Automatic Gain Fuzzy Logic Controller for Pulse RadarAGC HMC992LP5E fuzzy logic gain control is not...

International Journal on Electrical Engineering and Informatics - Volume 8, Number 1, March 2016

Automatic Gain Fuzzy Logic Controller for Pulse Radar Receiver System

Eko Joni Pristianto1 and Pranoto Hidaya Rusmin

2

1Research Center for Electronics and Telecommunication, Indonesian Institute of Sciences, Indonesia 2School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia

[email protected],

[email protected]

Abstract: Pulse radar is a device for emitting short pulse with high power and receiving echo

signal from the target. At receiver side, pulse radar has an Automatic Gain Control (AGC)

module to adjust receiver sensitivity and get the best amplitude level for the next process. The

AGC type used in this research is HMC992LP5E. In previous research, gain settings used a

classical closed loop AGC system. The disadvantage of this system is slow response of the

AGC to change the input signal. As the result, it is difficult to distinguish between noise and

target signal and differentiate amplitude level signal for different targets on radar system

application. This paper explains the design of Automatic Gain Control (AGC) system using

HMC992LP5E and fuzzy logic controller. The gain value of AGC is determined by input and

output signal amplitude level of AGC and also the maximum range of radar targets. AGC gain

value setting is provided by a certain voltage value at the pin VCTRL. VCTRL value is

calculated by a 32-bit microcontroller using fuzzy logic algorithms. This controller will

generate more dynamic value of AGC gain. The AGC output signal amplitude level can be

determined based on fuzzy logic algorithms. So that, the target signal captured by the pulse

radar receiver system will be more easily identified and can be used for other applications.

Keywords: pulse radar, automatic gain control, HMC992LP5E, fuzzy controller,

microcontroller.

1. Introduction

Pulse radar is a device that emits high power short wave and in some periods will receive

echo signal. Its receiver part receive back the reflected electromagnetic wave from object

signal that was detected by radar through antenna reflector. Generally, receiver has ability to

filter the echo signal and make it suitable with the preferred detection, amplify weak object

signal and pass it to signal processing system, and display the final data to the display system

[1].

On the receiver part, there is an Automatic Gain Control Module (AGC). AGC module

works to adjust receiving sensitivity and gain best signal amplitude before further process [2].

In this research used HMC992LP5E AGC type, which is produced by Hittite Microwave

Coorporation. This AGC is a Intermediate Frequency (IF) analog signal Variable Gain

Amplifier (VGA) controller. It is consisting of two identical variable attenuators combined

with Monolithic Microwave Integrated Circuit (MMIC) amplifier that works on frequency

50MHz to 800MHz. The gain controlled value has ranges from -10 dB to 40 dB [3].

In the classical closed loop AGC system, the amplification setting has implemented by

setting the VGA, which is integrated with the logarithmic detector (log detector for short).

Then, the amplitude of AGC output signal adjust by giving a voltage set value VSET for set

point ranging from 0.2 to 1.2 VDC. In previous research, the AGC gain setting was

implemented conventionally by giving a constant voltage for VSET, so that AGC output signal

amplitude will be fixed to the given set point value [4]. Classical AGC has several drawbacks.

It has slow response compared to fast change on input signal. This system result similar

amplitude for different targets or noise which causes difficulty to determine different target or

distinguish noise from target signal.

Received: January 10th

, 2016. Accepted: March 17th

, 2016

DOI: 10.15676/ijeei.2016.8.1.5

62

mailto:[email protected],

mailto:[email protected]

Figure 1. RCS on stealth aircraft Boeing UCAV X-45 (S Band)

Every pulse radar system target has Radar Cross Section (RCS), a ratio of backscatter

density in the direction of the radar (from target) to the power density of signal. This value is a

density parameter of compactness of the detected object [2]. As an example, Figure 1 shows

RCS value of a Boeing UCAV X-45 [5]. The following Table 1 shows some RCS values of

different objects.

Table 1. RCS value of different objects

According to the Table 1, an aircraft carrier has greatest RCS value which is 100000 m2 (50

dBsm), followed by cruiser, large airline, medium airliner, large fighter, Man, until insects with

RCS value of -60dBsm [5]. The RCS value, which is target signal amplitude, will be received

by radar receiver system. Thus, to obtain variety of target signal, the output amplitude of AGC

cannot be fixed all the time. With this potential advantage, an AGC HMC992LP5E controller using fuzzy logic needs to

be designed. In previous researches, AGC fuzzy controller has been developed in many

systems such as the application of fuzzy logic to improve the performance of AGC circuit. For

this case, AGC simulation using amplifier circuit [6]. The AGC based on fuzzy algorithm is

applied for Single Side Band (SSB) radio communication system [7]. AGC control system

based on fuzzy logic for Erbium Doped Fibre Amplifier (EDFAs) [8]. And, the fuzzy control

for AGC microphone in Autonomous Support System For Elderly [9]. The following Table 2

describes previous results from input-process-output perspectives.

Eko Joni Pristianto, et al.

63

Table 2. Recent Researches on AGC Fuzzy

No Research Features

1 AGC simulation using

amplifier circuit

Input: two inputs: input dan output signal as ac signal

with specific frequency.

Process: one module fuzzy but the fuzzy inference system

is not explained.

Output: the Vagc voltage to determine op-amp gain on

the transistor basis.

2 The AGCbased on

fuzzy algorithm is

applied for Single

Side Band (SSB) radio

communication

system

Input: two inputs: audio signal strength and its derivative.

Process: one module fuzzy using mamdani inference

system.

Output: the signal gain factor

3 AGC control system

based on fuzzy logic

for Erbium Doped

Fibre Amplifier

(EDFAs)

Input: two inputs, signal power and wavelength

Process: one module fuzzy using mamdani inference

fuzzy model

Output: current of the pump laser

4 Fuzzy control for

AGC microphone in

Autonomous Support

System For Elderly

Input: one input, Level of Noise

Proses: one module fuzzy but the fuzzy inference system

is not explained.

Output: Microphone sensitivity.

In this research, the value of AGC gain is determined according to input signal amplitude,

AGC output, and maximum range of radar target. The setting of AGC HMC992LP5E gain

value can be done by giving a certain voltage value to pin VCTRL. Calculation of VCTRL

value using fuzzy algorithm will be executed by 32 bit microcontroller. This control system

generate dynamic AGC gain value and AGC output signal based on the fuzzy logic algorithm.

2. Classic closed loop AGC HMC992LP5E system

HMC992LP5E can be configured as AGC amplifier by VGA core and log detector in single

chip. The following Figure 2 shows a block diagram and classic closed loop

AGCHMC992LP5E circuit.

In this configuration, input signal is amplified by VGA. Then, output of VGA core is fed

back to log detector input RFDETIN (pin 16) through an external cupler to decrease the

maximum or minimum amplitude of VGA output such that the value in the dynamic range of

log detector. Log detector generate voltage at DETOUT (pin 13) proportional to VGA output

power amplitude. Output pin DETOUT with high impedance is connected to gain controller on

pin VCTRL or VGA to form AGC loop. Pin VSET has output power constant which is the set

point value from AGC amplifier. VSET value is a negative feedback, so the VGA gain

automatically will be adjusted to make AGC output signal power remains constant. Output

signal power is determined by value of VSET AGC, regardless of input signal variance.

Figure 3 shows the correlation between input and output power at different VSET values in

classic AGC configuration [3].


64

Automatic Gain Control

AGC HMC992LP5E

50 - 800 MHz

Variable

voltage

Coupler -10 dB

RFINRFout

VSET RF det IN

INPUT OUTPUT

RF signal

DC signal

(a)

(b)

Figure 2. (a) Block diagram, (b) loop AGC HMC992LP5E schematic with two attenuators and

two amplifiers configuration [3].

Figure 3. The Correlation between input and output power at different VSET values in closed

loop AGC configuration

Input power under and over the range value of attenuation will be saturated, so that the

output is a linier function of input.


65

3. Proposed AGC System using Fuzzy Logic Controller

This fuzzy logic controller makes the gain value of AGC fluctuate. While, the value of gain

on classical closed loop AGC system is static because it is only has one gain value. In general,

this algorithm can only be applied in receiver system of non pulse compression Radar and it is

not yet tested for other type of Radar such as Frequency-Modulated Continuous-Wave

(FMCW) radar. The block diagram of AGC HMC993LP5E gain control using fuzzy logic can

be seen on below Figure 4.

OUTPUT

signal

Microcontroller

STM32F401RE

RF Power Detector

(Output)

Splitter 600 Mhz(output)

RF Power Detector

(Input)

Splitter 600 Mhz

(Input)

u2u1

y


AGC HMC992LP5E

50 - 800 MHz

RFIN RFout

VCTRL

Figure 4. AGC HMC993LP5E gain control using fuzzy logic

There are two two-way splitters, which split input signal into AGC input port and RF power

detector input port – which is used to read signal amplitude to the AGC. Output splitter split

AGC output signal into RF power detector port and system output port which is used to read

signal amplitude the AGC.

RF power detector convert RF signal into DC signal. Then, the DC signal will be the input

for microcontroller’s ADC and fuzzy logic variable. Where, the fuzzy logic controller inputs

are detection signals, u1and u2. The microcontroller process the value of u1 and u2 by fuzzy

algorithm and generate PWM output signal. This PWM signal is connected to VCTRL pin at

the AGC. The detailed circuit of system on Figure 4 is shown on Figure 5.

Figure 5. Schematic of AGC HMC992LP5E using fuzzy logic controller

To apply this method, log detector circuit has to be deactivated by giving 0V to DETEN pin

(no 18). This configuration uses two attenuators and two amplifiers.

4. AGC Signal and System

AGC HMC992LP5E fuzzy logic gain control is not only using input and output signal

amplitude. Other variables outside AGC system can also be used as fuzzy input value to

determine VCTRL value. In this research, AGC gain control method with fuzzy logic will be


66

applied to control pulse radar target signal amplitude. A fuzzy input variable which is a

maximum range of desired radar target will be added. Figure 6 shows a block diagram of AGC

application with fuzzy control on pulse radar target signal amplitude control.


AGC HMC992LP5E

50 - 800 MHz

RF Power

Detector

(Input)

Microcontroller

STM32F401RE

RF Power

Detector

(Output)

Splitter 600 Mhz(Input)

Splitter 600 Mhz(output)

LCDPC

1 2

3

4

5

6

7

8

9

11

12

PWM to

Analog

10

RF signal

DC signal

DataPWM signal

Figure 6. AGC HMC992LP5E using fuzzy logic controller on pulse radar target amplitude

control

On the first process, signal 1 in the form of continuous wave will be generated by signal

generator. Signal 1 pass to a input splitter to generate signal 2 and signal 3; each will be

attenuated by -3 dB. Then, signal 3 pass to RF power detector input. On RF power detector

input, signal 3 will be converted to DC signal to generate signal 4. Signal 4 is connected to

ADC 0 input pin of microcontroller. Then, ADC data 0 is converted again to input signal

which will be the value of fuzzy input, u1.

On the second process, microcontroller receive data signal in serial format from personal

computer. This data is the maximum detected range value. This value become the value of

fuzzy input, u2. Then, microcontroller calculate the first fuzzy algorithm with u1 and u2 as input

variables to generate first fuzzy output, signal 9, which is a PWM signal. First, this PWM

signal is converted to DC signal (signal 10) and will be the input for VCTRL AGC.

On the third process, after AGC gets the VCTRL value from microcontroller, the gain

adjustment will be executed by HMC992LP5E and signal 6 is generated. Signal 6 will be

processed by similar method to the first process to generate signal 12 and signal 7. Signal 12 is

AGC output signal with -3 dB attenuation and signal 7 will generate the second fuzzy input

value, u3 as feedback from system. Microcontroller calculate the second fuzzy algorithm with

y1 and u3 as input variables. The result update signal 9. All systems shown on above Figure 6,

operate on time domain and there is no frequency change. In this research, the frequency of

input signal is 600 MHz.

5. Radar Target Simulator

This module generate signal that represents target which will be received by pulse radar.

The generated target signal refers to the following radar equations (1).

𝑆𝑚𝑖𝑛 = 𝑃𝑡𝐺𝐴𝑒𝜎

(4𝜋)2 (𝑅𝑚𝑎𝑥)4 (1)

Where

Rmax = Maximum range.

Pt = Transmitter amplitude.

G = Maximum gain antenna.

Ae = Effective area of the receiving antenna.


67

σ = Radar cross section.

Smin = Minimum detectable signal.

Smin is amplitude of target with the change of Rmax value. The values of Pt, G, Ae, and σ are

constant. The block diagram of pulse radar target signal generator is shown in Figure 7 below.

Signal

Generator

Microcontroller

Digital

AttenuatorAGC

INPUT OUTPUT

Figure 7. Pulse Radar Target Signal Generator

The Microcontroller control the attenuation value of digital attenuator by assigning 6 bit

biner configuration attenuation, which has value ranging from -0.5 dB to -31.5 dB, that is

randomly generated within certain period. The module output is a 600 MHz signal that has

been modulated with different amplitude level. Figure 8 shows several waveform of pulse radar

target signal generator.

IF = 600MHz

PULSE MODULATOR

RF SWITCH

PM

IF

TX

PW PRF

TX

IF = 600MHz

PULSE MODULATOR

RF SWITCH

PM

IF

TX

PW P RF

TX

IF = 60 0MH z

PU LSE M O DU LATO R

R F SW ITC H

PM

IF

TX

P W P RF

TX

I F = 6 0 0 M H z

P U L S E M O D U L A T O R

R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

IF = 6 0 0 M H z


R F S W IT C H

P M

IF

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

Signal 1

Signal 2

IF = 60 0MH z

PU LSE M O DU LATO R

R F SW ITC H

PM

IF

TX

P W P RF

TX

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

IF = 600MHz

PULSE MODULATOR

RF SWITCH

PM

IF

TX

PW PRF

TX

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

IF = 6 0 0 M H z


R F S W IT C H

P M

IF

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

I F = 6 0 0 M H z


R F S W I T C H

P M

I F

T X

P W P R F

T X

Figure 8. Several waveform of pulse radar target signal generator

Signal 1 is an input signal coming from generator. Signal 2 is a modulated signal with

different amplitude. This signal represents the radar target [4].

6. Proposed Fuzzy Controller

Fuzzy algorithm used in some of the literature above had two inputs and one output, some

even used only one input and one output. Where as, for the process used one inference system,

that is Mamdani or Sugeno models. The proposed Fuzzy controller has two modules fuzzy,

those are fuzzy A module using mamdani model and fuzzy B module using Sugeno models.

Each module has two inputs and one output. Fuzzy rules in the fuzzy A module are based on

the knowledge of an expert and the experimental results. Meanwhile, fuzzy B module is

developed to read the output response of AGC and to maintain the module A output. The

detailed design will be explained below.

Power Input

AGC

Maximum

Range

Fuzzy A

Power Output

AGC

Fuuzy B

Gain

output

VCTRL

AGC

Figure 9. Fuzzy Logic for AGC HM992LP5E


68

AGC HMC992LP5E gain control has two fuzzy modules: fuzzy A and fuzzy B module.

Fuzzy A module uses AGC Power input and maximum target range reading as the input and

generate output gain value. The input of fuzzy B module are fuzzy A output and AGC power

output signal. The block diagram of fuzzy logic for AGC is shown on Figure 9 below.

The first input of Fuzzy A is the maximum target range, which will be divided into 4 fuzzy

sets by triangle function as shown on Figure 10(a). The second input is AGC input amplitude

which is divided into 5 fuzzy sets as shown on Figure 10(b). Then, the output of Fuzzy A will

be divided into 4 fuzzy sets as shown on Figure 10(c). The Fuzzy A rules is shown on the

following Table 3. It can be shown on Table 3, there will be 20 fuzzy rules and the

defuzzyfication process of Fuzzy A module uses Mean of Maximum method.

(a)

(b)

(c)

Figure 10. Membership function (a) maximum range, (b) Input amplitude, (c) output gain

Table 3. Fuzzy rules for Fuzzy A module

Maximum

radar target

range

Power Input AGC

Very Small Small Moderate Big VeryBig

Near SR R S S T

Moderate SR R S T T

Far SR S S T T

Very Far SR S S T T


69

Fuzzy A Output:

SR = very low output gain

R = low output gain

S = moderate output gain

T = high output gain

Fuzzy B uses Sugeno inference fuzzy model. The first input value of Fuzzy B is output gain

and the second input is AGC output amplitude. Each of inputs are divided into 4 and 5 fuzzy

sets with triangle function. Membership function of first and second input of Fuzzy B is shown

on Figure 11(a) and Figure 11(b).

(a)

(b)

(c)

Figure 11. (a) Membership function of output gain, (b) output signal level, (c) VCTRL

Output membership function of Fuzzy B module is VCTRL. That is divided into 7 fuzzy

sets as shown on Figure 11(c). Because Fuzzy B module uses Sugeno inference fuzzy model,

the fuzzy set output is a singleton function. Further rules for Fuzzy B module will be

constructed based on Table 4.

Table 4. Fuzzy rules for Fuzzy B module

Gain output Output signal level

Very low Low Moderate High Very High

Very low V2 V2.5 V3 V3.5 V4

Low V2 V2.5 V3 V3.5 V4

Moderate V1 V1.5 V2.5 V3.5 V4

High V1 V1.5 V2.5 V3 V4


70

By defining fuzzy rules according to Table 4, there will be 20 fuzzy rules on Fuzzy B

module and the defuzzyfication process of Fuzzy B uses Weighted Average method.

7. The Implementation

The hardwares of AGC consist of several circuit blocks i.e. power supply, RF power

detector, PWM to analog converter, two-way splitter 600 MHz, AGC HMC992LP5E,

microcontroller, and LCD 20x4. Figure 12 shows the implementation of schematic circuit for

AGC HMC992LP5E gain control system with fuzzy logic. Power supply circuit is a 5V DC

regulator with 2A current using integrated circuit LM2576. RF power detector, AD8313, is

connected to input and output amplitude sensor. This circuit converts input and output into DC

signal.

Volt meter

(VCTRL AGC)

Regulator

5V DC

AGC HMC992LP5E Output Splitter

Input Splitter

RF Power

Detector (input)

RF Power

Detector (output)

LCD 20x4

USB Cable

(to PC)

Microcontroller

PWM

to Analog

Input Port

(to SigGen)

Output Port

(to Spectrum)

DC Input

9-15 V

Figure 12. Implementation of AGC HMC992LP5E gain control system with fuzzy logic

RF power detector input and output will be connected to analog pin 0 (PA0) and analog pin

1 (PA1) of microcontroller. PWM-to-analog converter circuit uses LTC2645 and is used to

convert PWM value on pin PB10 of microcontroller into analog value with conversion time

about 8 us [13]. 600 MHz splitter is used to split RF signal with -3 dB attenuation on both

input and output side [14]. AGC HMC992LP5e will be controlled by fuzzy logic system. This

implementation used ARM 32 bit STM32F401RE microcontroller with LCD 20x4 circuit to

monitor sensor value and fuzzy calculation, which are connected to pin B9, B8, C9, C6, and C5

of microcontroller. Microcontroller will also be connected to computer through serial RS 232

channel.

8. Result and Analysis

Fuzzy A and Fuzzy B testing used 32 data samples for each maximum range value. Figure

13 shows relations between Fuzzy A output, from MATLAB and microcontroller calculation,

and input signal change. Figure 13(a) is a comparison between Fuzzy A output as a result of

microcontroller and MATLAB calculation for maximum target range of 20 NM. In this figure,

plot of microcontroller calculation coincide with plot of MATLAB calculation. From these

data can be concluded that average error on range of 24 NM is 2.39%.

Figure 13(b) shows result of Fuzzy A module testing on maximum target range which is

192 NM. On this range, the change Fuzzy A output is about -45 dBm to -15 dBm and the

generated Fuzzy A output is -12 dBm to -10 dBm. The significant change of output compared

to 24 NM range happens at input signal value -55 dBm to -45 dBm. On range of 24 NM, Fuzzy

A output is -60 dBm to -52 dBm. For 192 NM range, Fuzzy A output is -40 dBm to -35 dBm.

The average error at this range is 4.37%.


71

-70,00

-60,00

-50,00

-40,00

-30,00

-20,00

-10,00

0,00

-60,00 -50,00 -40,00 -30,00 -20,00 -10,00 0,00 10,00

Ou

tpu

t F

uzz

y A

(d

Bm

)

Daya Sinyal Input (dBm)

Fuzzy A_24 NM_uC

Fuzzy A_24 NM_Matlab

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

-60,00 -50,00 -40,00 -30,00 -20,00 -10,00 0,00 10,00

Ou

tpu

t F

uzz

y B

(V

olt

)


Fuzzy B_24 NM_uC

Fuzzy B_24 NM_Matlab

(a)

-45,00

-40,00

-35,00

-30,00

-25,00

-20,00

-15,00

-10,00

-5,00

0,00

-60 -50 -40 -30 -20 -10 0 10

Ou

tpu

t F

uzz

y A

(d

Bm

)


Fuzzy A_192 NM_uC


0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

-60 -50 -40 -30 -20 -10 0 10

Ou

tpu

t F

uzz

y B

(V

olt

)


Fuzzy B_192 NM_uC


(b)

Figure 13. The Relations between Fuzzy A output and input signal

Figure 14 shows a correlation between Fuzzy B output from microcontroller and MATLAB

calculation and input signal change. Figure 14(a) shows a comparison of Fuzzy B output from

microcontroller calculation with MATLAB calculation at 24 NM range. There are some

difference between the two calculations. On the third data when the input is about -52 dBm. At

this point, MATLAB calculation generates Fuzzy B output of -25 Volts. Whereas,

microcontroller calculation generates -2.8 Volt value. At this range, the deviation often

happens at input signal about -10 dBm to 0 dBm and above. Average error at range of 24 NM

is 4.23%.

-70,00

-60,00

-50,00

-40,00

-30,00

-20,00

-10,00

0,00

-60,00 -50,00 -40,00 -30,00 -20,00 -10,00 0,00 10,00

Ou

tpu

t F

uzz

y A

(d

Bm

)


Fuzzy A_24 NM_uC


0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

-60,00 -50,00 -40,00 -30,00 -20,00 -10,00 0,00 10,00

Ou

tpu

t F

uzz

y B

(V

olt

)


Fuzzy B_24 NM_uC


(a)

-45,00

-40,00

-35,00

-30,00

-25,00

-20,00

-15,00

-10,00

-5,00

0,00

-60 -50 -40 -30 -20 -10 0 10

Ou

tpu

t F

uzz

y A

(d

Bm

)


Fuzzy A_192 NM_uC


0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

4,50

-60 -50 -40 -30 -20 -10 0 10

Ou

tpu

t F

uzz

y B

(V

olt

)


Fuzzy B_192 NM_uC


(b)

Figure 14. The Correlation between Fuzzy B output and input signal

Figure 14(b) shows Fuzzy B module testing on maximum target range that is 192 NM.

There are 4 points with significant deviation which are at input signal of -39, -32, -25, and 3

dBm. The average error at 192 NM is 3.96%. The average error of Fuzzy B module is greater

than the average error for Fuzzy A module because the second input value of Fuzzy B has a

feedback signal from AGC fuzzy system. This change will directly affect the calculation on

Fuzzy B module which results in the change of the generated output value. There are several

possible factors i.e. RF power detector output and the accuracy of ADC microcontroller.

The generated signal from pulse radar target simulator consist of 5 signals. For easy

observation, first the input signal is modulated by RF detector diode. By this process, a pure

pulse signal will be obtained without carrier signal and its negative part will be cut. The

generated signal from this process is shown on Figure 15(a). Signal 1 up to 5 has same width of

2 ms with the distance between signal is 10 ms. Amplitude of each signal is distinguished to

test the response of AGC system. The amplitude of each signal are:

Signal 1 = 80 mV (-8,93 dBm),


72

Signal 2 = 100 mV (-6,99 dBm),

Signal 3 and 4 = 20 mV (-20,99 dBm),

Signal 5 = 50 mV (-13,01 dBm)

The testing of classic AGC system is illustrated on Figure 15(b). According to the

characteristics of classic AGC system, all input signal amplitudes within dynamic range will be

fixed to a certain point set. In this system, the magnitude of signal 1 to signal 5 have same

amplitude that is 150 mV (-3.47 dBm). On pulse radar receiver system, this system can be used

for pulse radar that only calculates radar target range. It will be less effective for pulse radar

which calculates target range and the intensity of detected target amplitude.

(c)

Figure 15. (a) Input signal, (b) the Output signal of classic AGC system,

(c) AGC Fuzzy system output signal on maximum target range at 192 NM

Figure 15(c) shows AGC fuzzy system output signal on maximum target range at 192 NM.

If it is compared to the classic AGC system, there are output signal differences:

Signal 1 has initially value 80 mV (-8.92 dBm) and increases to 150 mV (-3.47 dBm).

Signal 2 has initially value 100 mV (-6.99 dBm) and increases to 140 mV (-4.07 dBm).


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Signal 3 and 4 has initially value 20 mV (-20.99 dBm) and both increase to 150 mV (-3.47

dBm).

To prove this fuzzy system has worked well, it can be analyzed on signal 3. When input

signal is at -20.99 dBm and maximum target rang is 192 NM, Fuzzy B output will generate

VCTRL value about 2.5 V. According to AGC gain characteristic, this VCTRL value will

amplify AGC to 13 dB. So, the signal 1 which has amplitude -20.99 dBm, when passing

through AGC fuzzy will have amplified -7.99 dB. This can bee seen in the measured signal on

the osiloscope.

Output signals on classic AGC system will have the same amplitude when the system is

given varied amplitude input signal. Meanwhile, output signal amplitude of AGC fuzzy

system is calculated according to the fuzzy algorithm. As the result, output signal amplitude is

dynamic. The most important of these two fuzzy systems – classic AGC and Fuzzy AGC – is

AGC output must have same width and distance between signals with its input. This is

important to prevent target range calculation error in pulse radar system application. This is

already done at the testing, where the width of output does not change, which is 2 ms and the

distance between signals is 10 ms.

9. Conclusion

In this paper, AGC HMC992LP5E gain controller with fuzzy logic has been designed and

implemented successfully on microcontroller with input variables are AGC input-output signal

and maximum radar target range. The average error of Fuzzy A module is 3.51% and Fuzzy B

is 4.47%. This system has been tested using pulse radar target simulator. AGC Fuzzy system

has only changed the input signal amplitude and kept constant the width and distance between

signals. This system is more effective to be applied on pulse radar systems which calculates

target range and intensity of detected target’s amplitude. Thus, the target captured by receiver

will be more easily identified.

10. References

[1]. Skolnik, M. I.,“Introduction to Radar Systems”, 3rd

ed, 1-5, McGraw-Hill Book Company

Inc., 2001.

[2]. ”Automatic Gain Control Methods”,

http://www.radartutorial.eu/09.receivers/rx08.en.html, Accessed at 1 May 2015.

[3]. “Datasheet IC HMC992LP5E”, v03.1013, Hittite Microwave Coorporation,

https://www.hittite.com/content/documents/data_sheet/.

[4]. Design of air surveillance radar, Radar Consortium (2014), 2nd

Report Progress Sinas

Research, PPET-LIPI, Bandung.

[5]. http://www.intechopen.com/books/aeronautics-and g stronautics/the-assessment-method-

for-multi-azimuth-and-multi-frequency dynamic-integrated-stealth-performance-o, 25

Mei 2015.

[6]. Kim, J.W., Zhang, L., Seo, J.Y., Cho, H.C., Seo, H.I., Cho, T.H., dan Jung, J.D.,

“Improved Automatic Gain Control Circuit Using Fuzzy Logic”, Fuzzy Systems and

Knowledge Discovery, pp. 159-168, 2006.

[7]. Barajas, J.R., Assad, G.D., dan Soto, R., “A fuzzy logic based AGC algorithm for a radio

communication system”, The Ninth IEEE International Conference on Fuzzy Systems, vol

2, pp 977-980, 2000.

[8]. Murat, Y., Goktas, H.H., dan Celebi, F.V., “Design and implementation of fuzzy logic

based automatic gain controller for (EDFAs)”, International Journal for Light and

Electron Optics, vol 125, issue 18, pp 5450–5453, 2014.

[9]. Delgado, L.G., Redrovan, D.V., Bykbaev, V.R., Delgado, N.G., dan Panzner, T., “Fuzzy

Controller for Automatic Microphone Gain Control in an Autonomous Support System

for Elderly”, The 2nd

International Workshop on Service Science for e-Health (SSH), pp

77-81, 2014.

[10]. Suyanto, Soft Computing, 27-56, Informatika, Bandung, 2008


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[11]. Zadeh, L. A., “Fuzzy Sets”, Journal Information and Control, 8, 338-353, 1965.

[12]. Datasheet IC AD8313, 0.1 - 2.5 GHz, Analog Device,

http://www.analog.com/en/products/rf-microwave/rf-power-detectors/non-rms-

responding-detector/ad8313.html

[13]. Demo Manual DC2197A, Linear Technology, http://cds.linear.com/docs/en/demo-board-

manual/DC2197AF.PDF, 5 Mei 2015.

[14]. Marbun, A. J., Design of Chebyshev Power Combiner 2:1 at 2400 Mhz, Thesis,

Universitas Indonesia, 2008.

Eko Joni Pristianto, is a researcher at Research Center for Electronics and

Telecommunications LIPI, Indonesia. He was graduated from Universitas

Jember (UNEJ) in 2008. He obtained his master degree from Electrical

Engineering program, Bandung Institute of Technology (ITB), in 2015. His

research interests include radar system, underwater communication system,

automation system and control system.

Pranoto Hidaya Rusmin was born in Magelang, Indonesia in 1972. He

received B.Eng., M.Eng., and Doctor degrees in electrical engineering from

Institut Teknologi Bandung (ITB), Indonesia, in 1996, 1999, 2009,

respectively. Since 1998, He is a Lecturer at School of Electrical Engineering

and Informatics ITB, Indonesia. His research interest is Internet Congestion

Control.


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Automatic Gain Fuzzy Logic Controller for Pulse RadarAGC HMC992LP5E fuzzy logic gain control is not...

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