Connexions

You are here: Home » Content » Range Results
Content Actions
Lenses

What is a lens?

Lenses

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

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...
Affiliated with (?)
This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.
  • This module is included inLens: Rice University ELEC 301 Project Lens
    By: Rice University ELEC 301As a part of collection:"ECE 301 Projects Fall 2003"

    Click the "Rice University ELEC 301 Projects" link to see all content affiliated with them.

    Rice University ELEC 301 Projects
  • This module is included inLens: Rice University OpenCourseWare
    By: OpenCourseWare ConsortiumAs a part of collection:"ECE 301 Projects Fall 2003"

    Click the "Rice University OCW" link to see all content affiliated with them.

    Rice University OCW
Tags

(?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Range Results

Module by: Amit Aggarwal, Erlend Hansen

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 1Simulate 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 2Simulate 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 3Simulate 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 4Simulate 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 5Simulate 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 6Simulate 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)

Transmitted Chirptrain Noise Free
TransmittedchirptrainNOISEfree.jpg
Figure 1: Transmitted Chirptrain Noise Free
ShiftedTime-DelayedChirptrainwithNOISE
ShiftedTime-DelayedChirptrainwithNOISE.jpg
Figure 2: ShiftedTime-DelayedChirptrainwithNOISE
Matchedfilteroutputfortransmittedsignal
Matchedfilteroutputfortransmittedsignal.jpg
Figure 3: Matchedfilteroutputfortransmittedsignal
Matchedfilteroutputforrecievedsignal
Matchedfilteroutputforrecievedsignal.jpg
Figure 4: Matchedfilteroutputforrecievedsignal
Locationofpickedoutpeaksintransmittedsignal
Locationofpickedoutpeaksintransmittedsignal.jpg
Figure 5: Locationofpickedoutpeaksintransmittedsignal
Locationsofpickedoutpeaksinrecievedsignal
Locationsofpickedoutpeaksinrecievedsignal.jpg
Figure 6: Locationsofpickedoutpeaksinrecievedsignal
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)
%errorinrange2
errorinrange2.jpg
Figure 7: %errorinrange2
Method of Error CalculationThe % 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 % ErrorThe % 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.

Comments, questions, feedback, criticisms?

Send feedback