1. Sonar Signal Processing
ECE 5525 Speech Processing
Cheryl Skibski
Ocean Engineering
Florida Institute of Technology

2. Overview Review SONAR Correlation and auto-correlation
Monday, September 17, 2018
Overview
Review SONAR
Correlation and auto-correlation
Matched filtering
Data acquisition toolbox in MATLAB commands
Implementation
Results
Conclusions
Future Work
References

3. Introduction Implement basic sonar
Monday, September 17, 2018
Introduction
Implement basic sonar
Send a chirp sound through a speaker
Record the bounce off the target
Data processing using Data Acquisition Toolbox and Signal Processing Toolbox.

4. Sound Navigation And Ranging (SONAR)
Monday, September 17, 2018
Sound Navigation And Ranging (SONAR)
Used underwater to detect objects and for navigation
Essential in areas where light and clear visibility are not available
2 types of sonar:
Active sonar: A sound ping is transmitted by the transducer that operates as a transmitter and receiver.
Passive sonar: Listens to sound by using a hydrophone.

5. Correlation and Autocorrelation
Monday, September 17, 2018
Correlation and Autocorrelation
Autocorrelation is the cross-correlation of a signal by itself.
Cross-Correlation measures how similar two waveforms are by recognizing the time delay between the two signals
The amplitude in the cross correlated signal measures how much the received signal resembles the target signal at the location
Used to find the location of echoes
𝑥 𝑛 ∗𝑡 −𝑛 =𝑦[𝑛]
MATLAB function: xcorr(x)

6. Monday, September 17, 2018
Matched Filtering
Matched filtering is a type that uses correlation to detect a known waveform.
This is done even when random noise is introduced to the signal
Maximizes signal-to-noise ratio and decreases noise

7. Data Acquisition Toolbox - MATLAB
Monday, September 17, 2018
Data Acquisition Toolbox - MATLAB
ai=analoginput('winsound', 0);
addchannel(ai,1);
ao=analogoutput('winsound', 0);
addchannel(ao, 1);
duration=4.5;
SampleRate=44100;
set([ai ao], 'SampleRate', SampleRate);
set(ai,'SamplesPerTrigger',N)
putdata(ao,x);
start([ai ao]);
clear ai

8. Monday, September 17, 2018
Implementation
Perform autocorrelation of the original chirp. The chirp is correlation with itself in order to view the perfect echo
Each received signal is correlated with the original chirp in order to eliminate noise and to get the location of the received signal.
s5=getdata(ai);
plot(t , s5); axis tight; grid on;
xs5=xcorr(s5, pulse);
plot(t2, xs5(1:length(t2))); hold on;

9. Implementation A spike is ideal for the cross-correlation procedure
Monday, September 17, 2018
Implementation
A spike is ideal for the cross-correlation procedure
whenever a chirp echo is detected.
Knowing the speed of sound in air, the time step can be converted to meters and inches. The time is the difference in seconds between the two spikes of the cross-correlation
C=343;
Dmeter=(C*time)/2
Dinch=Dmeter/0.0254;

10. Results Monday, September 17, 2018 f0 = 4e3; % low frequenchy (Hz)
N = 2^ceil(log2(Tmax*SampleRate)); % buffer size (samples) for trigger
t = (0:(1/SampleRate):p)'; % time index (sec)
w = length(t); % pulse length (samples)
pulse = chirp(t,f0,pw,f1); % chrip waveform using matlab built in function
pulse = pulse.*hanning(w); % pulse envelope of a hanning window
z = zeros(N-w,1); % create zeros to the end of chirp window
x = [pulse; z]; %Create zeros at the end of the chirp signal

11. Monday, September 17, 2018
Results

12. Results Results 5 inches from target: T1=2.75 T2=3.57
Monday, September 17, 2018
Results
Results 5 inches from target:
T1=2.75
T2=3.57
Time= = 0.82 ms
Dmeter= (0.82x10-3 * 343)/2 = 0.14 m
Dinch=5.5 inches

13. Monday, September 17, 2018
Results

14. Conclusion Successful for distances closer to the sound source
Monday, September 17, 2018
Conclusion
Successful for distances closer to the sound source
Scattering of the reflected signal, noise in the room, and errors due to the hardware microphone and speaker created errors in testing
The speaker did not have enough power powerful sound outputted would be beneficiary to this implementation

15. Monday, September 17, 2018
Future Work
Get more familiar with the Data Acquisition Toolbox in MATLAB
Add in a microphone array and sonar beamforming and account for more sonar parameters
Use MATLAB to create a plot like an underwater sonar image and use different colors to represent the intensities of the reflected signal
Implement underwater using a hydrophone

16. Monday, September 17, 2018
Questions?
Thank You

17. Monday, September 17, 2018
References
Matejowsky, Eddie. “Eddie's Lounge room sonar project”. 12 Jan Web. 7 Dec 2010 <
Quatieri, Thomas. “Discrete-Time Speech Signal Processing: Principles and Practice,” Prentice Hall, 2002
Smith, Stephen. “The Scientist and Engineer’s Guide to Digital Signal Processing.” California Technical Publishing, <
Tohyama, Mikio and Tsunehiko Koike. “Fundamentals of Acoustic Signal Processing,” Academic Press, 1998.
Urick, Robert. “Principles of Underwater Sound 3rd ed,” McGraw-Hill, Inc, 1983.