frequency or phase before transmission and the received signal is passed through a ﬁlter to accumulate the energy in a short pulse. The pulse compression ratio (PCR) is deﬁned as
PCR =  width of the pulse before compression 
width of the pulse after compression 
 (21)
The block diagram of a pulse compression radar system is shown in Figure 1.5. The transmitted pulse is either frequency or phase modulated to increase the bandwidth. Transreceiver (TR) is a switching unit which regulates the function of the antenna as a transmitting and receiving device. The pulse compression ﬁlter is usually a matched ﬁlter whose frequency response matches with the spectrum of the transmitted waveform. The ﬁlter performs a correlation between the transmitted and the received pulses. The received pulses with similar characteristics to the transmitted pulses are picked up by the matched ﬁlter whereas other received signals are comparatively ignored by the receiver (Vansi Krishma M, 2011).
Figure 1.5: Block diagram of a pulse compression radar system
1.5 MATCHED FILTER MODEL
In radar applications, the reﬂected signal is used to determine the existence of the target. The reﬂected signal is corrupted by additive white Gaussian noise (AWGN). The probability of detection is related to signaltonoise ratio (SNR) rather than exact shape of the signal received. Hence, it is required to maximize the SNR rather than preserving the shape of the signal. A ﬁlter which maximizes the output SNR is called matched ﬁlter. A matched ﬁlter is a linear ﬁlter whose impulse response is determined for a signal in such way that the output of the ﬁlter yields maximum SNR when the signal along with AWGN is passed through the ﬁlter. An input signal s(t) along with AWGN is given as input to the matched ﬁlter as shown in Figure 3.12.
Let N_{o}/2 be the two sided power spectral density (PSD) of AWGN. We are required to ﬁnd out the impulse response h(t) or the frequency response H(f) (Fourier transform of h(t)) that yields maximum SNR at a predetermined delay t_{o}
In other words, h(t) or H(f) is determined to maximize the output SNR which is given by
Figure 1.6: Block diagram of matched filter.
Equation 22
Where SP is the signal power, NP is the output noise power, S_{o}(t_{o}) is the value of the output signal S_{o}(t) at
t = t_{o} and N_{o}2(t) is the mean square value of the noise.
If S(f) is the fourier transform of S(t), then S_{o}(t) is obtain as
Equation 23
The value of S_{o}(t) at t = t_{o} is
Equation 24
The mean square value N_{o}2(t) of the noise is evaluated as
Equation 25
Substituting equ (24) and equ (25) into equ (22)
Equation 26
Using Schwartz inequality, the numerator of equ (26) can be written as
Equation 27
In equ (27), the equality holds good if
Equation 28
Where K_{1} is an arbitrary constant and * stands for complex conjugate. Using the equality sign of equ (27) which corresponds to maximum output SNR in equ (26)
Equation 29
Where E is the energy of the infinite time signal and defined as
Equation 30
From equ(29), it is obvious that the maximum SNR is a function of the energy of the signal but not the shape. Taking inverse Fourier Transform of equ(30), the impulse response of matched filter is obtained as
Equation 31
From equ (31), it is clear that the impulse response of matched filter is a delayed mirror image of the conjugate of the input signal from equ (24) and equ (28).
The output at t = t_{o} is given as
Equation 32
Equ (32) states that regardless of the type of waveform, at the predefined delay t = t_{o}, the output is the energy of the wave form for K_{1} = 1. The output of the matched filter is evaluated as
Equation 33
Where ⊗ denotes the linear convolution operation
Equation 34
Equ (34) represents auto correction function (ACF) of the input signal S(t)
Thus, it is obvious that the autocorrelation operation is mathematically equivalent to the matched filter with a time reversed complex conjugate of the signal. In the frequency domain, the product of the fourier transform of the signal S(t) and its timereversed complex conjugate can represent the matched filter.
So(t) = F 1{F[S(τ)] FS*(τ)}  (35)
Equation 36
Since the autocorrection sequence is symmetric, it is sufficient to consider only the positive lags.
The above operation can be implemented using matched filter operation.
By defining; h(n) = S*(n), we have
Equation 37
Equation 38
So(m) = IFFT [FFT{S(n)} x FFT{S*(n)}]  (39)
Where the FFT and IFFT operations were used to simplify the correlation operation
The digital correlation processor operates on the principle that the spectrum of the time convolution of two waveforms is equal to the product of the spectrum of these two signals as shown in figure 1.7
Figure 1.7: Digital matched filter correction processor
If M range samples are to be provided by one correlation processor, the number of samples in the Fast Fourier Transform (FFT) must equal M plus the number of samples in the reference waveform. These added M samples are filled with zeros in the reference waveform FFT. For extended range coverage, repeated correlation processor operations are required with range delays of M samples between adjacent operations. A Fast Fourier Transform in the pulsecompressor function correlates the received signal return spectrum with the known spectrum of the transmitted signal. The FFT is analogous to a spectrum analyzer. Thus, the inverse FFT is the autocorrelation function of the input signal which represents the output of the matched filter (FanWarg, Huotao Gao, 2011).
1.6 MATCHED FILTER SIMULATION RESULT AND ANALYSISFigure 1.8: Graph of autocorrection function vs delayed time
A pulse compression matched filter is applied when one wants to pass the received radar echo through a filter whose output will optimize the signaltonoise ratio (SNR). For a rectangular pulse, matched filter is a simple pass band filter. (Diagram)
In digital system, matched filter is implemented by ‘’convolving’’ the reflected echo with the ‘’time reversed’’ transmit pulse as illustrated using three bits. (Diagram)
The processes involve in the convolution are:
 Move digitized pulses by each other in steps
 When data overlaps, multiply samples and sum them up to obtain the output signal.
1) When there is no overlap in the filter, the output of the matched filter is zero
2) When one sample overlaps in the filter, the output of the matched filter is 1
3) When two samples overlap, the output of the matched filter is 2
4) When three samples overlap, the output of the matched filter is 35) When two samples overlap, the output of the matched filter is 2
6) When one sample overlaps, the output of the matched filter is 1
7) When no sample overlaps, the output of the matched filter is zero (0)
Matched filter is used to maximize the signaltonoise ratio (SNR) so that targets can be detected even at high precipitation when there is an increase in sensitivity.
1.7 CONCLUSION
With an improved radar performance, pilots and air traffic controllers can observe immediately and precisely where surrounding air traffic is at a given time within their surveillance volume. Future research work can be carried out in developing a model for suppressing autocorrelation side lobes of matched filter to enhance its performance.
1.8 RECOMMENDATION
There is need to keep airplanes spaced at least five nautical miles horizontally apart when flying across a country and no closer than three nautical miles when preparing to land. Therefore, safety rules must be followed without compromise to avoid much traffic in the air which might lead to plane crash. A welldesigned radar system, with all other factors at maximum efficiency, should be able to distinguish targets separated by onehalf the pulse width time. The degree of range resolution depends on the width of the transmitted pulse, the types and sizes of targets, and the efficiency of the receiver and indicator. Pulse width is the primary factor in range resolution and detection. Its increase will enhance the performance of the Nigeria radar system by improving its effectiveness in detecting targets even at heavy precipitation.
However, the state of PortHarcourt airport and navigational aids calls for urgent attention especially in the areas of:
 Infrastructural development such as rehabilitation of the runway.
 Upgrade of VHF/UHF radio communication gadgets of the air traffic controllers.
 Uninterrupted electric power supply both in the radar system and the entire airport.
 Upgrade of Eurocat C ATM System for APP positions used by air traffic controllers
 Upgrade of RSM 970 MSSR to the one with higher pulse width.
 Training and retraining of members of the air traffic controllers for optimum performance.
I vehemently believe that with a careful implementation of these observations and more, our airspace will be very safe to fly.
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 Bassem, R. and Mahafza (2000), “Radar systems Analysis and Design Using MATLAB”, chapman and hall/CRC.
 Blake, L. M. (1991), Radar Range Performance Analysis, Silver Spring, Maryland, Munro.
 FanWarg, Huotao Gao, Lin Zhou, Qingchen Zhou, Jie Shi Yuxiang Sun (2011), “Design and FPGA implementation of digital pulse compression for HF chirp radar based on modified orthogonal transformation”, IEICE Electronics Express, Vol 8
 Levanon N, and Mozesn E. (2004), “Radar Systems”, Wiley New York.
 Masanoni Shinriki and Hironori Susaki (2008),”Pulse Compression for simple pulse”,IEEE Transactions and aerospace electronic systems, Vol 44.
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 Sridhar G and K. D. Rao (1995), “Improving Performance in Pulse Radar Detection Using Neutral Networks”, IEEE Trans. Aerosp. Electron System Vol. AES32.
 Vansi Krishma M, K Ravi Kumar, K. Suresh, V.Rejesh (2011),”Radar pulse compression”, International Journal of electronics and communication technology”, Vol 2.
 Varshney Lar (2002),”Radar System Components and System Design”, North Syracuse New York.
 Yichum P, Shirui P, Kefeng Y and D. Wenfeng (2005),”Optimisation Design of NLFM Signal and its Pulse Compression Simulation”,IEEE Int. Conf.
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APPENDIX A : MATLAB PROGRAM LISTINGS
No. 1% Sensitivity versus range curve as it varies with pulse width
R = [1 50 200 1000]; % values of range
S1 = [27.47 95.43 119.51 147.47]; % values of sensitivity at 0.8pulse width
S2 = [23.49 91.45 115.53 143.49]; % values of sensitivity at 2.0pulse width
S3 = [19.51 87.47 111.55 139.51]; % values of sensitivity at 5.0pulse width
S4 = [16.50 84.46 108.54 136.50]; % values of sensitivity at 10.0pulse width
plot(R,S1,'k') % to be plotted with black colour
hold on
grid off
xlabel('Range[km]'); % xaxis label
ylabel('Sensitivity[dBm]'); % yaxis label
plot(R,S2,'r') % to be plotted with red colour
plot(R,S3,'b') % to be plotted with blue colour
plot(R,S4,'g') % to be plotted with magenta colour
legend('0.8us','2.0us','5.0us','10.0us')
title('Graph of Sensitivity versus Range')
No. 2
% Graphical illustration of how pulse width varies with range and snr
Pt = 2570; % value of peak power
G = 4381; % value of gain(36.4dB)
L = 0.3; % value of wavelength
Rcs = 1.0; % value of radar cross section
w1 = 2.0; % 2.0us pulse width
w2 = 5.0; % 5.0us pulse width
w3 = 10.0; % 10.0us pulse width
K = 1.38*10^(23); % Boltzman constant
Te = 290; % effective temperature
F = 2.0; % value of noise figure(3dB)
lo = 128.8; % value of radar system loss(21.1dB)
snr = 1:1:18; % range of values of snr
R1 = ((Pt*G^2*L^2*Rcs*w1)./((4*pi)^3*K*Te*F*lo*snr)).^(1/4); % function of R1
R2 = ((Pt*G^2*L^2*Rcs*w2)./((4*pi)^3*K*Te*F*lo*snr)).^(1/4); % function of R2
R3 = ((Pt*G^2*L^2*Rcs*w3)./((4*pi)^3*K*Te*F*lo*snr)).^(1/4); % function of R3
pSlot (snr,R1,'k',snr,R2,'r',snr,R3,'b'); % for graph plotting
hold on
grid off
xlabel('SignaltoNoise Ratio[dB]')% xaxis label
ylabel('Range[M]') % yaxis label
legend('2.0us','5.0us','10.0us')
title('Graph of Range vs SNR for three different Pulse Width')
No. 3
% Graph of the matched filter output simulation
wt = 2*pi:0.01:2*pi;
So = sinc(wt); % So is the output of the signal
plot (wt,so,'k'); % Output signal vs the phase angle of function
hold on
grid on
xlabel('Delayed time') % xaxis label
ylabel('Autocorrelation function') % yaxis label
title('Graph of ACF against delayed time')
How to
cite this article: Biebuma JJ, Asiagwu PN. Performance Analysis of Radar for Effective Air Traffic Control System. International
Journal of Science & Healthcare Research. 2016; 1(1):5465.
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