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Ship radiated noise (SRN) recognition is challenging due to environmental noise and the broad frequency range of underwater signals. Existing deep learning models often include irrelevant frequencies and use red, green, and blue (RGB) channel configurations in convolutional networks, which are unsuitable for SRN data and computationally intensive. To address these limitations, we propose FCResNet5, a neural network optimized for SRN classification. FCResNet5 adopts a streamlined architecture that focuses on the critical frequency band and applies frequency channelization to enhance spectral representation. Its compact design achieves greater computational efficiency while maintaining comparable accuracy. Ablation studies confirm the contribution of each component, and comparative results demonstrate that FCResNet5 offers a more efficient alternative to existing models without compromising performance.

A unitary transformation-based algorithm is proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation of coherent signals. The problem is solved by reorganizing the covariance matrix into a block Hankel one for decorrelation first and then reconstructing a new matrix to facilitate the unitary transformation. By multiplying unitary matrices, eigenvalue decomposition and singular value decomposition are both transformed into real-valued, so that the computational complexity can be reduced significantly. In addition, a fast and computationally attractive realization of the 2-D unitary transformation is given by making a Kronecker product of the 1-D matrices. Compared with the existing 2-D algorithms, our scheme is more efficient in computation and less restrictive on the array geometry. The processing of the received data matrix before unitary transformation combines the estimation of signal parameters via rotational invariance techniques (ESPRIT)-Like method and the forward-backward averaging, which can decorrelate the impinging signalsmore thoroughly. Simulation results and computational order analysis are presented to verify the validity and effectiveness of the proposed algorithm.

Ship-radiated noise classification is critical in ocean acoustics. Recently, the feature extraction method combined with time–frequency spectrograms and convolutional neural networks (CNNs) has effectively described the differences between various underwater targets. However, many existing CNNs are challenging to apply to embedded devices because of their high computational costs. This paper introduces a lightweight network based on multi-scale asymmetric CNNs with an attention mechanism (MA-CNN-A) for ship-radiated noise classification. Specifically, according to the multi-resolution analysis relying on the relationship between multi-scale convolution kernels and feature maps, MA-CNN-A can autonomously extract more fine-grained multi-scale features from the time–frequency domain. Meanwhile, the MA-CNN-A maintains its light weight by employing asymmetric convolutions to balance accuracy and efficiency. The number of parameters introduced by the attention mechanism only accounts for 0.02‰ of the model parameters. Experiments on the DeepShip dataset demonstrate that the MA-CNN-A outperforms some state-of-the-art networks with a recognition accuracy of 98.2% and significantly decreases the parameters. Compared with the CNN based on three-scale square convolutions, our method has a 68.1% reduction in parameters with improved recognition accuracy. The results of ablation explorations prove that the improvements benefit from asymmetric convolution, multi-scale block, and attention mechanism. Additionally, MA-CNN-A shows a robust performance against various interferences.

Orthogonal frequency division multiplexing (OFDM) multihop system is a promising way to increase capacity and coverage. In this paper, we propose an optimally joint subcarrier matching and power allocation scheme to further maximize the total channel capacity with the constrained total system power. First, the problem is formulated as a mixed binary integer programming problem, which is prohibitive to find the global optimum in terms of complexity. Second, by making use of the equivalent channel power gain for any matched subcarrier pair, a low complexity scheme is proposed. The optimal subcarrier matching is to match subcarriers by the order of the channel power gains. The optimal power allocation among the matched subcarrier pairs is water-filling. An analytical argument is given to prove that the two steps achieve the optimally joint subcarrier matching and power allocation. The simulation results show that the proposed scheme achieves the largest total channel capacity as compared to the other schemes, where there is no subcarrier matching or no power allocation.

Subsurface mooring allows researchers to measure the ocean properties such as water temperature, salinity, and velocity at several depths of the water column for a long period. Traditional subsurface mooring can release data only after recovered, which constrains the usage of the subsurface and deep layer data in the ocean and climate predictions. Recently, we developed a new real-time subsurface mooring (RTSM). Velocity profiles over upper 1 000 m depth and layered data from sensors up to 5 000 m depth can be real-time transmitted to the small surface buoy through underwater acoustic communication and then to the office through Beidou or Iridium satellite. To verify and refine their design and data transmission process, we deployed more than 30 sets of RTSMs in the western Pacific to do a 1-year continuous run during 2016–2018. The continuous running period of RTSM in a 1-year cycle can reach more than 260 days on average, and more than 95% of observed data can be successfully transmitted back to the office. Compared to the widely-used inductive coupling communication, wireless acoustic communication has been shown more applicable to the underwater sensor network with large depth intervals and long transmission distance to the surface.

Frequency-domain and time-domain processing approaches todirection-of-arrival (DOA) estimation for multiple broadband farfield signals using beamspace preprocessing structures areproposed. The technique is based on constant mainlobe responsebeamforming. A set of frequency-domain and time-domain beamformerswith constant (frequency independent) mainlobe response andcontrolled sidelobes is designed to cover the spatial sector ofinterest using optimal array pattern synthesis technique andoptimal FIR filters design technique. These techniques lead theresulting beampatterns higher mainlobe approximation accuracy andyet lower sidelobes. For the scenario of strong out-of-sectorinterfering sources, our approaches can form nulls or notches inthe direction of them and yet guarantee that the mainlobe responseof the beamformers is constant over the design band. Numericalresults show that the proposed time-domain processing DOAestimator has comparable performance with the proposedfrequency-domain processing method, and that both of them are ableto resolve correlated source signals and provide better resolutionat lower signal-to-noise ratio (SNR) and lower root-mean-squareerror (RMSE) of the DOA estimate compared with the existingmethod. Our beamspace DOA estimators maintain good DOA estimationand spatial resolution capability in the scenario of strongout-of-sector interfering sources.

We consider the problem of adaptive signal detection in the presence of Gaussian noise with unknown covariance matrix. We propose a parametric radar detector by introducing a design parameter to trade off the target sensitivity with sidelobes energy rejection. The resulting detector merges the statistics of Kelly's GLRT and of the Rao test and so covers Kelly's GLRT and the Rao test as special cases. Both invariance properties and constant false alarm rate (CFAR) behavior for this detector are studied. At the analysis stage, the performance of the new receiver is assessed and compared with several traditional adaptive detectors. The results highlight better rejection capabilities of this proposed detector for mismatched signals. Further, we develop two two-stage detectors, one of which consists of an adaptive matched filter (AMF) followed by the aforementioned detector, and the other is obtained by cascading a GLRT-based Subspace Detector (SD) and the proposed adaptive detector. We show that the former two-stage detector outperforms traditional two-stage detectors in terms of selectivity, and the latter yields more robustness.

Temporal filters and spatial filters are widely used in many areas of signal processing. A number of optimal design criteria to these problems are available in the literature. Various computational techniques are also presented to optimize these criteria chosen. There are many drawbacks in these methods. In this paper, we introduce a unified framework for optimal design of temporal and spatial filters. Most of the optimal design problems of FIR filters and beamformers are included in the framework. It is shown that all the design problems can be reformulated as convex optimization form as the second-order cone programming (SOCP) and solved efficiently via the well-established interior point methods. The main advantage of our SOCP approach as compared with earlier approaches is that it can include most of the existing methods as its special cases, which leads to more flexible designs. Furthermore, the SOCP approach can optimize multiple required performance measures, which is the drawback of earlier approaches. The SOCP approach is also developed to optimally design temporal and spatial two-dimensional filter and spatial matrix filter. Numerical results demonstrate the effectiveness of the proposed approach.

In this study, the authors deal with the problem of adaptive detection of point-like targets in Gaussian disturbance with unknown but persymmetric structured covariance matrix induced by the space and/or time symmetry of the sensing system. In this framework, they devise and assess two selective receivers exploiting the Rao test and the generalised likelihood ratio test design criteria. The performance assessment, conducted by Monte Carlo simulation, has shown that the proposed receivers can significantly outperform their unstructured counterparts and guarantee enhanced rejection performance of unwanted signals with respect to their natural competitors.

Frequency-domain and time-domain processing approaches to direction-of-arrival (DOA) estimation for multiple broadband far field signals using beamspace preprocessing structures are proposed. The technique is based on constant mainlobe response beamforming. A set of frequency-domain and time-domain beamformers with constant (frequency independent) mainlobe response and controlled sidelobes is designed to cover the spatial sector of interest using optimal array pattern synthesis technique and optimal FIR filters design technique. These techniques lead the resulting beampatterns higher mainlobe approximation accuracy and yet lower sidelobes. For the scenario of strong out-of-sector interfering sources, our approaches can form nulls or notches in the direction of them and yet guarantee that the mainlobe response of the beamformers is constant over the design band. Numerical results show that the proposed time-domain processing DOA estimator has comparable performance with the proposed frequency-domain processing method, and that both of them are able to resolve correlated source signals and provide better resolution at lower signal-to-noise ratio (SNR) and lower root-mean-square error (RMSE) of the DOA estimate compared with the existing method. Our beamspace DOA estimators maintain good DOA estimation and spatial resolution capability in the scenario of strong out-of-sector interfering sources.