A Novel Approach for Three-Dimensional Acoustic Source Localization in Orthotropic Medium

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.

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

This paper presentsanew approach for the three-dimensional (3-D) localization of sound sources. An acoustic camera uses an angular beamforming to measure the Direction of Arrival (Do A) of an incoming signal, to localize the emission source. The acoustic sensor used in this article is the Brüel & Kjaer acoustic camera transformed to operate inabistatic mode. The transformation consists inaplacing of one of the microphones of the acoustic camera outside of its microphone array. This allows simultaneous estimation of the Do Aand the Time Difference of Arrival (TDo A) of the incoming signal(s). Such sensors were not found. The paper proposes emitter localization in range - cross range - elevation coordinates by combining estimates of TDo Aand Do Aand presents the signal processing method for that purpose. The range resolution of 0.2mwas achieved in an experiment. Experimental results were obtained using different emission sources. Adescription of resolution cell limitations is presented. The obtained results show acoustic noise source localization without the pre-metering of the range of the imaging plane, i.e., withoutaneed to use the additional range meter which is notapart of the acoustic camera. The latter is important in tasks of non-destructive testing.

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.

We present a novel Fast Direct Source Localization (FDSL) approach for acoustic isotropic sensor array applications. Unlike previous approximate maximum likelihood (AML) approach, the proposed FDSL focuses on the phase shift caused by time delays among sensors and obtains an analytical result without exhaustive search for all the possible locations. Using this phase shift model, conventional phase uncertainty problem can be solved by a light-weight linear search in the limited phase space, which greatly reduces the computational complexity of array processing. Theoretical analysis of the FDSL has been proposed and compared with the Cramer Rao Bound (CRB) of the AML approach. Simulations using real bird call data validate the advantage of proposed method.

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.

The human ability for spatial hearing encourages the development of spatial audio technology to support many human activities. Spatial audio mimics the real-life sound and provides a more immersive hearing experience as if the listener were present in the environment where the sound source was recorded. Recently, research on how spatial hearing processes are encoded in the brain has begun to be developed. As a modality for brain activity measurement that is non-invasive and has a high temporal resolution, Electroencephalography (EEG) is suitable for studying brain responses to moving sound stimuli. This study compared brain activity in hearing stereo and spatial audio stimulus. Through measurements using 21 EEG electrodes on the scalp, the results showed an increase in the mean PSD for all Theta, Alpha, and Beta waves in the spatial audio stimulus compared to stereo audio. The most significant increase in the mean PSD occurred in the Beta wave of 93.8008 µV2/Hz. Furthermore, the source localization approach with Independent Component Analysis (ICA) and Low-resolution Brain Electromagnetic Tomography (LORETA) method was used to estimate and quantify the active brain area of this process. The results showed an activation of the non-auditory cortex when the subject was hearing spatial audio stimulus. By using Paired T-Test of current density for both hearing processes, the results showed there was no significant difference (p > 0.05) in Brodmann area (BA) 41 (Primary Auditory Cortex) and BA 42 (Secondary Auditory Cortex). Meanwhile, for the spatial audio hearing process, there was a significant difference (p < 0.05) in BA 6 (Premotor Cortex), which is related to spatial orientation, and BA 9 (Dorsolateral Prefrontal Cortex), which is associated with executive functions, including working memory and selective attention. This study offers potential insights into spatial hearing research and immersive audio production.

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.

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

Mode coupling due to scattering by weak random inhomogeneities in waveguides leads to loss of coherence of wave fields at long distances of propagation. This in turn leads to serious deterioration of coherent source localization methods, such as matched field. We study with analysis and numerical simulations how such deterioration occurs and introduce a novel incoherent approach for long range source localization in random waveguides. It is based on a special form of transport theory for the incoherent fluctuations of the wave field. We study theoretically the statistical stability of the method and illustrate its performance with numerical simulations. We also show how it can be used to estimate the correlation function of the random fluctuations of the wave speed.

Optimization of number of microphones and microphone spacing using time delay based multilateration approach for explosive sound source localization

Localization of short-range acoustic and seismic wideband sources: Algorithms and experiments

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.

This paper proposes a new approach for odor source localization using a low computational controller in the micro quadcopter. Searching for an odor source is an engineering problem, it can be a simple task if a high-computational controller is implemented. However, in reality, a micro quadcopter has a major constraint: the payload limitation where the high-computational controller can be out of the option. In this case, a low computational controller is employed to complement the searching requirement. In this research, the searching algorithm based on bio-inspired behavior of the silkmoth and noise reduction based on the Savitzky-Golay filter is employed. The odor source localization in the micro quadcopter shows a satisfactory result where the test is validated through experimental validation.