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.

In a reverberant scenario, phase transformed weighted algorithms are more robust than Maximum Likelihood (ML) because of the insufficiency of the data model to incorporate reverberant information. This transformation has been applied to General Cross-Correlation and Steered Response Power algorithms; the latter has been shown to be more robust. For a multiple known number of sources, both algorithms have problems separating the sources that are close together because of the limitation caused by the resolution. Recently, an approach was made using simple well-known statistical room acoustics to model room reverberation, and another parametric approach called the Approximate Maximum Likelihood was made that was designed for multi-source estimations. By combining these methods, we developed an ML algorithm that is suitable for multi-source target estimates in a reverberant room.

Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bioacoustic data has traditionally been a difficult and time consuming process in which researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we implement an AML-based source localization algorithm, and use it to localize marmot alarm-calls. We assess the performance of these techniques based on results from two field experiments: (1) a controlled test of direction-of-arrival (DOA) accuracy using a pre-recorded source signal, and (2) an experiment to detect and localize actual animals in their habitat, with a comparison to ground truth gathered from human observations. Although small arrays yield ambiguities from spatial aliasing of high frequency signals, we show that these ambiguities are readily eliminated by proper bearing crossings of the DOAs from several arrays. These results show that the AML source localization algorithm can be used to localize actual animals in their natural habitat, using a platform that is practical to deploy.

Field biologists use animal sounds to discover the presence of individuals and to study their behavior. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we demonstrate a real-time wireless sensor system that implements an AML-based source localization algorithm. We will demonstrate a system that is easy to set up and that can localize a whistle in the demonstration area in real time. This demonstration will use the techniques we described in our corresponding paper [1].

We propose a novel algorithm employing particle filters for acoustic source tracking in a reverberant environment. By incorporating the likelihood function computed through approximate maximum-likelihood (AML) method, the proposed algorithm is applicable to wideband sources and can be implemented for multiple sources tracking. Both computer simulation and experimental results show the effectiveness of the proposed algorithm

Sensor network technology can revolutionize the study of animal ecology by providing a means of non-intrusive, simultaneous, unmanned monitoring. In this paper, we investigate the design, analysis, and testing of acoustic arrays for localizing bird vocalizations of different species. The spectra of the bird waveforms affect the desired dimension of the array. Microphones are placed in a uniform circular array and are finely synchronized within a few microseconds. We apply the Approximate Maximum Likelihood (AML) method to estimate the source direction-of-arrival (DOA) and perform beamforming for signal enhancement. The crossing of the distributed DOA estimated bearings is used to localize the birds, and the enhanced signals are used for training and estimation for the classification of the birds. The experimental results demonstrate the practicality and robustness of our array design.