Summary1. Animals produce sounds for diverse biological functions such as defending territories, attracting mates, deterring predators, navigation, finding food and maintaining contact with members of their social group. Biologists can take advantage of these acoustic behaviours to gain valuable insights into the spatial and temporal scales over which individuals and populations interact. Advances in bioacoustic technology, including the development of autonomous cabled and wireless recording arrays, permit data collection at multiple locations over time. These systems are transforming the way we study individuals and populations of animals and are leading to significant advances in our understandings of the complex interactions between animals and their habitats. 2. Here, we review questions that can be addressed using bioacoustic approaches, by providing a primer on technologies and approaches used to study animals at multiple organizational levels by ecologists, behaviourists and conservation biologists. 3. Spatially dispersed groups of microphones (arrays) enable users to study signal directionality on a small scale or to locate animals and track their movements on a larger scale. 4. Advances in algorithm development can allow users to discriminate among species, sexes, age groups and individuals. 5. With such technology, users can remotely and non-invasively survey populations, describe the soundscape, quantify anthropogenic noise, study species interactions, gain new insights into the social dynamics of sound-producing animals and track the effects of factors such as climate change and habitat fragmentation on phenology and biodiversity. 6. There remain many challenges in the use of acoustic monitoring, including the difficulties in performing signal recognition across taxa. The bioacoustics community should focus on developing a *Correspondence author. E-mail: marmots@ucla.edu 2011, 48, 758-767 doi: 10.1111/j.1365-2664.2011.01993.x Ó 2011 The Authors. Journal of Applied Ecology Ó 2011 British Ecological Society common framework for signal recognition that allows for various species' data to be analysed by any recognition system supporting a set of common standards. 7. Synthesis and applications. Microphone arrays are increasingly used to remotely monitor acoustically active animals. We provide examples from a variety of taxa where acoustic arrays have been used for ecological, behavioural and conservation studies. We discuss the technologies used, the methodologies for automating signal recognition and some of the remaining challenges. We also make recommendations for using this technology to aid in wildlife management. Journal of Applied Ecology
In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramér-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator.
istributed sensor networks have been proposed for a wide range of applications. The main purpose of a sensor network is to monitor an area, including detecting, identifying, localizing, and tracking one or more objects of interest. These networks may be used by the military in surveillance, reconnaissance, and combat scenarios or around the perimeter of a manufacturing plant for intrusion detection. In other applications such as hearing aids and multimedia, microphone networks are capable of enhancing audio signals under noisy conditions for improved intelligibility, recognition, and cuing for camera aiming. Recent developments in integrated circuit technology have allowed the construction of low-cost miniature sensor nodes with signal processing and wireless communication capabilities. These technological advances not only open up many possibilities but also introduce challenging issues for the collaborative processing of wideband acoustic and seismic signals for source localization and beamforming in an energy-constrained distributed sensor network. The purpose of this article is to provide an overview of these issues. Some prior systems include: WINS at RSC/UCLA [1], AWAIRS at UCLA/RSC [2]-[4], Smart Dust at UC Berkeley [5], USC-ISI network [6], MIT network [7], SensIT systems/networks [8], and ARL Federated Laboratory Advanced Sensor Program systems/networks [9]. In the first section, we consider the physical features of the sources and their propagation properties and discuss the system features of the sensor network. The next section introduces some early works in source localization, DOA estimation, and beamforming. Other topics discussed include the closed-form least-squares source localization problem, iterative ML source localization, and DOA estimation. In the final section a brief conclusion is given. Microsensor Networks Physical Features We first characterize the basic physical characteristics and features of the sources and their propagation properties as shown in Table 1. These features are outside the control of the designer of the architecture and algorithm for the sensor network. In this article, we will deal with these features in terms of acoustic or seismic (i.e., vibrational) sources. While these two sources have some common features, they also have some distinct differences. Radio frequency (RF), visual, infrared, and magnetic sources have other distinct features but will not be considered here. The movement of personnel, car, truck, wheeled/tracked vehicle, as well as vibrating machinery can all generate acoustic or seismic waveforms. These waveforms are referred to as wideband signals since the ratio of highest to lowest frequency component is quite large. For audio waveforms (i.e., 30 Hz-15 KHz), the ratio is about 500, and these waveforms are wideband. Dominant acoustical waveforms generated from wheeled and tracked vehicles may range from 20 Hz-2 KHz, resulting in a ratio of about 100. Similarly, dominant seismic waveforms generated from wheeled vehicles may range from 5 Hz-500 Hz, al...
We propose an entropy-based sensor selection heuristic for localization. Given 1) a prior probability distribution of the target location, and 2) the locations and the sensing models of a set of candidate sensors for selection, the heuristic selects an informative sensor such that the fusion of the selected sensor observation with the prior target location distribution would yield on average the greatest or nearly the greatest reduction in the entropy of the target location distribution. The heuristic greedily selects one sensor in each step without retrieving any actual sensor observations. The heuristic is also computationally much simpler than the mutual-information-based approaches. The effectiveness of the heuristic is evaluated using localization simulations in which Gaussian sensing models are assumed for simplicity. The heuristic is more effective when the optimal candidate sensor is more informative.
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