Multisource passive acoustic tracking is useful in animal bio-behavioral study by replacing or enhancing human
involvement during and after field data collection. Multiple simultaneous vocalizations are a common occurrence
in a forest or a jungle, where many species are encountered. Given a set of nodes that are capable of producing
multiple direction-of-arrivals (DOAs), such data needs to be combined into meaningful estimates. Random
Finite Set provides the mathematical probabilistic model, which is suitable for analysis and optimal estimation
algorithm synthesis. Then the proposed algorithm has been verified using a simulation and a controlled test
Variation in the number of targets and sensors needs to be addressed in any realistic sensor system. Targets may
come in or out of a region or may suddenly stop emitting detectable signal. Sensors can be subject to failure for
many reasons. We derive a tracking algorithm with a model that includes these variations using Random Finite
Set Theory (RFST). RFST is a generalization of standard probability theory into the finite set theory domain.
This generalization does come with additional mathematical complexity. However, many of the manipulations
in RSFT are similar in behavior and intuition to those of standard probability theory.
Joint estimation and detection for multi-sensor and multi-target algorithms are often hybrids of both analytical
and ad-hoc approaches at various levels. The intricacies of the resulting solution formulation often obscures
design intuition leaving many design choices to a largely trial and error based approach. Random Finite Set
Theory (RFST)1,2 is a formal generalization of classical probability theory to the random set domain. By treating
multi-target and multi-sensor jointly, RFST is able to provide a systematic theoretical framework for rigorous
mathematical analysis. Because of its set theory domain, RFST is able to model the randomness of missed
detection, sensor failure, target appearance and disappearance, clutter, jammer, ambiguous measurements, and
other practical artifacts within its probability framework. Furthermore, a rigorous statistical framework, the
Finite Set Statistics, has been developed for RFST that includes statistical operations such as: Maximum
Likelihood, Bayesian prediction-correction filter, sensor fusion, and even the Cramer-Rao Lower Bound (CRB).
In this paper we will apply RFST to jointly detect and locate a target in a power-aware wireless sensor network
setting. We will further derive the CRB using both classical and RFST approaches as verification. Then we will
use analytical results in conjunction with simulations to develop insights for choosing the design parameters.
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.
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.
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.