Search Results (326)

Uniform circular arrays (UCAs) are widely used in underwater source localization due to their omnidirectional coverage. However, random sensor position errors caused by installation inaccuracies and environmental disturbances convert UCAs into non-uniform circular arrays (NCAs), severely degrading the performance of high-resolution direction of arrival (DOA) estimation algorithms. To address this issue, this paper proposes a robust DOA estimation method that integrates empirical mode decomposition (EMD) denoising with prior-weighted iterative least squares (PWLS) correction. The method first applies EMD to adaptively denoise received signals by selecting intrinsic mode functions based on a combined energy-correlation criterion. An initial DOA estimate is then obtained using the MUSIC algorithm. Finally, a PWLS correction algorithm leverages prior knowledge of deviated sensors to iteratively fit the circle center and gradually pull sensor positions toward the ideal circumference, using a differentiated relaxation mechanism to suppress outliers while preserving geometric features. Systematic Monte Carlo simulations compare five correction algorithms under multi-frequency and wideband signals. The results show that both multi-frequency and wideband signals reduce estimation errors to below 0.1°, with the proposed PWLS achieving the best accuracy under multi-frequency signals, while all algorithms approach zero error under wideband signals. The PWLS algorithm converges in about 10 iterations with high computational efficiency, providing a reliable solution for practical underwater NCA applications. Full article

Rapid assessment of ground surface conditions is essential in disaster response and search-and-rescue operations, where drones are increasingly deployed for aerial inspection and victim localization. This paper proposes an active acoustic sensing method for estimating ground surface conditions using a drone-mounted speaker and microphone array. The method is based on the multiple signal classification framework and enables three-dimensional localization of reflection points according to the principle of echolocation. A key feature of the proposed approach is that it shares both hardware and signal processing components with acoustic-based victim search, allowing simultaneous execution of surface sensing and sound source localization (SSL) on a single drone platform without increasing system complexity. Outdoor experiments were conducted to evaluate sensing performance for ground surface anomalies, specifically ground surface depressions and cracks. The experimental results clarify the achievable sensing performance and coverage in real environments and reveal key factors affecting detection performance. The feasibility of simultaneous execution of active acoustic sensing and SSL was also investigated, and the mutual interactions between sensing and localization performance were clarified. These findings highlight both the potential and the practical limitations of integrating environmental sensing and victim localization on a single drone platform. Full article

The position of a non-cooperative underwater pulse signal source can be estimated by applying target motion analysis techniques to the direction of arrival (DOA) and frequency of arrival (FOA) measurements obtained from a hydrophone array. However, the harsh underwater acoustic environment, with its pronounced multipath propagation, high signal attenuation, and sparse detectable pulses, introduces considerable errors into the estimation of DOA and FOA. These errors can degrade the performance of conventional estimators such as the pseudolinear estimation (PLE) method, leading to significant bias and divergence issues. To address these issues, this paper proposes a method based on nonlinear batch processing for underwater non-cooperative target localization. A cost function is constructed based on a nonlinear observation model and the weighted least squares principle to ensure high modeling fidelity. Subsequently, a multi-start grid search combined with a trust region dogleg algorithm is employed for global iterative optimization of the cost function, enhancing the accuracy and stability of the final position estimate. Numerical simulation results demonstrate that the proposed method achieves high convergence speed and localization accuracy under adverse noise conditions and with a limited number of received pulses. Moreover, the sea trial results confirm that the algorithm attained a convergence rate of 93% with only 25 received pulses, and outperformed the conventional PLE method by approximately 80% in terms of positioning accuracy. Full article

During continuous navigation of the mother ship, an autonomous underwater vehicle (AUV) can be recovered through an underwater hangar, and the accurate localization of the AUV relative to the mother ship is a key step in the recovery process. To address the AUV localization problem, an n-shaped hydrophone array is designed based on the spatial configuration of the underwater hangar. Since underwater acoustic signals are susceptible to multipath propagation, co-channel interference, and other transmission impairments, the signals received by the array often exhibit coherence. Accordingly, a far-field sound source localization method based on the n-shaped array is proposed. The proposed algorithm first applies spatial smoothing to the x-axis and y-axis subarrays and jointly constructs a received data vector, followed by eigenvalue decomposition of the corresponding covariance matrix. The Multiple Signal Classification (MUSIC) algorithm is then employed to obtain coarse estimates of the source angles. These coarse estimates are subsequently used as initial values for the Space-Alternating Generalized Expectation-maximization (SAGE) algorithm, which performs refined optimization of the angular parameters in a continuous parameter space, thereby effectively improving estimation accuracy. Furthermore, the proposed algorithm is extended from far-field scenarios to near-field localization. Simulation results demonstrate that the proposed method achieves good parameter estimation performance. Full article

The rapid integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace has introduced complex security challenges, particularly regarding the protection of critical infrastructure and personal privacy. While conventional detection mechanisms such as radar and optical sensors are widely deployed, they are frequently limited by line-of-sight obstructions and the small radar cross-section of modern commercial drones. Acoustic analysis presents a viable passive alternative; however, accurate three-dimensional localization remains a computationally demanding task, further complicated by the use of directional sensors with non-uniform sensitivity patterns. In this paper, a deep learning framework is proposed to address these ambiguities. The method involves the fusion of raw acoustic data with explicit sensor geometry metadata within a neural network architecture. To enhance localization precision, a composite loss function is introduced, which independently optimizes planar and altitude coordinates while penalizing outlier predictions. Experimental validation was conducted using a custom dataset of real-world drone flights, utilizing a distributed array of directional microphones. The results demonstrate that the proposed system effectively mitigates the spatial irregularities of ad hoc sensor deployment, achieving robust localization performance in complex acoustic environments. Full article

Beyond supporting ultra-high-capacity data transmission, metropolitan and access networks are expected to enable real-time infrastructure monitoring, driving the emergence of integrated sensing and communication (ISAC). Distributed acoustic sensing (DAS) has proven to be well-suited to urban sensing application requirements, yet its seamless integration into ISAC remains challenging—conventional high-peak-power sensing pulses in DAS induce nonlinear crosstalk in communication channels. DAS inherently suffers from interference fading due to single-frequency laser sources, which limits sensitivity. Here, we propose an ISAC architecture based on an electro-optic (EO) comb and a 7-core fiber, achieving nonlinearity-suppressed self-homodyne transmission and fading-suppressed DAS. Unmodulated comb lines and sensing pulses are polarization-multiplexed into orthogonal polarization states within the central core to minimize nonlinear crosstalk while delivering local oscillators (LOs) for wavelength division multiplexing (WDM) coherent transmission within six outer cores—achieving 10.56 Tbit/s capacity. In addition to supporting WDM transmission, the EO comb’s wavelength diversity is also exploited to enhance DAS performance. Specifically, a dual-pulse probe loaded onto four comb lines yields a 6 dB signal-to-noise ratio gain and a 64% reduction in fading occurrences, achieving a sensitivity of 1.72 $p\epsilon /\sqrt{\mathrm{Hz}}$ with 8 m spatial resolution. Moreover, our system supports simultaneous multi-wavelength backscatter detection in sensing and simplified digital signal processing in self-homodyne communication, reducing receiver complexity and cost. Our work presents a scalable, energy-efficient ISAC framework that unifies high-capacity communication with high-sensitivity sensing, providing a blueprint for future intelligent optical networks. Full article

This paper presents a high-sensitivity two-dimensional fiber-optic hydrophone designed for the detection and localization of underwater acoustic sources. The device comprises two sensing heads, each incorporating a fiber Bragg grating (FBG) embedded within a customized 3D-printed encapsulation. To enhance acoustic sensitivity, the design utilizes a silicone thin-film coupled with a pyramidal channel that spatially concentrates acoustic energy from the base to the apex, where the FBG is positioned. Incident acoustic pressure induces vibrations in the film, which are amplified by the channel structure, imparting strain on the FBG and resulting in a shift in the Bragg wavelength. The acoustic frequency response is demodulated by converting the overlapping optical power between the sensing and reference gratings into an electrical signal via a photodetector. By arranging the two sensing heads orthogonally, the system effectively determines the direction and angle of the acoustic source. Experimental results show a peak sensitivity of −210.59 dB re 1 V/μPa, with a FWHM of 57.92–66.27 Hz and a figure of merit (FOM) up to 3.64 dB/Hz. In addition, the acoustic-field SNR is approximately 26 dB in the dominant band, and the LOD is 64.19 dB re 1 μPa (10–400 Hz). Experimental validation confirms the hydrophone’s high sensitivity and localization accuracy, demonstrating its significant potential for underwater acoustic sensing applications. Full article

The central auditory system integrates signals received from both ears to derive information about the spatial and spectral features of the emitting sound source. This binaural processing of acoustic information is critical for both communication and environmental awareness. However, these binaural computations may become disrupted in individuals diagnosed with autism spectrum disorder (ASD), potentially leading to difficulties with speech perception, sound attention, and sensory hypersensitivity. Here, we present a narrative review of the emerging evidence regarding binaural processing deficits in ASD. These deficits include elevated thresholds for interaural time and level differences and reduced sound localization accuracy. In addition, physiological data suggests that these behavioral traits correspond with abnormal activity in central auditory structures. Molecular and cellular alterations to central auditory circuits may underlie these behavioral and physiological features, which could arise from both genetic and environmental factors. Overall, binaural processing alterations in ASD remain under-studied, with a need for future studies to identify neural circuit-level mechanisms and potential interventions. Full article

Microseismic identification and localization of signals from single-component distributed optical fiber acoustic sensors (DAS) in vertical wells are limited by low signal-to-noise ratio and lack of directional information, making effective signal identification and accurate localization difficult. Improving the detection rate and accuracy of such data events is helpful for analyzing the effect of fracturing. To address this, this paper proposes a method for automatically picking and locating microseismic events based on dual fitting modeling and waveform inversion. First, empirical mode decomposition (EMD) is used to adaptively decompose and reconstruct the original DAS signal to filter out approximately 80% of high-frequency noise (noise above 200 Hz). Second, the classic short-time average/long-time average energy ratio algorithm is used to pick all “event points.” Finally, DBSCAN density clustering and RANSAC robust fitting are combined to perform secondary screening and fitting modeling of the “event points” to obtain the continuous event arrival time distribution along the well section direction, and the spatial location of the seismic source is inverted based on the fitting results. Tested with experimental data from Well XX, the automatic detection rate reached 96%, and the accuracy of machine detection compared with manual judgment reached 95%. Full article

Urban traffic noise poses escalating environmental challenges in rapidly urbanizing regions with high-density buildings, yet systematic investigations into its spatiotemporal characteristics remain relatively scarce. This study addresses this research gap via the synchronized on-site monitoring of traffic noise and traffic flow on a representative arterial road in Guangzhou, China. The analysis reveals that nighttime equivalent continuous A-weighted sound levels (L_Aeq) are 3.0–4.0 dB(A) higher than those during the congested daytime peak, a phenomenon primarily driven by higher vehicle speeds under nighttime free-flow traffic conditions. The spatial analysis uncovers complex three-dimensional noise propagation dynamics specific to urban street canyons. Vertical profiling demonstrates a counterintuitive pattern where noise levels do not attenuate with building height, and upper floors experience marginally higher noise exposure than the ground floor, which is attributed to the canyon effect, where multiple sound wave reflections offset the natural distance attenuation. A validated three-dimensional computational model was further employed to evaluate the efficacy of noise mitigation strategies, showing that an integrated intervention combining porous asphalt pavement and acoustic barriers achieves a maximum noise attenuation of 19.9 dB(A) at ground-level receptors. This significant reduction stems from a synergistic effect: porous asphalt reduces noise at the source on a global scale, while acoustic barriers provide localized shielding for the lower floors of adjacent buildings. This research concludes that effective traffic noise control in high-density urban areas requires three-dimensional, multi-faceted strategies addressing noise source characteristics, transmission pathways, and receptor vulnerabilities. Full article

Plastic shrinkage and self-desiccation, along with the associated early-age cracking, are still among the most important factors that influence long-term performance of concrete structures, including durability. Superabsorbent polymers (SAPs) have been widely researched for application in concrete to mitigate shrinkage through facilitating effective internal curing by releasing water into the mixture to promote continuous hydration of cement. The acoustic emission (AE) monitoring technique, due to its high sensitivity, has proven very effective in tracking the process of water release by SAPs in concrete during early-stage curing. Typically, AE parameters such as cumulative activity, amplitude and energy are utilized to characterize the kinetics of curing processes. While these parameters indicate well the internal activity of SAPs in time, they do not offer information on the precise location of the active sources within the material’s volume, leaving a crucial gap in the understanding of the ongoing microstructural changes caused by internal water distribution and cement hydration. In this sense, AE event source localization can offer information about the active zones of water hydration activity in the material 3D domain, allowing detection of their evolution during concrete curing. Meanwhile, Acoustic Emission Tomography (AET) computes ultrasonic velocity distributions in different periods of monitoring, which are governed by acoustic characteristics of the concrete mixtures, to visualize material stiffness development spatially and temporally. This level of insight is particularly important for SAP concrete, where uniformity of internal water curing is essential for ensuring long-term durability and material soundness. By visualizing how the hydration sources evolve in real time, these methods offer an effective, non-destructive, and cost-effective solution for early-age concrete quality control, which would be challenging to achieve through other techniques. Full article

Sound source localization in dynamic environments with multiple moving speakers presents significant challenges due to reverberation, noise, and unknown source counts. To address these issues, this paper proposes an integrated deep-learning framework combining spatial spectrum estimation with blind source detection. The method employs a causal convolution–recurrent network (SRP-DPCRN) to extract robust spatial features from multichannel audio signals under adverse acoustic conditions. Subsequently, an iterative attention-based detection network (IASDNet) automatically identifies active sources from the estimated spatial spectrum without requiring prior knowledge of source quantity. Evaluated on both simulated datasets and the real-recorded LOCATA benchmark, the proposed system demonstrates superior performance in multi-source tracking scenarios, achieving an average detection accuracy of 96% with mean angular error below 3.5 degrees. The results confirm that joint optimization of feature learning and source counting provides an effective solution for blind localization in practical applications, significantly outperforming conventional and deep-learning baselines. Full article

Thermal runaway events in energy storage power stations exhibit distinct acoustic characteristic signals. Three-dimensional localization of the sound source is of significant importance for achieving precise firefighting interventions. This study proposes an internal fault localization method for power stations based on the acoustic signals from pressure relief valves of energy storage battery packs. By deploying four microphones to capture the acoustic signals from the battery pack pressure relief valves, the spatial location of the faulty pack can be calculated using a three-dimensional localization model trained on a Back Propagation (BP) neural network. The localization accuracy of this model is better than 0.5 m, with the majority of measurement points achieving an accuracy of less than 0.3 m, meeting the requirements for battery pack-level localization. A key advantage of this method is its low sensitivity to time delay measurement errors caused by reverberation and reflections in enclosed spaces. Reliable and stable localization of pressure relief sound sources can be achieved through multiple training sessions within the battery cabin, which facilitates practical deployment. Full article

For high-speed compound helicopters, such as the S-97 Raider, the reflection and diffraction effects of vertical/horizontal tails on pusher propeller noise are inevitable. To investigate the noise distortion effect of the rear-mounted pusher propeller, this study first relies on the Chinese Laboratory of Rotorcraft Navier-Stokes (CLORNS) solver, adopting the high-resolution Perturbed polynomial reconstructed Targeted Essentially Non-Oscillatory scheme (TENO-P) combined with the Delayed Detached Eddy Simulation based on the Spalart–Allmaras (SA-DDES) turbulence model to resolve the multi-scale rotor flowfield. Additionally, a continuous and conserved acoustic source extraction method is proposed to eliminate non-physical waves at the one-way Computational Fluid Dynamics and Computational AeroAcoustics (CFD–CAA) coupling interface, addressing the temporal inconsistency between flowfield evolution and acoustic propagation. Finally, numerical investigations are conducted on the instantaneous acoustic wave propagation and acoustic directivity of the pusher propeller under the influence of vertical/horizontal tails. The results show that significant acoustic distortion occurs when pusher propeller-generated noise interacts with vertical/horizontal tails. This interaction not only produces reflected and diffracted acoustic waves but also leads to wavefront discontinuities, the formation of short acoustic waves, and changes in acoustic directivity. The maximum variation in the sound pressure level reaches 10 dB at local azimuths. The distortion effect of tails on pusher propeller noise is closely correlated with the number of propeller blades. The interaction process between the propeller and tails becomes more complex with the increase in blade count, resulting in the generation of shorter acoustic waves. For the six-blade rotor, the originally continuous acoustic wave branch can be split into up to four short waves. This study confirms that the proposed Hybrid Computational AeroAcoustics (HCAA) method holds significant application prospects in the aeroacoustic research of compound helicopters. Full article

Acoustic precipitation enhancement (APE) is an emerging non-chemical weather-modification technique, yet quantitative three-dimensional evidence of its impact on rainy clouds remains scarce. This study investigates a stratiform precipitation event over the Bayinbuluke Basin in the central Tianshan Mountains of northwestern China, 29–30 August 2024, using an X-band phased-array weather radar (X-PAR) coordinated with an upward-directed acoustic source. Rapid volumetric scans and sector-aligned range-height indicators were combined to reconstruct the three-dimensional cloud structure before, during, and after acoustic operation. During acoustic operation, the results were stronger and more persistent than during the non-operation period, with localized values exceeding 40 dBZ. Within the 3 km influence zone, low-level reflectivity increased across all azimuthal sectors with clear directional dependence. Dual-ratio analysis showed statistically significant enhancement in the windward sector (247°, $DR$ = 1.91, $p$ = 0.0004) and the leeward sector (137°, $DR$ = 1.51, $p$ = 0.008), indicating that acoustic-induced responses extended beyond the primary radiation sector and propagated downstream with cloud advection. These results, based on a single stratiform precipitation case, demonstrate that volumetric X-PAR observations can detect localized cloud-structure responses during acoustic operation. Full article