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الكتاب الطبي Advanced signal processing handbook


Advanced signal processing handbook : theory and implementation for radar, sonar, and medical imagin





Recent advances in digital signal processing algorithms and computer technology have combined to
provide the ability to produce real-time systems that have capabilities far exceeding those of a few years
ago. The writing of this handbook was prompted by a desire to bring together some of the recent
theoretical developments on advanced signal processing, and to provide a glimpse of how modern
technology can be applied to the development of current and next-generation active and passive realtime
systems.
The handbook is intended to serve as an introduction to the principles and applications of advanced
signal processing. It will focus on the development of a generic processing structure that exploits the
great degree of processing concept similarities existing among the radar, sonar, and medical imaging
systems. A high-level view of the above real-time systems consists of a high-speed
Signal Processor
to
provide mainstream signal processing for detection and initial parameter estimation, a
Data Manager
which supports the data and information processing functionality of the system, and a
Display Sub-
System
through which the system operator can interact with the data structures in the data manager to
make the most effective use of the resources at his command.
The
Signal Processor
normally incorporates a few fundamental operations. For example, the sonar and
radar signal processors include beamforming, “matched” filtering, data normalization, and image processing.
The first two processes are used to improve both the signal-to-noise ratio (SNR) and parameter
estimation capability through spatial and temporal processing techniques. Data normalization is required
to map the resulting data into the dynamic range of the display devices in a manner which provides a
CFAR (constant false alarm rate) capability across the analysis cells.
The processing algorithms for spatial and temporal spectral analysis in real-time systems are based on
conventional FFT and vector dot product operations because they are computationally cheaper and more
robust than the modern non-linear high resolution adaptive methods. However, these non-linear algorithms
trade robustness for improved array gain performance. Thus, the challenge is to develop a concept which
allows an appropriate mixture of these algorithms to be implemented in practical real-time systems.
The non-linear processing schemes are adaptive and synthetic aperture beamformers that have been
shown experimentally to provide improvements in array gain for signals embedded in partially correlated
noise fields. Using system image outputs, target tracking, and localization results as performance criteria,
the impact and merits of these techniques are contrasted with those obtained using the conventional
processing schemes. The reported real data results show that the advanced processing schemes provide
improvements in array gain for signals embedded in anisotropic noise fields. However, the same set of
results demonstrates that these processing schemes are not adequate enough to be considered as a
replacement for conventional processing. This restriction adds an additional element in our generic signal
processing structure, in that the conventional and the advanced signal processing schemes should run
in parallel in a real-time system in order to achieve optimum use of the advanced signal processing
schemes of this study.

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الكتاب الطبي Advanced signal processing handbook.txt (461 بايت, عدد مرات التحميل : 23)

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