高级信号处理技术：雷达、声呐与医学图像解析

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"高级信号处理技术在雷达、声呐和医学图像领域的应用" 在现代科技领域，高级信号处理技术是至关重要的一个部分，尤其在雷达、声呐和医学图像这三个领域中，它扮演着不可替代的角色。斯特吉奥普洛斯（Stergios Stergiopoulos）编辑的《高级信号处理手册》提供了深入的理论与实践知识，为相关领域的学习者和专业人士提供了宝贵的资料。在雷达系统中，信号处理是确保目标检测、识别和跟踪的关键。它涉及到信号的发射、接收、滤波、增强以及目标特征提取等多个环节。例如，使用数字信号处理技术可以提高雷达的分辨率和抗干扰能力，通过匹配滤波等方法优化目标回波信号，从而实现更精确的目标定位和识别。此外，多普勒处理技术也是雷达系统中的重要一环，能够分析目标的速度和运动方向。声呐系统则主要应用于水下探测，如海洋生物研究、潜艇定位和海底地形测绘等。在声呐中，信号处理技术用于改善信号质量，降低噪声影响，同时通过回声定位技术实现对水下物体的识别和距离测量。其中，脉冲压缩、波束形成和自适应滤波等技术是声呐信号处理的核心，它们能提高信号的信噪比，增强目标的探测能力。医学图像领域同样离不开信号处理技术，尤其是在影像诊断中。例如，在CT、MRI或超声成像中，原始数据通常需要经过复杂的信号处理才能转化为可供医生分析的图像。图像增强技术可以改善图像质量，去除噪声，突出关键的病灶信息。此外，通过特征提取和模式识别技术，可以自动检测病变，辅助医生进行早期诊断。斯特吉奥普洛斯的著作涵盖了这些领域的广泛内容，不仅包括基础理论，还涉及实际应用案例，旨在帮助读者理解并掌握高级信号处理技术。尽管书籍的部分内容涉及版权，但读者仍可以通过合法途径获取并学习，以提升在信号处理领域的专业素养。无论是雷达系统的优化，还是声呐系统的改进，或是医学图像分析的精准化，都离不开这些先进技术的支持。因此，《高级信号处理手册》对于从事相关工作或者对此感兴趣的人士来说，无疑是一本极具价值的参考书。

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 their imple-

mentation.

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 trans-

ducers can refer to other publications on the topic.

16–19

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

FIGURE 1.1

Overview of a generic real-time system. It consists of an array of transducers, a

signal processor

provide mainstream signal processing for detection and initial parameter estimation; a

data manager

, which supports

the data, information processing functionality, and data fusion; and a

display sub-system

through which the system

operator can interact with the manager to make the most effective use of the information available at his command.

Existing SIGNAL

PROCESSOR

DATA

MANAGER

DISPLAY

SUB-SYSTEM

OPERATOR-MACHINE

INTERFACE

New SIGNAL

PROCESSOR

Transducer # 1

Transducer # n

MEDIUM

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 state-

of-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 technolog-

ical 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 1970s 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.

1,4,15

As ﬂoating-point central processing units (CPUs)

and supporting memory devices were introduced in the mid to late 1970s, multi-processor digital systems

and modern signal processing algorithms could be considered for implementation in real-time systems.

This major breakthrough expanded in the 1980s into massively parallel architectures supporting multi-

sensor 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 imple-

mentation 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.

2,3,15,20–25

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 avail-

able.

Recent frequent announcements include successful developments of super-scalar and massively

parallel signal processing computers that have throughput capabilities of hundred of billions of ﬂoating-

point operations per second (GFLOPS).

This resulted in a resurgence of interest in algorithm develop-

ment of new covariance-based, high-resolution, adaptive

15,20–22,25

and synthetic aperture beamforming

algorithms,

15,23

and time-frequency analysis techniques.

Chapters 2, 3, 6, and 11 discuss in some detail the recent developments in adaptive, high-resolution,

and synthetic aperture array signal processing and their advantages for real-time system applications. In

particular, Chapter 2 reviews the basic issues involved in the study of adaptive systems for signal pro-

cessing. The virtues of this approach to statistical signal processing may be summarized as follows:

• The use of an adaptive ﬁltering algorithm, which enables the system to adjust its free parameters

(in a supervised or unsupervised manner) in accordance with the underlying statistics of the

environment in which the system operates, hence, avoiding the need for determining the statistical

characteristics of the environment

• Tracking capability, which permits the system to follow statistical variations (i.e., non-stationarity)

of the environment

• The availability of many different adaptive ﬁltering algorithms, both linear and non-linear, which

can be used to deal with a wide variety of signal processing applications in radar, sonar, and

biomedical imaging

• Digital implementation of the adaptive ﬁltering algorithms, which can be carried out in hardware

or software form

In many cases, however, special attention is required for non-linear, non-Gaussian signal processing

applications. Chapter 3 addresses this topic by introducing a Gaussian mixture approach as a model in

such problems where data can be viewed as arising from two or more populations mixed in varying

proportions. Using the Gaussian mixture formulation, problems are treated from a global viewpoint that

readily yields and uniﬁes previous, seemingly unrelated results. Chapter 3 introduces novel signal pro-

cessing techniques applied in applications problems, such as target tracking in polar coordinates and

interference rejection in impulsive channels. In other cases these advanced algorithms, introduced in

Chapters 2 and 3, trade robustness for improved performance.

15,25,26

Furthermore, the improvements

achieved are generally not uniform across all signal and noise environments of operational scenarios.

The challenge is to develop a concept which allows an appropriate mixture of these algorithms to be

implemented in practical real-time systems. The advent of new adaptive processing techniques is only

the ﬁrst step in the utilization of

a priori

information as well as more detailed information for the mediums

of the propagating signals of interest. Of particular interest is the rapidly growing ﬁeld of matched ﬁeld

processing (MFP).

The use of linear models will also be challenged by techniques that utilize higher

order statistics,

neural networks,

fuzzy systems,

chaos, and other non-linear approaches. Although

these concerns have been discussed

in a special issue of the

IEEE Journal of Oceanic Engineering

devoted

to sonar system technology, it should be noted that a detailed examination of MFP can be found also in

the July 1993 issue of this journal which has been devoted to detection and estimation of MFP.

The discussion in Chapter 4 focuses on the class of problems for which there is some information

about the signal propagation model. From the basic formalism of blind system identiﬁcation process,

signal processing methods are derived that can be used to determine the unknown parameters of the

medium transfer function and to demonstrate its performance for estimating the source location and

the environmental parameters of a shallow water waveguide. Moreover, the system concept similarities

between sonar and ultrasound systems are analyzed in order to exploit the use of model-based sonar

signal processing concepts in ultrasound problems.

The discussion on model-based signal processing is extended in Chapter 5 to determine the most

appropriate signal processing approaches for measurements that are contaminated with noise and under-

lying uncertainties. In general, if the SNR of the measurements is high, then simple non-physical tech-

niques such as Fourier transform-based temporal and spatial processing schemes can be used to extract

the desired information. However, if the SNR is extremely low and/or the propagation medium is

uncertain, then more of the underlying propagation physics must be incorporated somehow into the

processor to extract the information. These are issues that are discussed in Chapter 5, which introduces

a generic development of model-based processing schemes and then concentrates speciﬁcally on those

designed for sonar system applications.

Thus, Chapters 2, 3, 4, 5, 6, and 11 address a major issue: the implementation of advanced processing

schemes in real-time systems of interest. The starting point will be to identify the signal processing concept

similarities among radar, sonar, and medical imaging systems by deﬁning a generic signal processing

structure integrating the processing functionalities of the real-time systems of interest. The deﬁnition of a

generic signal processing structure for a variety of systems will address the above continuing interest that

is supported by the fact that synthetic aperture and adaptive processing techniques provide new gain.

2,15,20,21,23

This kind of improvement in array gain is equivalent to improvements in system performance.

In general, improvements in system performance or array gain improvements are required when the

noise environment of an operational system is non-isotropic, such as the noise environment of (1)

atmospheric noise or clutter (radar applications), (2) cluttered coastal waters and areas with high shipping

density in which sonar systems operate (sonar applications), and (3) the complexity of the human body

(medical imaging applications). An alternative approach to improve the array gain of a real-time system

requires the deployment of very large aperture arrays, which leads to technical and operational implica-

tions. Thus, the implementation of non-conventional signal processing schemes in operational systems

will minimize very costly H/W requirements associated with array gain improvements.

Figure 1.2 shows the conﬁguration of a generic signal processing scheme integrating the functionality

of radar, sonar, ultrasound, medical tomography CT/X-ray, and magnetic resonance imaging (MRI)

systems. There are ﬁve major and distinct processing blocks in the generic structure. Moreover, recon-

ﬁguration of the different processing blocks of Figure 1.2 allows the application of the proposed concepts

to a variety of active or passive digital signal processing (DSP) systems.

The

ﬁrst point

of the generic processing ﬂow conﬁguration is that its implementation is in the

frequency domain. The

second point

is that with proper selection of ﬁltering weights and careful data

partitioning, the frequency domain outputs of conventional or advanced processing schemes can be made

equivalent to the FFT of the broadband outputs. This equivalence corresponds to implementing ﬁnite

impulse response (FIR) ﬁlters via circular convolution with the FFT, and it allows spatial-temporal

processing of narrowband and broadband types of signals,

2,15,30

as deﬁned in Chapter 6. Thus, each

processing block in the generic DSP structure provides continuous time series; this is the central point

of the implementation concept that allows the integration of quite diverse processing schemes, such as

those shown in Figure 1.2.

More speciﬁcally, the details of the generic processing ﬂow of Figure 1.2 are discussed very brieﬂy in

the following sections.

1.3.1 Signal Conditioning of Array Sensor Time Series

The block titled

Signal Conditioning for Array Sensor Time Series

in Figure 1.2 includes the partitioning of

the time series from the receiving sensor array, their initial spectral FFT, the selection of the signal’s frequency

band of interest via bandpass FIR ﬁlters, and downsampling. The output of this block provides continuous

time series at a reduced sampling rate for improved temporal spectral resolution. In many system applica-

tions including moving arrays of sensors, array shape estimation or the sensor coordinates would be required

to be integrated with the signal processing functionality of the system, as shown in this block.

Typical system requirements of this kind are towed array sonars,

which are discussed in Chapters 6,

10, and 11; CT/X-ray tomography systems,

6–8

which are analyzed in Chapters 15 and 16; and ultrasound

imaging systems deploying long line or planar arrays,

8–10

which are discussed in Chapters 6, 7, 13, and 14.

The processing details of this block will be illustrated in schematic diagrams in Chapter 6. The FIR band

selection processing of this block is typical in all the real-time systems of interest. As a result, its output can

be provided as input to the blocks named

Sonar, Radar & Ultrasound Systems

Tomography Imaging Systems

1.3.2 Tomography Imaging CT/X-Ray and MRI Systems

The block at the right-hand side of Figure 1.2, which is titled

Tomography Imaging Systems

, includes image

reconstruction algorithms for medical imaging CT/X-ray and MRI systems. The processing details of these

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