Neural Computation
Sonar recognition of targets embedded in sediment
Neural Networks - Special issue: automatic target recognition
Echo Signal Processing
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Robust Wigner distribution with application to the instantaneousfrequency estimation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Underwater target classification using wavelet packets and neural networks
IEEE Transactions on Neural Networks
Underwater target classification in changing environments using an adaptive feature mapping
IEEE Transactions on Neural Networks
Comparison of different classification algorithms for underwater target discrimination
IEEE Transactions on Neural Networks
Multi-aspect target discrimination using hidden Markov models and neural networks
IEEE Transactions on Neural Networks
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A novel approach to the problem of detecting and classifying underwater bottom mine objects in littoral environments from acoustic backscattered signals is considered. We begin by defining robust short-time Fourier transform to convert the received echo into a time-frequency (TF) plane. Identify interest local region in spectrogram, then features in TF plane with robustness to reverberation and noise disturbances are built. Finally, echo features are sent to a relevance vector machine (RVM) classifier that represents a Bayesian extension of support vector machine (SVM). To evaluate the performace of the classifier based on this approach, the classification experiment of two typical types of mines lying on the bottom have been performed with a broad bandwidth active sonar. Each of the targets was lying on the lake bottom at a depth of 20 m. The case study exploits the robustness of a feature extraction scheme, and furthermore, RVM yields a much sparser solution and improves the classification accuracy than SVM in an impulse noise environment.