Localization of sound sources : a systematic review

Sound localization is a vast field of research and advancement which is used in many useful applications to facilitate communication, radars, medical aid, and speech enhancement to but name a few. Many different methods are presented in recent times in this field to gain benefits. Various types of microphone arrays serve the purpose of sensing the incoming sound. This paper presents an overview of the importance of using sound localization in different applications along with the use and limitations of ad-hoc microphones over other microphones. In order to overcome these limitations certain approaches are also presented. Detailed explanation of some of the existing methods that are used for sound localization using microphone arrays in the recent literature is given. Existing methods are studied in a comparative fashion along with the factors that influence the choice of one method over the others. This review is done in order to form a basis for choosing the best fit method for our use

Maximizing the Number of Spatial Nulls with Minimum Sensors

In this paper, we attempt to unify two array processing frameworks viz, Acoustic Vector Sensor (AVS) and two level nested array to enhance the Degrees of Freedom (DoF) significantly beyond the limit that is attained by a Uniform Linear Hydrophone Array (ULA) with specified number of sensors. The major focus is to design a line array architecture which provides high resolution unambiguous bearing estimation with increased number of spatial nulls to mitigate the multiple interferences in a deep ocean scenario. AVS can provide more information about the propagating acoustic field intensity vector by simultaneously measuring the acoustic pressure along with tri-axial particle velocity components. In this work, we have developed Nested AVS array (NAVS) ocean data model to demonstrate the performance enhancement. Conventional and MVDR spatial filters are used as the response function to evaluate the performance of the proposed architecture. Simulation results show significant improvement in performance viz, increase of DoF, and localization of more number of acoustic sources and high resolution bearing estimation with reduced side lobe level

Sensor Array Optimization for Multiple Harmonic Sound Source Separation and DOA

INTRODUCTION In the last years a lot of researches about source separation have been realized, like extraction of a signal of interest (vocal recognition application), identification of which source gives which sound (motor engine applications) or noise source characterization (environmental application). Most of these techniques for sound source estimation use the signal-subspace approach, where the number of emitting sources is determined by the multiplicity of the lowest eigenvalue of the correlation matrix. The problems arise when the number of microphones is equal to the number of sources radiating, hence the noise subspace could not exist. This Master Thesis investigates how to realize a Goniometer Antenna to record communications, as well as the implementation of an algorithm to optimize the location of the sensors with the intend of separating the different sound sources in the at-worst case(number of sources equal number of sensors). It has been achieve using the eigenvalues of the correlation matrix of the received signals and the delay between microphones. Finally, measurements in the anechoic chamber verified the proposed approach. METHODS An acoustic goniometer is a system that measures the angle between a source and a receptor using the phase delay, thereby obtaining the source direction. The design dwell on two sensors (microphones) collocated in the 2D space in a concrete geometry. The implementation of each algorithm was done in Matlab based on two parts: the time delay estimation used in source localization by computing the azimuth in [2], and also an adaptation of the MPE block carried out in [4]. Likewise different methods based on the properties of the correlation matrix have been studied for delay estimating like in [3]. Apart from that, in [1] is explored the relation between sensor array geometry and eigenvalues to obtain the optimal sound sources separation and detection. This theory has been put into practice in programming in Matlab: minimization of the distance between microphones such that accomplish the condition of sources separation or sources detection. The optimization procedure has been done using two different SQP Methods: Active Set and Interior Point. Moreover, an optimization approach is presented for a system composed by two sensors and three sound sources. Several options based on mathematical theory has been considered for solving the problem. Eventually, taking advantage of the procedure followed in [1] and combined with the circumcenter calculation, the optimal distance for the microphones can be found. RESULTS Afterwards all this work, different simulations with the code in Matlab were tested reaching successful results. Then, a process of validation is required in the anechoic chamber for more realistic measurements. CONCLUSIONS In conclusion is demonstrated by theoretical calculation at first and then by experimental measurements that the optimal array geometry could help to improve the sound source separation approach. Forthcoming works will consist in extending this work for larger bandwidth and much more sound sources. Also, taking into consideration a more realistic model with reflections, interfering signals or noise corrupted

Signal direction-of-arrival and amplitude estimation for multiple-row bathymetric sidescan sonars

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 1998In practical applications with bathymetric sidescan sonars, the multipath reflections and other directional interferences are the key limiting factors for a better performance. This thesis proposes a new scheme to deal with the interferences using a multiple-row bathymetric sidescan sonar. Instead of smoothing the measurements over some time or angle intervals, which was previously widely investigated, we resolve the multipath interferences from the direct signal. Two approaches on signal direction-of-arrival DOA and amplitude estimation are developed, the correlated signal direction estimate CSDE for three-row systems and the ESPRIT-based method. These approaches are compared using different sonar data models, including a stochastic model from the statistical analysis on bottom scattering and a coherent model from the analysis on interference field; the simulations show the ESPRIT-based approach is quite robust at the angular separation of 100 between two sources and at the signal-to-noise ratio above 10dB except for highly coherent or temporally correlated signals, for which CSDE works very well. The computer simulation results and the discussions on practical algorithm implementation indicate the proposed scheme can be applied to a real multiple-row bathymetric sidescan sonar. With the capability to simultaneously resolve two or more directional signals, the new sonar model should work better for a wider variety of practical situations in shallow water with out significant increase of the system cost.Funding supporting my thesis research project was provided by the Office of Naval Research ONR

Two-dimensional direction-of-arrival estimation with time-modulated arrays

Two-dimensional direction-of-arrival estimation with time-modulated array

Generalized DOA and Source Number Estimation Techniques for Acoustics and Radar

The purpose of this thesis is to emphasize the lacking areas in the field of direction of arrival estimation and to propose building blocks for continued solution development in the area. A review of current methods are discussed and their pitfalls are emphasized. DOA estimators are compared to each other for usage on a conformal microphone array which receives impulsive, wideband signals. Further, many DOA estimators rely on the number of source signals prior to DOA estimation. Though techniques exist to achieve this, they lack robustness to estimate for certain signal types, particularly in the case where multiple radar targets exist in the same range bin. A deep neural network approach is proposed and evaluated for this particular case. The studies detailed in this thesis are specific to acoustic and radar applications for DOA estimation

Vector sensors for underwater : acoustic communications

Acoustic vector sensors measure acoustic pressure and directional components separately. A claimed advantage of vector sensors over pressure-only arrays is the directional information in a collocated device, making it an attractive option for size-restricted applications. The employment of vector sensors as a receiver for underwater communications is relatively new, where the inherent directionality, usually related to particle velocity, is used for signal-to-noise gain and intersymbol interference mitigation. The fundamental question is how to use vector sensor directional components to bene t communications, which this work seeks to answer and to which it contributes by performing: analysis of acoustic pressure and particle velocity components; comparison of vector sensor receiver structures exploring beamforming and diversity; quanti cation of adapted receiver structures in distinct acoustic scenarios and using di erent types of vector sensors. Analytic expressions are shown for pressure and particle velocity channels, revealing extreme cases of correlation between vector sensors' components. Based on the correlation hypothesis, receiver structures are tested with simulated and experimental data. In a rst approach, called vector sensor passive time-reversal, we take advantage of the channel diversity provided by the inherent directivity of vector sensors' components. In a second approach named vector sensor beam steering, pressure and particle velocity components are combined, resulting in a steered beam for a speci c direction. At last, a joint beam steering and passive time-reversal is proposed, adapted for vector sensors. Tested with two distinct experimental datasets, where vector sensors are either positioned on the bottom or tied to a vessel, a broad performance comparison shows the potential of each receiver structure. Analysis of results suggests that the beam steering structure is preferable for shorter source-receiver ranges, whereas the passive time-reversal is preferable for longer ranges. Results show that the joint beam steering and passive time-reversal is the best option to reduce communication error with robustness along the range.Sensores vetoriais acústicos (em inglês, acoustic vector sensors) são dispositivos que medem, alem da pressão acústica, a velocidade de partícula. Esta ultima, é uma medida que se refere a um eixo, portando, esta associada a uma direção. Ao combinar pressão acústica com componentes de velocidade de partícula pode-se estimar a direção de uma fonte sonora utilizando apenas um sensor vetorial. Na realidade, \um" sensor vetorial é composto de um sensor de pressão (hidrofone) e um ou mais sensores que medem componentes da velocidade de partícula. Como podemos notar, o aspecto inovador está na medição da velocidade de partícula, dado que os hidrofones já são conhecidos.(...)This PhD thesis was supported by the Brazilian Navy Postgraduate Study Abroad Program Port. 227/MB-14/08/2019

Intelligent joint channel parameter estimation techniques for mobile wireless positioning applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile wireless positioning has recently received great attention. For mobile wireless communication networks, an inherently suitable approach is to obtain the parameters that are used for positioning estimates from the radio signal measurements between a mobile device and one or more xed base stations. However, obtaining accurate estimates of these location-dependent channel parameters is a challenging task. The focus of this thesis is on the estimation of these channel parameters for mobile wireless positioning applications. In particular, we investigate novel estimators that jointly estimate more than one type of channel parameters. We rst perform a comprehensive critical review on the most recent and popular joint channel parameter estimation techniques. Secondly, we improve a state-of-the-art technique, namely the Space Alternating Generalised Expectation maximisation (SAGE) algorithm by employing adaptive interference cancellation to improve the estimation accuracy of weaker paths. Thirdly, a novel intelligent channel parameter estimation technique using Evolution Strategy (ES) is proposed to overcome the drawbacks of the existing iterative maximum likelihood methods. Furthermore, given that in reality it is di cult to obtain the number of multipath in advance, we propose a two tier Hierarchically Organised ES to jointly estimate the number of multipath as well as the channel parameters. Finally, we extend the proposed ES method to further estimate the Doppler shift in mobile environments. Our proposed intelligent joint channel estimation techniques are shown to exhibit excellent performance even with low Signal to Noise Ratio (SNR) channel conditions as well as robust against uncertainties in initialisations.EPSRC and Cambridge Silicon Radi