Underwater Acoustic Communication Signals Research Articles

Modulation recognition of underwater acoustic communication signals based on neural architecture search

Modulation recognition of underwater communication signals is a critical aspect of underwater information confrontation. However, the current deep learning-based methods for underwater communication modulation recognition often mimic neural network architectures used in image processing and speech processing, tending to perform poorly in low signal-to-noise ratio (SNR) conditions. This paper introduces a novel approach by utilizing neural architecture search (NAS) method. By automatically searching for neural architectures that are well-suited for modulation recognition in underwater environment, our proposed method improves the classification performance particularly in low SNR conditions. Furthermore, we have also proposed a recognition method based on attention mechanism and feature fusion, which substantially enhances the accuracy of identifying phase-modulated signals. Numerical simulation and experimental data are used to demonstrate performance of proposed methods.

Direction of Arrival Joint Prediction of Underwater Acoustic Communication Signals Using Faster R-CNN and Frequency–Azimuth Spectrum

Utilizing hydrophone arrays for detecting underwater acoustic communication (UWAC) signals leverages spatial information to enhance detection efficiency and expand the perceptual range. This study redefines the task of UWAC signal detection as an object detection problem within the frequency–azimuth (FRAZ) spectrum. Employing Faster R-CNN as a signal detector, the proposed method facilitates the joint prediction of UWAC signals, including estimates of the number of sources, modulation type, frequency band, and direction of arrival (DOA). The proposed method extracts precise frequency and DOA features of the signals without requiring prior knowledge of the number of signals or frequency bands. Instead, it extracts these features jointly during training and applies them to perform joint predictions during testing. Numerical studies demonstrate that the proposed method consistently outperforms existing techniques across all signal-to-noise ratios (SNRs), particularly excelling in low SNRs. It achieves a detection F1 score of 0.96 at an SNR of −15 dB. We further verified its performance under varying modulation types, numbers of sources, grating lobe interference, strong signal interference, and array structure parameters. Furthermore, the practicality and robustness of our approach were evaluated in lake-based UWAC experiments, and the model trained solely on simulated signals performed competitively in the trials.

A metric to quantify the time-varying characteristics of underwater acoustic communication channels.

Underwater acoustic communication signals suffer from time dispersion due to time-varying multipath propagation in the ocean. This leads to intersymbol interference, which in turn degrades the performance of the communication system. Typically, the channel correlation functions are employed to describe these characteristics. In this paper, a metric called the channel average correlation coefficient (CACC) is proposed from the correlation function to quantify the time-varying characteristics. It has a theoretical negative relationship with communication performance. Comparative analysis involving simulations and experimental data processing highlights the superior effectiveness of CACC over the traditional metric, the channel coherence time.

Measurement of distance and speed between transmitter and receiver using the propagation time of underwater acoustic communication signals with orthogonal signal division multiplexing

This paper proposes a procedure for measuring transmitter–receiver (Tx–Rx) distance using acoustic communication signals to realize communication and positioning underwater with the same signal and device. Specifically, we focus on an underwater acoustic communication using orthogonal signal division multiplexing, where a pilot signal and messages are placed in the time–frequency domain. In this paper, we show that measurements of both Tx–Rx distance and speed are available by utilizing the pilot signal. We conducted a sea trial and evaluated the performance of the proposed method. During the experiment, the Tx–Rx distance was 50–550 m. The obtained results suggest that the proposed method can measure the Tx–Rx distance and speed in a specific environment. The mean absolute error and the standard deviation error of ranging are 4.0 and 2.7 m, respectively, while those of measurement speed are 0.13 and 0.22 m s−1, respectively.

A Modulation Recognition System for Underwater Acoustic Communication Signals Based on Higher-Order Cumulants and Deep Learning

Underwater acoustic channels, influenced by time-varying, space-varying, frequency-varying, and multipath effects, pose significant interference challenges to underwater acoustic communication (UWAC) signals, especially in non-cooperative scenarios. The task of modulating and identifying distorted signals faces huge challenges. Although traditional modulation recognition methods can be useful in the radio field, they often prove inadequate in underwater environments. This paper introduces a modulation recognition system for recognizing UWAC signals based on higher-order cumulants and deep learning. The system achieves blind recognition of received UWAC signals even under non-cooperative conditions. Higher-order cumulants are employed due to their excellent noise resistance, enabling the differentiation of OFDM signals from PSK and FSK signals. Additionally, the high-order spectra differences among signals are utilized for the intra-class recognition of PSK and FSK signals. Both simulation and lake test results substantiate the effectiveness of the proposed method.

Stacking Ensemble Learning for the Modulation Recognition of Underwater Acoustic Communication Signals

Effective detection and recognition of underwater acoustic communication signals is of great significance in the field of ocean exploration. In this paper, a novel deep-learning recognition method of modulation modes of underwater acoustic communication signals is proposed. The stacking ensemble learning framework is established on the basis of several single deep-learning classifiers with different network structures and parameter configurations. The proposed method achieved an average recognition accuracy rate of more than 95% on the dataset generated under a simulation environment. The performance is also verified by means of a comparison with the classic SVM method.

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication.

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

Modulation Recognition Method for Underwater Acoustic Communication Signals Based on Passive Time Reversal-Autoencoder with the Synchronous Signals.

In the process of the modulation recognition of underwater acoustic communication signals, the multipath effect seriously interferes with the signal characteristics, reducing modulation recognition accuracy. The existing methods passively improve the accuracy from the perspective of selecting appropriate signal features, lacking specialized preprocessing for suppressing multipath effects. So, the accuracy improvement of the designed modulation recognition models is limited, and the adaptability to environmental changes is poor. The method proposed in this paper actively utilizes common synchronous signals in underwater acoustic communication as detection signals to achieve passive time reversal without external signals and designs a passive time reversal-autoencoder to suppress multipath effects, enhance signals' features, and improve modulation recognition accuracy and environmental adaptability. Firstly, synchronous signals are identified and estimated. Subsequently, a passive time reversal-autoencoder is designed to enhance power spectrum and square spectrum features. Finally, a modulation classification is performed using a convolutional neural network. The model is trained in simulation channels generated by Bellhop and tested in actual channels which are different from the training period. The average recognition accuracy of the six modulated signals is improved by 10% compared to existing passive modulation recognition methods, indicating good environmental adaptability as well.

Recognition of Whistle-like Signals Based on FM Harmonic Characteristics

While human hearing can easily identify whistle-like sounds made by dolphins and whales, manually identifying them from the increasingly variety and quantity of underwater acoustic signals (such as other marine life sounds and underwater acoustic communication signals) is undoubtedly laborious. To solve this question, a recognition method based on the frequency modulated feature and harmonic characteristic is analyzed in this paper. A harmonic energy analysis algorithm is proposed and is compared to a harmonic detection method. The experimental results prove that the proposed method could identify the whistle-like sounds with an accuracy over 90% and the traditional one could only reach 50%. The recognition method may be applied to the investigation of marine biological habits and the monitoring of marine ecological environment, and can also potentially be further applied to the detection of bionic communication signal.

Modulation Signal Recognition of Underwater Acoustic Communication Based on Archimedes Optimization Algorithm and Random Forest

This paper researches the recognition of modulation signals in underwater acoustic communication, which is the fundamental prerequisite for achieving noncooperative underwater communication. In order to improve the accuracy of signal modulation mode recognition and the recognition effects of traditional signal classifiers, the article proposes a classifier based on the Archimedes Optimization Algorithm (AOA) and Random Forest (RF). Seven different types of signals are selected as recognition targets, and 11 feature parameters are extracted from them. The decision tree and depth obtained by the AOA algorithm are calculated, and the optimized random forest after the AOA algorithm is used as the classifier to achieve the recognition of underwater acoustic communication signal modulation mode. Simulation experiments show that when the signal-to-noise ratio (SNR) is higher than -5dB, the recognition accuracy of the algorithm can reach 95%. The proposed method is compared with other classification and recognition methods, and the results show that the proposed method can ensure high recognition accuracy and stability.

Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks

Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.

Research on modulation recognition of underwater acoustic communication signal based on deep learning

Marine information technology plays a important role in the development of marine resources development, marine climate early warning, and other industries. Underwater acoustic communication technology can help us better access marine information. The performance of the underwater acoustic signal modulation recognition algorithm depends on the accuracy of feature extraction. however, due to excessive underwater noise, many traditional algorithms can not recognize features well. For this reason, this paper proposes a modulation recognition network for aquatic communication signals based on deep learning. ResNet can capture the characteristics of deep features and combines ResNet with a modulation recognition network. Finally, the experiment proves the effectiveness of this method.

Multihydrophone Fusion Network for Modulation Recognition.

Deep learning (DL)-based modulation recognition methods of underwater acoustic communication signals are mostly applied to a single hydrophone reception scenario. In this paper, we propose a novel end-to-end multihydrophone fusion network (MHFNet) for multisensory reception scenarios. MHFNet consists of a feature extraction module and a fusion module. The feature extraction module extracts the features of the signals received by the multiple hydrophones. Then, through the neural network, the fusion module fuses and classifies the features of the multiple signals. MHFNet takes full advantage of neural networks and multihydrophone reception to effectively fuse signal features for realizing improved modulation recognition performance. Experimental results on simulation and practical data show that MHFNet is superior to other fusion methods. The classification accuracy is improved by about 16%.

Modulation Mode Recognition Method of Non-Cooperative Underwater Acoustic Communication Signal Based on Spectral Peak Feature Extraction and Random Forest

The modulation mode recognition of non-cooperative underwater acoustic (UWA) communication signal faces great challenges due to the influence of the UWA channel and the demand for efficient recognition. This work proposes a recognition method for UWA orthogonal frequency division multiplexing (OFDM), binary frequency shift keying (2FSK), four-frequency shift keying (4FSK), and eight-frequency shift keying (8FSK) by using spectral peak feature extraction combined with random forest (RF). First, a new spectral peak feature extraction method is proposed. In this method, pre-processing, waveform optimization, and feature extraction are used to ensure that the extracted feature maintains high robustness in the UWA channel. Then, we designed an RF classifier that can meet the demand for high-efficiency recognition and good performance. Finally, simulation and experimental results verified the feasibility of the recognition method.

Underwater Acoustic Signal Classification Based on Sparse Time–Frequency Representation and Deep Learning

For classification of underwater acoustic signals, we propose a novel sparse anisotropic chirplet transform (ACT) to reveal fine time-frequency structures. The signal features in the form of a time-frequency map are fed into a deep convolutional neural network, referred to as a time-frequency feature network (TFFNet), which brings flexibility to signal classification. The TFFNet is based on a novel efficient feature pyramid enhancing feature (EFP) maps by aggregating the context information at different scales. To remove the gridding artifacts on enhanced feature maps, a form of aggregating transformation, a forward feature fusion, is utilized to merge the forward feature maps. Main contributions of this work are a novel sparse ACT, a TFFNet classifier, and an EFP with forward feature fusion. Experimental results demonstrate that the sparse ACT provides a high-resolution time-frequency representation of underwater signals and the TFFNet improves the classification performance compared to known networks and two machine learning methods (random forest and support vector machine with radial basis function kernel) on two real data sets, an underwater acoustic communication signal data set and whale sounds data set.

Stochastic resonance with reinforcement learning for underwater acoustic communication signal

In the underwater acoustic communication (UAC), the receive signal is submerged in the heavy noise which make it hard to be detected. Stochastic resonance (SR) utilizes noise instead of eliminating it to improve the signal to noise ratio (SNR) and has been an attractive topic in the field of weak signal detection. However, as a nonlinear system, the SR requires sophisticated system design and critical parameter choice to meet its oscillatory condition so as to keep the balance among signal, noise and the nonlinear system. To solve this problem, the parameters that influence SR system have been analyzed in this paper and an adaptive method called as Reinforcement Learning & Genetic Algorithm (RLGA) for adjusting the parameters of the SR system has been proposed. By combining the reinforcement learning (RL) with genetic algorithm (GA), the method ameliorates the local search ability and accelerates the convergence speed of the traditional GA, which make it more suitable for the signal detection in UAC. Numerical simulations and outfields experiments shown that the proposed RLGA_SR can increase the output signal to noise ratio (OSNR) evidently and has robust in different kinds of marine noise and UAC channels.

Research on spatiotemporal fluctuation characteristics of underwater acoustic communication signals

In underwater acoustic communication systems, channel characteristics are mainly affected by changes in space and time. The changes embody the random fluctuations of the seabed and sea surface, and the effects of refraction and scattering caused by seawater layered media on the sound field. As the channel is time-varying and space-varying, the rapid fluctuation of communication signals, which is shown in the frequency selective fading in frequency-domain and the signal waveform distortion in the time domain, leads to a bad effect on the performance of the underwater acoustic communication system. The techniques to compensate or offset the effects of deep fading such as coding error correction and spatial diversity are widely used in underwater acoustic communication systems, which may cause a significant waste of limited communication efficiency. This paper analyzes the spatiotemporal fluctuation characteristics of both signal field and noise field, and summarizes the temporal and spatial variation rules. The effects of signal spatiotemporal fluctuation on communication systems are significantly reduced by reasonably selecting the communication signal parameters, equipment deployment depth and horizontal distance, which will guide the parameter configuration and network protocol optimization of communication systems.

Multimodal Sparse Time–Frequency Representation for Underwater Acoustic Signals

Multiple features can be extracted from time-frequency representation of signals for the purpose of acoustic event detection. However, many underwater acoustic signals are formed by multiple events (impulsive and tonal), which generates difficulty on the high-resolution TFR for each component. For the characterization of such different events, we propose an anisotropic chirplet transform to achieve the TFR with high energy concentration. Such transform applies a time-frequency varying Gaussian window to compensate the energy of each component while suppressing unwanted noise. Using a set of directional chirplet ridges from the obtained TFR, a structure-split-merge algorithm is designed to reconstruct a multimodal sparse representation, which provides instantaneous frequency and time features. Specifically, a pulsed-to-tonal ratio, based on these features, is computed to distinguish two acoustic signals. The presented method is validated using shallow water experimental underwater acoustic communication signals and large sequences of harmonics and pulsed bursts from common whales.

Automatic Modulation Classification for Short Burst Underwater Acoustic Communication Signals Based on Hybrid Neural Networks

Automatic modulation classification (AMC) is challenging for short burst underwater acoustic (UWA) communication signals. Difficulties include but are not limited to the poor UWA channels, impulsive noise, and data scarcity. To address these problems, a method based on hybrid neural networks (HNNs) is proposed in this paper. First, an impulsive noise preprocessor is adopted to mitigate the impulse in the received signals. Subsequently, an HNN consisting of an attention aided convolutional neural network (Att-CNN) and a sparse auto-encoder is built to extract features from the temporal waveforms and square spectra of the preprocessed signals after burst detection. Finally, a late fusion is made to combine the prediction results of the two sub-networks. To overcome the variable signal duration relative to the fixed input size of the Att-CNN, a data-reusing approach is proposed to perform dimension preprocessing on the waveforms. Moreover, a transfer learning strategy is introduced to resolve the issue of insufficient training data from the testing channel. The results of simulation experiments and practical signal tests both demonstrate that the proposed method is robust against UWA channels and ambient noise. Our approach significantly outperforms existing deep learning-based methods in dealing with short and weak signal bursts, while requiring less training data from the testing channel.

Blind Detection of Underwater Acoustic Communication Signals Based on Deep Learning

Blind detection of underwater acoustic communication (UWAC) signals is challenging in non-cooperative reception scenarios. Difficulties include but not limited to complex underwater acoustic channels, diversity of signal categories, and data scarcity. To address these problems, we propose a novel blind detection method for UWAC signals based on deep learning (DL). First, an impulsive noise preprocessor and a signal denoising generative adversarial network are built to mitigate the noise in the received signals. Second, a convolutional neural network-based binary classification network is built to automatically extract features and distinguish between the UWAC signals and noise. Moreover, a transfer data model is presented to overcome the insufficient data issue in the target water regions. The results of simulation experiments and practical signal tests both demonstrate that the proposed method is robust to ambient noise with wide dynamic ranges and complex distributions. Our approach significantly outperforms conventional algorithms and existing DL-based algorithms at low signal-to-noise ratios, while requiring no prior information about the testing channel.