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Active and passive sonar are the two types of empirical underwater acoustic channel models (UWACMs). Passive sonar UWACMs have applications in military, ocean exploration, and search and rescue (SAR) activities. However, high transmission loss (TL), multipath propagation, and ambient noise pose significant challenges to signal-to-noise ratio (SNR) and communication effectiveness. To address these challenges, this paper develops a UWACM based on the passive sonar equation method to determine SNR in deep-sea environments, specifically for SAR operations. Determining SNR involves characterizing signal propagation in terms of TL. Existing models lack analysis of TL and SNR for various deep-sea multipath propagation scenarios relevant to SAR applications. Therefore, this paper analyses TL and SNR for both direct and various multipath propagation modes, including surface reflection (SR), surface duct (SD), bottom bounce (BB), convergence zone (CZ), deep sound channel (DSC), and reliable acoustic paths (RAPs) in the deep sea. This work aims to quantify the detection capabilities of underwater location beacons (ULBs) under various deep-sea scenarios and configurations. By analyzing ULB signal propagation characteristics, this research seeks to address key challenges related to ULB performance and ultimately improve SAR operations. The results of the proposed model significantly correlate with existing literature, confirming its accuracy. [ABSTRACT FROM AUTHOR]

Selecting the number of sensors to be actually used in acoustical signal beamformings that employ a given sensor array with a fixed number of elements is important for performance optimization in signal detection applications. Adding sensors to the beamforming computation might introduce excess noise into the measured signal, but selecting too few sensors reduces the beampattern resolution. Additionally, the sensor signals may be weighted to compensate for the background noise or known signal sources. In this paper, a maximum likelihood estimator is developed for the signal beamforming and the signal-to-noise ratio is used as a key performance indicator for determining the point at which adding sensors deteriorates the overall signal readout efficiency. As a case study, an experimental onshore passive sonar readout chain is analyzed through signal beamforming with a circular array with 32 sensors. The experimental results were supported by simulations and showed that the described method was able to calibrate the delay-and-sum algorithm for its best performance under noisy conditions. Good agreement has been obtained with a heuristic approximation from expert knowledge, and the method expands it to general applications. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Beamforming technology is very important for passive sonar to detect targets. However, the performance of a beamformer is seriously degraded in practical applications due to the complex and changeable underwater environment. In this paper, a null broadening algorithm for passive sonar based on a weighted similarity vector is proposed for underwater fast-moving strong interference signals. First, the covariance matrix was reconstructed through the correlation between the steering vector and the subspace eigenvector, which was used to calculate the similarity vector. Then, the maximum power in the interference angle sector was used as the virtual interference source power to broaden the null in the angle sector. Next, the difference between the optimal weight vector and the similar vector was minimized, the interference-plus-noise power constraints and norm constraints were added, and the equation was written as a quadratic constrained quadratic programming (QCQP) problem, which was converted into a convex optimization problem by using the semidefinite relaxation technique. Finally, the optimal solution was calculated by using eigen decomposition. The simulation results show that the algorithm can guarantee deep nulling and effectively suppress sidelobe height under various error conditions, which shows that the proposed algorithm has a good suppression effect and strong robustness for fast strong interference. [ABSTRACT FROM AUTHOR]

The low-frequency narrowband components (known as lines) in the radiated noise of underwater acoustic targets are an important feature of passive sonar detection. Conventional adaptive line enhancer (ALE) based on the least mean square algorithm has limited performance under colored background noise and low signal-to-noise ratio (SNR). In this paper, by combining the frequency domain sparse model of lines and maximum correntropy criterion (MCC), a β-adaptive l0-MCC-ALE is proposed to solve the above-mentioned problem. The proposed ALE uses a sparse-driven MCC algorithm to update the weight vector in the frequency domain to further suppress the colored background noise. For the problem that the value of parameter β is sensitive to the performance, β is updated adaptively according to the frequency response of ALE in each iteration. Simulation and real data processing results show that the proposed ALE is insensitive to the given parameter β and has excellent performance for line enhancement. Compared with conventional ALE, the SNR of lines can be improved by 7~8 dB. [ABSTRACT FROM AUTHOR]

In submarine warfare systems, passive SONAR is commonly used to detect enemy targets while concealing one's own submarine. The bearing information of a target obtained from passive SONAR can be accumulated over time and visually represented as a two-dimensional image known as a BTR image. Accurate measurement of bearing–time information is crucial in obtaining precise information on enemy targets. However, due to various underwater environmental noises, signal reception rates are low, which makes it challenging to detect the directional angle of enemy targets from noisy BTR images. In this paper, we propose a deep-learning-based segmentation network for BTR images to improve the accuracy of enemy detection in underwater environments. Specifically, we utilized the spatial convolutional layer to effectively extract target objects. Additionally, we propose novel loss functions for network training to resolve a strong class imbalance problem observed in BTR images. In addition, due to the difficulty of obtaining actual target bearing data as military information, we created a synthesized BTR dataset that simulates various underwater scenarios. We conducted comprehensive experiments and related discussions using our synthesized BTR dataset, which demonstrate that the proposed network provides superior target segmentation performance compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A closer look into the history of maritime geomedia reveals a historical intermezzo in which navigation in cases of unstable sight was realized with "Submarine Signaling" (SS). This infrastructure for the purpose of safe wayfinding along coasts operated from around 1900 to the early 1920s, relying upon submarine bells in the global oceans, and hydrophones and telephonic user interfaces in vessels. SS can be regarded as a starting point to the era of electrotechnological navigation, and is of media-historical interest insofar as its operability was based on undersea acoustics: shifting its epistemic focus from sight to sound, SS thus lay beyond the "visual regime" that characterizes our current digital geomedia cultures. Herein, I will reconstruct the genesis and history of SS, use it as a contrasting foil to our postmodern geomedia practices, and finally argue for an understanding of geomedia as historically variable phenomena. [ABSTRACT FROM AUTHOR]

The detection of tonal signals with unknown frequencies is an important area of study in underwater signal processing. A common approach to address this issue is to use the Discrete Fourier Transform (DFT) for observations. When a tone does not lie precisely at the discrete DFT frequency point, its energy will leak to adjacent frequency point. This phenomenon is known as scalloping loss or Picket Fence Effect (PFE). PFE leads to the degradation of detection performance based on DFT. This paper studies the problem of robust detection in the case of PFE. A coherently-averaged power processor utilizing the information of adjacent frequency bins is designed. The results of simulations and experiments show that the proposed method is robust against PFE, and is highly suitable for tone detection in practical circumstances. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the results of an innovative approach in the underwater domain of research related to the identification, classification and recognition of maritime targets using acoustic data processed. The "Acoustic Signature" is specific to each target type; it is usually produced by the vibration of the propulsion system of surface vessels caused by their radiation into the water. Therefore, the processing of the frequencies generated by these vibrations is essential for the analysis and the classification of different target type. The purpose of this study is to build an alternative method to identify and classify targets with passive sonars using the TPWS (Two - Pass Split - Windows) filter. In this process, the signal generated by the target is processed in time frequency domain. Then a TPSW algorithm is applied in the frequency domain to reduce the background noise and enhance the frequency lines of the target noise. Finally, an artificial intelligence model is applied to classify targets, taking as inputs the narrowband and the broadband analysis. This classification is based on deep learning process, relied on the training, validation, and test phases in order to enhance the accuracy and reduce the loss. Our results showed that the suggested method is accurate (appx 83.5% SNR = 0db), depending essentially on the signal/noise ratio. [ABSTRACT FROM AUTHOR]

This article describes the development and testing of a passive sonar, multitarget tracker, and adaptive behavior that enable an autonomous underwater vehicle (AUV) to detect and actively track nearby surface vessels. A planar hull-mounted hydrophone array, originally designed for active sonar, is repurposed for passive sonar use and provides acoustic data to a time-delay-and-sum beamformer that generates multiple angle-only contacts. A particle filter tracker assimilates these contacts with a single-hypothesis data association strategy to estimate the position and velocity of targets. Summary statistics of each track are periodically reported to an onboard database along with qualitative labels. To improve tracking performance, detections trigger an adaptive behavior that maneuvers the AUV to maintain multiple targets in the field of view by minimizing the worst case aspect angle deviation from broadside (across all targets). The tracking system is demonstrated through at-sea experiments in which a Bluefin-21 AUV adaptively tracks multiple surface vessels, including another autonomous platform, in the approaches to Boston Harbor. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we describe an unmanned underwater vehicle (UUV) behavior designed to track nearby vessels using bearing-only measurements obtained from a rigidly mounted planar hydrophone array—one that was originally designed for active sonar use but is repurposed for passive sonar use. Upon detecting a target, a maneuver is executed to resolve the port/starboard bearing ambiguity. The maneuver is heuristically designed with the aim of reducing the effects of end-fire, self-noise during maneuvers, and other noise/spurious measurements on the bearing ambiguity decision. After resolving the bearing ambiguity, the vehicle continuously adjusts its heading to track the target by keeping it broadside to the array. The performance of this behavior, as well as that of the passive sonar itself, is evaluated through field trials in the approaches to Boston Harbor using a Bluefin-21 UUV. In total, 19 successful behavior tests are described where the UUV resolves the bearing ambiguity and tracks either a static source emitting a predefined waveform or the platform noise of a medium-sized catamaran underway. [ABSTRACT FROM AUTHOR]

The SPICE (Sparse Iterative Covariance-based Estimation) algorithm estimates the azimuth angle by applying a sparse recovery method to the covariance matrix in the time domain. In this paper, we show how the SPICE algorithm, which was originally formulated in the time domain, can be extended to the frequency domain. Furthermore, we demonstrate, through numerical results, that the performance of the proposed algorithm is superior to that of the conventional frequency domain algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the results of an experimental investigation about target detecting with passive sonar in Persian Gulf. Detecting propagated sounds in the water is one of the basic challenges of the researchers in sonar field. This challenge will be complex in shallow water (like Persian Gulf) and noise less vessels. Generally, in passive sonar, the targets are detected by sonar equation (with constant threshold) that increases the detection error in shallow water. The purpose of this study is proposed a new method for detecting targets in passive sonars using adaptive threshold. In this method, target signal (sound) is processed in time and frequency domain. For classifying, Bayesian classification is used and posterior distribution is estimated by Maximum Likelihood Estimation algorithm. Finally, target was detected by combining the detection points in both domains using Least Mean Square (LMS) adaptive filter. Results of this paper has showed that the proposed method has improved true detection rate by about 24% when compared other the best detection method. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for ship recognition and classification using sound produced and radiated underwater. To do so, a three-step procedure is proposed. First, the preprocessing operations are utilized to reduce noise effects and provide signal for feature extraction. Second, a binary image, made from frequency spectrum of signal segmentation, is formed to extract effective features. Third, a neural classifier is designed to classify the signals. Two approaches, the proposed method and the fractal-based method are compared and tested on real data. The comparative results indicated better recognition ability and more robust performance of the proposed method than the fractal-based method. Therefore, the proposed method could improve the recognition accuracy of underwater acoustic targets. [ABSTRACT FROM AUTHOR]

In passive sonar, narrowband adaptive beamforming techniques can be exploited to increase the signal-to-interference-plus-noise ratio (SINR), providing that array steering vector (ASV) errors and cross-spectral density matrix (CSDM) estimation errors can be controlled. When beamforming large aperture, many-element arrays in dynamic scenarios, the number of stationary snapshots available for CSDM estimation can be small compared to the number of array elements, leading to the problem of snapshot deficiency. Furthermore, common narrowband approaches become computationally prohibitive for large bandwidths. Here, we exploit the wideband nature of passive sonar signals to alleviate snapshot deficiency and reduce computational complexity. Narrowband robust Capon beamformers (RCBs), which exploit ellipsoidal ASV uncertainty sets to maintain high SINR, are extended to the wideband problem via the steered covariance matrix (STCM) method, yielding wideband RCBs (WBRCBs). To further reduce computational complexity and speed up algorithm convergence, subarray techniques are also incorporated, yielding wideband subarray RCBs (WBSARCBs). These algorithms, which are applicable to arbitrary array geometries, are evaluated using simulated and experimental passive sonar data. [ABSTRACT FROM PUBLISHER]

In this paper, a Bayesian approach to tracking a single target of interest (TOI) using passive sonar is presented. The TOI is assumed to be in the presence of other loud interfering targets, or interferers. To account for the interferers, a single-signal likelihood function (SSLF) is proposed which uses maximum-likelihood estimates (MLEs) in place of nuisance parameters. Since there is uncertainty in signal origin, we propose a computationally efficient method for computing association probabilities. The final proposed SSLF accounts for sidelobe interference from other signals, reflects the uncertainty caused by the array beampattern, is signal-to-noise ratio (SNR) dependent, and reflects uncertainty caused by unknown signal origin. Various examples are considered, which include moving and stationary targets. For the examples, the sensors are assumed to be uniformly spaced linear arrays. The arrays may be stationary or moving and there may be one or more present. [ABSTRACT FROM AUTHOR]

Information theory provides a novel perspective on passive sonar performance analysis. This approach begins by partitioning the search space and then considers the problem of assigning a source to the correct partition based on pressure observations from a hydrophone array. Prior work described necessary conditions for achieving arbitrarily small probability of error (Pe) as a tradeoff between signal-to-noise ratio (SNR) and the range accuracy of the partitions. This paper presents a method to extend these results using the rate-distortion function to find necessary conditions for any Pe. The Gaussian channel bound sets an upper limit on the information rate received at the array. Through the rate-distortion function, this upper limit on the information rate implies a lower bound on Fe for a given partition. Furthermore, the current work describes a tradeoff between range accuracy, Pe and SNR. Examples of this tradeoff are evaluated for both one-dimensional (bearing) and two-dimensional (range-depth) estimation problems. [ABSTRACT FROM AUTHOR]

The acoustic spectrum of a propeller-driven aircraft is dominated by a series of spectral lines that are harmonically related to the blade rate (which is equal to the product of the propeller rotation rate and the number of blades on the propeller). We show that an array of acoustic sensors towed below the sea surface can be used for the passive detection and localization of such an aircraft. The acoustic energy from an aircraft is found to reach the subsurface sensors via two propagation paths: a bottom reflection path that enables the aircraft to be detected at long ranges, and a direct path that is present only when the aircraft passes overhead. For each of these paths, the observed variation with horizontal range of the Doppler shift in the blade rate closely matches the variation predicted by the simple model presented in this paper. Good agreement between theory and experiment is also obtained for the variation with horizontal range of the aircraft's apparent bearing. Thus, by using the observed Doppler shift and apparent bearing information, we were able to estimate the aircraft's horizontal range, speed, direction, and altitude. [ABSTRACT FROM AUTHOR]

This paper describes a novel framework for classifying underwater transient signals recorded by passive sonar. The proposed approach involves two key ideas. Firstly, a feature-selection algorithm is used to identify those acoustic features that optimally model each class of transient sound. Secondly, features that are perceptually motivated are proposed, i.e., they encode information that human listeners are likely to use in transient classification tasks. Three perceptual features are proposed, which encode timbre, the physical material of the sound source, and the temporal context (pattern) in which the transient occurred. The authors show how these features, which are computed over different temporal windows, can be combined to make classification decisions. The performance of the proposed classifier is evaluated on a corpus of transient signals extracted from passive sonar recordings. Specifically, the performance of the perceptual features is compared with spectral features and with those that encode statistics of time, frequency, and power. The present results show that the perceptual features provide valuable cues to the class of a transient. However, the best performing classifier was obtained by selecting a subset of perceptual, spectral, and statistical features in a class-dependent manner. [ABSTRACT FROM AUTHOR]

At present, the tracking accuracy of underwater passive target tracking is often limited due to models that are overly simple, with low complexity, poor universality, and an inability to learn. In this paper, a cubature Kalman filter (CKF) algorithm based on a gated recurrent unit (GRU) network is proposed. The filter innovation, prediction error, and filter gain obtained from the CKF are used as the input to the GRU network, and the filter error value is used as the output to train the network. End-to-end online learning is carried out using the designed fully connected network, and the current state of the target is predicted. In this paper, a deep neural network based on the GRU architecture is used to convert the tracking prediction problem into a time series prediction problem in the field of artificial intelligence, and its strong fitting ability is used to resolve the uncertainty of the target motion. Simulation results show that an unmanned underwater vehicle (UUV) state estimation method based on the GRU filter proposed in this paper offers better accuracy and stability than the traditional state estimation method. [ABSTRACT FROM AUTHOR]

This paper proposes a method to enhance the quality of detecting and classifying surface vehicle propeller cavitation noise (VPCN) in shallow water by using the improved Detection Envelope Modulation On Noise (DEMON) algorithm in combination with the modified Convolution Neural Network (CNN). To improve the quality of the VPCN spectrogram signal, we apply the DEMON algorithm while analyzing the amplitude variation (AV) to detect the fundamental frequencies of the VPCN signal. To enhance the performance of the traditional CNN, we adapt the size of the sliding window in accordance with the properties of the VPCN spectrogram data, and also reconstruct the CNN layer structure. As for the results, the fundamental frequencies contented in the VPCN spectrogram data can be detected. The analytical results based on the measured data show that the accuracy of the VPCN classification obtained by the proposed method is above 90%, which is higher than those obtained by traditional methods. [ABSTRACT FROM AUTHOR]

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition. [ABSTRACT FROM AUTHOR]

Frequency estimation of a tonal signal in passive sonar systems is crucial to the identification of the marine object. In the conventional techniques, a basis mismatch error caused by the discretization of the frequency domain is unavoidable, resulting in a severe degradation of the object detection quality. To overcome the basis mismatch error, we propose a tonal frequency estimation technique in the continuous frequency domain. Towards this end, we formulate the frequency estimation problem as an atomic norm minimization problem. From the numerical experiments, we show that the proposed technique is effective in identifying the tonal frequency components of marine objects. [ABSTRACT FROM AUTHOR]

The traditional passive azimuth estimation algorithm using two hydrophones, such as cross-correlation time-delay estimation and cross-spectral phase estimation, requires a high signal-to-noise ratio (SNR) to ensure the clarity of the estimated target trajectory. This paper proposes an algorithm to apply the frequency diversity technique to passive azimuth estimation. The algorithm also uses two hydrophones but can obtain clear trajectories at a lower SNR. Firstly, the initial phase of the signal at different frequencies is removed by calculating the cross-spectral density matrix. Then, phase information between frequencies is used for beamforming. In this way, the frequency dimension information is used to improve the signal processing gain. This paper theoretically analyzes the resolution and processing gain of the algorithm. The simulation results show that the proposed algorithm can estimate the target azimuth robustly under the conditions of a single target (SNR = −16 dB) and multiple targets (SNR = −10 dB), while the cross-correlation algorithm cannot. Finally, the algorithm is tested by the swell96 data and the South Sea experimental data. When dealing with rich frequency signals, the performance of the algorithm using two hydrophones is even better than that of the conventional broadband beamforming of the 64-element array. This further validates the effectiveness and advantages of the algorithm. [ABSTRACT FROM AUTHOR]

Capon beamforming is often applied in passive sonar to improve the detectability of weak underwater targets. However, we often have no accurate prior information of the direction-of-arrival (DOA) of the target in the practical applications of passive sonar. In this case, Capon beamformer will suffer from performance degradation due to the steering vector error dominated by large DOA mismatch. To solve this, a new robust Capon beamforming approach is proposed. The essence of the proposed method is to decompose the actual steering vector into two components by oblique projection onto a subspace and then estimate the actual steering vector in two steps. First, we estimate the oblique projection steering vector within the subspace by maximizing the output power while controlling the power from the sidelobe region. Subsequently, we search for the actual steering vector within the neighborhood of the estimated oblique projection steering vector by maximizing the output signal-to-interference-plus-noise ratio (SINR). Semidefinite relaxation and Charnes-Cooper transformation are utilized to derive convex formulations of the estimation problems, and the optimal solutions are obtained by the rank-one decomposition theorem. Numerical simulations demonstrate that the proposed method can provide superior performance, as compared with several previously proposed robust Capon beamformers in the presence of large DOA mismatch and other array imperfections. [ABSTRACT FROM AUTHOR]