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Underwater Ta

Binesh

Department of Electronics, C

bineshtbt@gm

Abstract— Underwatesystems and technologclassifier system is onof the classifier. The essential clues suitablenon-stochastic underwbased on modified Kproposed. The proposefunctional measure ofalgorithmic vector quaperformance has been for the proposed undBessel window.

Index Terms—MahalKaiser-Bessel window

1. Introduction

The ocean is considered tomix of many complex mclassification of underwater sfrom the ocean, necessitates in signal processing. An uclassifier system utilizing spback scattered data has beenAn autonomous underwateclassifier utilizing temporinformation of mingled spectr[2]. The selection of suitablfeatures in a classifier wiefficiency and efficacy of the environment. Frequency do

PROCEEDINGS O

arget Classifier Using Modified KBessel Window

T., Supriya M.H and P.R. Saseendran Pillai

Cochin University of Science and Technology, Kochi-6

mail.com, supriyadoe@gmail.com, prspillai@cusat.ac.in

er target classification has got numerous applications gies. The selection of suitable source specific featue of the major factors determining the efficiency andspectral features, when suitably modified, can provid

e for the design of underwater signal classifiers. In thiswater target classifier, making use of an efficient feKaiser-Bessel window, operating in the frequency doed classifier is making use of a Matching Parameter wf the Mahalanobis and Euclidean distances and utantization approach as well for cluster formation. Thstudied and fairly acceptable success rates have been erwater target classifier, making use of a modified

lanobis Distance, Frequency domain windowing, Mw, Underwater target classifier

o be a bewildering mechanisms. The ignals, originating robust techniques

underwater signal pectral analysis of n proposed in [1]. er vehicle based ral and spatial rum is proposed in le source specific ill influence the system in a given

omain windowing

technique has got applications inadaptive systems [3].

The principle of windowindomain using Kaiser-Bessel wcan be effectively applied in underwater target classifiers. Kaiser-Bessel window, whencause reduction in peak specunderwater target signals.classification capability cannot bbetter than a normal spectral clamodified Kaiser-Bessel windowbeen proposed, that can be madthe spectral components to prodfeature set. The feature set of unsignals, thus extracted, constitu

OF SYMPOL 2013

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Binesh T et al.: Underwater Target Classifier Using Modified Kaiser-Bessel Window 83

of major significance in the design and performance of the classifier. The spectral features of the target are extracted from a sequence of time samples and used in the design stage of the classifier. For this, the sampled underwater signal is partitioned into frames for the purpose of extracting source specific features. The power spectrum is filtered through a bank of filters to obtain a nonlinear resolution, as this provides good discrimination properties and also amenable to many analytical manipulations. This smoothed spectrum is altered by the modified Kaiser-Bessel window, which constitutes the extracted feature set for the non-stochastic classifier system.

Classification is a decision making process of identifying the unknown underwater signal based on the system-learned information. The classifier has been designed, making use of the modified features extracted from the training set. In the classification phase, the Matching Parameter, which is a measure of the similarity of the features extracted from the unknown signal and the trained feature set, is estimated.

The Matching Parameter adopted by this system is designed to be an optimized function of Mahalanobis Distance. Vector quantization has been carried out for the cluster formation and to obtain the centroids used for the system modeling. The system performance has been validated using simulation studies for the class of underwater noise sources. The various system parameters have been analyzed and quite encouraging simulation results have been obtained for the proposed underwater target classifier.

2. Methodology

For the proposed underwater target classifier, the methodology consists of various stages, which include the extraction of optimized feature set for solving the classifier problem.

2.1. Frequency Domain Windowing Based Feature Extraction

A suitable window function, when applied in the frequency domain modifies the spectral feature set in such a way that it can be effectively utilized in underwater target classifier design. The proposed system makes use of a modified Kaiser-Bessel window, defined as

(1)

for 0<n<M , and is zero elsewhere, α is an arbitrary real number determining the geometrical aspect of the window, γ is the transformation parameter which can be adaptive, M is an integer in which the length of the window is M+1 and Io is the zeroth order Modified Bessel function of the first kind[4], defined as ∑ ! (2)

The modified Kaiser-Bessel window function operates on the spectral components to generate a modified feature set. When γ =0, equation (1) reduces to a normal Kaiser-Bessel window which causes an undesirable reduction in the spectral power distribution. The modified window function operation is found to alter the spectral power values in such a way that it reduces the ambiguity in the classification process. It influences the Matching Parameter of the system and ensures increased classification capability compared to a normal spectral classifier. Careful selection of the transformation parameter γ is essential, since the peak spectral power is strongly influenced by the value of γ.

2.2. Effect of γ on the Classifier Performance

A mathematical framework elucidating the effect of the transformation parameter γ on

84 PROCEEDINGS OF SYMPOL 2013

the overall performance of the proposed classifier, making use of modified Kaiser-Bessel window, has been formulated.

If Mpwin denotes the Mp value for the proposed non-stochastic underwater target classifier and Mpnorm , is the corresponding value for a normal spectral classifier, then it can be shown that,

(3)

where W is the modified Kaiser-Bessel window to which the extracted spectral feature sets are applied. It can also be shown that

1 (4)

When 1,

1 (5)

And 1

Then 1, and hence

This implies that the performance of the proposed system will be better than that of a normal spectral feature based classifier.

When 0 , then 0 1 and also 01. So the factor may not always be greater than unity. Hence the condition in equation (6) is no longer holding good and as such, the proposed system cannot ensure reduced ambiguity in classification.

When 0 1, then 0 1 and also 0 1 .

Here again cannot be ensured to be greater than unity. Hence by satisfying the design criterion 1, the proposed system performs better compared to a normal spectral classifier.

2.3. Classifier Design The whole process of classification can be

generally divided into two phases, viz., the training phase and the classification phase [5]. The training phase consists of system- learning and forming different target classes based on the features extracted from a set of underwater signals, while the classification phase involves the process of estimating a matching score, which is a measure of the similarity of the features extracted from the unknown signal and that of the system-learned target classes.

The extracted feature set is utilized in the underwater target classifier design, making use of vector quantization process. Vector quantization removes redundancies and makes use of the inherent dependencies by clustering of the spectral parameters. It is a powerful method of mapping a large number of vectors to a predefined number of clusters. The clusters in turn can be defined with respect to their centroids. The required features of the underwater signals are extracted and making use of which, a comparatively smaller set of centroids are generated using algorithmic procedures. Figure 1 depicts the classifier system, which makes use of the extracted modified spectral features.

The classifier system makes use of Linde-Buzo-Gray (LBG) algorithm [6] in its design stage, which is a clustering algorithm that operates on a set of N input vectors and generates a representative subset of L vectors as the output. Upon random initiation of a code book, the N vectors are to be classified into L clusters. The cluster centers are updated and a distortion measure is computed. If the relative difference of the distortion measures of two iterations is greater than a small set threshold value, the iteration is repeated and cluster codebook gets updated. Or else the codebook, which corresponds to the cluster centers, generated from the earlier iteration will be chosen.

Binesh T et al.: Underwater Target Classifier Using Modified Kaiser-Bessel Window 85

Fig. 1: Classifier system for underwater target

signals

For the underwater signals, a knowledge base consisting of almost all the targets is created, with cluster centroid based codebooks[7][8]. This is achieved by extracting the required features and clustering them to generate the codebook. In the classification phase, the unknown target, represented by its extracted clustered feature set, is compared with the centroid based codebooks. For each codebook, a quantization distortion measure is computed. The proposed system makes use of a metric referred to as Matching Parameter(Mp), which is a functional measure of Mahalanobis distance and Euclidean distance. The Mahalanobis distance, is a function of the covariance matrix and is a measure of the dissimilarity between two vectors. The Matching Parameter can, thus, be defined for the purpose of minimizing the ambiguity in the classification process as || ||

where p and q are the vectors under consideration, S is the covariance matrix and k is a scalar. The classifier performs the final decision by identification of the trained target

signal to which the minimum value of Mp of the unknown target signal corresponds.

3. Simulation Results

The performance of the proposed underwater target classifier system has been analyzed using simulation studies and the results have been presented. The clustered feature set is used in the design of target classes for various training signals. In the training phase, codebooks consisting of centroids of the extracted features for different underwater target signals have been estimated.

With a transformation parameter γ =7, the peak spectral magnitude of the feature set for seacat noise is estimated to be 1304.70 while the same for Boat noise is found to be 688.00 The corresponding values for a similar classifier using normal spectral features were estimated to be 164.30 and 86.64 respectively. Increased spectral powers are advantageous in the sense that the undesirable convolution effects can be better compensated, in the proposed frequency domain windowing based system. The feature set is more robust compared to normal spectral features, by reducing the ambiguity in the classification process. Success rates of about 84% have been obtained for underwater target classification scenario, making use of the modified Kaiser-Bessel window based system with γ =7.

In Figure 2, the variation of estimated Matching Parameter set values, in a typical classification process of seacat noise by a normal spectral feature based system is shown while the variation of estimated Matching Parameters, in the corresponding classification by the proposed non-stochastic system is depicted in Figure 3.The minimum value in the plots corresponds to that system-trained signal, with which a close match has been obtained, for the unknown target signal. These minimum Matching Parameter values for the seacat noise were computationally found to be 0.30 and

Output

Target Class 1

Target Class 2

Target Class N

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cted

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ture

s

Target signal

.

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isio

n M

akin

g

86 PROCEEDINGS OF SYMPOL 2013

0.70 respectively for the normal spectral feature based system and the proposed system.

Fig. 2: Variation of Matching parameter(Mp) for classification of seacat noise by a normal spectral classifier.

Fig. 3: Variation of Matching parameter(Mp) for the classification of seacat noise by the proposed system, with γ =7.

The Matching Parameter (Mp) provides a measure of ambiguity in the classification process. Figure. 4 shows the estimated success rates for the modified Kaiser-Bessel window based classifier for various transformation parameter (γ ) values and the system is found to attain a stable behavior for γ =7, with a success rate of 84%. As has been established in section 2.2, the proposed system performs better for γ >1. By judiciously assigning a preferred value ofγ , one can expect a fairly

acceptable success rate of 84% for the proposed non-stochastic classifier. In this context, it is worth mentioning the fact that the success rate for a normal spectral classifier system was found to be 77.7%.

It has also been observed that the increased difference of Mp values among individual target signals corresponds to reduced probability of ambiguous classification, compared to that of a normal spectral feature based system for which the corresponding Mp values are lower. Moreover, increased difference of Mp values between the minimum and the next higher values will ensure improved classification capability.

Fig. 4: Variation of estimated success rates for different values of γ for the proposed system.

Unwanted time domain convolution effects, which might occur in real time environments, can be compensated to a greater degree because of the frequency domain windowing in the proposed system.

4. Conclusions The proposed underwater signal classifier

makes use of modified Kaiser-Bessel window function for judiciously modifying the extracted feature set so as to reduce the ambiguity in the classification process. The window operates on the spectral components in the frequency domain to generate a modified feature set. System training has been carried

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Binesh T et al.: Underwater Target Classifier Using Modified Kaiser-Bessel Window 87

out using LBG algorithm based vector quantization and a codebook is generated by clustering the extracted features for each underwater signal, thereby generating a codebook of centroids. In the classification phase, the Matching Parameter which is a functional measure of Mahalanobis distance has been used for obtaining a measure of similarity between the unknown codebook and the system-trained codebooks. The proposed approach has been found to be robust and efficient in underwater target classification scenario. The system can be augmented with other suitable architectures so as to configure an underwater target classifier with enhanced performance.

ACKNOWLEDGMENTS The authors gratefully acknowledge the

Department of Electronics, Cochin University of Science and Technology, Cochin, India, for providing the necessary facilities for carrying out this research work.

References [1]. Vray, D., Delachartre, P., Andrieux, N.,

and Gimenez, G., �Bottom classification using information in the spectral domain and time-frequency domain,� OCEANS '94. �Oceans Engineering for Today's Technology and Tomorrow's Preservation.� Proceedings , vol.2, pp. 13-16, 1994.

[2]. Yanwu Zhang., Baggeroer, A.B., and Bellingham, J.G., �Spectral-feature classification of oceanographic processes using an autonomous underwater vehicle,� Oceanic Engineering, IEEE

Journal of , vol.26, no.4, pp.726-741, 2001.

[3]. Rivera-Colon, Ramfis., Reddy, Sridhar P., and Lindquist, Claude S., �Frequency domain windowing analysis for class 3 adaptive filters,� Engineering in Medicine and Biology Society, 1993. Proceedings of the 15th Annual International Conference of the IEEE , vol. 1, pp.420-421, 1993.

[4]. Harris, F.J., �On the use of windows for harmonic analysis with the discrete Fourier transform,� Proceedings of the IEEE , vol.66, no.1, pp.51-83, 1978.

[5]. E.Karpov., �Real Time Speaker Identification�, Master’s Thesis, University of Joensuu, department of Computer Science, 2003.

[6]. Linde, Y., Buzo, A., and Gray, R.M., �An Algorithm for Vector Quantizer Design�, IEEE Trans. on Comm., vol.28, pp. 84-94, 1980.

[7]. Chin-Chuan Han., Ying-Nong Chen., Chih-Chung Lo., Cheng-Tzu Wang., and Kuo-Chin Fan., �A Novel Approach for VQ Using a Neural Network, Mean Shift, and Principal Component Analysis,� Intelligent Vehicles Symposium, 2006 IEEE , vol.1, pp.244-249, 2006.

[8]. Majot Kaur Gill., Reetkamal Kaur., and Jagdev Kaur., �Vector Quantization based Speaker Identification�, Int. Journal of Comp. Applications, vol. 4, no. 2, pp. 1-4, 2010.