T echniques adopted in the post processing of active sonar data from Royapuram

A buried object detection SONAR has been developed by the marine sensors and systems group of National Institute of Ocean Technology and the analysis of the data from a specific site is reported in the paper. Handling the unpredictable noise is a major concern in sonar signal processing, especially in buried object detection sonar systems. To improve the signal to noise ratio and also to preserve the boundaries of targets, special post processing techniques are to be applied. Signal averaging is found to be a useful technique in this regard and this paper compares and analyzes various averaging techniques including moving averaging, exponential averaging, and median filter. The exponential averaging with median filter is found to be one of the best suitable methods for noise reduction in detecting buried objects in shallow waters, since it significantly i mproves the signal to noise ratio by preserving the boundaries of targets. It is observed that the original sonar image with 6% noise level is improved to 0.03 to 0.04 noise variance using the combination of exponential moving average and median f ilter and the same trend is observed up to 35% noise level when corrupted by Gaussian noise. Performance evaluation of the techniques has been carried out and is quantitatively verified with the data collected during the sea trials.

[1]  Georgios Haralabus,et al.  Signal processing for an active sonar system suitable for advanced sensor technology applications and environmental adaptation schemes , 2006, 2006 14th European Signal Processing Conference.

[2]  Thomas S. Huang,et al.  A fast two-dimensional median filtering algorithm , 1979 .

[3]  Thomas S. Huang,et al.  The Effect of Median Filtering on Edge Estimation and Detection , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Richard A. Haddad,et al.  Adaptive median filters: new algorithms and results , 1995, IEEE Trans. Image Process..

[5]  Chul Park,et al.  AN EFFICIENT IMAGE ENHANCEMENT ALGORITHM FOR SONAR DATA , 2013 .

[6]  U. Datta,et al.  Performance Evaluation of Different Averaging based Filter Designs Using Digital Signal Processor and its Synthesis on FPGA , 2012 .

[7]  Rolf Unbehauen,et al.  An adaptive recursive 2-D filter for removal of Gaussian noise in images , 1992, IEEE Trans. Image Process..

[8]  Steven W. Smith,et al.  The Scientist and Engineer's Guide to Digital Signal Processing , 1997 .

[9]  Tadeusz Gudra,et al.  New Approach in Bats' Sonar Signals Parametrization and Modelling , 2010 .

[10]  Steven G. Schock,et al.  Buried object scanning sonar , 2001 .

[11]  Gnanambal Ilango,et al.  NEW HYBRID FILTERING TECHNIQUES FOR REMOVAL OF GAUSSIAN NOISE FROM MEDICAL IMAGES , 2011 .

[12]  Reuven Cohen,et al.  Nonlinear exponential smoothing (NLES) algorithm for noise filtering and edge preservation , 2005 .

[13]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Nirwan Ansari,et al.  Nonlinear filtering by threshold decomposition , 1999, IEEE Trans. Image Process..

[15]  B. Shoushtarian.  A PRACTICAL APPROACH TO REAL-TIME DYNAMIC BACKGROUND GENERATION BASED ON A TEMPORAL MEDIAN FILTER , 2003 .