Sonar Echoes Research Articles

Data Compression and Damage Evaluation of Underground Pipeline With Musicalized Sonar GMM

The underground cement pipeline network is critical in urban infrastructure, such as sewage drainage, fluid transportation, etc. Pipelines are deeply buried, so the damage is difficult to be detected. It is of great significant to monitor the crack of pipeline in real time. In this research, a data compression and damage evaluation method of underground pipeline using musicalized sonar GMM (Gaussian Mixed Model) was proposed. First, the sonar echo signal was sampled and discretized into four musicalized indicators by musicalized model. Then, the distribution of multi parameters were analyzed by GMM. The Frechet similarity was constructed to compare the difference between each working condition and baseline condition, so that the damage state could be identified. The engineering experiment shown that this method could greatly compress the monitoring data and evaluate four crack levels.

A construction method of reverberation suppression filter using an end-to-end network.

Reverberation is the primary background interference of active sonar systems in shallow water environments, affecting target position detection accuracy. Reverberation suppression is a signal processing technique used to improve the clarity and accuracy of echo by eliminating the echoes, reverberations, and noise that occur during underwater propagation.This paper proposes an end-to-end network structure called the Reverberation Suppression Network (RS-U-Net) to suppress the reverberation of underwater echo signals. The proposed method effectively improves the signal-to-reverberation ratio (SRR) of the echo signal, outperforming existing methods in the literature. The RS-U-Net architecture uses sonar echo signal data as input, and a one-dimensional convolutional network (1D-CNN) is used in the network to train and extract signal features to learn the main features. The algorithm's effectiveness is verified by the pool experiment echo data, which shows that the filter can improve the detection of echo signals by about 10 dB. The weights of reverberation suppression tasks are initialized with an auto-encoder, which effectively uses the training time and improves performance. By comparing with the experimental pool data, it is found that the proposed method can improve the reverberation suppression by about 2 dB compared with other excellent methods.

Sonar echo detection based on two-dimensional matched filtering and convolutional neural network

In underwater acoustics, the performance of sonar echo detection is limited when the echo-to-noise ratio or echo-to-reverberation ratio is low. As an attempt to improve the echo detection rate and maintain a low false alarm rate, a method based on two-dimensional matched filtering (2-D-MF) and convolutional neural network (CNN) is proposed. The 2-D-MF divides the replica signal into multiple sub-replicas, each with different frequency components, and utilizes the sub-replicas to perform the matched filtering individually, obtaining 2-D-MF features that better represent the amplitude-frequency characteristics of the echo signal. The CNN is utilized as an echo detector to extract echo information from the 2-D-MF features and determine the presence of an echo. The proposed method is tested using data collected in the South China Sea, 2021. During the experiment, a transducer transmitted linear frequency modulation (LFM) signals, and a transponder, acting as an analog target, forwarded the LFM signals as echoes. The detection results demonstrate that this method can improve the echo detection rate by approximately 7% while maintaining a constant false alarm rate of 1‰.

Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes

Many bat species navigate in complex, heavily vegetated habitats. To achieve this, the animal relies on a sensory basis that is very different from what is typically done in engineered systems that are designed for outdoor navigation. Whereas the engineered systems rely on data-heavy senses such as lidar, bats make do with echoes triggered by short, ultrasonic pulses. Prior work has shown that ‘clutter echoes’ originating from vegetation can convey information on the environment they were recorded in—despite their unpredictable nature. The current work has investigated the spatial granularity that these clutter echoes can convey by applying deep-learning location identification to an echo data set that resulted from the dense spatial sampling of a forest environment. The Global Positioning System (GPS) location corresponding to the echo collection events was clustered to break the survey area into the number of spatial patches ranging from two to 100. A convolutional neural network (Resnet 152) was used to identify the patch associated with echo sets ranging from one to ten echoes. The results demonstrate a spatial resolution that is comparable to the accuracy of recreation-grade GPS operating under foliage cover. This demonstrates that fine-grained location identification can be accomplished at very low data rates.

Motion acoustic flow field: Underwater small target motion characterization for active sonar echograph of harbor environment.

Spatial-temporal variations of active sonar echo intensity can provide effective motion information for characterizing intruding small targets and play a key role in follow-up tracking, behavioral analysis, and recognition, etc. Inspired by the idea of optical flow, which can be used to calculate subtle spatial-temporal variations of each pixel in image sequences, a different motion acoustic flow field (MAFF) is proposed for estimating the motion of underwater small targets in successive active sonar echographs from harbor environments. This is because directly applying current calculation framework for optical flow presents two challenges in this case. The first challenge is that the echo intensity fluctuation breaks its potential assumption of brightness consistency in the pre-processing stage. The second challenge is that the small size of the blob targets could be smoothed out as outliers by its median filter-based post-processing. Hence starting from the classical optical flow equation, MAFF introduces a novel spatial-temporal connected component pre-processing and a novel blob shape segmentation refinement post-processing. Experiments on a set of real-world harbor datasets demonstrate the efficacy of our MAFF calculation framework.

Jammer robust model-aided deep learning-based target detection for cognitive sonar

This work extends the development of an adaptive deep learning-based target detection algorithm for a channel matrix-based cognitive sonar framework that makes use of both a primary and secondary dataset. The channel matrix-based framework models all waveform dependent components, i.e. the sonar echo and clutter, via transfer function matrices, whereas all other waveform independent noise is characterized via the data covariance. By leveraging "lessons learned" from a derived model-based generalized likelihood ratio test (GLRT), a data driven deep learning algorithm was made adaptive to waveform dependent clutter returns in our previous work. The algorithm, however, remains fragile to waveform independent jamming and interference, and the extensive training required to obtain robust performance is practically infeasible. Thus, in this work further modifications are included to robustify the algorithm against these waveform independent components by leveraging knowledge from the model-based GLRT. The performance of the proposed algorithm is tested using various scenarios with jamming and interference signals.

Autonomous localization and mapping based on biomimetic sonar in natural environments

Bats navigating in dense vegetation based on biosonar have to obtain the necessary sensory information from “clutter echoes,” i.e., echoes that are superpositions of contributions of many reflecting facets (e.g., leaves). Since the locations and reflective properties of the individual facets are unknown, clutter echoes have to be treated as random signals that can neither be predicted nor—under most practical circumstance—be replicated. Nevertheless, prior research has shown that deep neural networks are capable of extracting fairly precise location information from clutter echoes. This raises the question whether clutter echoes could be used to provide navigational guidance in a conventional simultaneous localization and mapping (SLAM) framework, which is to a large extent dependent on precise and deterministic measurement to generate a localized map. Our hypothesis is that biomimetic sonar echoes indeed contain the information that is necessary support to local path planning, which can be utilized by a suitable deep learning architecture and training process. To investigate this issue, we have collected data in natural, heavily vegetated environments using a biomimetic sonar head that mimics the periphery of the biosonar system in horseshoe bats. The annotation of the data and the network training process is currently undergoing.

Large-scale recognition of natural landmarks with deep learning based on biomimetic sonar echoes

The ability to identify natural landmarks on a regional scale could contribute to the navigation skills of echolocating bats and also advance the quest for autonomy in natural environments with man-made systems. However, recognizing natural landmarks based on biosonar echoes has to deal with the unpredictable nature of echoes that are typically superpositions of contributions from many different reflectors with unknown properties. The results presented here show that a deep neural network (ResNet50) was able to classify ten different field sites and 20 different tracks (two at each site) distributed over an area about 40 km in diameter. Based on spectrogram representations of single echoes, classification accuracies up to 99.6% for different sites and 94.7% for different tracks have been achieved. Classification performance was found to depend on the used pulse component (constant-frequency-CF vs frequency-modulated-FM) and the trade-off between time and frequency resolution in the spectrogram representations of the echoes. For the former, classification performance increased monotonically with better time resolution. For the latter, classification performance peaked at an intermediate trade-off point between time and frequency resolution indicating that both dimensions contained relevant information. Future work will be needed to further characterize the quality of the spatial information contained in the echoes, e.g. in terms of spatial resolution and potential ambiguities.

Coherence-Induced Bias Reduction in Synthetic Aperture Sonar Along-Track Micronavigation

Subwavelength motion estimation is vital for the production of focused synthetic aperture sonar (SAS) imagery. The required precision is obtainable from the sonar data itself through a process termed micronavigation. Along-track micronavigation is achieved by a similar technique to that used in correlation velocity logs (CVLs), where sparse estimates of the spatial coherence function are interpolated to estimate the location of the peak coherence and hence estimate the interping vehicle motion. However, along-track micronavigation estimates made using this technique are biased, which limits the utility of these measurements for long-term navigation of autonomous underwater vehicles (AUVs). Three sources of along-track motion estimation bias are considered in this article. First, imperfect temporal registration between the signals results in coherence estimates that are negatively biased as a function of the temporal offset. Second, the sparse estimates of the spatial coherence function are obtained by cross-correlation of complex baseband signals, a process which is known to result in positively biased coherence estimates, especially when the true coherence is low. Finally, mismatches between the underlying spatial coherence function and the interpolation kernel used to estimate the peak coherence location also result in along-track micronavigation bias. In this article, we describe and evaluate three methods for reducing along-track micronavigation bias. We introduce a temporal registration of the signals before coherence estimation, which reduces the impact of negative coherence bias due to temporal offsets. The remaining coherence estimation bias is reduced by combining multiple coherence estimates in a Bayesian coherence estimator. Additionally, an improved interpolation kernel is derived with a significantly improved fit compared to the current gold standard Gaussian interpolation kernel. The improvements in along-track micronavigation accuracy are demonstrated using two simulated data sets, which both allow comparison with ground truth. The first involves direct simulation of the spatial coherence from a given interping geometry using the pulse-echo formulation of the van Cittert–Zernike theorem, while the second involves simulation of raw sonar echo data using a point-scatterer model. Using these simulations, a reduction in along-track micronavigation bias of 48.5%–99.5% is demonstrated, with reductions in along-track micronavigation error standard deviation of up to 34%. This improvement expands the potential for SAS-equipped AUVs to reduce their long-term navigation drift, facilitating longer underwater transits, improved target localization, and reduced track misalignment in repeat-pass operations.

Egyptian fruit bats exhibit precise 3D spatial memory that eclipses sensory information

Egyptian fruit bats (Rousettus aegyptiacus) have well-developed vision and lingual echolocation, which provide complementary sensory information to execute natural behaviors, such as obstacle avoidance and landing. However, spatial memory can reduce the computational load in natural tasks and there is evidence that bats sometimes fail to use sensory information when they operate in familiar surroundings. Here we investigate the precision of this bat species’ spatial memory for a landing location when it has access to vision and sonar echoes and when it has access to sonar echoes alone. Animals were trained for about 10 days to find a 5 cm diameter landing platform at a fixed location in a 2.5 × 6 m2 flight corridor and later tested when the landing platform was shifted in azimuth and elevation by 5–30 cm. The bat’s 3D flight trajectory was tracked with high-speed video cameras and its sonar inspection of objects was quantified with an ultrasound microphone array that permits reconstruction of the sonar beam aim. Our data suggest that spatial memory in Egyptian fruit bats is highly precise and eclipses multimodal sensory information. This bat species’ robust reliance on 3D spatial memory may support long-distance navigation and forage in the natural environment.

Chaotic based Pteropus algorithm for solving optimal reactive power problem

<span lang="EN-IN">In this work, a Chaotic based Pteropus algorithm (CPA) has been proposed for solving optimal reactive power problem. Pteropus algorithm imitates deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles by using sonar echoes, particularly utilize time delay. To the original Pteropus algorithm chaotic disturbance has been applied and the optimal capability of the algorithm has been improved in search of global solution. In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance is added at the time of stagnation. Furthermore exploration and exploitation capability of the proposed algorithm has been improved. Proposed CPA technique has been tested in standard IEEE 14,300 bus systems & real power loss has been considerably reduced.</span>

Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan

Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan

Reconstruction of Ultrasound Signals Using Randomly Acquired Samples in a Sparse Environment

Abstract Sonar echoes can provide more than only range information, but recording the full sonar echo is challenging in resource constrained systems. This paper introduces an approach for reconstructing under-sampled sonar echo signals in environments that are not cluttered using Compressive Sensing. This technique requires sampling only around 20% of the total samples in order to achieve good reconstruction results. An experimental validation of the approach is presented.

Classification of sonar echo signals in their reduced sparse forms using complex-valued wavelet neural network

This study aims to identify a method for classifying signals using their reduced sparse forms with a higher degree of accuracy. Many signals, such as sonar, radar, or seismic signals, are either sparse or can be made sparse in the sense that they have sparse or compressible representations when expressed in the appropriate basis. They have a convenient transform domain in which a small number of sparse coefficients express them as linear sums of sinusoidals, wavelets, or other bases. Although real-valued artificial neural networks (ANNs) have been frequently used in the classification of sonar signals for a long time, complex-valued wavelet neural network (CVWANN) is used for these complex reduced sparse forms of sonar signals in this study. Before the classification, the number of inputs was reduced to 1/3 dimension. Complex-valued sparse coefficients (CVSCs) obtained from the reduced form were classified by CVWANN. The performance of the proposed method is presented and compared to other classification methods. Our method, CVSCs + CVWANN, is very successful as 94.23% by tenfold cross-validation data selection and 95.19% by 50–50% training–testing data selection.

Parameter state estimation for bistatic sonar systems

The use of autonomous underwater vehicles (AUVs) cooperating in a network for anti-submarine warfare surveillance operations is of topical interest. Each AUV has to localise and track targets (submarines) robustly and precisely. This can be realised by a bistatic sonar configuration. The precise knowledge of the bistatic system parameters is mandatory for target tracking. These are in particular the positions of the acoustic sources, transmission times, and the position and heading of the AUV sonar sensor. However, these parameters often are not precisely known, e.g. for non-cooperative acoustic sources or due to navigation uncertainties of the AUV. Therefore, the authors consider the inverse problem, i.e. they estimate the bistatic system parameters by exploiting sonar echoes from known stationary `targets' like wrecks or small islands. Their implementation of the estimation method is based on the multihypothesis tracking technique. Results are discussed for two applications: The first one is estimation of the parameters of a non-cooperative source. The second application focuses on the estimation of the receiver parameters; in particular they show that their approach can be used to increase robustness of AUV navigation. Their algorithms are tested with simulated and real data recorded by the Centre for Maritime Research and Experimentation.

Validating Sidescan Sonar as a Fish Survey Tool over Artificial Reefs

ABSTRACT Bollinger, M.A. and Kline, R.J., 2017. Validating sidescan sonar as a fish survey tool over artificial reefs. Visual observation methods via SCUBA are commonly used to survey artificial reef fish, although conditions in the Gulf of Mexico often make surveys difficult or even dangerous for divers. In this study, sidescan sonar was used to quantify water-column fish abundance and was compared to the established visual observation methods on SCUBA over four reef sites. Calibrated intensity values measured from sidescan sonar echo returns were used to estimate fish body length and to calculate scaled biomass (g/m2 reef) from a pooled fish length–weight relationship of commonly observed reef fish in the area. Sidescan sonar methods were equivalent to SCUBA surveys for measuring fish abundance over the same reef areas; however, overall reef-associated abundances measured with sidescan were significantly higher because the sidescan could measure a larger water-column area and furthermore allowed for a r...

Noise and Interference Suppression in Sonar Echoes Using the Fractional Fourier Transform

The Fractional Fourier Transform (FrFT) enables separation of signals from noise and interference by utilizing the entire time-frequency space. Signals are filtered by rotating to a new time axis ‘ta’, with rotational parameter ‘a’, selected using some metric such as mean-square error (MSE) between a desired signal-of-interest (SOI) and its estimate. The FrFT has been applied to numerous problems, but it is most suited for applications such as sonar and radar, when the time-frequency distribution of the SOI and the undesired environment are different. It can greatly outperform the conventional fast Fourier Transform (FFT), which is solely a frequency domain method (a=1), as well as conventional time-based MMSE adaptive filtering (a=0). In this paper, we present a simple FrFT-based algorithm that separates sonar echoes of a desired SOI, e.g. a chirp, from the cluttered background, which could be noise or interference (i.e. another signal). We exploit the fact that we can find the best time axis ‘ta’ in which the SOI becomes a tone, or close to it, with the FrFT, enabling easy notching (zeroing) of the clutter. By searching for the tone peak and notching everywhere except the peak, we can successfully and easily remove the clutter. This algorithm is robust because clutter typically does not correlate with the signal in the FrFT domain, and thus does not impair our ability to estimate the peaks and notch the clutter. We compute the MSE between the true transmitted signal and the received echo with and without this algorithm as a function of signal-to-noise ratio (SNR) and show that 5 dB reduction in MSE is possible with the FrFT.

Noise and Interference Suppression in Sonar Echoes Using the Fractional Fourier Transform

The Fractional Fourier Transform (FrFT) enables separation of signals from noise and interference by utilizing the entire time-frequency space. Signals are filtered by rotating to a new time axis ‘ta’, with rotational parameter ‘a’, selected using some metric such as mean-square error (MSE) between a desired signal-of-interest (SOI) and its estimate. The FrFT has been applied to numerous problems, but it is most suited for applications such as sonar and radar, when the time-frequency distribution of the SOI and the undesired environment are different. It can greatly outperform the conventional fast Fourier Transform (FFT), which is solely a frequency domain method (a=1), as well as conventional time-based MMSE adaptive filtering (a=0). In this paper, we present a simple FrFT-based algorithm that separates sonar echoes of a desired SOI, e.g. a chirp, from the cluttered background, which could be noise or interference (i.e. another signal). We exploit the fact that we can find the best time axis ‘ta’ in which the SOI becomes a tone, or close to it, with the FrFT, enabling easy notching (zeroing) of the clutter. By searching for the tone peak and notching everywhere except the peak, we can successfully and easily remove the clutter. This algorithm is robust because clutter typically does not correlate with the signal in the FrFT domain, and thus does not impair our ability to estimate the peaks and notch the clutter. We compute the MSE between the true transmitted signal and the received echo with and without this algorithm as a function of signal-to-noise ratio (SNR) and show that 5 dB reduction in MSE is possible with the FrFT.

A Unified Analysis of Structured Sonar-Terrain Data Using Bayesian Functional Mixed Models

ABSTRACTSonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. Although existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this article, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design. Supplementary materials for this article are available online.

Advances in Remote Sensing Research on Oil and Ice from the IOGP Arctic Oil Spill Response Technology JIP

ABSTRACT (2017-044) The IOGP Arctic Oil Spill Response Technology Joint Industry Program’s Remote Sensing Technical Working Group was initiated in 2012 with the objective to expand the oil industry’s detection and monitoring capabilities for spills on, under, around or in ice. The first phase produced two state-of-knowledge reports assessing sensor capabilities above and below the ice. A key finding from these studies was that many existing remote sensing platforms and sensors originally developed for oil on open water can also provide effective sensing in a broad range of ice conditions. The second phase covered an integrated experiment that included sensor testing in a cold basin, followed by modeling to determine potential applicability of different sensors in a wider range of sea-ice conditions. Five above-ice (Frequency Modulated Continuous Wave Radar (FMCW), ground penetrating radar (GPR), visible and infrared cameras and laser fluorescence polarization [LP] sensor) and seven below-ice (high dynamic range optical camera, visible and infrared spectrometer, LP sensor, broadband and narrowband sonar and multibeam echo sounder) sensors were tested with varying ice thickness and oil concentrations at the US Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL) over a three-month period. All of the sensors used during this experiment showed some ability to detect oil on, in, or below ice under certain conditions and major advances in the knowledge of sensor applicability were made. Three follow-on projects (late 2016) include an operations guide providing a concise operationally oriented review of the different sensor technologies in key oil and ice scenarios, and additional field testing with medium to long-wave infrared, and the FMCW radar.