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Bistatic active sonar enables robust and precise target position and tracking, making it a key technology for autonomous underwater vehicles (AUVs) in underwater surveillance. This paper proposes a multi-information-assisted target tracking algorithm for bistatic active sonar, leveraging spatial and temporal echo signal structures to address the challenges of AUVs in shallow water. First, broadened cluster formations in sonar echoes are analyzed, leading to the integration of a spatial clustering-based data association. This paper departs from conventional methods by fusing target position, echo amplitude, and Doppler information during the movement of AUVs, which can improve the efficiency of association probability computation. The re-derived multi-information-assisted association probability calculation method and algorithmic workflow are explicitly designed for real-time implementation in AUV systems. Simulation experiments verify the feasibility of integrating Doppler and amplitude information. The sea trial data from simulated AUV-deployed bistatic sonar contained only amplitude information due to experimental limitations. By utilizing this amplitude information, the algorithm proposed in this paper demonstrates a 23.95% performance improvement over the traditional probabilistic data association algorithm. The proposed algorithm provides AUVs with enhanced tracking autonomy, significantly advancing their capability in ocean engineering applications.

Mass strandings of beaked whales (family Ziphiidae) have been reported in the scientific literature since 1874. Several recent mass strandings of beaked whales have been reported to coincide with naval active sonar exercises. To obtain the broadest assessment of surface ship naval active sonar operations coinciding with beaked whale mass strandings, a list of global naval training and anti-submarine warfare exercises was compiled from openly available sources and compared by location and time with historic stranding records. This list includes activities of navies of other nations but emphasizes recent U.S. activities because of what is available in publicly accessible sources. Of 136 beaked whale mass stranding events reported from 1874 to 2004, 126 occurred between 1950 and 2004, after the introduction and implementation of modern, high-power mid-frequency active sonar (MFAS). Of these 126 reports, only two reported details on the use, timing, and location of sonar in relation to mass strandings. Ten other mass strandings coincided in space and time with naval exercises that may have included MFAS. An additional 27 mass stranding events occurred near a naval base or ship but with no direct evidence of sonar use. The remaining 87 mass strandings have no evidence for a link with any naval activity. Six of these 87 cases have evidence for a cause unrelated to active sonar. The large number of global naval activities annually with potential MFAS usage in comparison to the relative rarity of mass stranding events suggests that most MFAS operations take place with no reported stranding events and that for an MFAS operation to cause a mass stranding of beaked whales, a confluence of several risk factors is probably required. Identification of these risk factors will help in the development of measures to reduce the risk of sonar-related strandings. [PUBLICATION ABSTRACT]

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.

Machine perception uses advanced sensors to collect information about the surrounding scene for situational awareness 1 – 7 . State-of-the-art machine perception 8 using active sonar, radar and LiDAR to enhance camera vision 9 faces difficulties when the number of intelligent agents scales up 10 , 11 . Exploiting omnipresent heat signal could be a new frontier for scalable perception. However, objects and their environment constantly emit and scatter thermal radiation, leading to textureless images famously known as the ‘ghosting effect’ 12 . Thermal vision thus has no specificity limited by information loss, whereas thermal ranging—crucial for navigation—has been elusive even when combined with artificial intelligence (AI) 13 . Here we propose and experimentally demonstrate heat-assisted detection and ranging (HADAR) overcoming this open challenge of ghosting and benchmark it against AI-enhanced thermal sensing. HADAR not only sees texture and depth through the darkness as if it were day but also perceives decluttered physical attributes beyond RGB or thermal vision, paving the way to fully passive and physics-aware machine perception. We develop HADAR estimation theory and address its photonic shot-noise limits depicting information-theoretic bounds to HADAR-based AI performance. HADAR ranging at night beats thermal ranging and shows an accuracy comparable with RGB stereovision in daylight. Our automated HADAR thermography reaches the Cramér–Rao bound on temperature accuracy, beating existing thermography techniques. Our work leads to a disruptive technology that can accelerate the Fourth Industrial Revolution (Industry 4.0) 14 with HADAR-based autonomous navigation and human–robot social interactions. Heat-assisted detection and ranging is experimentally shown to see texture and depth through darkness as if it were day, and also perceives decluttered physical attributes beyond RGB or thermal vision.

Mid-frequency active sonar (MFAS), used for antisubmarine warfare (ASW), has been associated with multiple beaked whale (BW) mass stranding events. Multinational naval ASW exercises have used MFAS offshore of the Mariana Archipelago semi-annually since 2006. We report BW and MFAS acoustic activity near the islands of Saipan and Tinian from March 2010 to November 2014. Signals from Cuvier's (Ziphius cavirostris) and Blainville's beaked whales (Mesoplodon densirostris), and a third unidentified BW species, were detected throughout the recording period. Both recorders documented MFAS on 21 August 2011 before two Cuvier's beaked whales stranded on 22–23 August 2011. We compared the history of known naval operations and BW strandings from the Mariana Archipelago to consider potential threats to BW populations. Eight BW stranding events between June 2006 and January 2019 each included one to three animals. Half of these strandings occurred during or within 6 days after naval activities, and this co-occurrence is highly significant. We highlight strandings of individual BWs can be associated with ASW, and emphasize the value of ongoing passive acoustic monitoring, especially for beaked whales that are difficult to visually detect at sea. We strongly recommend more visual monitoring efforts, at sea and along coastlines, for stranded cetaceans before, during and after naval exercises.

The covertness of the active sonar is a very important issue and the sonar signal waveform design problem was studied to improve covertness of the system. Many marine mammals produce call pulses for communication and echolocation, and existing interception systems normally classify these biological signals as ocean noise and filter them out. Based on this, a bio-inspired covert active sonar strategy was proposed. The true, rather than man-made sperm whale, call pulses were used to serve as sonar waveforms so as to ensure the camouflage ability of sonar waveforms. A range and velocity measurement combination (RVMC) was designed by using two true sperm whale call pulses which had excellent range resolution (RR) and large Doppler tolerance (DT). The range and velocity estimation methods were developed based on the RVMC. In the sonar receiver, the correlation technology was used to confirm the start and end time of sonar signals and their echoes, and then based on the developed range and velocity estimation method, the range and velocity of the underwater target were obtained. Then, the RVMC was embedded into the true sperm whale call-train to improve the camouflage ability of the sonar signal-train. Finally, experiment results were provided to verify the performance of the proposed method.

As background for this special issue on strandings and mid-frequency active sonar (MFAS), this paper presents a brief history of active sonar, tracing the development of MFAS from its origins in the early 20th century through the development of current tactical MFAS.

Kvadsheim et al outlines the 3S (Sea mammals, Sonar, Safety) project's ongoing research into how naval sonar affects cetaceans. Since 2006, 3S has conducted 157 controlled sonar exposure experiments, deployed 242 tags across six species, and published over 30 papers. The initial goal was to understand dose-response relationships to assess disturbance levels in marine mammals. The latest phase, 3S4, began in 2023 and focuses on comparing the behavioral impacts of Continuous Active Sonar (CAS) versus Pulsed Active Sonar (PAS). A sea trial off Norway in October 2023 tested repeated sonar exposures on killer and humpback whales using real-time GPS tracking. Researchers are analyzing responses such as feeding cessation and social sound masking. A second trial with similar methods is planned for October 2024.

Active sonar signal matching is a critical technique for measuring inter-signal similarity and enhancing target detection and classification performance. However, in complex underwater environments, noise, reverberation, and prolonged signal durations often degrade matching accuracy and computational efficiency. To address these challenges, this paper proposes an adaptive extremum-aligned boundary-constrained dynamic time warping (AEB-DTW) algorithm, based on the classical dynamic time warping (DTW) framework. The algorithm extracts significant extrema from signal envelopes to suppress noise and reverberation while capturing salient features. By integrating the position and amplitude of extrema, an adaptive weighted matching strategy is introduced to enhance feature discrimination. In addition, spline fitting is applied to the residuals of the extremum matching path to dynamically generate upper and lower boundary constraints, thus restricting DTW computation to a meaningful region and achieving a balance between accuracy and efficiency. Experiments using lake-trial active sonar data under signal-to-reverberation ratios (SRRs) from 0 dB to 30 dB show that AEB-DTW outperforms Euclidean distance (ED), DTW, and its variants in matching accuracy, robustness, and angular resolution, while significantly improving computational efficiency, particularly for long-duration signals.

Anthropogenic activities can lead to changes in animal behavior. Predicting population consequences of these behavioral changes requires integrating short-term individual responses into models that forecast population dynamics across multiple generations. This is especially challenging for long-lived animals, because of the different time scales involved. Beaked whales are a group of deep-diving odontocete whales that respond behaviorally when exposed to military mid-frequency active sonar (MFAS), but the effect of these nonlethal responses on beaked whale populations is unknown. Population consequences of aggregate exposure to MFAS was assessed for two beaked whale populations that are regularly present on U.S. Navy training ranges where MFAS is frequently used. Our approach integrates a wide range of data sources, including telemetry data, information on spatial variation in habitat quality, passive acoustic data on the temporal pattern of sonar use and its relationship to beaked whale foraging activity, into an individual-based model with a dynamic bioenergetic module that governs individual life history. The predicted effect of disturbance from MFAS on population abundance ranged between population extinction to a slight increase in population abundance. These effects were driven by the interaction between the temporal pattern of MFAS use, baseline movement patterns, the spatial distribution of prey, the nature of beaked whale behavioral response to MFAS and the top-down impact of whale foraging on prey abundance. Based on these findings, we provide recommendations for monitoring of marine mammal populations and highlight key uncertainties to help guide future directions for assessing population impacts of nonlethal disturbance for these and other long-lived animals.