The focus of most direction-of-arrival (DOA) estimation problems has been based mainly on a two-dimensional (2D)
scenario where we only need to estimate the azimuth angle. But in various practical situations we have to deal with a
three-dimensional scenario. The importance of being able to estimate both azimuth and elevation angles with high
accuracy and low complexity is of interest. We present the theoretical and the practical issues of DOA estimation using
the Approximate-Maximum-Likelihood (AML) algorithm in a 3D scenario. We show that the performance of the
proposed 3D AML algorithm converges to the Cramer-Rao Bound. We use the concept of an isotropic array to reduce
the complexity of the proposed algorithm by advocating a decoupled 3D version. We also explore a modified version of
the decoupled 3D AML algorithm which can be used for DOA estimation with non-isotropic arrays. Various numerical
results are presented. We use two acoustic arrays each consisting of 8 microphones to do some field measurements. The
processing of the measured data from the acoustic arrays for different azimuth and elevation angles confirms the
effectiveness of the proposed methods.
Distributed sensor networks have been proposed for a wide range of applications. In this paper, our goal is to locate a wideband source, generating both acoustic and seismic signals, using both seismic and acoustic sensors. For a far-field acoustic source, only the direction-of-arrival (DOA) in the coordinate system of the sensors is observable. We use the approximate Maximum-Likelihood (AML) method for DOA estimations from severalacoustic arrays. For a seismic source, we use data collected at a single tri-axial accelerometer to perform DOA estimation. Two seismic DOA estimation methods, the eigen-decomposition of the sample covariance matrix method and the surface wave method are used. Field measurements of acoustic and seismic signals generated by vertically striking a heavy metal plate placed on the ground in an open field are collected. Each acoustic array uses four low-cost microphones placed in a square configuration and separated by one meter. The microphone outputs of each array are collected by a synchronized A/D recording system and processed locally based on the AML algorithm for DOA estimation. An array of six tri-axial accelerometers arranged in two rows whose outputs are fed into an ultra low power and high resolution network-aware seismic recording system. Field measured data from the acoustic and seismic arrays show the estimated DOAs and consequent localizations of the source are quite accurate and useful.
Sensor network technology can revolutionize the study of animal ecology by providing a means of non-intrusive, simultaneous monitoring of interaction among multiple animals. In this paper, we investigate design, analysis, and testing of acoustic arrays for localizing acorn woodpeckers using their vocalizations. Each acoustic array consists of four microphones arranged in a square. All four audio channels within the same acoustic array are finely synchronized within a few micro seconds. We apply the approximate maximum likelihood (AML) method to synchronized audio channels of each acoustic array for estimating the direction-of-arrival (DOA) of woodpecker vocalizations. The woodpecker location is estimated by applying least square (LS) methods to DOA bearing crossings of multiple acoustic arrays. We have revealed the critical relation between microphone spacing of acoustic arrays and robustness of beamforming of woodpecker vocalizations. Woodpecker localization experiments using robust array element spacing in different types of environments are conducted and compared. Practical issues about calibration of acoustic array orientation are also discussed.