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Range Results

Module by: Amit Aggarwal, Erlend Hansen. E-mail the authors

Test Cases

General paramters

  • L=# of pulses = 4
  • TW = time-bandwidth product = 64
  • p = oversampling factor = 3
  • M = interpulse period = 300
  • n = noise factor = .2 (correspond to SNR of -10dB for received signal)
  • sampfreq = sampling frequency = (20*(10 ^6)) Hz

Note:

The sampfreq value of 20*(10^6) Hz was given in "Computer-Based Exerciese for Signal Processing Using MATLAB" by Burrus [ et al.] as an example sampling frequency to use

Note:

The paramter "Time Delay" (i.e. TD) is the amount by which the user wants to shift his vector by (i.e. not units of time)

Note:

The max range that could be accounted for according to equations based in "Approach for Range" module was 8190 meters (8.2 kilometers)

Test Case 1

Simulate a Time Delay of 5 (TD = 5)

Matlab function call

[noisytestecho,noisyshifttestecho,rsigmatchlocs,timedelay,range,h]=burst4(4,64,3,300,.2,(20*(10 ^6)),5);

Test Case 2

Simulate a Time Delay of 30 (TD = 30)

Matlab function call

[noisytestecho,noisyshifttestecho,rsigmatchlocs,timedelay,range,h]=burst4(4,64,3,300,.2,(20*(10 ^6)),30);

Test Case 3

Simulate a Time Delay of 45(TD = 45)

Matlab function call

[noisytestecho,noisyshifttestecho,rsigmatchlocs,timedelay,range,h]=burst4(4,64,3,300,.2,(20*(10 ^6)),45);

Test Case 4

Simulate a Time Delay of 65(TD = 65)

Matlab function call

[noisytestecho,noisyshifttestecho,rsigmatchlocs,timedelay,range,h]=burst4(4,64,3,300,.2,(20*(10 ^6)),65);

Test Case 5

Simulate a Time Delay of 100 (TD = 100)

Matlab function call

[noisytestecho,noisyshifttestecho,rsigmatchlocs,timedelay,range,h]=burst4(4,64,3,300,.2,(20*(10 ^6)),100);

Test Case 6

Simulate a Time Delay of 200 (TD = 200)

Matlab function call

[noisytestecho,noisyshifttestecho,rsigmatchlocs,timedelay,range,h]=burst4(4,64,3,300,.2,(20*(10 ^6)),200);

Plot examples (for test case 1)

Figure 1: Transmitted Chirptrain Noise Free
Transmitted Chirptrain Noise Free
Transmitted Chirptrain Noise Free (TransmittedchirptrainNOISEfree.jpg)
Figure 2: ShiftedTime-DelayedChirptrainwithNOISE
ShiftedTime-DelayedChirptrainwithNOISE
ShiftedTime-DelayedChirptrainwithNOISE (ShiftedTime-DelayedChirptrainwithNOISE.jpg)
Figure 3: Matchedfilteroutputfortransmittedsignal
Matchedfilteroutputfortransmittedsignal
Matchedfilteroutputfortransmittedsignal (Matchedfilteroutputfortransmittedsignal.jpg)
Figure 4: Matchedfilteroutputforrecievedsignal
Matchedfilteroutputforrecievedsignal
Matchedfilteroutputforrecievedsignal (Matchedfilteroutputforrecievedsignal.jpg)
Figure 5: Locationofpickedoutpeaksintransmittedsignal
Locationofpickedoutpeaksintransmittedsignal
Locationofpickedoutpeaksintransmittedsignal (Locationofpickedoutpeaksintransmittedsignal.jpg)
Figure 6: Locationsofpickedoutpeaksinrecievedsignal
Locationsofpickedoutpeaksinrecievedsignal
Locationsofpickedoutpeaksinrecievedsignal (Locationsofpickedoutpeaksinrecievedsignal.jpg)

Note:

The starting value of the past two graphs are at n = 1

Analysis of Results

Overall result of test cases given by a graph of the %error of the range approximation compared to "radar" from "Computer-Based Exerciese for Signal Processing Using MATLAB" by Burrus [ et al.] (see pages 328-329 for definition of parameters and function)

Figure 7: %errorinrange2
%errorinrange2
%errorinrange2 (errorinrange2.jpg)
Method of Error Calculation

The % error was calculated by first taking the returned value for range from our developed program and using that as an input to the "radar" function from Burrus [ et al.]. The resulting waveform generated is then put through the same match filter as our simulated received wave. A comparison of the location of the 1st peaks is then done and the absolute value of the difference is taken. The value is then divided by the difference from the right most edge of "radar" 's first chirp to the end of the signal.

Comment on % Error

The % error increased in a direct proportional to however larger we made the new TD value. Thus, if a shift of TD=900 was applied, the % error was 9 times greater than in test case 5 ( TD = 100), it was in fact about 55.89%.

Where to Next

Next, look at "RADAR:Velocity Analysis" as next step.

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Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

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