1 research outputs found
DESIGN OPTIMIZATION OF EMBEDDED SIGNAL PROCESSING SYSTEMS FOR TARGET DETECTION
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