1st Edition

Signal Processing in Radar Systems

By Vyacheslav Tuzlukov Copyright 2013
    632 Pages 197 B/W Illustrations
    by CRC Press

    632 Pages 197 B/W Illustrations
    by CRC Press

    An essential task in radar systems is to find an appropriate solution to the problems related to robust signal processing and the definition of signal parameters. Signal Processing in Radar Systems addresses robust signal processing problems in complex radar systems and digital signal processing subsystems. It also tackles the important issue of defining signal parameters.

    The book presents problems related to traditional methods of synthesis and analysis of the main digital signal processing operations. It also examines problems related to modern methods of robust signal processing in noise, with a focus on the generalized approach to signal processing in noise under coherent filtering. In addition, the book puts forth a new problem statement and new methods to solve problems of adaptation and control by functioning processes. Taking a systems approach to designing complex radar systems, it offers readers guidance in solving optimization problems.

    Organized into three parts, the book first discusses the main design principles of the modern robust digital signal processing algorithms used in complex radar systems. The second part covers the main principles of computer system design for these algorithms and provides real-world examples of systems. The third part deals with experimental measurements of the main statistical parameters of stochastic processes. It also defines their estimations for robust signal processing in complex radar systems.

    Written by an internationally recognized professor and expert in signal processing, this book summarizes investigations carried out over the past 30 years. It supplies practitioners, researchers, and students with general principles for designing the robust digital signal processing algorithms employed by complex radar systems.


    Part I Design of Radar Digital Signal Processing and Control Algorithms

    Principles of Systems Approach to Design Complex Radar Systems
    Methodology of Systems Approach
    Main Requirements to Complex Radar Systems
    Problems of System Design for Automated Complex Radar Systems
    Radar Signal Processing System as an Object of Design

    Signal Processing by Digital Generalized Detector in Complex Radar Systems
    Analog to Digital Signal Conversion: Main Principles
    Digital Generalized Detector for Coherent Impulse Signals
    Convolution in Time Domain
    Convolution in Frequency Domain
    Examples of Some DGD Types

    Digital Interperiod Signal Processing Algorithms
    Digital Moving-Target Indication Algorithms
    DGD for Coherent Impulse Signals with Known Parameters
    DGD for Coherent Impulse Signals with Unknown Parameters
    Digital Measurers of Target Return Signal Parameters
    Complex Generalized Algorithms of Digital Interperiod Signal Processing

    Algorithms of Target Range Track Detection and Tracking
    Main Stages and Signal Reprocessing Operations
    Target Range Track Detection Using Surveillance Radar Data
    Target Range Tracking Using Surveillance Radar Data

    Filtering and Extrapolation of Target Track Parameters Based on Radar Measure
    Initial Conditions
    Process Representation in Filtering Subsystems
    Statistical Approach to Solution of Filtering Problems of Stochastic (Unknown) Parameters
    Algorithms of Linear Filtering and Extrapolation under Fixed Sample Size of Measurements
    Recurrent Filtering Algorithms of Undistorted Polynomial Target Track Parameters
    Adaptive Filtering Algorithms of Maneuvering Target Track Parameters
    Logical Flowchart of Complex Radar Signal Reprocessing Algorithm

    Principles of Control Algorithm Design for Complex Radar System Functioning at Dynamical Mode
    Configuration and Flowchart of Radar Control Subsystem
    Direct Control of Complex Radar Subsystem Parameters
    Scan Control in New Target Searching Mode
    Power Resource Control under Target Tracking
    Distribution of Power Resources of Complex Radar System under Combination of Target Searching and Target Tracking Modes

    Part II Design Principles of Computer System for Radar Digital Signal Processing and Control Algorithms

    Design Principles of Complex Algorithm Computational Process in Radar Systems
    Design Considerations
    Complex Algorithm Assignment
    Evaluation of Work Content of Complex Digital Signal Processing Algorithm Realization by Microprocessor Subsystems
    Paralleling of Computational Process

    Design Principles of Digital Signal Processing Subsystems Employed by Complex Radar System
    Structure and Main Engineering Data of Digital Signal Processing Subsystems
    Requirements for Effective Speed of Operation
    Requirements for RAM Size and Structure
    Selection of Microprocessor for Designing the Microprocessor Subsystems
    Structure and Elements of Digital Signal Processing and Complex Radar System Control Microprocessor Subsystems
    High-Performance Centralized Microprocessor Subsystem for Digital Signal Processing of Target Return Signals in Complex Radar Systems
    Programmable Microprocessor for Digital Signal Preprocessing of Target Return Signals in Complex Radar Systems

    Digital Signal Processing Subsystem Design (Example)
    General Statements
    Design of Digital Signal Processing and Control Subsystem Structure
    Structure of Coherent Signal Preprocessing Microprocessor Subsystem
    Structure of Noncoherent Signal Preprocessing Microprocessor Subsystem
    Signal Reprocessing Microprocessor Subsystem Specifications
    Structure of Digital Signal Processing Subsystem

    Global Digital Signal Processing System Analysis
    Digital Signal Processing System Design
    Analysis of "n – 1 – 1" MTI System
    Analysis of "n – n – 1" MTI System
    Analysis of "n – m – 1" MTI System
    Comparative Analysis of Target Tracking Systems

    Part III Stochastic Processes Measuring in Radar Systems

    Main Statements of Statistical Estimation Theory
    Main Definitions and Problem Statement
    Point Estimate and Its Properties
    Effective Estimations
    Loss Function and Average Risk
    Bayesian Estimates for Various Loss Functions

    Estimation of Mathematical Expectation
    Conditional Functional
    Maximum Likelihood Estimate of Mathematical Expectation
    Bayesian Estimate of Mathematical Expectation: Quadratic Loss Function
    Applied Approaches to Estimate the Mathematical Expectation
    Estimate of Mathematical Expectation at Stochastic Process Sampling
    Mathematical Expectation Estimate under Stochastic Process Amplitude Quantization
    Optimal Estimate of Varying Mathematical Expectation of Gaussian Stochastic Process
    Varying Mathematical Expectation Estimate under Stochastic Process Averaging in Time
    Estimate of Mathematical Expectation by Iterative Methods
    Estimate of Mathematical Expectation with Unknown Period

    Estimation of Stochastic Process Variance
    Optimal Variance Estimate of Gaussian Stochastic Process
    Stochastic Process Variance Estimate under Averaging in Time
    Errors under Stochastic Process Variance Estimate
    Estimate of Time-Varying Stochastic Process Variance
    Measurement of Stochastic Process Variance in Noise

    Estimation of Probability Distribution and Density Functions of Stochastic Process
    Main Estimation Regularities
    Characteristics of Probability Distribution Function Estimate
    Variance of Probability Distribution Function Estimate
    Characteristics of the Probability Density Function Estimate
    Probability Density Function Estimate Based on Expansion in Series Coefficient Estimations
    Measurers of Probability Distribution and Density Functions: Design Principles

    Estimate of Stochastic Process Frequency-Time Parameters
    Estimate of Correlation Function
    Correlation Function Estimation Based on its Expansion in Series
    Optimal Estimation of Gaussian Stochastic Process Correlation Function Parameter
    Correlation Function Estimation Methods Based on Other Principles
    Spectral Density Estimate of Stationary Stochastic Process
    Estimate of Stochastic Process Spike Parameters
    Mean-Square Frequency Estimate of Spectral Density

    Notation Index

    Chapters include a summary and discussion as well as references.


    Dr. Vyacheslav Tuzlukov is currently a full professor in the Department of Information Technologies and Communication, School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea. He is an author of over 170 journal and conference papers and eight books on signal processing, including Signal Processing Noise (CRC Press, 2002) and Signal and Image Processing in Navigational Systems (CRC Press, 2004). He is a keynote speaker, chair of sessions, tutorial instructor, and plenary speaker at major international conferences on signal processing. Dr. Tuzlukov has been highly recommended by U.S. experts of Defense Research and Engineering (DDR&E) of the United States Department of Defense (U.S. DoD) for his expertise in the field of humanitarian demining and minefield-sensing technologies and was awarded the Special Prize of the U.S. DoD in 1999. His achievements have distinguished him as one of the leading experts from around the world by Marquis Who’s Who.