Acoustic Communication Signals Research Articles

Interception of Bio-Mimicking Underwater Acoustic Communications Signals

Interception of Bio-Mimicking Underwater Acoustic Communications Signals

Acoustic notch filter based on self-collimation sonic crystals

Utilizing the self-collimation effect in two-dimensional (2D) sonic crystals (SCs), we have proposed an acoustic notch filter which is composed of two perfect mirrors and a beam splitter. Numerical simulation shows that by properly designing the inside structure of the 2D SC, the self-collimated acoustic waves with a desired frequency can be trapped between the two mirrors, realizing the notch filtering function. Moreover, the frequency and bandwidth of the notch filter can be arbitrarily adjusted by changing the parameters of the internel structure, in a considerably broad band. Potential applications include acoustic communication and acoustic signal processing, especially for underwater circumstances.

Localization and detection of underwater acoustic communication signals using convolutional recursive neural networks

Abstract This paper employs a multi-task neural network (UWAELD) to simultaneously perform the Underwater Acoustic Communication Event Detection (UACED) and direction-of-arrival (DOA) mission. Typically, the UACED and DOA tasks are executed separately, as the UACED task is identified through time-frequency analysis, while the DOA task is identified through the amplitude and phase differences of the signal, making it very difficult to jointly perform both tasks. To overcome this, we utilize a logarithmic spectrogram for time-frequency analysis and stack the normalized eigenvectors obtained by processing the signal covariance matrix. We ultimately evaluate the network’s feasibility using simulation data and lake test data, utilizing acoustical signals measured in actual experiments. Finally, for the UADCED task, the error rate is only 6%, with an F1 score of 0.83; for the DOA task, the localization error is only 1.72°, with a localization recall score of 0.87. Additionally, for the DOA task, we employ the classical MUSIC algorithm to demonstrate the network’s superiority.

Time and place affect the acoustic structure of frog advertisement calls

Abstract Acoustic communication signals are important for species recognition and mate attraction across numerous taxa. For instance, most of the thousands of species of frogs have a species-specific advertisement call that females use to localize and discriminate among potential mates. Thus, the acoustic structure of the advertisement call is critical for reproductive success. The acoustic structure of calls will generally diverge over evolutionary time and can be influenced by the calls of sympatric species. While many studies have shown the influence of geography on contemporary call variation in populations of frogs, no study has compared the acoustic structure of frog calls across many species to ask whether we can detect an influence of divergence time and overall geographic overlap on the differences in acoustic structure of species-typical calls that we observe now. To this end, we compared acoustic features of the calls of 225 species of frogs within 4 families. Furthermore, we used a behavioral assay from 1 species of frog to determine which acoustic features to prioritize in our large-scale analyses. We found evidence that both phylogeny (time) and geography (place) relate to advertisement call acoustics albeit with large variation in these relationships across the 4 families in the analysis. Overall, these results suggest that, despite the many ecological and evolutionary forces that influence call structure, the broad forces of time and place can shape aspects of advertisement call acoustics.

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.

Improving the efficiency of channel evaluation during high-speed data transmission in underwater acoustic communication with OFDM

Problem statement. The organization of noise-resistant wireless underwater acoustic communication is not a simple and solved task. One of the main problems is the multipath propagation of a sound acoustic wave as a result of re–reflections from the seabed surface and a change in the frequency of signal vibrations when the receiver and transmitter move relative to each other. Thus, finding a channel evaluation solution that can improve the quality of communication is an urgent task for developers of underwater acoustic communication systems. Purpose. To consider the possibility of developing effective methods for processing underwater acoustic communication signals with high noise immunity by evaluating the communication channel. Results. The possibility of using an adaptive filter with a sliding window to evaluate the channel during high-speed data transmission in underwater acoustic communication is shown. The simulation results showed that the proposed adaptive filter with a sliding window surpasses the normalized adaptive filters in terms of the BER bit error and wins over additive filters with an exponential window in reducing the number of mathematical operations. When modeling wireless acoustic communication systems with OFDM, error-free data transmission with a spectral efficiency of 0.33 bits/s/Hz was achieved. Practical significance. The obtained results can be used to improve the wireless transmission of data in underwater acoustic communication due to the proposed algorithm of the adaptive filter.

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.

Behavioural plasticity compensates for adaptive loss of cricket song.

Behavioural flexibility might help animals cope with costs of genetic variants under selection, promoting genetic adaptation. However, it has proven challenging to experimentally link behavioural flexibility to the predicted compensation of population-level fitness. We tested this prediction using the field cricket Teleogryllus oceanicus. In Hawaiian populations, a mutation silences males and protects against eavesdropping parasitoids. To examine how the loss of this critical acoustic communication signal impacts offspring production and mate location, we developed a high-resolution, individual-based tracking system for low-light, naturalistic conditions. Offspring production did not differ significantly in replicate silent versus singing populations, and fitness compensation in silent conditions was associated with significantly increased locomotion in both sexes. Our results provide evidence that flexible behaviour can promote genetic adaptation via compensation in reproductive output and suggest that rapid evolution of animal communication systems may be less constrained than previously appreciated.

Calling for the future of conservation: a protocol for passive acoustic monitoring of small arboreal primates

Biodiversity conservation faces challenges due to a lack of accurate information on species occurrence. Various techniques have been used to survey species diversity and estimate population density, but monitoring species over large spatial and temporal scales remains challenging. Passive acoustic monitoring (PAM) has emerged as a cost-effective and non-invasive method for monitoring biodiversity. PAM utilizes autonomous recording units (ARUs) installed in different areas and is particularly relevant for monitoring threatened species in tropical forest regions. In the case of non-human primates, PAM has proven effective in detecting endangered species, monitoring populations, studying vocal behavior, and evaluating territory use. The black lion tamarin (Leontopithecus chrysopygus), an endangered species of the Atlantic Forest, relies on acoustic signals for communication. This study proposes a PAM protocol for monitoring arboreal primates using the black lion tamarin as a model. It reviews PAM's use in primate research and emphasizes defining target vocalizations. We recommend optimal recording conditions, including distance, height, and equipment. Recordings should be positioned high above the ground, considering the arboreal nature of primates. The choice of spatial distribution, including random placement, transects, and grids, depends on the research question and objectives. Lastly, the study addresses the recording schedule, considering periods of greater species activity, such as from sunrise to sunset. In summary, this study highlights PAM's potential for monitoring arboreal primates providing recommendations for vocalizations, recording conditions, equipment, spatial distribution, and schedules, contributing to effective monitoring, and supporting conservation efforts in tropical forests.

Detection and Recognition of Underwater Acoustic Communication Signal under Ocean Background Noise

Detection and Recognition of Underwater Acoustic Communication Signal under Ocean Background Noise

Coordination of marine multi robot systems with communication constraints

This article focuses on the coordination and communication aspects of multi-robot systems (MRS) and explores what, how and when robots should coordinate and communicate in marine environment. It includes a comparative analysis of MRS coordination under communication constraints in aerial, terrestrial, and marine environments. In the light of the gaps identified in the current MRS literature, we focus on how to tackle the inherent communication constraints presents in marine environments. Thus, a review of Marine Multi-Robot systems (MMRS) is presented, proposing a taxonomy that classifies them according to how they communicate and coordinate. In this line, the classification of different communication strategies found in the literature has received significant attention. Additionally, a MMRS coordination approach is proposed where a heterogeneous group of marine vehicles must explore an unknown area maximizing communication. The proposed approach uses a real characterization of the acoustic communication signal quality as input to the decision making process. To validate its effectiveness, our approach has been subjected to a series of highly realistic experiments.

Acoustic diode realized by asymmetric filter

Devices of one-way transport for acoustic waves are called acoustic diodes. They are able to promote the advancement of noise isolation, acoustic communication, and acoustic signal processing. A lot of designs of acoustic diodes based on various mechanisms have been given. However, most designs have problems of one kind or another, such as low efficiency, instability, bulky volume, complex structure, frequency change, waveform distortion, and so on. An asymmetric acoustic filter with only three layers is proposed in this work. The total length of the acoustic diode is less than half the wavelength. Its backward transmission is almost completely stopped. For the forward transmission, the amplitude of the transmitted wave is almost proportional to the driving voltage. This characteristic is better than its electronic counterpart which is often annoyed by the unavoidable nonlinearity at high driving voltage. A simple, compact, stable, broadband, frequency-preserved, highly efficient, linear acoustic diode is realized.

Direct acoustic communication between underwater and airborne nodes

Direct acoustic communication between underwater and airborne nodes has always been considered unfeasible due to the energy loss caused by the strong surface reflection of sound waves. However, contrary to popular belief, our study demonstrates that underwater transducers can effectively transmit detectable acoustic signals in the air. To investigate this phenomenon, we conducted an experiment involving the deployment of an underwater transducer at a depth of 1 m, while an unoccupied aerial vehicle equipped with a voice recorder was positioned at various altitudes ranging from 2 to 30 m and horizontal distances of 0–30 m. Sound pressure levels were measured at 20 different positions within the frequency range of 10–20kHz, and orthogonal frequency division multiplex acoustic communication signals were recorded at specific positions. Our findings reveal the successful establishment of a direct acoustic communication link between the water and air interface, achieving a data rate of 4.565 kbps. This study opens up new possibilities for practical applications in underwater-to-air communication systems.

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.