Cover image for Signal processing in radar systems
Title:
Signal processing in radar systems
Publication Information:
Boca Raton, FL : CRC Press/Taylor & Francis, c2013
Physical Description:
xxvi, 606 p. : ill. ; 26 cm.
ISBN:
9781439826072
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30000010322210 TK6580 T89 2013 Open Access Book Book
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Summary

Summary

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.


Author Notes

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.


Table of Contents

Prefacep. xv
Authorp. xix
Introductionp. xxi
Part I Design of Radar Digital Signal Processing and Control Algorithms
Chapter 1 Principles of Systems Approach to Design Complex Radar Systemsp. 3
1.1 Methodology of Systems Approachp. 3
1.2 Main Requirements of Complex Radar Systemsp. 6
1.3 Problems of System Design for Automated Complex Radar Systemsp. 13
1.4 Radar Signal Processing System as an Object of Designp. 15
1.5 Summary and Discussionp. 21
Referencesp. 23
Chapter 2 Signal Processing by Digital Generalized Detector in Complex Radar Systemsp. 25
2.1 Analog-to-Digital Signal Conversion: Main Principlesp. 25
2.1.1 Sampling Processp. 25
2.1.2 Quantization and Signal Sampling Conversionp. 29
2.1.3 Analog-to-Digital Conversion: Design Principles and Main Parametersp. 30
2.1.3.1 Sampling and Quantization Errorsp. 30
2.1.3.2 Reliabilityp. 32
2.2 Digital Generalized Detector for Coherent Impulse Signalsp. 32
2.2.1 Matched Filterp. 32
2.2.2 Generalized Detectorp. 34
2.2.3 Digital Generalized Detectorp. 36
2.3 Convolution in Time Domainp. 39
2.4 Convolution in Frequency Domainp. 47
2.5 Examples of Some DGD Typesp. 52
2.6 Summary and Discussionp. 53
Referencesp. 55
Chapter 3 Digital Interperiod Signal Processing Algorithmsp. 57
3.1 Digital Moving-Target Indication Algorithmsp. 57
3.1.1 Principles of Construction and Efficiency Indicesp. 57
3.1.2 Digital Rejector Filtersp. 60
3.1.3 Digital Moving-Target Indicator in Radar System with Variable Pulse Repetition Frequencyp. 66
3.1.4 Adaptation in Digital Moving-Target Indicatorsp. 67
3.2 DGD for Coherent Impulse Signals with Known Parametersp. 69
3.2.1 Initial Conditionsp. 69
3.2.2 DGD for Target Return Pulse Trainp. 72
3.2.3 DGD for Binary Quantized Target Return Pulse Trainp. 74
3.2.4 DGD Based on Methods of Sequential Analysisp. 75
3.2.5 Software DGD for Binary Quantized Target Return Pulse Trainp. 81
3.3 DGD for Coherent Impulse Signals with Unknown Parametersp. 82
3.3.1 Problem Statements of Digital Detector Synthesisp. 82
3.3.2 Adaptive DGDp. 84
3.3.3 Nonparametric DGDp. 87
3.3.3.1 Sign-Nonparametric DGDp. 88
3.3.3.2 Rank-Nonparametric DGDp. 89
3.3.4 Adaptive-Nonparametric DGDp. 91
3.4 Digital Measurers of Target Return Signal Parametersp. 93
3.4.1 Digital Measurer of Target Rangep. 94
3.4.2 Algorithms of Angular Coordinate Estimation under Uniform Radar Antenna Scanningp. 95
3.4.3 Algorithms of Angular Coordinate Estimation under Discrete Radar Antenna Scanningp. 100
3.4.4 Doppler Frequency Measurerp. 103
3.5 Complex Generalized Algorithms of Digital Interperiod Signal Processingp. 105
3.6 Summary and Discussionp. 108
Referencesp. 111
Chapter 4 Algorithms of Target Range Track Detection and Trackingp. 115
4.1 Main Stages and Signal Reprocessing Operationsp. 117
4.1.1 Target Pip Gating: Shape Selection and Dimensions of Gatesp. 119
4.1.2 Algorithm of Target Pip Indication by Minimal Deviation from Gate Centerp. 122
4.1.3 Target Pip Distribution and Binding within Overlapping Gatesp. 125
4.2 Target Range Track Detection Using Surveillance Radar Datap. 128
4.2.1 Main Operations under Target Range Track Detectionp. 128
4.2.2 Statistical Analysis of "2m + 1/n" Algorithms under False Target Range Track Detectionp. 129
4.2.3 Statistical Analysis of "2m + 1/n" Algorithms under True Target Range Track Detectionp. 135
4.3 Target Range Tracking Using Surveillance Radar Datap. 138
4.3.1 Target Range Autotracking Algorithmp. 138
4.3.2 United Algorithm of Detection and Target Range Trackingp. 142
4.4 Summary and Discussionp. 143
Referencesp. 146
Chapter 5 Filtering and Extrapolation of Target Track Parameters Based on Radar Measurep. 149
5.1 Initial Conditionsp. 150
5.2 Process Representation in Filtering Subsystemsp. 150
5.2.1 Target Track Modelp. 150
5.2.2 Measuring Process Modelp. 153
5.3 Statistical Approach to Solution of Filtering Problems of Stochastic (Unknown) Parametersp. 155
5.4 Algorithms of Linear Filtering and Extrapolation under Fixed Sample Size of Measurementsp. 156
5.4.1 Optimal Parameter Estimation Algorithm by Maximal Likelihood Criterion for Polynomial Target Track: A General Casep. 157
5.4.2 Algorithms of Optimal Estimation of Linear Target Track Parametersp. 159
5.4.3 Algorithm of Optimal Estimation of Second-Order Polynomial Target Track Parametersp. 162
5.4.4 Algorithm of Extrapolation of Target Track Parametersp. 166
5.4.5 Dynamic Errors of Target Track Parameter Estimation Using Polar Coordinate Systemp. 168
5.5 Recurrent Filtering Algorithms of Undistorted Polynomial Target Track Parametersp. 170
5.5.1 Optimal Filtering Algorithm Formula Flowchartp. 170
5.5.2 Filtering of Linear Target Track Parametersp. 174
5.5.3 Stabilization Methods for Linear Recurrent Filtersp. 177
5.5.3.1 Introduction of Additional Term into Correlation Matrix of Extrapolation Errorsp. 178
5.5.3.2 Introduction of Artificial Aging of Measuring Errorsp. 179
5.5.3.3 Gain Lower Boundp. 179
5.6 Adaptive Filtering Algorithms of Maneuvering Target Track Parametersp. 179
5.6.1 Principles of Designing the Filtering Algorithms of Maneuvering Target Track Parametersp. 179
5.6.1.1 First Approachp. 180
5.6.1.2 Second Approachp. 181
5.6.1.3 Third Approachp. 181
5.6.2 Implementation of Mixed Coordinate Systems under Adaptive Filteringp. 181
5.6.3 Adaptive Filtering Algorithm Version Based on Bayesian Approach in Maneuvering Targetp. 186
5.7 Logical Flowchart of Complex Radar Signal Reprocessing Algorithmp. 192
5.8 Summary and Discussionp. 193
Referencesp. 199
Chapter 6 Principles of Control Algorithm Design for Complex Radar System Functioning at Dynamical Modep. 201
6.1 Configuration and Flowchart of Radar Control Subsystemp. 202
6.2 Direct Control of Complex Radar Subsystem Parametersp. 207
6.2.1 Initial Conditionsp. 207
6.2.2 Control under Directional Scan in Mode of Searched New Targetsp. 207
6.2.3 Control Process under Refreshment of Target in Target Tracing Modep. 211
6.3 Scan Control in New Target Searching Modep. 213
6.3.1 Problem Statement and Criteria of Searching Control Optimalityp. 213
6.3.2 Optimal Scanning Control under Detection of Single Targetp. 214
6.3.3 Optimal Scanning Control under Detection of Unknown Number of Targetsp. 215
6.3.4 Example of Scanning Control Algorithm in Complex Radar Systems under Aerial Target Detection and Trackingp. 219
6.4 Power Resource Control under Target Trackingp. 222
6.4.1 Control Problem Statementp. 222
6.4.2 Example of Control Algorithm under Target Tracking Modep. 223
6.4.3 Control of Energy Expenditure under Accuracy Aligningp. 226
6.5 Distribution of Power Resources of Complex Radar System under Combination of Target Searching and Target Tracking Modesp. 228
6.6 Summary and Discussionp. 231
Referencesp. 233
Part II Design Principles of Computer System for Radar Digital Signal Processing and Control Algorithms
Chapter 7 Design Principles of Complex Algorithm Computational Process in Radar Systemsp. 237
7.1 Design Considerationsp. 237
7.1.1 Parallel General-Purpose Computersp. 238
7.1.2 Custom-Designed Hardwarep. 239
7.2 Complex Algorithm Assignmentp. 241
7.2.1 Logical and Matrix Algorithm Flowchartsp. 241
7.2.2 Algorithm Graph Flowchartsp. 243
7.2.3 Use of Network Model for Complex Algorithm Analysisp. 246
7.3 Evaluation of Work Content of Complex Digital Signal Processing Algorithm Realization by Microprocessor Subsystemsp. 249
7.3.1 Evaluation of Elementary Digital Signal Processing Algorithm Work Contentp. 249
7.3.2 Definition of Complex Algorithm Work Content Using Network Modelp. 250
7.3.3 Evaluation of Complex Digital Signal Reprocessing Algorithm Work Content in Radar Systemp. 252
7.4 Paralleling of Computational Processp. 257
7.4.1 Multilevel Graph of Complex Digital Signal Processing Algorithmp. 257
7.4.2 Paralleling of Linear Recurrent Filtering Algorithm Macro-Operationsp. 263
7.4.3 Paralleling Principles of Complex Digital Signal Processing Algorithm by Object Setp. 265
7.5 Summary and Discussionp. 267
Referencesp. 271
Chapter 8 Design Principles of Digital Signal Processing Subsystems Employed by a Complex Radar Systemp. 273
8.1 Structure and Main Engineering Data of Digital Signal Processing Subsystemsp. 273
8.1.1 Single-Computer Subsystemp. 273
8.1.2 Multicomputer Subsystemp. 276
8.1.3 Multimicroprocessor Subsystems for Digital Signal Processingp. 278
8.1.4 Microprocessor Subsystems for Digital Signal Processing in Radarp. 280
8.2 Requirements for Effective Speed of Operationp. 282
8.2.1 Microprocessor Subsystem as a Queuing Systemp. 282
8.2.2 Functioning Analysis of Single-Microprocessor Control Subsystem as Queuing Systemp. 285
8.2.3 Specifications for Effective Speed of Microprocessor Subsystem Operationp. 289
8.3 Requirements for RAM Size and Structurep. 293
8.4 Selection of Microprocessor for Designing the Microprocessor Subsystemsp. 295
8.5 Structure and Elements of Digital Signal Processing and Complex Radar System Control Microprocessor Subsystemsp. 296
8.6 High-Performance Centralized Microprocessor Subsystem for Digital Signal Processing of Target Return Signals in Complex Radar Systemsp. 299
8.7 Programmable Microprocessor for Digital Signal Preprocessing of Target Return Signals in Complex Radar Systemsp. 301
8.8 Summary and Discussionp. 302
Referencesp. 306
Chapter 9 Digital Signal Processing Subsystem Design (Example)p. 309
9.1 General Statementsp. 309
9.2 Design of Digital Signal Processing and Control Subsystem Structurep. 310
9.2.1 Initial Statementsp. 310
9.2.2 Main Problems of Digital Signal Processing and Control Subsystemp. 311
9.2.3 Central Computer System Structure for Signal Processing and Controlp. 314
9.3 Structure of Coherent Signal Preprocessing Microprocessor Subsystemp. 315
9.4 Structure of Noncoherent Signal Preprocessing Microprocessor Subsystemp. 318
9.4.1 Noncoherent Signal Preprocessing Problemsp. 318
9.4.2 Noncoherent Signal Preprocessing Microprocessor Subsystem Requirementsp. 321
9.5 Signal Reprocessing Microprocessor Subsystem Specificationsp. 322
9.6 Structure of Digital Signal Processing Subsystemp. 325
9.7 Summary and Discussionp. 327
Referencesp. 329
Chapter 10 Global Digital Signal Processing System Analysisp. 331
10.1 Digital Signal Processing System Designp. 331
10.1.1 Structure of Digital Signal Processing Systemp. 331
10.1.2 Structure and Operation of Nontracking MTIp. 332
10.1.3 MTI as Queuing Systemp. 335
10.2 Analysis of "n - 1 - 1" MTI Systemp. 339
10.2.1 Required Number of Memory Channelsp. 339
10.2.2 Performance Analysis of Detector-Selectorp. 340
10.2.3 Analysis of MTI Characteristicsp. 343
10.3 Analysis of "n - n - 1" MTI Systemp. 344
10.4 Analysis of "n - m - 1" MTI Systemp. 345
10.5 Comparative Analysis of Target Tracking Systemsp. 347
10.6 Summary and Discussionp. 349
Referencesp. 349
Part III Stochastic Processes Measuring in Radar Systems
Chapter 11 Main Statements of Statistical Estimation Theoryp. 353
11.1 Main Definitions and Problem Statementp. 353
11.2 Point Estimate and Its Propertiesp. 356
11.3 Effective Estimationsp. 358
11.4 Loss Function and Average Riskp. 359
11.5 Bayesian Estimates for Various Loss Functionsp. 362
11.5.1 Simple Loss Functionp. 363
11.5.2 Linear Module Loss Functionp. 364
11.5.3 Quadratic Loss Functionp. 365
11.5.4 Rectangle Loss Functionp. 366
11.6 Summary and Discussionp. 366
Referencesp. 368
Chapter 12 Estimation of Mathematical Expectationp. 369
12.1 Conditional Functionalp. 369
12.2 Maximum Likelihood Estimate of Mathematical Expectationp. 373
12.3 Bayesian Estimate of Mathematical Expectation: Quadratic Loss Functionp. 381
12.3.1 Low Signal-to-Noise Ratio (¿ 2 " 1)p. 383
12.3.2 High Signal-to-Noise Ratio (¿ 2 " 1)p. 385
12.4 Applied Approaches to Estimate the Mathematical Expectationp. 386
12.5 Estimate of Mathematical Expectation at Stochastic Process Samplingp. 397
12.6 Mathematical Expectation Estimate under Stochastic Process Amplitude Quantizationp. 408
12.7 Optimal Estimate of Varying Mathematical Expectation of Gaussian Stochastic Processp. 413
12.8 Varying Mathematical Expectation Estimate under Stochastic Process Averaging in Timep. 422
12.9 Estimate of Mathematical Expectation by Iterative Methodsp. 427
12.10 Estimate of Mathematical Expectation with Unknown Periodp. 430
12.11 Summary and Discussionp. 436
Referencesp. 439
Chapter 13 Estimation of Stochastic Process Variancep. 441
13.1 Optimal Variance Estimate of Gaussian Stochastic Processp. 441
13.2 Stochastic Process Variance Estimate under Averaging in Timep. 449
13.3 Errors under Stochastic Process Variance Estimatep. 455
13.4 Estimate of Time-Varying Stochastic Process Variancep. 460
13.5 Measurement of Stochastic Process Variance in Noisep. 468
13.5.1 Compensation Method of Variance Measurementp. 468
13.5.2 Method of Comparisonp. 473
13.5.3 Correlation Method of Variance Measurementp. 476
13.5.4 Modulation Method of Variance Measurementp. 478
13.6 Summary and Discussionp. 484
Referencesp. 486
Chapter 14 Estimation of Probability Distribution and Density Functions of Stochastic Processp. 487
14.1 Main Estimation Regularitiesp. 487
14.2 Characteristics of Probability Distribution Function Estimatep. 491
14.3 Variance of Probability Distribution Function Estimatep. 495
14.3.1 Gaussian Stochastic Processp. 495
14.3.2 Rayleigh Stochastic Processp. 499
14.4 Characteristics of the Probability Density Function Estimatep. 504
14.5 Probability Density Function Estimate Based on Expansion in Series Coefficient Estimationsp. 509
14.6 Measurers of Probability Distribution and Density Functions: Design Principlesp. 514
14.7 Summary and Discussionp. 520
Referencesp. 521
Chapter 15 Estimate of Stochastic Process Frequency-Time Parametersp. 523
15.1 Estimate of Correlation Functionp. 523
15.2 Correlation Function Estimation Based on Its Expansion in Seriesp. 531
15.3 Optimal Estimation of Gaussian Stochastic Process Correlation Function Parameterp. 539
15.4 Correlation Function Estimation Methods Based on Other Principlesp. 554
15.5 Spectral Density Estimate of Stationary Stochastic Processp. 561
15.6 Estimate of Stochastic Process Spike Parametersp. 570
15.6.1 Estimation of Spike Meanp. 571
15.6.2 Estimation of Average Spike Duration and Average Interval between Spikesp. 575
15.7 Mean-Square Frequency Estimate of Spectral Densityp. 579
15.8 Summary and Discussionp. 581
Referencesp. 582
Notation Indexp. 583
Indexp. 597