m tech project phase 3
DESCRIPTION
pptTRANSCRIPT
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Dayananda Sagar College of Engineering
Center for Post Graduation Studies
Department of Electronics and Communications Engineering
Project Seminar MTech Project Phase 3
Project Title : EVALUATION OF POLYPHASE FFT
ARCHITECTURE FOR PULSE DETECTION AND MEASUREMENT
Student Name: JEEVITHA T (1DS12LEC06)
Internal Project Guide: External Project Guide:Prof. Kiran Gupta Hemanth Vasant Paranjape
ECE dept,DSCE Scientist E, DARE,DRDO
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Problem statement
Time of Arrival(TOA)PDW parameter that assigns a time tag to the leading edge of
a received pulse at the receiver input.
For a digital EW receiver, the spectrum estimator (normally an FFT) is usually
the limiting factor in update rate due to its computational complexity.
update rate1/ frequency resolution.
Consider a monobit receiver:
256 samples at 2.56GHzTOA resolution100ns.
with 50% overlap at the same Fs TOA resolution50ns
increasing the number of FFTs.
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Phase1 ReviewPhase1 Review
Receiver considered is a Multi-bit Electronic Warfare Receiver
Receiver consists of a decimation filter and FFT block along with an encoder that
outputs the PDW(Pulse Descriptor Words).
The shaded block is realized as a polyphase DFT which uses decimation in frequency
domain.
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objective
the proposed method to increase the TOA resolution is to use decimation in the
frequency domain.
implement the decimation in frequency domain through a channelised polyphase
filtering method.
incorporate the channelised polyphase filtering method in the receiver model.
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Frequency Channelization
Filter bank:
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Polyphase Structure for FFT filter bank
USN: 1DS12LEC06 6
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Decimation filter design process:
STAGE 1: FFT filter bank
rectangular window in time domain sinc function in frequency domain
filter shape is not desirable
sidelobes are very high
Weighing function can be used in time domain
the detailed response of the single filter and the filter bank is as shown
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Continued..
STAGE 2 : Decimated FFT filter Bank
ALGORITHM: Decimation in Frequency Domain:
Step 1:
Step 2:
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Continued
Step 3:
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Step 4:
Step 5:
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Step 6:
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Results:
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Continued..
Stage 3: Windowed Decimated FFT filter Bank
To widen the individual filters and also suppress the sidelobes a window is applied
to the input data.
The corresponding effect in the frequency domain is the wide bandwidth of each
individual filter shape in the filter bank.
ALGORITHM:
Step 1:
Step 2:
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Continued..
Step 3:
Step 4: 32point FFT is performed on these y values , 16 individual filters aregenerated.
d f d
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Window function generated using an FDATOOL
Design Parameters:
N=256; M=8; 1= 0.01; 2=0.001; fs=3000MHz;
Fp =fs/(N/M) ; Fs = 2*Fp;
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Continued..
Step 1:
Step 2:
Park Mc-ClellansCriteria:
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Continued..
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Results of the stage 3:
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Advantages of Polyphase Filtering
First, by parallelizing the filter through polyphase decomposition, the sampling rate of
each individual filter is reduced by a factor of 1/D, where D is the number of filters.
A second significant advantage to using the channelized polyphase filtering
method is an increase in time resolution, which improves the TOA and PW calculations
in an EW receiver.
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Comparison:
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MATLAB simulation
Generation of the input signals:
FILE NAME DESCRIPTION
Known_pattern.m Accepts the number of pulsestransfers the control
test_case_generator.
Outputs the details of the input
info.txt,info1.txt
Test_case_pattern.m Provides the user a platform to enter the details of the input
pattern.
Transfers the control to cw_generator.m depending on the
user entries.
Cw_generator.m Used to generate cw pulses.
Info.txt, info1.txt Contains the details of the input signal.
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start
Enter the number of
pulses
1Test_case_generator(1st
pattern details)
2T_c_g(1st
pattern)
4
T_c_g(2nd
pattern)
3T_c_g(3rd
pattern)
T_c_g(2nd
pattern)
T_c_g(1st
pattern)
T_c_g T_c_gT_c_gT_c_g
Make a correct
entry
yes
no
no
no
no
yes
yes
yes
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Test_case_generator
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Testing of the input signal
filename description
Complete_model.m Imports the samples. Groups the samples
into frames. Performs decimation in
frequency domain.
Sidelobegaurd.m Peak detection algorithm implementation.
Track_detection.m Tracks the detected peaks.
Parameter_measurement.m Measurement of the input signal parameters
is done here
Pdw_info Results obtained from tha parameter
measurement are stored here.
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COMPLETE_MODEL.M ALGORITHM
1. The input data/ samples are stored in samples.txt
2. Grouping of the data begins :P = length(A);
P = P - mod(P,256);
L = (P/N);
where A is a variable which contains the imported data (samples.txt)
3. Design an equiripple low pass FIR filter with the following parameters: Fs=1350MHz
Fp=21.09375MHz
Fs=42.1875MHz
Wp=1/64
Ws=1/32
Fdatool is used to generate the filter co-efficients.H(n)co-efficients.
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Samples(14080)
L21
1 2 3256
P= P- mod(P,256) Mod(x,y)= x-n.*y
n = floor(x./y)
Consider an example:
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Continued.
4. Decimation in the frequency domain is applied on the incoming data:
Let x(n) be the input, h(n) be the filter co-efficients :
5. Calculate the 32 point FFT of each frame.
6. Compute the magnitude of complex data samples. Determine the first peak with the
highest magnitude. Done by using a MAX command.
fft_frame1 = (fft(yn,32))
[magn1 pos1]=max(fft_frame1(1:16))
7. Suppose X(k) is the peak sample, determine X(k-1)(magn5) and X(k+1)(magn6), only if the
pos1!=1.
8. Also calculate the inphase(I1) and quadrature phase(Q1) data of the fft_frame1 using the
REAL and IMAG commands respectively.
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Sidelobegaurd.m(peak detection algorithm)
1. The algorithm determine 4 peaks with highest magnitude from every frame, taking
into consideration side lobe rejection.
2. The parameters that are passed to sidelobegaurd are magnitude of the elements of
fft_frame1 and pos1.
3. Concept of bandpass sampling:
Used to sample a continuous bandpass signal centred about some frequency
other than zero.
The sampling freq in this case is calculated as:
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4. Check whether the pos1 lies within the detected dft bins.
5. If yes calculate the first differentialof the magnitude of the elements of the fft_frameif(D(i-1)> 0 && D(i) < 0)
d(i-1) = c(i-1);
else
d(i) = 0;
Detect a peak and eliminate its sidelobes simultaneously.
6. In case there are two or more pulsess8 = 0.0003; slg8 = c(pos1) * s8;
s7 = 0.0005;
s6 = 0.001;
s5 = 0.0501;
s4 = 0.1585;
s3 = 0.4585;s2 = 0.631;
s1 =1;
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For ex: let fc=1000MHz and B=500MHz
Then 1500MHz fs 1250MHz. Fs=1350MHz is used.
Now converting these translated frequencies into DFT bins we have:
fc-B/2 =750MHz fc=1000MHz fc+B/2=1250MHzfs- (fc-B/2) =600MHz fs-fc=350MHz fs-(fc+B/2)=100MHz
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Track detection:
1. Set four filter indices for four input pulses.
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Continued
THANK YOU