Source Localization in Random Acoustic Waveguides

Mode coupling in a random waveguide can be analyzed with asymptotic analysis based on separation of scales when the propagation distance is large compared to the size of the random inhomogeneities, which have small variance, and when the wavelength is comparable to the scale of the inhomogeneities. In this paper we study the asymptotic form of the joint distribution of the mode amplitudes at different frequencies. We derive a deterministic system of transport equations that describe the evolution of mode powers. This result is applied to the computations of pulse spreading in a random waveguide. It is also applied to the analysis of time reversal in a random waveguide. We show that randomness enhances spatial refocusing and that diffraction-limited focal spots can be obtained even with small-size time-reversal mirrors. The refocused field is statistically stable for broadband pulses in general. We show here that it is also stable for narrowband pulses, provided that the time-reversal mirror is large enough.

WAVE EVOLUTION, REFLECTION, AND TRANSMISSION ALONG INHOMOGENEOUS WAVEGUIDES

Due to its low implementation cost, the combination of the Received Signal Strength (RSS) with the Angle of Arrival (AOA) measurements is one of the solutions for Radio Frequency (RF) source localization, especially in a Non-Line of Sight (NLOS) environment. It is critical to determine the search space for a person who is lost in rural areas where the mobile network is unavailable due to a lack of Base Tower Stations (BTS) in order to reduce search time. In this paper, we introduce a new beacon-based approach for RF source localization, where the RF signal is received in NLOS after 1-bounce reflection, by combining the information coming from both the RSS-AOA sensors and the beacons, which are used as helpers- that move along a determined path. The proposed approach relies on determining the reflector’s pose first, after which the RF source is localized. The work has been verified in simulation and the Root Mean Square Error (RMSE) is used as a performance metric for RF source localization. Results show that our proposed approach has the lowest RMSE among localization methods mentioned in the literature under the same conditions.
 HIGHLIGHTS
 
 A new beacon-based approach for RF source localization in Non-Line Of Sight (NLOS) condition
 A reflector’s pose is determined based on the signal received from beacons
 The reflector’s pose is used to determine the location of the RF source
 One bounce reflection is considered since the chance of receiving RF signal with more reflections is very low
 
 GRAPHICAL ABSTRACT

Efficient and robust sound source recognition and localization is one of the basic techniques for humanoid robots in terms of reaction to environments. Due to the fixed sensor arrays and limited computation resources in humanoid robots, there comes challenge for sound source recognition and localization. This article proposes a sound source recognition and localization framework to realize real-time and precise sound source recognition and localization system using humanoid robots’ sensor arrays. The type of the audio is recognized according to the cross-correlation function. And steered response power-phase transform function in discrete angle space is used to search the sound source direction. The sound source recognition and localization framework presents a new multi-robots collaboration system to get the precise three-dimensional sound source position and introduces a distance weighting revision way to optimize the localization performance. Additionally, the experiment results carried out on humanoid robot NAO demonstrate that the proposed approaches can recognize and localize the sound source efficiently and robustly.

The use of Polynomial Chaos expansions to model the transfer of the statistical properties of the sound speed in ocean waveguides to those of acoustic waves passing through them is described. A perturbational framework approximating the interaction of acoustic normal modes with fluctuations around a background sound speed is used, with the fluctuations being represented vertically by empirical orthogonal functions and horizontally by correlation functions. The Polynomial Chaos expansions are derived to second order in the uncorrelated random variable representing the sound speed fluctuations for the generalized phase of the complex modal amplitudes, and to third order for the modal amplitudes themselves. The ability of the second order polynomial chaos expansion for the generalized model phase to accurately model acoustic propagation statistics is closely coupled to the accuracy of the adiabatic approximation in light scattering regimes. The weights of the Polynomial Chaos expansions are obtained using both direct (theoretical) and indirect (least-squares fit) methods. Divergence between these two sets of weights increases with combinations of increasing strength of sound speed fluctuations, increasing frequency, or increasing range, indicating where a higher order expansion may be necessary. Modelling results for the first and second moments of the acoustic intensity and the Scintillation Index in random waveguides are presented and compared to Monte Carlo results.

We consider the source and network localization problems, seeking to strengthen the relationship between the Weighted-Least-Square (WLS) and the Maximum-Likelihood (ML) solutions of these problems. To this end, we design an optimization algorithm for source and network localization under the principle that: a) the WLS and the ML objectives should be the same; and b) the solution of the ML-WLS objective does not depend on any information besides the set of given distance measurements (observations). The proposed Range-Global Distance Continuation (R-GDC) algorithm solves the localization problems via iterative minimizations of smoothed variations of the WLS objective, each obtained by convolution with a Gaussian kernel of progressively smaller variances. Since the last (not smoothed) WLS objective derives directly from the ML formulation of the localization problem, and the R-GDC requires no initial estimate to minimize it, final result is maximum-likelihood approach to source and network localization problems. The performance of the R-GDC method is compared to that of state-of-the-art techniques such as semidefinite programming (SDP), nonlinear Newton least squares (NLS), and the Stress-of-a-MAjorizing-Complex-Objective-Function (SMACOF) algorithms, as well as to the Cramér-Rao Lower Bound (CRLB). The comparison reveals that the solutions obtained with the R-GDC algorithm is insensitive to initial estimates and provides a localization error that closely approaches that of the corresponding fundamental bounds. The R-GDC is also found to achieve a complexity order comparable to that of the SMACOF, which is known for its efficiency.

Data from a moving source received by three horizontally separated bottomed sensors in a shallow-water environment are analyzed using a broadband matched-field approach to source localization. The water depth was approximately 120 m, and the low bottom loss in the area results in low spatial coherence of the broadband field. The modeled acoustic fields are constructed via a ray model with wave-like corrections. Localizations are made in the x-y plane. The time evolution of the matched-field surfaces produced from the data agree well with simulated surfaces. Matched-field surfaces are generated successively in intervals of about 1.6 s in small search areas with a fine spatial grid to test the possibility of tracking a source. Results of the source tracking will be presented. The analysis also considers the effects of the sea floor and environmental mismatch of sea floor descriptions on localization in the context of spatial coherence. In addition the effect of varying the receiver separation on the ability to make a localization when a source is in the ‘‘broadside’’ region of the array is discussed. [This work is supported by the Office of Naval Research, Code 23, through Block RL3B.]

Coded moderator approach for fast neutron source detection and localization at standoff

The reflection and transmission of guided mode in the 2-D single-mode waveguide whose boundary is random shape is numerically simulated. Since the boundary element method based on the guided-mode extracted integral equation (GMEIE) is employed, the boundary condition on the random boundary is rigorously satisfied. The dependence of the transmission coefficient on the amplitude of the random boundary is investigated. It is found that strong resonances with high Q-factor occur even for slightly corrugated boundaries and cause the extremely small transmission (high reflection). These resonances are created by the existence of the region where the second mode can propagate in the random waveguide. The frequency characteristics of these resonances are examined in detail.

In the unmanned aerial vehicle (UAV) based localization of slow-moving radio frequency (RF) sources with unknown transmitted strength of signal, such as a person with a cell phone in a search and rescue mission, the UAV navigation errors are significant sources of localization error. Although the use of a global positioning system (GPS) can reduce the UAV’s localization error significantly resulting in more accurate RF source localization, if the GPS signal is lost temporarily or permanently, the accuracy of the UAV-based localization decreases rapidly. In this paper, a simultaneous localization and mapping (SLAM)-inspired approach for simultaneous localization of UAV and RF sources (SLUS) is proposed. The proposed approach solves these two connected problems, i.e. the UAV localization and RF source localization, simultaneously to decrease the error of the UAV position estimation and the error of the RF source localization. In the proposed approach, beside the UAV position prediction, the RF source position prediction is also performed. Then the predicted states are augmented and the augmented predicted state information is corrected using range-ratio and bearing observations, i.e. RF source features, considering the unknown transmitted power. The proposed approach is simulated and the results show that the normal divergence of a target localization and the divergence of the UAV navigation in latitude and longitude channels have been eliminated using this approach. In other words, simultaneous localization of the UAV and RF sources uses the RF sources, as features in the environment, to aid the navigation system. Although the approach is similar to the mapping of RF sources in the environment, the created map would be useless after finding the RF sources. That is why SLUS has been used instead of SLAM. The main contributions of this work are: 1) performing simultaneous localization of a UAV and targets using RF signals, especially in a non-line of sight (NLOS) condition; 2) using difference of signal strength, i.e. differential received strength signal indicator (DRSSI), to eliminate the impact of unknown target signal power; and 3) simultaneous multi-target localization and tracking.

A source near the deep sound channel axis excites mode groups (or paths) that involve both deep sound channel and boundary interacting propagation. Dispersion from a broadband source as measured on a single hydrophone can be used to estimate source range. Furthermore, modal group speeds have a functional transition when passing through purely refractive to boundary reflecting phase speed regions which, under certain conditions, provides additional arrival structure to aid in source localization. This additional arrival structure is in the form of a focal region in a spectrogram. Indeed, different data sets from the Acoustic Thermometry of the Ocean Climate (ATOC) Program [ATOC Consortium, Science 281, 1327-1332 (1998)] show that localization can be accomplished using this focal region and/or the overall dispersion properties as originally suggested fifty years ago [M. Ewing and J. L. Worzel, Geo. Soc. Am., Memoir 27 (1948)].

Multiple microphone devices aimed at hands-free capabilities and speech recognition via acoustic beamforming require reliable estimates of the position of the acoustic source. Two-stage approaches based on the timedifference- of-arrival (TDOA) are computationally simple but lack robustness in practical environments. This chapter presents a family of broadband source localization algorithms based on parameterized spatiotemporal correlation, including the popular and robust steered response power (SRP) algorithm. Before forming the conventional spatial correlation matrix, the vector of microphone signals is time-aligned with respect to a hypothesized source location. It is shown that this parametrization easily generalizes classical narrowband techniques to the broadband setting. Methods based on minimum information entropy and temporally constrained minimum variance are developed. In order to ease the high computational demands imposed by a location parameterized scheme, a sparse representation of the parameterized spatial correlation matrix is proposed.

The nuclear imaging system at the National Ignition Facility (NIF) is a crucial diagnostic for determining the geometry of inertial confinement fusion implosions. The geometry is reconstructed from a neutron aperture image via a set of reconstruction algorithms using an iterative Bayesian inference approach. An important step in these reconstruction algorithms is finding the fusion source location within the camera field-of-view. Currently, source localization is achieved via an iterative optimization algorithm. In this paper, we introduce a machine learning approach for source localization. Specifically, we train a convolutional neural network to predict source locations given a neutron aperture image. We show that this approach decreases computation time by several orders of magnitude compared to the current optimization-based source localization while achieving similar accuracy on both synthetic data and a collection of recent NIF deuterium-tritium shots.

Aircraft structures are exposed to impact damage caused by debris and hail during their service life. One of the design concerns in composite structures is the resistance of layered surfaces to damage, which occurs from impacts with various foreign objects. Therefore, the impact localization and damage quantification of impacts should be studied and considered to address flight safety and to reduce costs associated with a regularly scheduled visual inspection. Since the structural components of the aircraft are large scale, visual inspection and monitoring are challenging and subject to human error. This paper presents a promising solution that can automatically detect and localize an impact that may occur during flight. To achieve this goal, acoustic emission (AE) is employed as an impact monitoring approach. Random forest and deep learning were adopted for training the source location models. An AE dataset was collected by conducting an impact experiment on a full-size thermoplastic aircraft elevator in a laboratory environment. A dataset consisting of AE parametric features and a dataset consisting of AE waveforms were assigned to a random forest classifier and deep learning network for the investigation of their applicability of impact source localization. The results obtained were compared using the source localization approach in previous research using a conventional artificial neural network. The analysis of results shows the random forest and deep learning leads to better event localization performance. In addition, the random forest model can provide the importance of features. By deleting the least important features, the storage required to save the input and the computing time for the random forest is greatly reduced, and an acceptable localization performance can still be obtained.

Corticomuscular coherence (CMC) quantifies the frequency-specific coupling between cortical and muscular activity and is increasingly used to probe motor-control mechanisms. However, the factors that consistently influence CMC in axial and lower-limb muscles remain unclear. The objective of this study is to map and critically describe experimental factors and methodological choices that have been studied and their potential influence on CMC of axial and lower limb muscles measured in healthy humans. A scoping review was conducted following PRISMA-ScR guidelines. CINAHL, MEDLINE (Ovid), Embase, and Web of Science were searched from the date of inception to March 5th, 2024. Eligible studies that (i) computed CMC, (ii) recorded cortical activity with EEG or MEG, (iii) analyzed trunk or lower limb EMG, and (iv) compared CMC across experimental conditions or participant groups were included. Two reviewers independently screened records, extracted demographic, neurophysiological, task, and signal-processing variables, and grouped studies by the factor examined; third reviewer resolved discrepancies. Results were synthesized descriptively. Four factors showed the most consistent influence on CMC: (1) Age: younger adults exhibit higher CMC than older adults (2) Muscle-specificity: the tibialis anterior (TA) displays stronger CMC than other axial or lower-limb muscles; (3) Contraction type: isotonic and eccentric/quasi-isotonic contractions elicit greater CMC than isometric contractions; (4) Athletic status: non-athletes demonstrate higher CMC than trained individuals. The effects of fatigue, contraction intensity, posture, or walking tasks were inconsistent. Methodologically, most studies employed EEG (single Cz channel) and rectified EMG; MEG, source localization, and longitudinal approaches were seldom used. Current evidence indicates that participant characteristics (age, athletic status) and task parameters (muscle tested, contraction type) can impact CMC, but heterogeneity in study design and analysis hampers direct comparison and causal inference. Future research should adopt longitudinal designs, standardized protocols, and advanced source localization techniques to clarify the mechanisms governing CMC in axial and lower-limb musculature.