Sonar Research Articles

Fast Sparse Bayesian Learning Based on Beamformer Power Outputs to Solve Wideband DOA Estimation in Underwater Strong Interference Environment

Wideband direction-of-arrival (DOA) estimation is an important task for passive sonar signal processing. Nowadays, sparse Bayesian learning (SBL) attracts much attention due to its good performance. However, performance degrades in the existence of strong interference. This problem can be solved by combining the beamformer and the SBL. The beamformer is a useful tool to suppress interference. Then, the SBL can easily estimate the DOA of the targets from the beamformer power outputs (BPO). Unfortunately, the latter step needs to compute the matrix inversion frequently, which brings some computational burden to the sonar system. In this paper, the BPO-based SBL is modified. A sequential solution is provided for the parameters in the BPO probabilistic model. In this manner, only one signal precision parameter involved in the probabilistic model is updated in each iteration and the matrix inversion is avoided during the iteration, thus reducing the computational burden. Simulation and experimental results show that the proposed method maintains high estimation precision in the interference environment. At the same time, its computational efficiency is almost three times higher in comparison with state-of-the-art methods.

The accuracy of length measurements made using imaging SONAR is inversely proportional to the beamwidth

Abstract Multibeam imaging SONARs have been used for a range of measurement applications, such as measurements of fish lengths. This study aimed to quantify the accuracy of imaging SONAR systems, that varied in frequency and beam geometry, to measure the length of synthetic targets positioned perpendicularly. Blueprint Oculus imaging SONAR systems, with four different (centre) frequencies (750 kHz, 1.2 MHz, 2.1 MHz and 3 MHz), were used to measure the length of three targets of nominal lengths: 10 cm, 20 cm and at ranges between 1 and 15.5 m. The effect of beam geometry on measurement error was then examined using regression analysis. This study found that there was an overestimation of the actual length of the target for all measurements that was inversely proportional to the horizontal beamwidth. The measurement error can be reduced by normalising for beamwidth. However, the variation of measurements (i.e. the precision) was found to also increase with range, which was attributed to the increasing beam separation. It is important that the effect of beamwidth on the accuracy of target length measurements, by imaging SONARs is acknowledged in future studies. One approach to mitigating this problem, is to limit the range at which length measurements are made to a beamwidth that produces an estimated level of error that is acceptable for that study.

Effects of Climate Change on Oceanic Sonar Use in the Upper European Continental Shelf

As the global effects of climate change become more known year by year, it becomes ever-more pertinent to examine the effects this may bring for every aspect of modern life we rely on. One topic of focus is that of multi-frequency sonar communication and navigational systems, which rely on well-established relationships relating to wave speed, signal intensity, and attenuation. We compiled data on oceanic temperature, acidity, and salinity in the Upper European Shelf, which includes the North Sea and Mediterranean Sea, from 2006 to 2072 using the CMIP5 future climate model in the RCP8.5 scenario. We calculate that the speed of sound in the northern European oceanic area will decrease by almost 18 m/s by 2072, with an average yearly decrease in sound speed by 0.37 m/s. The attenuation of sound through water will change year by year, calculated based on a higher-order polynomial regression dependent on the frequency of sonar used. The maximum operating ranges of active low-frequency, mid-frequency, and high-frequency sonar systems would theoretically change by +0.06%, -0.19%, and +0.71%, respectively per year, if no other factors are affected. Due to increased sound propagation, the ambient noise level of the ocean would also increase and have some counter-effect to the increased detection range however that increase in noise level was not quantitatively analyzed in this study.

Efficient configuration of heterogeneous multistatic sonar networks: A mixed-integer linear programming approach

The work undertaken in this paper pertains to the optimal spatial configuration of a heterogeneous Wireless Sensor Network (WSN) for the Area Coverage (AC) problem. Specifically, this research falls under the heading of Anti-Submarine Warfare (ASW) with an emphasis on active sonar systems and, more pointedly still, on a specific type of sensor: sonobuoys (portmanteau word formed by “sonar” and “buoy”). These buoys are further divided into three main categories: transmitter-only (Tx), receiver-only (Rx) and transmitter–receiver (TxRx). In this paper, we will therefore try to determine the geographical location of the different buoys comprising a Multistatic Sonar Network (MSN), special case of WSN, so as to maximize the overall surface area covered. To do this, we discretize an Area of Interest (AoI) into regular cells using bathymetric and altimetric data, and place a deployment position and a fictitious target at the center of each cell so that we can evaluate the network’s performance. More precisely, we are taking into account a limited number of sensors (buoys) with possible pairwise incompatibilities, variable performances, probabilistic detection models, an adverse masking effect (direct blast) as well as coastlines features. Finally, in order to solve this problem, we have developed several efficient Mixed-Integer Linear Programs (MILPs), all of which have been thoroughly tried-and-tested on a benchmark set of 100 instances derived from real elevation data. This has led us to identify an ideal model, i.e. one that is significantly better than all the others in the statistical sense.

High-Resolution Short Angle Weight Algorithm in Sonar Systems

High-Resolution Short Angle Weight Algorithm in Sonar Systems

Design of a charge-sensitive amplifier for MEMS piezoelectric hydrophone

Hydrophone is an important part of sonar system and convert acoustic signal into electrical signal. However, the electrical signal generated by the hydrophone is relatively weak and cannot be directly used for underwater detection and communication. Therefore, it is necessary to preprocess the generated electrical signal. In this paper, a switched capacitor charge-sensitive amplifier for MEMS piezoelectric hydrophone is designed. In the structure, two single-channel charge amplifiers are used for time-division multiplexing. The noise sampling and charge amplification are performed separately in the same half cycle to achieve the effect of alternating output, thereby increasing the sampling rate and solving the problem of discontinuous output of the switched capacitor circuit. In the circuit design, virtual switch and two-phase non-overlapping clock are adopted to reduce the error caused by charge injection and clock feedthrough effect of CMOS switch. What’s more, the correlated double sampling technique is used to reduce the offset and low frequency noise of the circuit. The gain of the charge-sensitive amplifier designed in this study is 26 dB, the bandwidth is 0.8Hz∼17.6kHz, the equivalent input noise power spectral density is 115.6 nV/Hz at 0.1Hz, and the charge sensitivity is 2 mV / pC. The fitting line between the input charge and the amplitude of the output curve is similar to the theory. The simulation results show that the charge-sensitive amplifier proposed in this study meets the design requirements of the MEMS piezoelectric hydrophone readout circuit.

Development of a low-power SAR ADC for analog front-end readout circuit of hydrophones

A low-power 16 bit 250KSa/s successive approximation analog-to-digital converter (SAR ADC) is designed. The capacitor array consists of a 2-segment sub-capacitor array and high sampling makes the coupling capacitance a unit capacitance, solving the problem of fractional capacitance mismatch. The power consumption is reduced by introducing a common-mode voltage during the switching process of the capacitor array. The circuit uses a 4-stage pre-amplifier and adds a dynamically latched comparator using output misalignment calibration to ensure high accuracy resolution. Simulated in DB Hitek 0.18 μm process, the Fast Fourier Transform(FFT) simulation results for 4096 points show that the signal-to-noise distortion ratio (SNDR) of the ADC can reach 90.2 dB and the effective number of bits (ENOB) can reach 14.69 bit. The average power consumption is 2.507mW at a supply voltage of 5 V, and this holds significant significance for the long-term deep-sea detection capabilities of sonar systems.

Research and fabrication of bimetallic plate transducer based on finite element analysis

By enhancing the type 1–3 piezoelectric composite material, a highly sensitive hydroacoustic transducer sensitive element structure is suggested for uses in sonar systems, such as small target identification and fine imaging. A lower metal plate, a piezoelectric ceramic column array, and an upper metal plate make up the piezoelectric material structure. The resonance frequency of the bimetal plate type piezoelectric ceramic array material was theoretically analyzed, the sensitive element geometry of the bimetal plate piezoelectric material was studied using finite element simulation, the material choice for the metal plate affected the material’s performance, and the parameters of each size of the sensitive element were determined. These findings served as a guide for the production of a highly sensitive hydroacoustic transducer, as well as the structure of the piezoelectric material transducer in the water performance test. Results indicate that the structure transducer has a maximum transmit voltage response of 178 dB and a −3 dB bandwidth of approximately 25 kHz; the transducer may be efficiently tuned for a maximum receive sensitivity of −172 dB.

Numerical investigation for the blockade capability of the multi-BWBUG cooperative system

ABSTRACT The multiple blended-wing-body underwater glider (multi-BWBUG) cooperative system features high endurance and low noise. This system is highly suitable for performing the blockade mission offshore. However, the research on this mission has not yet been reported. Thus, this study first proposes a blockade scenario for a multi-BWBUG cooperative system. The corresponding blockade mechanism is also described. Then, several typical topologies, the occurrence probability of network state, inherent capability, combat capability, and blockade capability, are defined. Finally, a novel method is proposed to calculate the blockade capability of the multi-BWBUG cooperative system. The simulation results indicate that the transmitting source level and communication frequency of communication equipment influence the occurrence probability of the network state. In addition, the failure ratio of communication links and the number of primary communication links affect the combat and blockade capabilities of the multi-BWBUG cooperative system. The blockade capability generally increases with the increase in the detection range of passive sonar.

Navigating the Unseen: Ultrasonic Technology for Blind Navigation

Navigation for the visually impaired is a critical aspect of ensuring independence and safety. This project introduces an "Ultrasonic Navigation System for the Blind" using ultrasonic sensors and a buzzer. The system aims to assist individuals with visual impairments by detecting obstacles in their path using ultrasonic sensors and providing feedback through a buzzer. This integration of sensors and an audible alert system offers an intelligent solution for enhancing mobility and reducing obstacles for the blind.On the other hand, and in order to reduce navigation difficulties of the blind, an obstacle detection system using ultrasounds and vibrators is added to this device. The proposed system detects the nearest obstacle via streoscopic sonar system and sends back vibro-tactile feedback to inform the blind about its localization.The system has one more advanced feature integrated to help the blind find their stick if they forgot where they kept it. Pressing the remote button sounds a buzzer on the stick which helps the blind person to find their stick. Thus this system allows obstacle detection as well as finding stick if misplaced by visually disabled people. This goal has been realized through the use of an ultrasonic sensor to determine the range of obstacles and also a microcontroller to act accordingly. The life of blind people is very difficult and challenging, This stick will be embedded by sensor to senses any object or human in front of them.

Multi-frame non-coherent track-before-detect method for weak tones in passive sonar

In order to solve the problem of performance degradation of track-before-detect (TBD) methods using single frame information when received signals exhibit a large fluctuation. A TBD method utilizing multi-frame information is proposed. Specifically, the novel measurement model and likelihood ratio function based on the non-coherent accumulation of multi-frame data are established. Multi-frame integration greatly improves the signal-to-noise ratio (SNR) of measurements. Furthermore, the derived likelihood ratio function can more effectively describe the statistical characteristics of the spectrum measurements. Combining these two advantages can improve the detection performance of weak tones for passive sonar. The simulation and experimental data results both demonstrate the advantages of the proposed method.

Application of attention-based transformer for ship-of-opportunity spectrogram prediction

Passive sonar is a useful tool for underwater acoustics that can be used to detect ships or other sound sources in the ocean. The input signals into the sonar system in turn can also be used to make inferences about the ocean environment. In recent work, ship noise has been used to infer seabed information from 15–20 minute spectrograms, with CPA in the center of the time window. Waiting for 15–20 minutes to record the full time window slows down real-time inference efforts. In this work, our goal is to complete ship-of-opportunity (SOO) spectrograms given the first few minutes. Our approach is to use a self-supervised attention-based transformer, which has been found to be effective for machine learning problems, particularly for natural language processing. The transformer is trained on simulated SOO spectrograms and tested on spectrograms from the Seabed Characterization Experiment in 2017 in the New England Mud Patch. The resulting predicted spectrograms contain the key features of the measured spectrograms over the full time window. If successful, this work may allow for real-time applications of ship detections and seabed inferencing methods. [Work supported by the Office of Naval Research, Grant N00014-22-12402.]

Performance analysis of passive acoustic ranging using a single hydrophone in shallow waters

Passive source range estimation is one of the important research fields on the sonar system. Recent research has carried out range estimation using single hydrophone. This study presents the result of analyzing the impact of environmental conditions on range estimation performance when estimating the range of a moving source with a single hydrophone. Range estimation of a moving source using a single hydrophone estimates source velocity from the cross-correlated fields of the signal measured at time intervals. This method of estimating the source range from estimated source velocity is used. Therefore, it is important to accurately estimate the source velocity for ranging, and the accuracy of estimating the source velocity is affected by the propagating mode characteristics in waveguide. This study investigates the changes in velocity estimation performance of a moving source according to various underwater environment and mode formation characteristics through simulation analysis and discusses ways to reduce velocity estimation error. [Work supported by KRIT (Contract No. 22-305-B00-001).]

Validation and verification of models for setting SONAR system requirements

When determining requirements for a SONAR system as a whole, typically very high-level requirements such as number of detections and total false alarms allowed per unit time must be converted into specific requirements for system components. These requirements may include array requirements like hydrophone count and position. Validation of probability of detection via sea test is fraught with complications, not least of which is environmental uncertainty. As a precursor, it is generally advisable to approach and attempt to validate models for each term in the SONAR equation one at a time. The approaches to validation vary depending on the term and can range in complexity from comparison to analytical solutions, to comparison with alternate available models, up through data collections at sea. Each of these approaches comes with its own challenges and limitations, which are discussed.

Design and construction of an in-air sonar for signal processing education

Sonar is an acoustic sensing method commonly used in underwater imaging and navigation. Students are often introduced to sonar theory, but few gain hands-on experience due to the cost and logistical difficulties associated with in-water experimentation. Alternatively, a sonar system can be designed for in-air use to mitigate some of the practical and operational challenges of acquiring sonar field data. Inspired by previous in-air sonar projects, the Pennsylvania State University student chapter of the Acoustical Society of America acquired $5,000 to construct an in-air sonar for exploring signal processing and mechanical design concepts. For a cost-effective solution, consumer audio equipment and additive manufacturing were leveraged. This presentation will walk through the sonar design and build process, discuss the project's current status, and present initial results. Plans for project development and potential inclusion into the curriculum will be discussed. Advice will be offered to those who may want to pursue similar projects.

Issues in mid-frequency sound intensity fluctuation on the Washington Shelf

Strong mid-frequency sound intensity fluctuations were found on the Washington shelf during an experiment in the summer of 2022. The Scintillation Index (SI) of integrated sound energy on single channels at 20 km range was near unity for both 3.5 kHz and 6.0 kHz signals. Accompanying oceanographic measurements from moorings and a towed profiling system revealed sound field variability at multiple time and spatial scales caused by linear and nonlinear internal waves, internal tides, and coastal trapped waves. While the 2022 dataset provides valuable information for addressing issues on intensity fluctuation, additional experimental data are needed to understand the impacts on active and passive mid-frequency sonar systems. To that end, a second Washington Shelf experiment is being planned for the summer of 2025. This talk will discuss the planned measurements and the range of issues to be addressed. These include the frequency-dependence of SI over 1–10 kHz, broadband fading, vertical and horizontal coherence, and the impacts of variability on propagation in subsurface ducts. These measurements are important for investigating fundamental oceanographic causes of sound fluctuation as well as for the design of appropriate signal processing methods. (Work supported by the Office of Naval Research.)

Enhancing ship noise reduction: a deep-learning based noise extraction model

In active sonar detection systems, the self-radiated noise from the platform itself is a primary factor that interferes with system performance. The platform self-noise exhibits non-Gaussian characteristics, comprising line spectrum and continuous spectrum, where line spectrum exhibits higher intensity and greater frequency stability. To reduce the ship noise, this study introduces a deep-learning based time-domain model with an encoder-separator-decoder architecture. In detail, the encoder and decoder modules utilize learnable convolutional layers to generate distinguishable features with a symmetrical structure. For separator module, we observe that noise components prominently predominate within the noisy signal in low signal-to-noise ratio (SNR) scenarios. The extraction of noise components is typically more tractable than of signal components. Therefore, in contrast to conventional frameworks that focus on extracting the signal subspace, this module is dedicated to extracting the noise subspace. Besides, the modified scale invariant SNR (SI-SNR) is proposed as the loss function for model optimization, which yields better performance than non-noise extraction structure. The robustness of the model is validated on the DeepShip dataset with four ship categories, demonstrating that the model consistently outperforms baselines, including matched filter, in all ship categories.

Acoustics at UMass Dartmouth

The University of Massachusetts Dartmouth has a long history spanning 5 decades of graduate courses offerings and research in underwater acoustics, transduction and signal processing leading to M.S. and Ph.D. degrees in Electrical Engineering. Collaborations between Marine Science, Physics, Mechanical, Bioengineering, and Electrical Engineering departments offer many interdisciplinary opportunities in Acoustics. Courses include Fundamentals of Acoustics, Vibrations, Underwater Acoustics, Electroacoustic Transducers, Medical Ultrasonics, Sonar, Digital Signal Processing, Array Processing, Random Signals, Information Theory, Communications, Detection and Estimation. Many unique facilities including an Underwater Acoustic Test Tank, Open-Ocean water access, and Unmanned Underwater Vehicles support both undergraduate and graduate projects. Research focuses include transducers and transduction science, materials characterization, calibration, array and sonar signal processing, animal bioacoustics, communications, detection and estimation, active and passive sonar funded by Office of Naval Research and Industry.

Surface and Underwater Acoustic Source Recognition Using Array Feature Extraction Based on Machine Learning

Sound source recognition is an important application of passive sonar. How to distinguish between surface and underwater sound sources is always a difficult problem. In order to solve the problem of S/U sound source identification, this paper proposes a machine learning method based on array feature extraction, which has certain innovative value. Firstly, according to the experimental environment of SACLANT 1993, simulation data is generated based on KRAKEN. Secondly, the simulation data and experimental data are used to extract the array features. Thirdly, the accuracy, recall rate, F1 and accuracy of GBDT classifier in three different frequency bands are evaluated. The results show that the training model established by using the array feature extraction method can effectively solve the problem of poor accuracy of some channels in the single channel classification, and obtain good experimental accuracy. Finally, the experimental accuracy of the three bandwidths is 0.9920, 0.9857 and 0.9713, respectively.

Using a biomimetic sonar bat robot to explore the sensory world of bats in the field and in the laboratory

Bat species such as those belonging to the families of horseshoe and Old World leaf-nosed bats make their navigation decisions based on echoes that are often received from dense vegetation. To gain an understanding of how these animals accomplish navigation based on such complicated clutter echoes, a biomimetic sonar system that replicates the auditory periphery of bats has been used as synthetic observer to probe the sensory stimulus ensemble of the bats in a flight tunnel as well as in the field. Both laboratory and field experiments were carried out in Brunei on the island of Borneo. For the flight tunnel, the biomimetic sonar robot has been used to record a large number of pulse and echo pairs in conjunction with an array consisting of 28 ultrasonic microphones. In this experiment, the sonar head was positioned at points spaced on a 3d grid that covers the tunnel's volume. This data set will be used to train a deep neural network to predict the inputs to the ears of a bat maneuvering in the tunnel. In the field experiments, the sonar head was carried off road in various tropical-forest habitats to record echo samples in conjunction with precise GPS location references.