Sonar Signal Research Articles

Marine Sediment Classification Spectral Ratio Technique From a Signal Decomposition View Based on Chirp Sonar Data

Sediment classification based on Chirp sonar data is very important in support of marine science and engineering. The traditional adopted spectral-ratio (SR) method is widely applied for its theory of simplicity and easy employment. However, the performance of SR method is heavily degraded by spectrum vibrations introduced by overlapping reflections. To solve this problem, a reflection signal decomposition method is proposed in this paper, which decomposes the overlapping reflection into separate reflection sub-signals to avoid the spectrum vibration. Firstly, we re-derive the overlapping reflection expression from the convolution model and give the initial model for the decomposition of the Chirp sonar signal. Then, introducing the spectrum smooth prior into consideration and incorporating it with the fidelity and the bandwidth terms, the decomposition model with constraints is proposed. After that, an iterative algorithm is introduced to solve the model optimization problem. The reflection sub-signal can be well obtained. Finally, through a least square linear fitting for the logarithm of the spectral ratio (the log-SR) of reflection sub-signals, quality factor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> ) which is applied for sediment classification can finally be achieved. Both real and simulated experiments have been conducted to verify the proposed method and some meaningful discussions are also drawn.

Application of Singular Value Decomposition and Adaptive Noise Cancellation Method in AUV Sonar Pulse

The detection signal-to-noise ratio determines the underwater acoustic reconnaissance ability of AUV in complex marine environments. In this paper, the singular value decomposition method and adaptive noise cancellation method are used to improve the signal-to-noise ratio of dual channel sonar signal. The experimental data processing results show that the singular value decomposition method has a stronger noise reduction ability, but the noise reduction effect is greatly affected by the singular value threshold, and the performance of the algorithm is unstable; Although the noise reduction ability of adaptive noise cancellation method is limited, it has strong environmental adaptability and more stable algorithm performance.

Performance analysis on the speed spectrum estimation based on dual-HFM waveform

Real-time Doppler estimation of sonar signals is of great significance to underwater communication and detection. The Hyperbolic-Frequency-Modulated (HFM) waveform-based speed spectrum estimation is one of the modern methods for underwater Doppler estimation, and this paper presents a detailed analyses of the derivation and the performance of this method. The analyzing results show the robustness of the method on multipath channels and multi-highlight targets and provide the main lobe width of the speed spectrum. Moreover, the paper examines the spectral properties of the HFM signal from the frequency-domain Doppler invariance property, which could provide insights for better understanding and utilization of this Doppler invariant waveform.

The Tacotron-Based Signal Synthesis Method for Active Sonar

The importance of active sonar is increasing due to the quieting of submarines and the increase in maritime traffic. However, the multipath propagation of sound waves and the low signal-to-noise ratio due to multiple clutter make it difficult to detect, track, and identify underwater targets using active sonar. To solve this problem, machine learning and deep learning techniques that have recently been in the spotlight are being applied, but these techniques require a large amount of data. In order to supplement insufficient active sonar data, methods based on mathematical modeling are primarily utilized. However, mathematical modeling-based methods have limitations in accurately simulating complicated underwater phenomena. Therefore, an artificial intelligence-based sonar signal synthesis technique is proposed in this paper. The proposed method modified the major modules of the Tacotron model, which is widely used in the field of speech synthesis, in order to apply the Tacotron model to the field of sonar signal synthesis. To prove the validity of the proposed method, spectrograms of synthesized sonar signals are analyzed and the mean opinion score was measured. Through the evaluation, we confirmed that the proposed method can synthesize active sonar data similar to the trained one.

Ecomusicology and the Potential of Music and Sound for Environmental Education

Ecomusicology is an interdisciplinary field drawing together scholars from sciences and humanities who share a research interest in the overlapping of music, culture, and nature. Human and non-human animals constantly rely on sonic signals to relate with other animals and the environment. Considering this auditory connection, and the contribution that ecocritical music and music about places may have in nurturing a sentiment of environmental stewardship, I propose an applied environmental education methodology that relies on participatory music making and critical listening to foster environmental awareness.

ВДОСКОНАЛЕННЯ ПОШУКУ СКУПЧЕНЬ РИБИ ЗАСОБАМИ КІНЕМАТИЧНОГО ПРОЕКТУВАННЯ

The article deals with sea and river commercial fisheries. It describes a possible way to increase the efficiency of the movements of fishing vessels when they are looking for accumulations (school) of fish. The improvement of the search movements of ships is based on the use of means and methods of kinematic design, which is part of modern descriptive geometry. In kinematic design, all design components are in independent spatial displacements at different speeds. In this case, the created mathematical dependencies make it possible to determine the coordinates of the spatial displacements of the design object, which here is the wanted accumulation (school) of fish. The basis of the proposed method for searching fish concentrations is the use of an additional search floating facility, such as a boat or motor boat. The fishing vessel and the auxiliary boat are equipped with search sonar equipment. A fishing vessel in the water area of the search area can drift or move at a slow speed along a given trajectory, and the search boat can accompany it and move around it along a circle of a certain radius, due to the range of the search equipment. The aim of the study was to create a methodology for increasing the efficiency of the movement of fishing vessels in search of fish concentrations. Among the main objectives of the study was the development of mathematical dependencies for determining the coordinates of the identified fish aggregations and creating a methodology for an approximate estimate of the volume of fish in its identified aggregation. The advantages of the proposed methodology for improving the search movements of fishing vessels are: - reduction of fuel consumption for the movement of the vessel in the water area of the investigated fish search area; - levelling by the kinematic design of the negative impact of false sonar signals reflected from the bottom of the reservoir. Keywords: marine fishing, fish search, sonar, sketch geometry, kinematic design, search trajectory, mathematical dependence.

Office of Naval Research Undersea Signal Processing, Contributions of Dr. Lisa Zurk

The Undersea Signal Processing team at the Office of Naval Research (ONR 321US) is tasked with developing advanced active/passive sonar signal and information processing technologies that improve the anti-submarine warfare (ASW) performance of US Navy sonar systems. Pursuant to this goal ONR supports academics whose research, being on the cutting edge, lead to high-risk technologies with potential for the greatest advancement of fleet capability. Dr. Lisa Zurk was such an academic, first supported by ONR 321US in 2005. Dr. Zurk’s research focused on developing signal and information processing algorithms which exploited the physics of the underwater operating environment. She believed the path to significant gains in sonar system performance would be obtained through exploitation of environmental knowledge. This belief is now a realization within the Naval Research Enterprise (NRE) and was a primary motivation for initiation of the ONR Task Force Ocean (TFO) Program in 2017. From establishment of the NEAR-Lab at Portland State University in 2005 until her transition to a Program Manager at the Defense Advanced Research Projects Agency in 2016, Dr. Zurk made significant contributions to the NRE. This presentation looks to highlight these past contributions and to consider their present impact.

Directional detection of simulated whale calls and ambient noise in a busy harbour

Timely information on marine mammal presence in, or near, sonar training areas can be used to minimize the risk of harming these animals. Defence Research and Development Canada (DRDC) has recently been investigating a variety of acoustic technologies to facilitate real-time marine mammal monitoring in high noise (including ship noise and sonar signals) environments. DRDC assessed the directional detection and localization capability of a network of sub-surface volumetric and vertical line arrays against the noisy backdrop of the busy harbour during an experiment conducted in Bedford Basin, NS. Playbacks of Southern Resident killer whale calls and North Atlantic right whale upcalls, as well as simulated calls, were transmitted. To assess variability in bearing estimates for whale calls in different frequency bands, an acoustic source was fixed at a single location for half of the experiment and dipped over the side of a boat at seven locations around the recorders during the other half. During this talk, ambient noise and environmental measurements will be presented along with acoustic localization results.

Tracking in ocean acoustics: Insights from the work of Lisa Zurk

Tracking is of paramount importance in ocean acoustics. One of the goals is the continuous location estimation of moving sources. Tracking needs also arise in identifying the structure of dispersion curves for long-range sound propagation and multipath arrival time identification across vertically separated hydrophones. Zurk’s work has shown that tracking can play a significant role in invariance estimation from striation patterns in spectrograms. We investigated ideas from tracking across the spectrum of ocean acoustics and looked into this latter problem. We found novel approaches for the estimation of the passive and active waveguide invariants, building on a seminal contribution of Lisa Zurk in the field of sonar signal processing.

Focusing on motion and averaging the diagonal: Lisa Zurk’s many contributions to passive sonar array processing

Lisa Zurk’s numerous contributions to passive sonar signal processing include motion compensation and regularizing sample covariance matrices (SCM) in snapshot deficient environments. While working at MIT Lincoln Labs, Lisa and colleagues compared the performance of several motion compensation methods including mode-based rank reduction and target motion compensation, or focusing, on the Santa Barbara Channel Experiment dataset [Zurk, Lee &amp; Ward, JASA, 2003]. Later at Portland State, Jorge Quijano and Lisa developed methods for regularizing the SCM through Toeplitz averaging, followed by maximum entropy extrapolation of the SCM to additional lags, [Quijano &amp; Zurk, JASA, 2017]. Toeplitz regularization found significant application in developing “augmented covariance matrices” for DOA estimators on sparse arrays. This talk reviews some of Lisa’s contributions to adaptive beamforming and highlights how some of her contributions inspired research in the UMass Dartmouth Signal Processing Group. [Work supported by ONR Code 321US.]

Lisa Zurk’s contributions to striation-based signal processing for active sonar

Strong multipath propagation adds considerable complication to acoustic signal processing in the ocean. When mapped in range-frequency space, the acoustic field exhibits striations, alternating bands of high and low intensity due to constructive and destructive interference between the paths. Nearly forty years ago, Russian scientists showed how this striation pattern could be described by a single scalar parameter, the so-called waveguide invariant that subsequently became a staple of their passive sonar signal processing methods. Lisa Zurk’s contribution was to show how a striation-based approach could be adapted to active sonar processing. Together with her students, she did tank experiments and analysis for monostatic and bistatic configurations. For a horizontal array, she developed a beamformer featuring a linear frequency shift across the array designed to align with high-intensity striations. The result was improved performance in noisy environments. She showed how the waveguide invariant can improve tracking accuracy by providing a constraint on possible tracks. She and her students demonstrated the improved tracking with continuous active sonar data. Lisa Zurk’s innovative work on these topics continues to inspire present day research.

Application of Internet of Things Technology in the Construction of Bored Pile Foundation in a High Altitude Environment

In order to solve the problem that the current geophysical method is difficult to locate in the mud of bored piles, the problem of effective exploration of karst caves at the bottom of piles, the author puts forward the application of Internet of Things technology and sonar technology in the construction of bored pile foundation in a high altitude environment. Combined with the propagation characteristics of sonar stress waves, a sonar detection method for karst caves at the bottom of bored piles is proposed, and a sonar detector for karst caves at the bottom of piles (JL-SONAR) and signal analysis software (PBCA) are developed. The JL-SONAR detector realizes the emission and acquisition of onsite sonar signals, and the PBCA software completes the analysis and arrangement of the detection data. This technology makes full use of the mud conditions of bored piles, the development of karst caves within 10 m of the pile bottom can be tracked and detected in the process of hole formation, which has the advantages of low cost, high speed, and high precision. Experimental results show that Project A has been drilled in multiple directions at the bottom of the pile, the rock cores on the west side of the pile bottom are mostly fragmented, the rock quality index RQD is 15–30, and the quality of the foundation rock at the pile bottom is extremely poor, which verify the results of sonar detection. No slurry leakage occurred in Project B after drilling, and the cores were mostly short columns. The rock quality index RQD was 75 to 90, and the quality of the foundation rock at the bottom of the pile was good, which verifies the results of sonar detection. Conclusion. The research results provide a new solution for karst cave exploration in karst areas, especially in liquid environments.

A Research on Road and Bridge Detection Based on Test Detection Technology

The existence of karst cave at the bottom of bored piles has a great impact on projects under construction and the surrounding buildings. Since bored piles require slurry wall protection, the current geophysical exploration method cannot effectively detect the karst cave at the bottom of the piles in the slurry. Combined with the characteristics of stress wave propagation, the sonar detection method is proposed. JL sonar detector can realize the transmission and acquisition of on-site sonar signals. This method makes full use of the mud conditions of bored cast-in-place piles, and the development of karst caves can be tracked and detected within 10 meters at the pile bottom during the drilling process. It has several advantages, including low cost, high speed, and high precision. This paper verifies the application of sonar detection technology in practical engineering through specific engineering cases. The research results put forward a new solution for cave exploration in karst areas, especially in liquid environment.

High resolution DOA estimation of acoustic plane waves: An innovative comparison among Cuckoo search heuristics and subspace based algorithms

SONAR signal processing plays an indispensable role when it comes to parameter estimation of Direction of Arrival (DOA) of acoustic plane waves for closely spaced target exclusively under severe noisy environments. Resolution performance of classical MUSIC and ESPRIT algorithms and other subspace-based algorithms decreases under scenarios like low SNR, smaller number of snapshots and closely spaced targets. In this study, optimization strength of swarm intelligence of Cuckoo Search Algorithm (CSA) is accomplished for viable DOA estimation in different scenarios of underwater environment using a Uniform Linear Array (ULA). Higher resolution for closely spaced targets is achieved using smaller number of snapshots viably with CSA by investigating global minima of the highly nonlinear cost function of ULA. Performance analysis of CSA for different number of targets employing estimation accuracy, higher resolution, variance analysis, frequency distribution of RMSE over the monte Carlo runs and robustness against noise in the presence of additive-white Gaussian measurement noise is achieved. Comparative studies of CSA with Root MUSIC and ESPRIT along with Crammer Rao Bound analysis witnesses better results for estimating DOA parameters which are further endorsed from the results of Monte Carlo simulations.

Deep Learning Models for Passive Sonar Signal Classification of Military Data

The noise radiated from ships can be used for their identification and classification using passive sonar systems. Several techniques have been proposed for military ship classification based on acoustic signatures, which can be acquired through controlled experiments performed in an acoustic lane. The cost for such data acquisition is a significant issue since the ship and crew have to be dislocated from the fleet. In addition, the experiments have to be repeated for different operational conditions, taking a considerable amount of time. Even with this massive effort, the scarce amount of data produced by these controlled experiments may limit further detailed analyses. In this paper, deep learning models are used for full exploitation of such acquired data, envisaging passive sonar signal classification. A drawback of such models is the large number of parameters, which requires extensive data volumes for parameter tuning along the training phase. Thus, generative adversarial networks (GANs) are used to synthesize data so that a larger data volume can be produced for training convolutional neural networks (CNNs), which are used for the classification task. Different GAN design approaches were evaluated and both maximum probability and class-expert strategies were exploited for signal classification. Special attention was paid to how the expert knowledge might give a handle on analyzing the performance of the various deep learning models through tests that mirrored actual deployment. An accuracy as high as 99.0±0.4% was achieved using experimental data, which improves upon previous machine learning designs in the field.

Robust underwater direction-of-arrival tracking with uncertain environmental disturbances using a uniform circular hydrophone array.

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, considering that an unknown underwater environment results in uncertain disturbances to the measurements, robust underwater DOA tracking with regard to uncertain environmental disturbances was studied. Because the uniform circular array (UCA) is free from the port and starboard ambiguity problem, a UCA was used to obtain the measurements for a long-time tracking scenario. First, a kinematic model of an underwater target and a measurement model based on the received signal of the UCA were established. Then, a DOA tracking algorithm was derived based on the extended Kalman filter (EKF), whose performance is significantly affected by the accuracy of the measurement noise covariance matrix (MNCM). Finally, considering that uncertain disturbances carry out unstable measurement noise, the modified Sage-Husa algorithm was used to obtain accurate MNCMs during the process of the derived EKF-based DOA tracking algorithm. Thus, a robust DOA tracking method with uncertain environmental disturbances using a UCA was proposed. The accuracy and reliability of the suggested method was verified via Monte Carlo simulations of a DOA tracking scenario and an experiment in the South China Sea in July 2021.

Sonar data classification by using few-shot learning and concept extraction

Given the heterogeneity and difficulty of classifying sonar targets, classifying these targets is a challenging problem in practice. In this paper, we propose a novel classification of sonar targets based on few-shot learning (FSL). The FSL can classify the data with only a few labeled training data. We also use the wavelet transform to denoise sonar signals and apply the short-time Fourier transform (STFT) to denoised signals. We propose a concept extraction method based on the STFT and find concept points to improve the performance of FSL classification. We evaluate the impact of concept numbers on accuracy. The main distinguishing feature of our proposed technique is the low sonar data requirement compared to other classification techniques. Our numerical experiments show that the proposed classification technique using the concept extraction method significantly improves the system performance in terms of accuracy.

Experimental Study on Shear Wave Transmission in Fractured Media.

Unconventional oil and gas reservoirs have broad exploration and development prospects. Fracture parameters and effectiveness evaluation are two of the key tasks for the evaluation of these types of reservoirs. Array acoustic logging can be used for fracture evaluation to compensate for the deficiencies of the image logging fracture evaluation method. Therefore, to develop acoustic logging evaluation methods as well as nondestructive testing methods for fractures, experiments were conducted to study the shear wave transmission in fractured media. Experiment data demonstrate a good correlation between the shear wave attenuation coefficient and fracture width, and the shear wave attenuation coefficients rise logarithmically with the increase in the fracture width for all models with different porosities and distinct dip angles of fractures. The shear wave attenuation coefficient changes relatively faster with the fracture width when the fracture width is within 250 μm. In addition, the shear wave attenuation is affected by the core porosity and fracture dip angle. When the fracture width is constant, the shear wave attenuation caused by the 0° fracture is relatively larger and is obviously greater than that of the fractures at other angles, which is consistent with the existing experimental results. The results of this study can be used to guide further research on amplitude compensation methods for sonic signal transmission in fractured media and fracture evaluation methods.

Changes in the acoustic activity of beaked whales and sperm whales recorded during a naval training exercise off eastern Canada

Experimental research has shown that beaked whales exhibit strong avoidance reactions to naval active sonars used during antisubmarine warfare training exercises, including cessation of echolocation and foraging activity. Behavioural responses to sonar have also been linked to strandings and mortality. Much of the research on the responses of beaked whales and other cetaceans to naval active sonar has occurred on or near U.S. naval training ranges, and the impacts of sonar in other regions remain poorly understood, particularly as these impacts, including mortality, are likely to go unobserved in offshore areas. In September 2016 the multinational naval exercise ‘CUTLASS FURY 2016’ (CF16) was conducted off eastern Canada. We used passive acoustic recordings collected in the region to quantify the occurrence and characteristics of sonar signals, measure ambient noise levels, and assess changes in the acoustic activity of beaked and sperm whales. The number of hours per day with echolocation clicks from Cuvier’s beaked whales and sperm whales were significantly reduced during CF16, compared to the pre-exercise period in 2016 (sperm whales) and to control data from 2015 (both species). Clicks from an unidentified Mesoplodont beaked whale species, sporadically detected prior to CF16, were absent during the exercise and for 7 days afterward. These results suggest that beaked and sperm whales ceased foraging in the vicinity of CF16 and likely avoided the affected area. Such disturbance may have energetic, health, and fitness consequences.

Natural acoustic stimuli evoke selective responses in the hippocampus of passive listening bats

A growing body of research details spatial representation in bat hippocampus, and experiments have yet to explore hippocampal neuron responses to sonar signals in animals that rely on echolocation for spatial navigation. To bridge this gap, we investigated bat hippocampal responses to natural echolocation sounds in a non‐spatial context. In this experiment, we recorded from CA1 of the hippocampus of three awake bats that listened passively to single echolocation calls, call‐echo pairs, or natural echolocation sequences. Our data analysis identified a subset of neurons showing response selectivity to the duration of single echolocation calls. However, the sampled population of CA1 neurons did not respond selectively to call‐echo delay, a stimulus dimension posited to simulate target distance in recordings from auditory brain regions of bats. A population analysis revealed ensemble coding of call duration and sequence identity. These findings open the door to many new investigations of auditory coding in the mammalian hippocampus.