Energy-Based Acoustic Source Localization Methods: A Survey <s> Energy-Based Sensing Model <s> Fundamentals of Vibration. Transverse Motion: The Vibrating String. Vibrations of Bars. The Two--Dimensional Wave Equation: Vibrations of Membranes and Plates. The Acoustic Wave Equation and Simple Solutions. Reflection and Transmission. Radiation and Reception of Acoustic Waves. Absorption and Attenuation of Sound. Cavities and Waveguides. Pipes, Resonators, and Filters. Noise, Signal Detection, Hearing, and Speech. Architectural Acoustics. Environmental Acoustics. Transduction. Underwater Acoustics. Selected Nonlinear Acoustic Effects. Shock Waves and Explosions. Appendices. Answers to Odd--Numbered Problems. Index. <s> BIB001 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Energy-Based Sensing Model <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> BIB002 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Energy-Based Sensing Model <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> BIB003 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Energy-Based Sensing Model <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> BIB004 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Energy-Based Sensing Model <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> BIB005
The model below is widely used for acoustic signals BIB004  BIB002 BIB001 . The acoustic signal received by a sensor at time instant n can be expressed as: where z(n) represents the acoustic intensity, γ is the gain factor of the sensor, P(n) denotes the intensity of the source signal measured at a location with distance of 1 m from the source, d(n) is the distance between the sensor and source and (n) is modeled as a zero-mean additive white Gaussian (AWGN) noise with variance ς 2 . The energy-based model was first presented in BIB004 BIB002  , which is derived from the acoustic RSS Equation BIB003 . In essence, the energy is the average intensity measurements over the time window [t − T/2, t + T/2]. Assume that (n) and P(n) are uncorrelated, such that: E{P(n) (n)} = E{P(n)}E{ (n)} = 0. Then, one may represent the acoustic energy as (setting g = γ BIB005 , and S(n) = E{P 2 (n)}): In practice, the expectation is realized using the ensemble average over a time window T = M/ f s , where M is the number of sample points used for estimating the acoustic energy received by the sensor during the time interval T and f s is the sampling frequency. The average energy measurement over the time window [t − T/2, t + T/2] denoted by y(t) is given by: Then, according to the result in Equation (2), Equation (3) can be rewritten as: The square of the background noise 2 (n) in Equation (3) will have a χ 2 distribution with the mean equal to E{ 2 (n)} = ζ 2 and the variance equal to 2ζ 4 /M. If M is sufficiently large (M 30), according to the central limit theorem, w can be approximated well with a normal distribution, namely w ∼ N(ζ 2 , 2ζ 4 /M).