Ambient Noise Imaging; Enhanced Spatial Correlation Algorithms and a way

to Combine Independent Images of Improved Stability and False-Alarm Rejection

A0079 Choon Kiat Lim Acoustic Research Laboratory, Tropical Marine Science Institute, NUS

A0080 John R. Potter Acoustic Research Laboratory, Tropical Marine Science Institute, NUS

In the growing class of underwater Ambient Noise Imaging (ANI)

algorithms, three stochastic techniques have so far been proposed and

found effective:

. The original first-order Acoustic Daylight algorithm (which images

the mean received intensity as a function of look direction)

. Second-order temporal intensity imaging (which uses the standard

deviation of the intensity)

. Second-order spatial intensity imaging (which calculates spatial

cross correlations of pixel intensity at zero time lags).

This paper addresses the challenges of second-order spatial imaging,

and how to combine images formed by independent algorithms. The

original second-order spatial algorithm calculates the correlation

matrix of beams for some fixed time window and takes the matrix

element with the smallest coefficient to identify two seed channels.

The extent to which other beams correlate to these two seed channels

is then used to form the image. Two enhancements to this base

technique are proposed. Furthermore, we propose a method of combining

images from different algorithms based on Kmean clustering.

The first image algorithm enhancement is based on the original spatial

correlation algorithm, except that the procedure is repeated with the

seed channels reselected from a small group of leading candidates each

time. For each selection, an image is produced. The final image is

constructed as a weighted product of these images. Results indicate

that this algorithm produces considerable improvement (as much as 15dB

in some cases).

The second enhancement attempts to classify the channels into target

and non-target sets in such a way as to maximise the intra-correlation

within each set while simultaneously minimising the inter-correlation

between the sets. The seed channels, corresponding to the matrix

element with the smallest cross correlation, are found first. The

beams are then classified into target and non-target sets by either a

simple clustering method or setting a correlation coefficient

threshold. The intra and inter-correlations are then modelled as a

linear objective optimisation with non-linear and linear constraints.

The aim is to find a representative time series signal for each set by

maximising the intra-correlation within each set. Finally, the time

series of the seed channels are replaced by their respective

representative time series and the image is generated by finding the

new correlation matrix. The procedure is repeated until convergence.

Results indicate only slight improvements in image quality when

compared to the original method (2 dB or less).

Lastly, this paper proposes a method of combining images produced by

different algorithms (i.e. Acoustic Daylight, second-order temporal

and spatial imaging). We apply Kmean clustering, with a validity

measure. The result obtained by a particular algorithm is assigned to

an element of a feature vector. The best results are obtained by

first normalizing the input feature for each algorithm to zero mean

and unit variance. The elements within each feature vector are then

sorted before clustering. The final image is obtained by a weighted

sum of the individual images, the weights being determined by the

clustering algorithm. Results show an improvement of up to 4dB

compared to the clustering method without sorting.