Localization Of Multiple Sources Research Articles

Localization and Characterization of Multiple Harmonic Sources

We introduce a new and intuitive algorithm to characterize and localize multiple harmonic sources intersecting in the spatial and frequency domains. It jointly estimates their fundamental frequencies, their respective amplitudes, and their directions of arrival based on an intelligent non-parametric signal representation. To obtain these parameters, we first apply variable-scale sampling on unbiased cross-correlation functions between pairs of microphone signals to generate a joint parameter space. Then, we employ a multidimensional maxima detector to represent the parameters in a sparse joint parameter space. In comparison to others, our algorithm solves the issue of pitch-period doubling when using cross-correlation functions, it estimates multiple harmonic sources with a signal power smaller than the signal power of the dominant harmonic source, and it associates the estimated parameters to their corresponding sources in a multidimensional sparse joint parameter space, which can be directly fed into a tracker. We tested our algorithm and three others on synthetic data and speech data recorded in a real reverberant environment and evaluated their performance by employing the joint recall measure, the root-mean-square error, and the cumulative distribution function of fundamental frequencies and directions of arrival. The evaluations show promising results: Our algorithm outperforms the others in terms of the joint recall measure, and it can achieve root-mean-square errors of 1 Hz or 1 $^\circ$ and smaller, which facilitates, e.g., distant-speech enhancement or source separation.

Accurate Localization of Multiple Sources Using Semidefinite Programming Based on Incomplete Range Matrix

We address the problem of locating multiple sources from the Euclidean distance matrix (EDM), which can be obtained from the received signal strength or time of arrival measurements. In EDM-based localization, EDM is usually corrupted by some inevitable factors, such as non-line-of-sight propagation, hardware failures, and strong interference. Note that EDM is a low-rank matrix but not a positive semidefinitematrix, classical semidefinite programming (SDP)-based algorithms cannot be implemented directly to handle the case. We derive an SDP-based low-rank solution to reconstruct EDM based on the semidefinite embedding lemma. Based on the recovered EDM, unlike some existing conventional non-convex estimators, a semidefinite relaxation method is developed to fix the locations of sources. In particular, we relax the non-convex localization problem into convex one by using square range information. Numerical simulation results demonstrate that the proposed algorithm performs higher accuracy while increasing slightly computational complexity as compared with the other existing approaches.

3D Localization and Frequency Band Estimation of Multiple Unknown RF Sources Using Particle Filters and a Wireless Sensor Network

This paper presents a methodology for the 3D localization and the frequency band estimation of multiple unknown RF sources, using received signal strength (RSS) measurements from a wireless network of static sensors and a moving sensor. Using a particle filter based Bayesian approach, the static sensor network is used to continuously monitor an urban area and provide an estimate of the probability distribution for the states of the unknown sources. Based on this probability distribution, a moving sensor is guided towards locations where it is more likely to find an unknown source. The RSS measurements in the vicinity of the unknown source are used to provide the final estimation and confirmation of the source state. The method is analytically developed and evaluated in terms of accuracy in estimating the number, the 3D location and the frequency band of the unknown sources under different Monte Carlo simulation scenarios.

Real-time multiple sound source localization and counting using a soundfield microphone

In this work, a multiple sound source localization and counting method based on a relaxed sparsity of speech signal is presented. A soundfield microphone is adopted to overcome the redundancy and complexity of microphone array in this paper. After establishing an effective measure, the relaxed sparsity of speech signals is investigated. According to this relaxed sparsity, we can obtain an extensive assumption that “single-source” zones always exist among the soundfield microphone signals, which is validated by statistical analysis. Based on “single-source” zone detecting, the proposed method jointly estimates the number of active sources and their corresponding DOAs by applying a peak searching approach to the normalized histogram of estimated DOA. The cross distortions caused by multiple simultaneously occurring sources are solved by estimating DOA in these “single-source” zones. The evaluations reveal that the proposed method achieves a higher accuracy of DOA estimation and source counting compared with the existing techniques. Furthermore, the proposed method has higher efficiency and lower complexity, which makes it suitable for real-time applications.

Convex Optimization Algorithms for Multiple Source Localization Based on Received Signal Strength Measurements

Decaying with the increasing of signal propagation distance, Received Signal Strength (RSS) is used in the wireless localization due to its low cost and easily implementation. When the transmit power is unavailable, two convex optimization algorithms including semi definite programming (SDP), second order cone and semi definite programming (SOC/SDP) are designed to estimate the source locations by relaxing the non-convex problem as convex optimization. The corresponding CramerRao lower bound (CRLB) of the problem is derived. The simulations demonstrate that the SOC/SDP algorithm provides the similar accuracy performance compared with the SDP algorithm. However the computational complexity of SOC/SDP is lower than that of the SDP due to the less variables and equality constraints. When perfect knowledge of the path loss exponent is available, the simulations also show that the accuracy performance of the proposed convex optimization algorithms degrades as the path loss exponent increases.

Acoustic Sources Localization in 3D Using Multiple Spherical Arrays

Direction of arrival (DOA) estimation of multiple sources using sensor arrays has been widely studied in the last few decades, particularly, the spherical harmonic analysis utilizing a spherical array. Both the number of sensors on the aperture and size of the sphere can affect the estimation accuracy dramatically. However, those two factors are conflicted to each other in a single spherical array. In this paper, a multiple spherical arrays structure is proposed to provide an alternative design to the traditional single spherical array for the spherical harmonic decomposition, to obtain better localization performance. The new structure consists of several identical spheres in a given area, and the microphones are placed identically on each sphere. The spherical harmonic analysis algorithm using the new multiple array structure for the problem of multiple acoustic sources localization is presented. Simulation results show that the multiple spherical arrays can provide a more accurate direction of arrival (DOA) estimation for the multiple sources than that of a single spherical array, distinguish several adjacent sources more efficiently, and reduce the number of microphones on each sphere without decreasing its’ estimation accuracy.

Sparse Approximation-Based Maximum Likelihood Approach for Estimation of Radiological Source Terms

A computationally efficient and accurate method is presented for identifying the number, intensity and location of stationary multiple radiological sources. The proposed method uniformly grids the region of interest resulting in a finite set of solutions for the source locations. The resulting problem is a sparse convex optimization problem based on ${{\cal L}_1}$ -norm minimization. The solution of this convex optimization encapsulates all information needed for the estimation of source terms; the values of the nonzero elements of the solution vector approximates the source intensity, the grid points corresponding to the nonzero elements approximates the source locations, and the number of nonzero elements is the number of sources. The accuracy limited by the resolution of the grid is further improved by making use of the maximum likelihood estimation approach. The performance of sparse approximation based maximum likelihood estimation is verified using real experimental data acquired from radiological field trials in the presence of up to three point sources of gamma radiation. The numerical results show that the proposed approach efficiently and accurately identifies the source terms simultaneously, and it outperforms existing methods which have been used for stationary multiple radiological source terms estimation.

Application of time reversal mirror and minimum variance distortionless response technique for source localization

Time difference of arrival (TDOA) and time reversal mirror (TRM) are commonly used in acoustic source localization. TDOA has excellent results in free field, but fair results in multiple reflections environment. TRM is applicable in multiple reflections environment and for multiple sources localization. The study continues the advantage of TRM and combines MVDR technique of noise reduction in order to find source locations. The results indicate that the combined MVDR technique gives better noise reduction than TRM in multiple reflections environment. From the drawing of energy diagram of acoustic waveguide, the combined MVDR technique will focus energy on signal source and it also reduces the noise energy in the other region. Comparing between TRM and the combined MVDR for source localization, the combined MVDR gives better resolution than TRM does. In addition to give better resolution, the combined MVDR will find the source location by finding the maximum energy location, on the other hand, TRM is unabl...

Bayesian localization, tracking, and environmental inference

This paper presents an overview of a general Bayesian inference approach to source localization, tracking and/or environmental estimation. Source location and spectral parameters together with environmental parameters are all considered unknown random variables to be estimated from prior information and observed acoustic data. The relative level of prior information for various parameters differentiates applications of interest. For instance, controlled-source geoacoustic inversion typically involves large prior uncertainties for seabed parameters but small uncertainties for source locations, although some applications, such as inverting noise from ships-of-opportunity, may involve larger location uncertainties. Alternatively, source localization in an uncertain environment typically involves non-informative location priors and environmental priors that reflect available knowledge. Tracking applications include additional prior constraints on source speed. In all cases, the goal is to compute marginals of the posterior probability density for source and environmental parameters, quantifying the information content of the data and prior. This is typically carried out with Markov-chain Monte Carlo methods including Metropolis-Hastings sampling and/or Gibbs sampling, with various approaches applied to improve efficiency (e.g., principal-component sampling, parallel tempering) and generality (trans-dimensional inversion). Multiple-source localization minimizes the Bayesian information criterion to estimate the number of sources.

Detection, localization and classification of multiple dipole-like magnetic sources using magnetic gradient tensor data

We have developed a method for automatic detection, localization and classification (DLC) of multiple dipole sources using magnetic gradient tensor data. First, we define modified tilt angles to estimate the approximate horizontal locations of the multiple dipole-like magnetic sources simultaneously and detect the number of magnetic sources using a fixed threshold. Secondly, based on the isotropy of the normalized source strength (NSS) response of a dipole, we obtain accurate horizontal locations of the dipoles. Then the vertical locations are calculated using magnitude magnetic transforms of magnetic gradient tensor data. Finally, we invert for the magnetic moments of the sources using the measured magnetic gradient tensor data and forward model. Synthetic and field data sets demonstrate effectiveness and practicality of the proposed method.

Auditory masking of speech in reverberant multi-talker environments.

Auditory localization research needs to be performed in more realistic testing environments to better capture the real-world abilities of listeners and their hearing devices. However, there are significant challenges involved in controlling the audibility of relevant target signals in realistic environments. To understand the important aspects influencing target detection in more complex environments, a reverberant room with a multi-talker background was simulated and presented to the listener in a loudspeaker-based virtual sound environment. Masked thresholds of a short speech stimulus were measured adaptively for multiple target source locations in this scenario. It was found that both distance and azimuth of the target source have a strong influence on the masked threshold. Subsequently, a functional model was applied to analyze the factors influencing target detectability. The model is comprised of an auditory front-end that generates an internal representation of the stimuli in both ears, followed by a decision device combining d' information across time, frequency and both ears. The model predictions of the masked thresholds were overall in very good agreement with the experimental results. An analysis of the model processes showed that head shadow effects, signal spectrum, and reverberation have a strong impact on target audibility in the given scenario.

Method for estimating closed-form solutions of the light diffusion equation for turbid media of any boundary shape.

This paper reports a method of estimating an approximate closed-form solution to the light diffusion equation for any type of geometry involving Dirichlet's boundary condition with known source location. It is based on estimating the optimum locations of multiple imaginary point sources to cancel the fluence at the extrapolated boundary by constrained optimization using a genetic algorithm. The mathematical derivation of the problem to approach the optimum solution for the direct-current type of diffuse optical systems is described in detail. Our method is first applied to slab geometry and compared with a truncated series solution. After that, it is applied to hemispherical geometry and compared with Monte Carlo simulation results. The method provides a fast and sufficiently accurate fluence distribution for optical reconstruction.

Moving source parameter estimation in an uncertain environment

Environmental uncertainty is one of the limiting factors in the matched-field localization. Within a Bayesian framework, environmental focalization has been widely used to solve the augmented localization problem, in which the environmental parameters, source ranges and depths are considered to be the unknown variables. However, the position of the moving source varies with time, which limits the observation interval and the number of acoustic signals. Therefore, it has to estimate lots of unknown parameters with the limited observation information. When the source moves fast or the environment has great uncertainty, the environmental focalization gets worse. Taking the parameter estimation of Kalman filter in the non-stationary process as a reference, the acoustic signals from a series of observations are treated in a simultaneous inversion. This provides the most informative solution, since data from multiple source locations are brought to bear simultaneously on the environmental unknowns, which in turn constrain the source locations better. In this article, the time-unvarying parameters are introduced to describe the source position. The source positions are inverted indirectly by the time-unvarying parameters, which reduces the estimated parameter dimension effectively. At the same time, the current estimated results are treated as the priori information of the next inversion, which establishes the new prior distribution and cost function. It could compensate for some individual abnormal data effectively and realize continuous localization of the moving source. The noise signals radiated from a surface ship target are processed and analyzed. The Bayesian tracking algorithm greatly increases the observation interval and the number of acoustic signals, and enhances the localization accuracy in an uncertain water environment. Tracking results of the ship noise indicate that simultaneous inversion of multiple acoustic observations with constant velocity track model and the Thikhonov regularization provides a better solution than sequential independent inversions. It is indicated that the Bayesian tracking method learns the uncertain environment as more observations become available. It is discovered that the maximum a posteriori solution and the two-dimensional solution have similar results according to the global positioning system value. The reason is that the source locations are treated implicitly by the source speed, which is similar to the marginal probability distribution by reducing the multidimensional posterior probability density to the representative two-dimensional probability distributions.

PSO Algorithm for Single and Multiple Odor Sources Localization Problems: Progress and Challenge

Abstract Odor sensing technology in robotic research introduce two research field namely odor recognition and odor source localization. Odor source localization research also includes the odor recognition ability with localization method. This paper shows some experiment had been done to localize odor source using single agent and multiple agents. Experiment shows that single agent can’t be used in dynamic environment, hence also can’t be used in real life application. This paper promotes an algorithm known as Particle Swarm Optimization (PSO) to solve these problems. The experiment conducted using PSO shows that PSO able to localize the odor source in the same condition where single agent failed. However, PSO still need to be modified before it can be use widely. This paper shows modification that has been proposed by the authors to enhance it’s ability. The research also has been push to solve multiple odor sources using parallel localization. To verify proposed method, software simulator was used. Results from these experiment show that Modified PSO is able to localize all four odor sources in dynamic environment in 651.900 seconds within 7 x 7 meters search area. The modification being applied in this research not limited by searching technic but also creating two types of robot.

A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks

In this paper, we address the challenging problem of multiple source localization in wireless sensor networks (WSN). We develop an efficient statistical algorithm, based on the novel application of sequential Monte Carlo (SMC) sampler methodology, that is able to deal with an unknown number of sources given quantized data obtained at the fusion center from different sensors with imperfect wireless channels. We also derive the posterior Cramér-Rao bound (PCRB) of the source location estimate. The PCRB is used to analyze the accuracy of the proposed SMC sampler algorithm and the impact that quantization has on the accuracy of location estimates of the sources. Extensive experiments show the benefits of the proposed scheme in terms of the accuracy of the estimation method that is required for model selection (i.e., the number of sources) and the estimation of the source characteristics compared to the classical importance sampling method.

A Self-Adaptive Particle Swarm Optimization Based Multiple Source Localization Algorithm in Binary Sensor Networks

With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective function which is used to estimate the location of sources. Therefore, the multiple-source localization problem is transformed into optimization problem. In order to improve the ability of global search of particle swarm optimization, the self-adaptive particle swarm optimization is used to solve this problem. Various simulations have been conducted, and the results show that the proposed method owns higher localization accuracy in comparison with other methods.

Usefulness of multiple frequency band source localizations in ictal MEG

ObjectiveWe evaluated the diagnostic value of multiple frequency band MEG source localization within a wide time window during the preictal period. MethodsData for 13 epilepsy patients who showed an ictal event during MEG were analyzed. Several seconds of preictal data were localized in the theta, alpha, beta, and gamma bands by using wavelet transformation and the sLORETA algorithm. The same analysis was performed with narrow time and frequency band. Localization concordances to the surgically resected area were compared. ResultsSource localization in the gamma band for a 10s window before ictal onset showed best concordance to the resection cavity. Eight of 13 patients showed sub-lobar concordance in the 10s gamma band localization, whereas 3 showed concordance in the narrow time and frequency analysis. Four of 7 patients with focal cortical dysplasia (FCD) achieved seizure-free outcome, and all 4 showed sub-lobar concordance. ConclusionsA 10s time window gamma source localization method can be used to delineate the epileptogenic zone. SignificanceThe use of a long period during preictal gamma source localization has the potential to become a localizing biomarker of the epileptogenic zone in candidates for surgical intervention, especially in MRI-suspected FCD.

Multiview Soundfield Imaging in the Projective Ray Space

A soundfield image a data structure that efficiently encodes and represents the wave field captured by a microphone array. Its representation based on the directional plenacoustic function, which defined the radiance of the acoustic paths (rays) that cross the segment that the array lies upon. The soundfield image can be processed as is to develop a variety of applications. In its original formulation, the soundfield image based on a Euclidean parameterization that can accommodate a limited range of rays and suitable for managing a single array only. In this paper, we generalize this methodology to the case of multiple microphone arrays deployed in space. The use of multiple arrays allows us to capture truly global information on the sound field but requires us to rethink the ray space, and adopt a global representation of the acoustic rays based on projective geometry. After introducing the new parameterization, we present two examples of applications: the estimation of the mutual poses of two or more arrays (self-calibration); and the localization of multiple acoustic sources. The effectiveness of these applications proven through simulations well real data experiments.

3D imaging in volumetric scattering media using phase-space measurements.

We demonstrate the use of phase-space imaging for 3D localization of multiple point sources inside scattering material. The effect of scattering is to spread angular (spatial frequency) information, which can be measured by phase space imaging. We derive a multi-slice forward model for homogenous volumetric scattering, then develop a reconstruction algorithm that exploits sparsity in order to further constrain the problem. By using 4D measurements for 3D reconstruction, the dimensionality mismatch provides significant robustness to multiple scattering, with either static or dynamic diffusers. Experimentally, our high-resolution 4D phase-space data is collected by a spectrogram setup, with results successfully recovering the 3D positions of multiple LEDs embedded in turbid scattering media.

Multiple sound source localization when sounds are stationary or rotating

In the field of spatial hearing, it is known that the sound source localization error increases when distractors are present. This study first determined the maximum number of sound sources that can be perceived. In one experiment, listeners determined how many sources (loudspeakers) presented sounds and these loudspeaker locations. In another experiment, listeners were asked to locate a new sound source which was added to an existing set of fixed sources. Listeners determined up to about four sources producing simultaneous unrelated speech sounds in both experiments. A second part of the research examined the effect of rotating the sound sources on the listener’s perception. If multiple unrelated sounds such as speech rotated, listeners could much more easily determine sound rotation than if the sounds were related to each other. If the sounds were related (e.g., all sounds were harmonics of a fundamental) but were still presented from a number of different loudspeakers, listeners could hardly process the individual sounds even when sounds rotated among the loudspeakers. Thus, the ability to locate multiple sound sources depends on the total number and motion of sources and the type of sound. [Research supported by an AFOSR grant.]