Automatic Gain Fuzzy Logic Controller for Pulse Radar Receiver System
Eko Joni Pristianto1 and Pranoto Hidaya Rusmin2
1Research Center for Electronics and Telecommunication, Indonesian Institute of Sciences, Indonesia
2School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia
1[email protected], 2[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
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.
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].
Automatic Gain Control AGC HMC992LP5E
50 - 800 MHz
Variable voltage
Coupler -10 dB
RFIN RFout
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.
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)
u2
u1
y Automatic Gain Control
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
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.
Automatic Gain Control AGC HMC992LP5E
50 - 800 MHz
RF Power Detector (Input)
Microcontroller STM32F401RE
RF Power Detector (Output) Splitter
600 Mhz (Input)
Splitter 600 Mhz (output)
PC LCD
1 2
3
4
5
6
7
8 9
11
12
PWM to Analog
10 RF signal
DC signal Data PWM 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 = (4𝜋)𝑃2𝑡 (𝑅𝐺𝐴𝑒𝜎
𝑚𝑎𝑥)4 (1)
Where
Rmax = Maximum range.
Pt = Transmitter amplitude.
G = Maximum gain antenna.
Ae = Effective area of the receiving antenna.
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
Attenuator AGC
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 = 60 0MHz
PULSE MODULATOR RF SWITCH
PM IF
TX
PW P RF
TX
IF = 6 0 0 MH z
P U L S E M O DU L A TO R
R F S W ITC H P M
IF TX
P W P R F
TX
IF = 6 0 0M H z
P U L S E M O D U L A T O R
R F S W ITC H
P M
IF
T X
P W P RF
T X
IF = 6 0 0 M H z
P U LS E M O D U LA T O R
R F S W IT C H
P M
IF
T X
PW P R F
T X
IF = 6 0 0M H z
P U L S E M O D U L A T O R
R F S W ITC H
P M
IF
T X
P W P RF
T X
IF = 60 0 M H z
P U L S E M O D U L A TO R
R F S W ITC H
P M
IF
TX
P W P RF
T X
I F = 6 00 M Hz
PUL SE M O D UL AT OR
RF SW I T C H PM
I F T X
PW PRF
I F = 6 00 MH z T X
PUL SE M OD U L AT OR
RF SW I T C H PM
I F T X
PW PRF
T X
IF = 6 0 0 M H z
P U L S E M O D U L A T O R
R F S W IT C H P M
IF T X
P W P R F
T X
IF = 6 0 0 M H z
P U LS E M O D U LA T O R
R F S W IT C H
P M
IF
T X
P W P R F
T X
IF = 60 0 M H z
P U L S E M O D U L A TO R
R F S W ITC H
P M
IF
TX
P W P RF
T X
IF = 6 0 0 M H z
P U L S E M O D U L A T O R
R F S W IT C H
P M
IF
T X
PW P R F
TX
IF = 60 0 M H z
P U L S E M O D U L A TO R
R F S W ITC H
P M
IF
TX
P W P RF
T X
Signal 1
Signal 2 IF = 6 0 0 MH z
P U L S E M O DU L A TO R R F S W ITC H
P M IF
TX
P W P R F
TX
IF = 6 0 0 M H z
P U L S E M O D U L A T O R
R F S W IT C H
P M
IF
T X
PW P R F
TX
IF = 60 0 M H z
P U L S E M O D U L A TO R
R F S W IT C H
P M
IF
TX
P W P R F
T X
IF = 6 0 0 M H z
P U L S E M O D U L A T O R
R F S W IT C H
P M
IF
T X
PW P R F
TX
IF = 6 0 0M H z
P U L S E M O D U L A T O R
R F S W IT C H
P M
IF
T X
P W P R F
T X
IF = 600MHz
PULSE MODULATOR RF SWITCH
PM IF
TX
PW PRF
TX
I F = 6 00 MHz
PUL SE M O D UL AT OR R F SW I T C H
PM I F
T X
PW PRF
I F = 6 00 MHz T X
PUL SE M O D UL AT OR R F SW I T C H
PM I F
T X
PW PRF
T X
IF = 6 0 0 M H z
P U L S E M O D U L A T O R R F S W IT C H
P M IF
T X
P W P R F
T X
IF = 60 0 M H z
P U L S E M O D U L A TO R
R F S W ITC H
P M
IF
TX
P W P RF
T X
IF = 6 0 0M H z
P U L S E M O D U L A T O R
R F S W IT C H
P M
IF
T X
P W P R F
T X
IF = 6 0 0 M H z
P U LS E M O D U LA T O R
R F S W IT C H
P M
IF
T X
P W P R F
T X
IF = 6 0 0M H z
P U L S E M O D U L A T O R
R F S W IT C H
P M
IF
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
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
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
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%.
-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
Output Fuzzy A (dBm)
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
Output Fuzzy B (Volt)
Daya Sinyal Input (dBm)
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
Output Fuzzy A (dBm)
Daya Sinyal Input (dBm)
Fuzzy A_192 NM_uC Fuzzy A_192 NM_Matlab
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
Output Fuzzy B (Volt)
Daya Sinyal Input (dBm)
Fuzzy B_192 NM_uC Fuzzy B_192 NM_Matlab
(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
Output Fuzzy A (dBm)
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
Output Fuzzy B (Volt)
Daya Sinyal Input (dBm)
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
Output Fuzzy A (dBm)
Daya Sinyal Input (dBm)
Fuzzy A_192 NM_uC Fuzzy A_192 NM_Matlab
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
Output Fuzzy B (Volt)
Daya Sinyal Input (dBm)
Fuzzy B_192 NM_uC Fuzzy B_192 NM_Matlab
(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),
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).
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.
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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.