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Ultrasonic bat detectors are useful for research and monitoring purposes to assess occupancy and relative activity of bat communities. Environmental “clutter” such as tree boles and foliage can affect the recording quality and identification of bat echolocation calls collected using ultrasonic detectors. It can also affect the transmission of calls and recognition by bats when using acoustic lure devices to attract bats to mist-nets. Bat detectors are often placed in forests, yet automatic identification programs are trained on call libraries using echolocation passes recorded largely from open spaces. Research indicates that using clutter-recorded calls can increase classification accuracy for some bat species and decrease accuracy for others, but a detailed understanding of how clutter impacts the recording and identification of echolocation calls remains elusive. To clarify this, we experimentally investigated how two measures of clutter (i.e., total basal area and number of stems of simulated woody growth, as well as recording angle) affected the recording and classification of a synthesized echolocation signal under controlled conditions in an anechoic chamber. Recording angle (i.e., receiver position relative to emitter) significantly influenced the probability of correct classification and differed significantly for many of the call parameters measured. The probability of recording echo pulses was also a function of clutter but only for the detector angle at 0° from the emitter that could receive deflected pulses. Overall, the two clutter metrics were overshadowed by proximity and angle of the receiver to the sound source but some deviations from the synthesized call in terms of maximum, minimum, and mean frequency parameters were observed. Results from our work may aid efforts to better understand underlying environmental conditions that produce false-positive and -negative identifications for bat species of interest and how this could be used to adjust survey accuracy estimates. Our results also help pave the way for future research into the development of acoustic lure technology by exploring the effects of environmental clutter on ultrasound transmission. 

Abstract Sound source localization is the ability to successfully understand the bearing and distance of a sound in space. The challenge of sound source localization has been a major are of research for engineers, especially those studying robotics, for decades. One of the main topics of focus is the ability for robots to track objects, human voices, or other robots robustly and accurately. Common ways to accomplish this goal may use large arrays, computationally intensive machine learning methods, or known dynamic models of a system which may not always be available. We seek to simplify this problem using a minimal amount of inexpensive equipment alongside a Bayesian estimator, capable of localizing an emitter using easily available a-priori information and timing data received from a prototype binaural sensor. We perform an experiment in a full anechoic chamber with a sound source moving at a constant speed; this experimental environment provides a space that allows us to isolate the performance of the sensor. We find that, while our current system isn’t perfect, it is able to track the general motion of a sound source and the path to even more accurate tracking in the future is clear. 

The problem of sound source localization has attracted the interest of researchers from different disciplines ranging from biology to robotics and navigation. It is in essence an estimation problem trying to estimate the location of the sound source using the information available to sound receivers. It is common practice to design Bayesian estimators based on a dynamic model of the system. Nevertheless, in some practical situations, such a dynamic model may not be available in the case of a moving sound source and instead, some a priori information about the sound source may be known. This paper considers a case study of designing an estimator using available a priori information, along with measurement signals received from a bearing-only sensor, to track a moving sound source in two dimensions. 

Abstract Communication inspired by animals is a timely topic of research in the modeling and control of multi-agent systems. Examples of such bio-inspired communication methods include pheromone trails used by ants to forage for food and echolocation used by bats to orient themselves and hunt. Source searching is one of many challenges in the field of swarm robotics that tackles an analogous problem to animals foraging for food. This paper seeks to compare two communication methods, inspired by sonar and pheromones, in the context of a multi-agent foraging problem. We explore which model is more effective at recruiting agents to forage from a found target. The results of this work begin to uncover the complicated relationship between sensing modality, collective tasks, and spontaneous cooperation in groups.