Target tracking is a broad subject area extensively studied in many engineering disciplines. In this thesis, target tracking implies the temporal estimation of target features such as the target's direction-of-arrival (DOA), the target's boundary pixels in a sequence of images, and/or the target's position in space. For multiple target tracking, we have introduced a new motion model that incorporates an acceleration component along the heading direction of the target. We have also shown that the target motion parameters can be considered part of a more general feature set for target tracking, e.g., target frequencies, which may be unrelated to the target motion, can be used to improve the tracking performance. We have introduced an acoustic multiple-target tracker using a flexible observation model based on an image tracking approach by assuming that the DOA observations might be spurious and that some of the DOAs might be missing in the observation set. We have also addressed the acoustic calibration problem from sources of opportunity such as beacons or a moving source. We have derived and compared several calibration methods for the case where the node can hear a moving source whose position can be reported back to the node. The particle filter, as a recursive algorithm, requires an initialization phase prior to tracking a state vector. The Metropolis-Hastings (MH) algorithm has been used for sampling from intractable multivariate target distributions and is well suited for the initialization problem. Since the particle filter only needs samples around the mode, we have modified the MH algorithm to generate samples distributed around the modes of the target posterior. By simulations, we show that this mode hungry algorithm converges an order of magnitude faster than the original MH scheme. Finally, we have developed a general framework for the joint state-space tracking problem. A proposal strategy for joint state-space tracking using the particle filters is defined by carefully placing the random support of the joint filter in the region where the final posterior is likely to lie. Computer simulations demonstrate improved performance and robustness of the joint state-space when using the new particle proposal strategy.

Top predators can have large effects on community and population dynamics but we still know relatively little about their roles in ecosystems and which biotic and abiotic factors potentially affect their behavioral patterns. Understanding the roles played by top predators is a pressing issue because many top predator populations around the world are declining rapidly yet we do not fully understand what the consequences of their potential extirpation could be for ecosystem structure and function. In addition, individual behavioral specialization is commonplace across many taxa, but studies of its prevalence, causes, and consequences in top predator populations are lacking. In this dissertation I investigated the movement, feeding patterns, and drivers and implications of individual specialization in an American alligator (Alligator mississippiensis) population inhabiting a dynamic subtropical estuary. I found that alligator movement and feeding behaviors in this population were largely regulated by a combination of biotic and abiotic factors that varied seasonally. I also found that the population consisted of individuals that displayed an extremely wide range of movement and feeding behaviors, indicating that individual specialization is potentially an important determinant of the varied roles of alligators in ecosystems. Ultimately, I found that assuming top predator populations consist of individuals that all behave in similar ways in terms of their feeding, movements, and potential roles in ecosystems is likely incorrect. As climate change and ecosystem restoration and conservation activities continue to affect top predator populations worldwide, individuals will likely respond in different and possibly unexpected ways.

<p dir="ltr">The design of hardware platform in acoustic sensing is critical. The number and the spatial arrangement of microphones play a huge role in sensing performance. All microphones should be properly processed for simultaneous recording. This work introduces an FPGA-based acoustic transceiver system supporting acoustic sensing with custom acoustic signals. The system contains 16 microphones and a speaker synchronized during sensing processes. The microphones were arranged into an ‘L’ shape with eight microphones on each line for a better resolution of angle estimation on two dimensions. The microphones were placed on a specifically designed PCB to achieve an optimal distance of the half-wavelength of acoustic signals for optimized sensing performance. A microphone interface was implemented on Ultra96-V2 FPGA for handling the simultaneous high-speed data streams. The system features an implementation of full-level data transmission up to the top-level Python program. To evaluate the sensing performance of the system, we conducted an experiment used Frequency Modulated Continuous Wave (FMCW) as the transmitted acoustic signal. The result of evaluation shown the accurate sensing of range, velocity and relative angle of a moving hand on the two dimensions corresponding to the microphone groups.</p>