AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Design Considerations of Sonar Projector Arrays with Improved Performance

Systems which employ underwater acoustic energy for observation or communication are called sonar systems. The active and passive sonars are the two types of systems used for the detection and localisation of targets in underwater. Active sonar involves the transmission of an acoustic signal which, when reflected from a target, provides the sonar receiver with a basis for the detection and estimation. Passive sonar bases its detection and estimation on sounds which emanate from the target itself--Machinery noise, flow noise, transmission from its own active sonar etc.Electroacoustic transducers are used in sonar systems for the transmission and detection of acoustic energy. The transducer which is used for the transmission of acoustic energy is called projector and the one used for reception is called hydrophone. Since a single transducer is not sufficient enough for long range and directional transmission, a properly distributed array of transducers are to be used [9-11].The need and requirement for spatial processing to generate the most favourable directivity patterns for transducer systems used in underwater applications have already been analysed by several investigators [12-21].The desired directivity pattern can be either generated by the use of suitable focussing techniques or by an array of non-directional sensor elements, whose arrangements, spacing and the mode of excitation provide the required radiation pattern or by the combination of these.While computing that the directivity pattern, it is assumed strength of the elements are unaffected by the the source acoustic pressure at each source. However, in closely packed a r r a y s , the acoustic interaction effects experienced among the elements will modify the behaviour of individual elements and in turn will reduce the acoust ic source leve 1 wi t h respect to the maximum t heoret i cal va 1ue a s well as degrade the beam pa t tern. Th i s ef fect shou 1d be reduced in systems that are intended to generate high acoustic power output and unperturbed beam patterns [2,22-31].The work herein presented includes an approach for designing efficient and well behaved underwater transd~cer arrays, taking into account the acoustic interaction effect experienced among the closely packed multielement arrays.Architectural modifications reducing the interaction effect different radiating apertures.

Development of Time - Frequency Techniques for Sonar Applications

Sonar signal processing comprises of a large number of signal processing algorithms for implementing functions such as Target Detection, Localisation, Classification, Tracking and Parameter estimation. Current implementations of these functions rely on conventional techniques largely based on Fourier Techniques, primarily meant for stationary signals. Interestingly enough, the signals received by the sonar sensors are often non-stationary and hence processing methods capable of handling the non-stationarity will definitely fare better than Fourier transform based methods.Time-frequency methods(TFMs) are known as one of the best DSP tools for nonstationary signal processing, with which one can analyze signals in time and frequency domains simultaneously. But, other than STFT, TFMs have been largely limited to academic research because of the complexity of the algorithms and the limitations of computing power. With the availability of fast processors, many applications of TFMs have been reported in the fields of speech and image processing and biomedical applications, but not many in sonar processing. A structured effort, to fill these lacunae by exploring the potential of TFMs in sonar applications, is the net outcome of this thesis. To this end, four TFMs have been explored in detail viz. Wavelet Transform, Fractional Fourier Transfonn, Wigner Ville Distribution and Ambiguity Function and their potential in implementing five major sonar functions has been demonstrated with very promising results. What has been conclusively brought out in this thesis, is that there is no "one best TFM" for all applications, but there is "one best TFM" for each application. Accordingly, the TFM has to be adapted and tailored in many ways in order to develop specific algorithms for each of the applications.