Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems / Edition 1 available in Hardcover, Paperback, eBook
Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems / Edition 1
- ISBN-10:
- 113855748X
- ISBN-13:
- 9781138557482
- Pub. Date:
- 02/04/2019
- Publisher:
- Taylor & Francis
- ISBN-10:
- 113855748X
- ISBN-13:
- 9781138557482
- Pub. Date:
- 02/04/2019
- Publisher:
- Taylor & Francis
Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems / Edition 1
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Overview
The Advanced Signal Processing Handbook helps you meet that challenge. Beyond offering an outstanding introduction to the principles and applications of advanced signal processing, it develops a generic processing structure that takes advantage of the similarities that exist among radar, sonar, and medical imaging systems and integrates conventional and nonlinear processing schemes.
Product Details
| ISBN-13: | 9781138557482 |
|---|---|
| Publisher: | Taylor & Francis |
| Publication date: | 02/04/2019 |
| Series: | CRC Press Revivals |
| Pages: | 750 |
| Product dimensions: | 7.00(w) x 10.00(h) x (d) |
About the Author
Dr. Stergios Stergiopoulos
is the Acting Chief Scientist at the Defence R&D Canada (DRDC) Toronto, an Associate Professor (Status Only) at the Edward S. Rogers Sr. Department of Electrical & Computer Engineering of the University of Toronto, and the main innovator of the Defence R&D Canada (DRDC) medical diagnostic technologies and patents that have been licensed for commercialization.Read an Excerpt
Chapter 1: Signal Concept Similarities among Sonar, Radar, and Medical Imaging Systems
1.1 IntroductionSeveral review articles on sonar, radar, and medical imaging system technologies have provided a detailed description of the mainstream signal processing functions along with their associated implementation considerations. The attempt of this handbook is to extend the scope of these articles by introducing an implementation effort of non-mainstream processing schemes in real-time systems. To a large degree, work in the area of sonar and radar system technology has traditionally been funded either directly or indirectly by governments and military agencies in an attempt to improve the capability of anti-submarine warfare (ASW) sonar and radar systems. A secondary aim of this handbook is to promote, where possible, wider dissemination of this military-inspired research.
1.2 Overview of a Real-Time System
In order to provide a context for the material contained in this handbook, it would seem appropriate to briefly review the basic requirements of a high-performance real-time system 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.
In this handbook, we will be limiting our attention to the signal processor, the data manager, and display sub-system, which consist of the algorithms and the processing architectures required for theirimplementation. Arrays of sources and sensors include devices of varying degrees of complexity that illuminate the medium of interest and sense the existence of signals of interest. These devices are arrays of transducers having cylindrical, spherical, planar, or linear geometric configurations, depending on the application of interest. Quantitative estimates of the various benefits that result from the deployment of arrays of transducers are obtained by the array gain term, which will be discussed in Chapters 6, 10, and 11. Sensor array design concepts, however, are beyond the scope of this handbook and readers interested in transducers can refer to other publications on the topic.
The signal processor is probably the single, most important component of a real-time system of interest for this handbook. In order to satisfy the basic requirements, the processor normally incorporates the following fundamental operations:
- Multi-dimensional beamforming
- Matched filtering
- Temporal and spatial spectral analysis
- Tomography image reconstruction processing
- Multi- dimensional image processing
The first three processes are used to improve both the signal-to-noise ratio (SNR) and parameter estimation capability through spatial and the temporal processing techniques. The next two operations are image reconstruction and processing schemes associated mainly with image processing applications. As indicated in Figure 1.1, the replacement of the existing signal processor with a new signal processor, which would include advanced processing schemes, could lead to improved performance functionality of a real-time system of interest, while the associated development cost could be significantly lower than using other hardware (H/W) alternatives. In a sense, this statement highlights the future trends of stateof-the-art investigations on advanced real-time signal processing functionalities that are the subject of the handbook.
Furthemore, post-processing of the information provided by the previous operations includes mainly the following:
- Signal tracking and target motion analysis
- Image post-processing and data fusion
- Data normalization
- OR-ing
These operations form the functionality of the data manager of sonar and radar systems. However, identification of the processing concept similarities between sonar, radar, and medical imaging systems may be valuable in identifying the implementation of these operations in other medical imaging system applications. In particular, the operation of data normalization in sonar and radar systems is required to map the resulting data into the dynamic range of the display devices in a manner which provides a constant false alarm rate (CFAR) capability across the analysis cells. The same operation, however, is required in the display functionality of medical ultrasound imaging systems as well.
In what follows, each sub-system, shown in Figure 1.1, is examined briefly by associating the evolution of its functionality and characteristics with the corresponding signal processing technological developments.
1.3 Signal Processor
The implementation of signal processing concepts in real-time systems is heavily dependent on the computing architecture characteristics, and, therefore, it is limited by the progress made in this field. While the mathematical foundations of the signal processing algorithms have been known for many years, it was the introduction of the microprocessor and high-speed multiplier-accumulator devices in the early 19705 which heralded the turning point in the development of digital systems. The first systems were primarily fixed-point machines with limited dynamic range and, hence, were constrained to use conventional beamforming and filtering techniques. 'A'S As floating-point central processing units (CPUs) and supporting memory devices were introduced in the mid to late 19705, multi-processor digital systems and modern signal processing algorithms could be considered for implementation in real-time systems. This major breakthrough expanded in the 19805 into massively parallel architectures supporting multisensor requirements.
The limitations associated with these massively parallel architectures became evident by the fact that they allow only fast- Fourier-transform (FFT), vector-based processing schemes because of efficient implementation and of their very cost-effective throughput characteristics. Thus, non-conventional schemes (i.e., adaptive, synthetic aperture, and high-resolution processing) could not be implemented in these types of real-time systems of interest, even though their theoretical and experimental developments suggest that they have advantages over existing conventional processing approaches. It is widely believed that these advantages can address the requirements associated with the difficult operational problems that next generation real-time sonar, radar, and medical imaging systems will have to solve.
New scalable computing architectures, however, which support both scalar and vector operations satisfying high input/output bandwidth requirements of large multi-sensor systems, are becoming available." Recent frequent announcements include successful developments of super-scalar and massively parallel signal processing computers that have throughput capabilities of hundred of billions of floatingpoint operations per second (GFLOPS). This resulted in a resurgence of interest in algorithm development of new covariance-based, high-resolution, adaptive 15,20-22,25 and synthetic aperture beamforming algorithms, 15,21 and time-frequency analysis techniques...