Wireless sensor networks have been employed in a wide variety of applications, despite the limited energy and communication resources at each sensor node. Low power custom VLSI chips implementing passive acoustic sensing algorithms have been successfully integrated into an acoustic surveillance unit and demonstrated for detection and location of sound sources.

In this dissertation, I explore active and passive acoustic sensing techniques, signal processing and classification algorithms for detection and classification in a multinodal sensor network environment.

I will present the design and characterization of a continuous-wave micro-Doppler sonar to image objects with articulated moving components. As an example application for this system, we use it to image gaits of humans and four-legged animals. I will present the micro-Doppler gait signatures of a walking person, a dog and a horse. I will discuss the resolution and range of this micro-Doppler sonar and use experimental results to support the theoretical analyses. In order to reduce the data rate and make the system amenable to wireless sensor networks, I will present a second micro-Doppler sonar that uses bandpass sampling for data acquisition. Speech recognition algorithms are explored for biometric identifications from one's gait, and I will present and compare the classification performance of the two systems. The acoustic micro-Doppler sonar design and biometric identification results are the first in the field as the previous work used either video camera or microwave technology. I will also review bearing estimation algorithms and present results of applying these algorithms for bearing estimation and tracking of moving vehicles.

Another major source of the power consumption at each sensor node is the wireless interface. To address the need of low power communications in a wireless sensor network, I will also discuss the design and implementation of ultra wideband transmitters in a three dimensional silicon on insulator process. Lastly, a prototype of neuromorphic interconnects using ultra wideband radio will be presented.