Energy-Based Acoustic Source Localization Methods: A Survey <s> Communication Burden <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> BIB001 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Communication Burden <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> BIB002
For the centralized methods, all of the sensor measurements have to be transmitted to a fusion center or one of the sensors. For the fixed quantized level of energy estimates, y i and sensor coordinates, conveying the information from the sensors to a fusion center requires the transmission of O(N) bits over a distance of O(1) per bit BIB001 . However, for POCS-based ones, O(N) bits are transmitted over a distance of O(log 2 L/L) per bit. Hence, it can be seen that the communication burden grows linearly with the number of sensors in the centralized approaches and sublinearly ( √ L log L) in the decentralized approach. Generally speaking, in sensor networks, it has been shown if the application and the sensor architecture permit, it is much more energy efficient to perform distributed local processing than to do central processing that requires extensive communication  . Of course, not all algorithms can use distributed/decentralized processing. It has been reported that POCS BIB001 and WDC BIB002 can be run in a distributed way.