Energy-Based Acoustic Source Localization Methods: A Survey <s> Discussion on Field Experiments <s> Acoustic energy based localization with wireless sensor networks is an interesting solution to locate sources and targets. For simplicity, localization formulation based on the maximum likelihood (ML) approach considers that the source and noise samples are uncorrelated and represented by a Gaussian distribution. However, the acoustic background noise can severely affect the accuracy of the location estimation. This paper proposes an accurate error estimate in which the correlation of the received signals at each wireless sensor is represented by a Hurst exponent and modeled by a fractional Gaussian noise (fGn). The experimental results show that the proposed solution is more appropriate for the source localization estimation under real acoustic noises and even for highly non-stationary sources. <s> BIB001 </s> Energy-Based Acoustic Source Localization Methods: A Survey <s> Discussion on Field Experiments <s> This paper considers the problem of identifying multiple sound sources from acoustical measurements obtained by an array of microphones. The problem is solved via maximum likelihood. In particular, an expectation-maximization (EM) approach is used to estimate the sound source locations and strengths, the pressure measured by a microphone being interpreted as a mixture of latent signals emitted by the sources. This work also considers two kinds of uncertainties pervading the sound propagation and measurement process: uncertain microphone locations and uncertain wavenumber. These uncertainties are transposed to the data in the belief functions framework. Then, the source locations and strengths can be estimated using a variant of the EM algorithm, known as the Evidential EM (E2M) algorithm. Eventually, both simulation and real experiments are shown to illustrate the advantage of using the EM in the case without uncertainty and the E2M in the case of uncertain measurement. <s> BIB002
For field experiments for energy-based approaches, actually in the literature, not much work has been done. In BIB001 , by using real corrupted or noisy signals collected from acoustic sources, such as a car, a helicopter and speech, source energy is then calculated. Simulation experiments are conducted by using ML-based methods. Measurement noise and the real communication channel will be different from our assumption in this paper. To improve the localization accuracy in real field implementations, the noise model and energy decay factor need to be considered carefully. Model uncertainty, data uncertainty and environment uncertainty are three key issues to be incorporated in the problem formulation for real implementations. Perhaps the EM algorithm can be used to estimate the unknown parameters, especially for the energy decay factor BIB002 . Time synchronization and synchronization errors caused may be a big challenge compared to signal-based approaches; since for the energy-based approaches, the time duration for averaging has to be consistent among the sensors. Processing speeds for the sensor computing processors are different. This will also be challenging to localize and track sources with higher moving speeds.