Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> An effective technique in locating a source based on intersections of hyperbolic curves defined by the time differences of arrival of a signal received at a number of sensors is proposed. The approach is noniterative and gives an explicit solution. It is an approximate realization of the maximum-likelihood estimator and is shown to attain the Cramer-Rao lower bound near the small error region. Comparisons of performance with existing techniques of beamformer, spherical-interpolation, divide and conquer, and iterative Taylor-series methods are made. The proposed technique performs significantly better than spherical-interpolation, and has a higher noise threshold than divide and conquer before performance breaks away from the Cramer-Rao lower bound. It provides an explicit solution form that is not available in the beamforming and Taylor-series methods. Computational complexity is comparable to spherical-interpolation but substantially less than the Taylor-series method. > <s> BIB001 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> Networks of small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications involving monitoring and the physical world in a tetherless fashion. Typically, each individual node can sense in multiple modalities but has limited communication and computation capabilities. Many challenges must be overcome before the concept of sensor networks In particular, there are two critical problems underlying successful operation of sensor networks: (1) efficient methods for exchanging information between the nodes and (2) collaborative signal processing (CSP) between the nodes to gather useful information about the physical world. This article describes the key ideas behind the CSP algorithms for distributed sensor networks being developed at the University of Wisconsin (UW). We also describe the basic ideas on how the CSP algorithms interface with the networking/routing algorithms being developed at Wisconsin (UW-API). We motivate the framework via the problem of detecting and tracking a single maneuvering target. This example illustrates the essential ideas behind the integration between UW-API and UW-CSP algorithms and also highlights the key aspects of detection and localization algorithms. We then build on these ideas to present our approach to tracking multiple targets that necessarily requires classification techniques becomes a reality. <s> BIB002 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator. <s> BIB003 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> A novel sensor network source localization method based on acoustic energy measurements is presented. This method makes use of the characteristics that the acoustic energy decays exponentially with respect to the distance from an omni-directional acoustic source. By comparing energy readings measured at surrounding acoustic sensors during the same time interval can be accurately estimated. We show that the potential target location is restricted to a hyper-sphere in the sensor field given the acoustic energy reading at a pair of sensors. Given multiple sensor acoustic energy readings, the target location is solved as the location that is closest (in the least square sense) to all the corresponding hyper-spheres. We further simplified this nonlinear least square problem to an unconstrained linear least square problem that yields a closed form solution. Experiment results using military vehicle acoustic data show great promise of this novel approach. <s> BIB004 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> This paper describes a new approach to the source localization and tracking problem in wireless sensor networks. A fast, easy-toimplement algorithm for localizing a source using received signal strength measurements is presented. The algorithm is based on incremental subgradient optimization methods. Using theory on the convergence rates of these methods we characterize the amount of in-network communication required to achieve an accurate estimate of the source’s location. In comparison to other localization and tracking algorithms described in the literature, the amount of communication (and thus energy and bandwidth) used by our algorithm is much lower than that used by other schemes, especially as network size grows. <s> BIB005 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method. <s> BIB006 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> We present a new approach to localizing an isotropic energy source using measurements from distributed sensors based on kernel averaging techniques. The location estimate is easily and efficiently calculated in a decentralized fashion. Statistical properties are derived for a very general measurement model. Experiments suggest that the proposed estimator is much more robust and exhibits better performance characteristics than the popular least squares estimator under a variety of conditions. <s> BIB007 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> The purpose of this paper is to present a technique for determining the coordinate locations of nodes in a distributed sensor network. This technique is based on the time difference of arrival (TDOA) of acoustic signals. In this scheme, several sound sources of known locations transmit while each node in the sensor network records the wave front time-of-arrival. Data from the nodes are transmitted to a central processor and the nonlinear TDOA equations are solved. Computational simulation results are presented in order to quantify the solution behavior and its sensitivity to likely error sources. Results based on experimentally collected data are also presented in order to demonstrate the potential for this approach in solving the self-localization problem. <s> BIB008 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> This correspondence addresses the problem of locating an acoustic source using a sensor network in a distributed manner, i.e., without transmitting the full data set to a central point for processing. This problem has been traditionally addressed through the maximum-likelihood framework or nonlinear least squares. These methods, even though asymptotically optimal under certain conditions, pose a difficult global optimization problem. It is shown that the associated objective function may have multiple local optima and saddle points, and hence any local search method might stagnate at a suboptimal solution. In this correspondence, we formulate the problem as a convex feasibility problem and apply a distributed version of the projection-onto-convex-sets (POCS) method. We give a closed-form expression for the projection phase, which usually constitutes the heaviest computational aspect of POCS. Conditions are given under which, when the number of samples increases to infinity or in the absence of measurement noise, the convex feasibility problem has a unique solution at the true source location. In general, the method converges to a limit point or a limit cycle in the neighborhood of the true location. Simulation results show convergence to the global optimum with extremely fast convergence rates compared to the previous methods <s> BIB009 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> The problem of source localization using distributed acoustic sensor networks is considered in this paper. The acoustic sensors detect and estimate the time of arrival (TOA) of the acoustic transient emitted from a source of interest. The estimated TOA estimates are then transmitted to a base station for collaborative source localization. In realistic scenarios, a subset of the TOA's could typically be erroneous owing to various detrimental factors. The standard least squares-based methods totally fail in presence of even only one outlier or erroneous TOA. By modeling the TOA estimation error as Cauchy-Lorentz distribution, a robust source localization algorithm is derived. Numerical experiments are provided to show the robustness and accuracy of localization of the proposed algorithm. <s> BIB010 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bio- acoustic data has traditionally been a difficult and time- consuming process in which researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deploy- able acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we implement an AML-based source localization algorithm, and use it to localize marmot alarm-calls. We assess the performance of these techniques based on results from two field experiments: (1) a controlled test of direction-of- arrival (DOA) accuracy using a pre-recorded source signal, and (2) an experiment to detect and localize actual animals in their habitat, with a comparison to ground truth gathered from human observations. Although small arrays yield ambiguities from spatial aliasing of high frequency signals, we show that these ambiguities are readily eliminated by proper bearing crossings of the DOAs from several arrays. These results show that the AML source localization algorithm can be used to localize actual animals in their natural habitat, using a platform that is practical to deploy. <s> BIB011 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> The localization of an acoustic source can be based on the energy measurements at a number of spatially separated microphones. This is because the amount of source energy attenuation at a microphone is proportional to the square of the distance between the source and the microphone. This paper develops an algebraic closed-form solution for the acoustic source localization problem using energy measurements, under the condition of direct line-of-sight and free space propagation. First-order analysis is applied to the proposed solution to study its performance, where only the linear noise terms are kept in obtaining the mean-square localization error. The first-order analytical results show that the proposed solution reaches the Cramer-Rao lower bound (CRLB) accuracy for Gaussian noise as the signal-to-noise ratio tends to infinity. In addition, the proposed solution provides much better accuracy than other closed-form solutions available in literature. Improvement on the proposed solution that extends its operating range beyond the threshold noise level was made by imposing nonnegative constraints. Simulations are included to corroborate the performance of the proposed method. <s> BIB012 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> In this paper, the use of low-power wireless sensor networks for the classification of signals is described. The objective is to accurately classify the target signals using a novel decentralized approach that aims to reduce the energy consumption of the network nodes as much as possible. Since, often, the most consuming part of such nodes is the radio module, the proposed solution performs the classification of the selected features of a detected signal directly on the board, thus transmitting only the classification results. As a case study, the identification of vehicles that emit acoustic signals is considered. For this case, the process of feature extraction and selection is based on a spectral analysis of the signals, whereas the classification is carried out by support vector machines, which were chosen for their flexibility in classifying patterns. In particular, the use of the -SVM classification algorithm is proposed because it is expected to provide a good performance in terms of both implementation cost and classification accuracy. Finally, the advantages of the proposed solution in terms of energy consumption are illustrated with reference to an implementation based on the Mica2 sensor node by Crossbow Technology Inc. <s> BIB013 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> We consider the problem of locating a single radiating source from several noisy measurements using a maximum likelihood (ML) criteria. The resulting optimization problem is nonconvex and nonsmooth, and thus finding its global solution is in principle a hard task. Exploiting the special structure of the objective function, we introduce and analyze two iterative schemes for solving this problem. The first algorithm is a very simple explicit fixed-point-based formula, and the second is based on solving at each iteration a nonlinear least squares problem, which can be solved globally and efficiently after transforming it into an equivalent quadratic minimization problem with a single quadratic constraint. We show that the nonsmoothness of the problem can be avoided by choosing a specific “good” starting point for both algorithms, and we prove the convergence of the two schemes to stationary points. We present empirical results that support the underlying theoretical analysis and suggest that, despite of its nonconvexity, the ML problem can effectively be solved globally using the devised schemes. <s> BIB014 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> We consider least squares (LS) approaches for locating a radiating source from range measurements (which we call R-LS) or from range-difference measurements (RD-LS) collected using an array of passive sensors. We also consider LS approaches based on squared range observations (SR-LS) and based on squared range-difference measurements (SRD-LS). Despite the fact that the resulting optimization problems are nonconvex, we provide exact solution procedures for efficiently computing the SR-LS and SRD-LS estimates. Numerical simulations suggest that the exact SR-LS and SRD-LS estimates outperform existing approximations of the SR-LS and SRD-LS solutions as well as approximations of the R-LS and RD-LS solutions which are based on a semidefinite relaxation. <s> BIB015 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm. <s> BIB016 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> A new incremental optimization algorithm called normalized incremental subgradient (NIS) algorithm is proposed in this letter, which can be used for distributed maximum likelihood estimation (MLE). Its convergence with a diminishing stepsize has been proved and analyzed theoretically. We then apply the NIS algorithm to the energy-based sensor network source localization problem where the decay factor of the energy decay model is unknown. Simulation results show it can achieve very high estimation performance, which is only somewhat lower than that of the centralized localization method based on global optimization techniques, but with hundreds of times lower computational complexity than the centralized method. <s> BIB017 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> In this paper, energy-based localization methods for source localization in sensor networks are examined. The focus is on least-squares-based approaches due to a good tradeoff between performance and complexity. A suite of methods are developed and compared. First, two previously proposed methods (quadratic elimination and one step) are shown to yield the same location estimate for a source. Next, it is shown that, as the errors which perturb the least-squares equations are nonidentically distributed, it is more appropriate to consider weighted least-squares methods, which are observed to yield significant performance gains over the unweighted methods. Finally, a new weighted direct least-squares formulation is presented and shown to outperform the previous methods with much less computational complexity. Unlike the quadratic elimination method, the weighted direct least-squares method is amenable to a correction technique which incorporates the dependence of unknown parameters leading to further performance gains. For a sufficiently large number of samples, simulations show that the weighted direct solution with correction (WDC) can be more accurate with significantly less computational complexity than the maximum-likelihood estimator and approaches Cramer-Rao bound (CRB). Furthermore, it is shown that WDC attains CRB for the case of a white source. <s> BIB018 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> Due to uncertainties in target motion and limited sensing regions of sensors, single-sensor-based collaborative target tracking in wireless sensor networks (WSNs), as addressed in many previous approaches, suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability, and energy consumed are affected by the sampling interval between two successive time steps. In this paper, an adaptive energy-efficient multisensor scheduling scheme is proposed for collaborative target tracking in WSNs. It calculates the optimal sampling interval to satisfy a specification on predicted tracking accuracy, selects the cluster of tasking sensors according to their joint detection probability, and designates one of the tasking sensors as the cluster head for estimation update and sensor scheduling according to a cluster head energy measure (CHEM) function. Simulation results show that, compared with existing single-sensor scheduling and multisensor scheduling with a uniform sampling interval, the proposed adaptive multisensor scheduling scheme can achieve superior energy efficiency and tracking reliability while satisfying the tracking accuracy requirement. It is also robust to the uncertainty of the process noise. <s> BIB019 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> Source localization, the problem of determining the physical location of an acoustic or wireless emitter, is commonly encountered in sensor networks which are attempting to locate and track an emitter. Similarly, in navigation systems that do not rely on the global positioning system (GPS), ldquosignals of opportunityrdquo (existing wireless infrastructure) can be used as ad hoc navigation beacons, and the goal is to determine their location relative to a receiver and thus deduce the receiver's position. These two research problems have a very similar mathematical structure. Specifically, in either the source localization or navigation problem, one common approach relies on time difference of arrival (TDOA) measurements to multiple sensors. In this paper, we investigate a bandwidth efficient method of TDOA computation when the signals of opportunity use multicarrier modulation. By exploiting the structure of the multicarrier transmission, much less information needs to be exchanged between sensors compared to the standard cross correlation approach. Analytic and simulation results quantify the performance of the proposed algorithm as a function of the signal-to-noise ratio (SNR) and the bandwidth between the sensors. <s> BIB020 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> This paper considers the problem of time difference-of-arrival (TDOA) source localization when the TDOA measurements from multiple disjoint sources are subject to the same sensor position displacements from the available sensor positions. This is a challenging problem and closed-form solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve this purpose. The proposed algorithm jointly estimates the unknown source and sensor positions to take the advantage that the TDOAs from different sources have the same sensor position displacements. The joint estimation is a highly nonlinear problem due to the coupling of source and sensor positions in the measurement equations. We introduce the novel idea of hypothesized source locations in the algorithm development to enable the formulation of psuedolinear equations, thereby leading to the establishment of closed-form solution for source location estimates. Besides the advantage of closed-form, the newly developed algorithm is shown analytically, under the condition that the TDOA measurement noise and the sensor position errors are sufficiently small, to reach the CRLB accuracy. For clarity, the localization of two disjoint sources is used in the algorithm development. The developed algorithm is then examined under the special case of a single source and extended to the more general case of more than two unknown sources. The theoretical developments are supported by simulations. <s> BIB021 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> In this paper, we analyze the Cramer-Rao Lower Bound (CRLB) of localization using Signal Strength Difference (SSD) as location fingerprint. This analysis has a dual purpose. Firstly, the properties of the bound on localization error may help to design efficient localization algorithm. For example, utilizing one of the properties, we propose a way to define weights for a weighted K-Nearest Neighbor (K-NN) scheme which is shown to perform better than the K-NN algorithm. Secondly, it provides suggestions for a positioning system design by revealing error trends associated with the system deployment. In both cases, detailed analysis as well as experimental results are presented in order to support our claims. <s> BIB022 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate source localization based on the practical time of arrival (TOA) measurement model. Unlike most existing works that transform TOA measurement into time differences before processing, we consider the original measurement model and investigate three methods for direct source localization. We also derive the Cramer-Rao lower bound (CRLB) under the TOA model and establish its connection with the CRLB under the more commonly used time-difference of arrival (TDOA) signal model. We present results that illustrate the performance advantage of source localization based on the original TOA model over the commonly used TDOA pre-processing. <s> BIB023 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> In this paper, we present a simple iterative algorithm for range-difference (RD) based localization. The RD-based localization is a kind of nonlinear optimization problem and generally it has no closed-form solution. Through auxiliary function approach, we derive iterative update rules without any tuning parameters, which just consists of 1) averaging source-sensor distances, 2) averaging the source positions estimated by updating source-sensor distance on each sensor with the source-direction fixed. Due to the resemblance of the iterative averaging to k-means clustering, we call it r-means localization. The convergence of the algorithm is guaranteed. The acceleration of the convergence is also investigated. <s> BIB024 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> The sparse nature of location finding problem makes the theory of compressive sensing desirable for indoor positioning in Wireless Local Area Networks (WLANs). In this paper, we address the received signal strength (RSS)-based localization problem in WLANs using the theory of compressive sensing (CS), which offers accurate recovery of sparse signals from a small number of measurements by solving an $\ell_1$-minimization problem. A pre-processing procedure of orthogonalization is used to induce incoherence needed in the CS theory. In order to mitigate the effects of RSS variations due to channel impediments, the proposed positioning system consists of two steps: coarse localization by exploiting affinity propagation, and fine localization by the CS theory. In the fine localization stage, access point selection problem is studied to further increase the accuracy. We implement the positioning system on a WiFi-integrated mobile device (HP iPAQ hx4700 with Windows Mobile 2003 Pocket PC) to evaluate the performance. Experimental results indicate that the proposed system leads to substantial improvements on localization accuracy and complexity over the widely used traditional fingerprinting methods. <s> BIB025 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> Distributed estimation of Gaussian mixtures has many applications in wireless sensor network (WSN), and its energy-efficient solution is still challenging. This paper presents a novel diffusion-based EM algorithm for this problem. A diffusion strategy is introduced for acquiring the global statistics in EM algorithm in which each sensor node only needs to communicate its local statistics to its neighboring nodes at each iteration. This improves the existing consensus-based distributed EM algorithm which may need much more communication overhead for consensus, especially in large scale networks. The robustness and scalability of the proposed approach can be achieved by distributed processing in the networks. In addition, we show that the proposed approach can be considered as a stochastic approximation method to find the maximum likelihood estimation for Gaussian mixtures. Simulation results show the efficiency of this approach. <s> BIB026 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> In this paper, we propose a new approach for localization in wireless sensor networks based on semi-supervised Laplacian regularized least squares algorithm. We consider two kinds of localization data: signal strength and pair-wise distance between nodes. When nodes are close within their physical location space, their localization data vectors should be similar. We first propose a solution using the alignment criterion to learn an appropriate kernel function in terms of the similarities between anchors, and the kernel function is used to measure the similarity between pair-wise sensor nodes in the networks. We then propose a semi-supervised learning algorithm based upon manifold regularization to obtain the locations of the non-anchors. We evaluate our algorithm under various network topology, transmission range and signal noise, and analyze its performance. We also compare our approach with several existing approaches, and demonstrate the high efficiency of our proposed algorithm in terms of location estimation error. <s> BIB027 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> The TDOA-based source localization problem in sensor networks is considered with sensor node location uncertainty. A total least squares (TLSs) algorithm is developed by a linear closed-form solution for this problem, and the uncertainty of the sensor location is formulated as a perturbation. The sensitivity of the TLS solution is also analyzed. Simulation results show its improved performance against the classic least squares approaches. <s> BIB028 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> Source localization based on energy measurements is an important problem in wireless sensor networks (WSNs). It is well known that the associated objective function is not convex and may have multiple local optima and saddle points. Most of the existing algorithms can not achieve global optima. In this paper, we formulate the source localization as a convex feasibility problem (CFP) and propose a diffusion based projection method as a solution. In the proposed method, no fusion center is required; the sensor nodes need to communicate only with their closest neighbors and all the sensors update their estimations simultaneously and finally they are able to achieve consensus on a possible minimizer asymptotically. The proposed method has low complexity and achieves global optimality. Theoretical analysis and simulation results show that the proposed method has good estimation performance whether or not the CFP is consistent or inconsistent. <s> BIB029 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> We consider source localization in a wireless sensor network in the absence of a fusion center, where each sensor makes hybrid time-of-arrival (TOA) and direction-of-arrival (DOA) measurements. Specially, in this paper, we focus on the bounded noise case. Distributed location estimation is posed as a consensus problem. The average consensus method in the literature can be used for a linear fusion estimator which can be written as the ratio of two averages. The disadvantage of this average consensus method is that it can only used for linear model and needs to wait for the final convergence to achieve an acceptable accuracy. However, generally its convergence rate is low. Alternatively, we present a convex projection consensus method where we show that the resulting estimator at each sensor converges to a common point lying in the intersection of convex regions that are determined based on local sensor measurements and also the minimum number of sensors required to meet a certain estimation error tolerance is derived. Numerical results are presented showing the convergence properties and root mean squared error under both the average consensus and the convex projection methods. <s> BIB030 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Introduction <s> Recent advances in wireless sensor networks have led to renewed interests in the problem of source localization. Source localization has broad range of applications such as emergency rescue, asset inventory, and resource management. Among various measurement models, one important and practical source signal measurement is the received signal time of arrival (TOA) at a group of collaborative wireless sensors. Without time-stamp at the transmitter, in traditional approaches, these received TOA measurements are subtracted pairwise to form time-difference of arrival (TDOA) data for source localization, thereby leading to a 3-dB loss in signal-to-noise ratio (SNR). We take a different approach by directly applying the original measurement model without the subtraction preprocessing. We present two new methods that utilize semidefinite programming (SDP) relaxation for direct source localization. We further address the issue of robust estimation given measurement errors and inaccuracy in the locations of receiving sensors. Our results demonstrate some potential advantages of source localization based on the direct TOA data over time-difference preprocessing. <s> BIB031
Wireless sensor networks (WSNs) are often used for monitoring tasks, such as detection, classification, localization and tracking of one or more targets in two-dimensional (2D) or three-dimensional (3D) sensor fields BIB002 BIB019 BIB026   . Due to the limited resources for sensing, communication and computation in WSNs, energy-efficient collaborative signal processing algorithms are needed  BIB013 . The localization problem has received considerable attention recently BIB006 BIB027  BIB028 . Source localization in wireless sensor networks (WSNs) is an important problem encountered in many indoor and outdoor applications BIB019  BIB006  BIB022 BIB029 BIB030 . There has been a rich history of published work that attempts to solve the source localization problem in WSNs. These solutions fall into three categories, in which the localization solutions are based on three different types of physical measurements: (1) time of arrival (TOA) BIB010 BIB014 BIB015  BIB023 BIB031 or time difference of arrival (TDOA) BIB001 BIB008 BIB024 BIB020 BIB021 ; (2) direction of arrival (DOA)  BIB003 BIB011 ; and (3) received signal strength (RSS) or energy BIB002 BIB006  BIB022  BIB004 BIB016  BIB007 BIB025 BIB005 BIB012 BIB017 BIB018 BIB009 . Among three types of physical measurements, TOA and TDOA requires high precision hardware for timing purposes. Usually, in passive source localization, we do not know the propagation time. Hence, TDOA-based methods are chosen instead of TOA-based ones, however, in which the time synchronization problem arises. This needs extra calibrations. Furthermore, these time-based methods require precise acquisition of the phases of the signals arriving at different nodes. However, the features of some signals tend to be narrow-band, making precise phase acquisition difficult in a noisy environment. DOA-based methods need sensors equipped with antenna sub-arrays, e.g., a microphone sensor in acoustic networks, each capable of independently detecting the source signal and producing a The paper is organized as follows. Section 2 briefly introduces the energy-based sensing model. Sections 3 and 4 present the algorithms for the single and multiple source localization problems, respectively. Section 5 concludes the paper and points out possible future research directions.