Enhanced wavelet-based methods for reducing complexity and calculation time in sonar measurements
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We introduce two enhanced wavelet-based methods to improve accuracy and optimize calculation efficiency for sonar time delay estimation. Existing time delay estimations provide poor results because of many environmental effects such as noise, multipath, and crosstalk. While advanced digital signal processing (DSP) techniques have been proposed to overcome these problems, they entail increased complexity and calculation time. We use prediction in the methods we propose; the position where the reflected wave starts to occur is predicted by a recognition technique. The results are much better and the calculation time is shorter than other methods that use DSP techniques. In the first method, the optimization procedure is applied in the time domain, while in the second method, the optimization is calculated in the wavelet domain. Numerical comparisons and simulations using synthetic signals are provided to demonstrate the effectiveness of the proposed enhanced methods. We also demonstrate that our new algorithms are more stable than the existing ones and that the calculation time is reduced dramatically while maintaining increased accuracy, especially in a high-noise environment.
KeywordsCross-correlation Prediction Recognition Time Delay Ultrasonic Sensor Wavelet Transform
predicted delay time
distance from the sensor to the obstacle
cross-correlation between y 1(t) and y 2(t)
velocity of sound in the environment
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- 2.Shoval, S. and Borenstein, J., “Using coded signals to benefit from ultrasonic sensor crosstalk in mobile robot obstacle avoidance,” Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 3, pp. 2879–2884, 2001.Google Scholar
- 4.Stanley, B. and McKerrow, P., “Measuring range and bearing with a binaural ultrasonic sensor,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 2, pp. 565–571, 1997.Google Scholar
- 6.Jeon, H. J. and Kim, B. K., “A study on world map building for mobile robots with tri-aural ultrasonic sensor system,” Proc. of the IEEE International Conference on Robotics and Automation, Vol. 3, pp. 2907–2912, 1995.Google Scholar
- 7.Moita, F. and Nunes, U., “Multi-echo technique for feature detection and identification using simple sonar configurations,” Proc. of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Vol. 1, pp. 389–394, 2001.Google Scholar
- 8.Wu, S. Q., So, H. C. and Ching, P. C., “Improvement of TDOA measurements using wavelet denoising with a novel thresholding technique,” Proc. IEEE International Conference on Acoustic, Speech, and Signal Processing, Vol. 1, pp. 539–542, 1997.Google Scholar
- 9.Barsanti, R. J. and Tummala, M., “Wavelet-based time delay estimates for transient signals,” Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 1, pp. 1173–1177, 2003.Google Scholar
- 12.Carter, G. C., “The smoothed coherent transform SCOT,” Technical Report TM TC-159-72, Naval Undersea Warfare Center, 1972.Google Scholar