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1.
PeerJ ; 6: e4249, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29340248

RESUMO

BACKGROUND: Passive acoustic telemetry using coded transmitter tags and stationary receivers is a popular method for tracking movements of aquatic animals. Understanding the performance of these systems is important in array design and in analysis. Close proximity detection interference (CPDI) is a condition where receivers fail to reliably detect tag transmissions. CPDI generally occurs when the tag and receiver are near one another in acoustically reverberant settings. Here we confirm transmission multipaths reflected off the environment arriving at a receiver with sufficient delay relative to the direct signal cause CPDI. We propose a ray-propagation based model to estimate the arrival of energy via multipaths to predict CPDI occurrence, and we show how deeper deployments are particularly susceptible. METHODS: A series of experiments were designed to develop and validate our model. Deep (300 m) and shallow (25 m) ranging experiments were conducted using Vemco V13 acoustic tags and VR2-W receivers. Probabilistic modeling of hourly detections was used to estimate the average distance a tag could be detected. A mechanistic model for predicting the arrival time of multipaths was developed using parameters from these experiments to calculate the direct and multipath path lengths. This model was retroactively applied to the previous ranging experiments to validate CPDI observations. Two additional experiments were designed to validate predictions of CPDI with respect to combinations of deployment depth and distance. Playback of recorded tags in a tank environment was used to confirm multipaths arriving after the receiver's blanking interval cause CPDI effects. RESULTS: Analysis of empirical data estimated the average maximum detection radius (AMDR), the farthest distance at which 95% of tag transmissions went undetected by receivers, was between 840 and 846 m for the deep ranging experiment across all factor permutations. From these results, CPDI was estimated within a 276.5 m radius of the receiver. These empirical estimations were consistent with mechanistic model predictions. CPDI affected detection at distances closer than 259-326 m from receivers. AMDR determined from the shallow ranging experiment was between 278 and 290 m with CPDI neither predicted nor observed. Results of validation experiments were consistent with mechanistic model predictions. Finally, we were able to predict detection/nondetection with 95.7% accuracy using the mechanistic model's criterion when simulating transmissions with and without multipaths. DISCUSSION: Close proximity detection interference results from combinations of depth and distance that produce reflected signals arriving after a receiver's blanking interval has ended. Deployment scenarios resulting in CPDI can be predicted with the proposed mechanistic model. For deeper deployments, sea-surface reflections can produce CPDI conditions, resulting in transmission rejection, regardless of the reflective properties of the seafloor.

2.
J Acoust Soc Am ; 139(1): 53-62, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26827004

RESUMO

The echolocation system of the Risso's dolphin (Grampus griseus) remains poorly studied compared to other odontocete species. In this study, echolocation signals were recorded from a stationary Risso's dolphin with an array of 16 hydrophones and the two-dimensional beam shape was explored using frequency-dependent amplitude plots. Click source parameters were similar to those already described for this species. Centroid frequency of click signals increased with increasing sound pressure level, while the beamwidth decreased with increasing center frequency. Analysis revealed primarily single-lobed, and occasionally vertically dual-lobed, beam shapes. Overall beam directivity was found to be greater than that of the harbor porpoise, bottlenose dolphin, and a false killer whale. The relationship between frequency content, beam directivity, and head size for this Risso's dolphin deviated from the trend described for other species. These are the first reported measurements of echolocation beam shape and directivity in G. griseus.


Assuntos
Golfinhos/fisiologia , Ecolocação/fisiologia , Animais , Feminino , Cabeça/anatomia & histologia , Análise de Regressão , Espectrografia do Som
3.
J Acoust Soc Am ; 134(3): 2534-45, 2013 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-23968051

RESUMO

This paper develops and applies a linearized Bayesian localization algorithm based on acoustic arrival times of marine mammal vocalizations at spatially-separated receivers which provides three-dimensional (3D) location estimates with rigorous uncertainty analysis. To properly account for uncertainty in receiver parameters (3D hydrophone locations and synchronization times) and environmental parameters (water depth and sound-speed correction), these quantities are treated as unknowns constrained by prior estimates and prior uncertainties. Unknown scaling factors on both the prior and arrival-time uncertainties are estimated by minimizing Akaike's Bayesian information criterion (a maximum entropy condition). Maximum a posteriori estimates for sound source locations and times, receiver parameters, and environmental parameters are calculated simultaneously using measurements of arrival times for direct and interface-reflected acoustic paths. Posterior uncertainties for all unknowns incorporate both arrival time and prior uncertainties. Monte Carlo simulation results demonstrate that, for the cases considered here, linearization errors are small and the lack of an accurate sound-speed profile does not cause significant biases in the estimated locations. A sequence of Pacific walrus vocalizations, recorded in the Chukchi Sea northwest of Alaska, is localized using this technique, yielding a track estimate and uncertainties with an estimated speed comparable to normal walrus swim speeds.


Assuntos
Acústica , Monitoramento Ambiental/métodos , Biologia Marinha/métodos , Vocalização Animal , Morsas/fisiologia , Acústica/instrumentação , Alaska , Algoritmos , Animais , Teorema de Bayes , Simulação por Computador , Monitoramento Ambiental/instrumentação , Modelos Lineares , Biologia Marinha/instrumentação , Método de Monte Carlo , Oceanos e Mares , Processamento de Sinais Assistido por Computador , Espectrografia do Som , Natação , Fatores de Tempo , Transdutores , Incerteza , Morsas/psicologia
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