This paper proposes a method for underwater target recognition based on micro‐Doppler effects (called STR_MD) using a majority voting ensemble classifier weighted with particle swarm optimization (PSO) (called MV‐PSO). The micro‐Doppler effect refers to amplitude/phase modulation of the received signal by rotating parts of a target such as propellers. Since different targets’ geometric and physical properties differ, their micro‐Doppler signature is different. This inconsistency can be considered an effective issue (especially in the frequency domain) for sonar target recognition. To demonstrate the effectiveness of the proposed method, both simulated and practical micro‐Doppler data are produced and applied to the designed STR_MD. Also, MV‐PSO with six well‐known basic classifiers, k‐nearest neighbors (k‐NN), Naive Bayes (NB), decision tree (DT), MLP_NN, support vector machine (SVM), and random forest (RF), has been used to evaluate the performance of the proposed method. This ensemble classifier assigns an instance to a class that most base classifiers agree on. However, basic classifiers in a set seldom work just as well. Therefore, in this case, one strategy is to weigh each classification depending on its performance using PSO. The performance parameters measured are the recognition score, reliability, and processing time. The simulation results showed that the correct recognition rate, reliability, and processing time for the simulated data at SNR = 5 dB and 10° viewing angle were 98.50, 98.89, and 9.81 s, respectively, and for the practical dataset with RPM = 1200, 100, 100, and 4.43, respectively. Thus, MV‐PSO has a more encouraging performance in STR_MD for simulated and practical micro‐Doppler sonar datasets.

Ensemble learners and deep neural networks are state-of-the-art schemes for classification applications. However, deep networks suffer from complex structure, need large amount of samples and also require plenty of time to be converged. In contrast, ensemble learners (especially AdaBoost) are fast to be trained, can work with small and large datasets and also benefit strong mathematical background. In this paper, we have developed a new orthogonal version of AdaBoost, termed as ORBoost, in order to desensitize its performance against noisy samples as well as exploiting low number of weak learners. In ORBoost, after reweighting the distribution of each learner, the Gram-Schmidt rule updates those weights to make a new samples’ distribution to be orthogonal to the former distributions. In contrast in AdaBoost, there is no orthogonality constraint even between two successive weak learners while there is a similarity between the distributions of samples in different learners. To assess the performance of ORBoost, 16 UCI-Repository datasets along with six big datasets are deployed. The performance of ORBoost is compared to the standard AdaBoost, LogitBoost and AveBoost-II over the selected datasets. The achieved results support the significant superiority of ORBoost to the counterparts in terms of accuracy, robustness, number of exploited weak learners and generalization on most of the datasets.

Random noise elimination acts as an important role in the seismic signal processing. Generally, noise in seismic data can be divided into two categories of coherent and incoherent or random noise. Suppression of wide-band noise which is characterized by random oscillation in seismic data over time is one of the challenging issues in the seismic data processing. This paper describes a new noise suppression algorithm for seismic data denoising. The seismic data, trace-by-trace are transformed into sparse subspace using the synchrosqueezed wavelet transform, then the obtained sparse time-frequency representation is decomposed into semilow-rank and sparse components using the Optshrink algorithm. Finally, the denoised seismic trace can be recovered by back-transforming the semilow-rank component to the time domain using inverse synchrosqueezed wavelet transform. The proposed method is assessed using a single synthetic seismic trace and a synthetic seismic section with two crossover linear and curve events with two discontinuities that are buried in the random noise. We have also evaluated the method using a prestack real seismic data set from an oil field in the southwest of Iran. A comparison is performed between the proposed method and the semisoft GoDec algorithm, classical f-x singular spectrum analysis, and prediction Wiener filter. The results visually and quantitatively confirmed the superiority of the proposed method in contrast to the other well-established noise reduction methods.