m Tech Project Phase 3

5/19/2018 m Tech Project Phase 3

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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


2/33Title of the Project

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.



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.



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.



Frequency Channelization

Filter bank:


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Polyphase Structure for FFT filter bank

USN: 1DS12LEC06 6


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Title of the Project

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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USN: 1DS12LEC06 8


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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:

USN: 1DS12LEC06 13


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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:

USN: 1DS12LEC06 19


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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

m Tech Project Phase 3

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