Sensor networks for automated detection of targets, such as pedestrians and

vehicles, are highly relevant in defense and surveillance applications. For

this purpose, a variety of target detection algorithms and systems using

different types of sensors have been proposed in the literature. Among them,

systems based on non-image sensors are of special interest in many practical

deployment scenarios because of their power efficiency and low computational

loads. In this thesis, we investigate low power sensor systems for detecting

people and vehicles using non-image sensors such as acoustic and seismic

sensors. Our investigation is focused on design optimization

across trade-offs including real-time performance, energy efficiency, and

target detection accuracy, which are key design evaluation metrics for this

class of systems.

Design and implementation of low power, embedded target detection systems

can be decomposed into two major,

inter-related subproblems: (a) algorithm development, which encompasses the

development or selection of detection algorithms and optimization of their

parameters, and (b) system development, which involves the mapping of the

algorithms derived from (a) into real-time, energy efficient implementations

on the targeted embedded platforms. In this thesis, we address both of these

subproblems in an integrated manner. That is, we investigate novel algorithmic

techniques for improvement of accuracy without excessive computational

complexity, and we develop new design methodologies, tools, and

implementations for efficient realization of target detection algorithms on

embedded platforms.

We focus specifically on target detection systems that employ acoustic and

seismic sensing modalities. These selected modalities support the low power

design objectives of our work. However, we envision that our developed

algorithms and implementation techniques can be extended readily to other

types or combinations of relevant sensing modalities.

Throughout this research, we have developed prototypes of our new algorithms

and design methods on embedded platforms, and we have experimented with these

prototypes to demonstrate our findings, and iteratively improve upon the

achieved implementation trade-offs. The main contributions of this thesis are

summarized in the following.

(1). Classification algorithm for acoustic and seismic signals. We have

developed a new classification algorithm for discrimination among people,

vehicles, and noise. The algorithm is based on a new fusion technique for

acoustic and seismic signals. Our new fusion technique was evaluated through

experiments using actual measured datasets, which were collected from different

sensors installed in different locations and at different times of day. Our

proposed classification algorithm was shown to achieve a significant reduction

in the number of false alarms compared to a baseline fusion approach.

(2). Joint target localization and classification framework using

sensor networks. We designed a joint framework for target localization and

classification using a single generalized model for non-imaging based multi-

modal sensor data. For target localization, we exploited both sensor data and

estimated dynamics within a local neighborhood. We validated the capabilities

of our framework by using an actual multi-modal dataset, which includes ground

truth GPS information (e.g., time and position) and data from co-located

seismic and acoustic sensors. Experimental results showed that our framework

achieves better classification accuracy compared to state of the art fusion

algorithms using temporal accumulation and achieves more accurate target

localizations than a baseline target localization approach.

(3). Design and optimization of target detection systems on embedded platforms

using dataflow methods. We developed a foundation for our system-level design

research by introducing a new rapid prototyping methodology and associated

software tool. Using this tool, we presented the design and implementation of a

novel, multi-mode embedded signal processing system for detection of people and

vehicles related to our algorithmic contributions. We applied a

strategically-configured suite of single- and dual-modality signal processing

techniques together with dataflow-based design optimization for

energy-efficient, real-time implementation. Through experiments using a

Raspberry Pi platform, we demonstrated the capability of our target detection

system to provide efficient operational trade-offs among detection accuracy,

energy efficiency, and processing speed.

(4). Software synthesis from dataflow schedule graphs on multicore platforms.

We developed new software synthesis methods and tools for design

and implementation of embedded signal processing systems using dataflow

schedule graphs (DSGs). DSGs provide formal representations of dataflow

schedules, which encapsulate information about the assignment of computational

tasks (signal processing modules) to processing resources and the ordering of

tasks that are assigned to the same resource. Building on fundamental DSG

modeling concepts from the literature, we developed the first algorithms and

supporting software synthesis tools for mapping DSG representations into

efficient multi-threaded implementations. Our tools replace ad-hoc multicore

signal processing system development processes with a structured process that

is rooted in dataflow formalisms and supported with a high degree of

automation. We evaluated our new DSG methods and tools through a

demonstration involving multi-threaded implementation of our proposed

classification algorithm and associated fusion technique for acoustic/seismic

signals.