Contents
PART I Design of radar Digital Signal Processing and Control algorithms
Chapter 1 Principles of Systems Approach to Design Complex Radar Systems
1.1 Methodology of Systems Approach
1.2 Main Requirements of Complex Radar Systems
1.3 Problems of System Design for Automated Complex Radar Systems
1.4 Radar Signal Processing System as an Object of Design
Chapter 2 Signal Processing by Digital Generalized Detector in Complex Radar Systems
2.1 Analog-to-Digital Signal Conversion: Main Principles
2.1.2 Quantization and Signal Sampling Conversion
2.1.3 Analog-to-Digital Conversion: Design Principles and Main Parameters
2.1.3.1 Sampling and Quantization Errors
2.2 Digital Generalized Detector for Coherent Impulse Signals
2.2.3 Digital Generalized Detector
2.3 Convolution in Time Domain
2.4 Convolution in Frequency Domain
2.5 Examples of Some DGD Types
Chapter 3 Digital Interperiod Signal Processing Algorithms
3.1 Digital Moving-Target Indication Algorithms
3.1.1 Principles of Construction and Efficiency Indices
3.1.2 Digital Rejector Filters
3.1.3 Digital Moving-Target Indicator in Radar System with Variable Pulse Repetition Frequency
3.1.4 Adaptation in Digital Moving-Target Indicators
3.2 DGD for Coherent Impulse Signals with Known Parameters
3.2.2 DGD for Target Return Pulse Train
3.2.3 DGD for Binary Quantized Target Return Pulse Train
3.2.4 DGD Based on Methods of Sequential Analysis
3.2.5 Software DGD for Binary Quantized Target Return Pulse Train
3.3 DGD for Coherent Impulse Signals with Unknown Parameters
3.3.1 Problem Statements of Digital Detector Synthesis
3.3.3.1 Sign-Nonparametric DGD
3.3.3.2 Rank-Nonparametric DGD
3.3.4 Adaptive-Nonparametric DGD
3.4 Digital Measurers of Target Return Signal Parameters
3.4.1 Digital Measurer of Target Range
3.4.2 Algorithms of Angular Coordinate Estimation under Uniform Radar Antenna Scanning
3.4.3 Algorithms of Angular Coordinate Estimation under Discrete Radar Antenna Scanning
3.4.4 Doppler Frequency Measurer
3.5 Complex Generalized Algorithms of Digital Interperiod Signal Processing
Chapter 4 Algorithms of Target Range Track Detection and Tracking
4.1 Main Stages and Signal Reprocessing Operations
4.1.1 Target Pip Gating: Shape Selection and Dimensions of Gates
4.1.2 Algorithm of Target Pip Indication by Minimal Deviation from Gate Center
4.1.3 Target Pip Distribution and Binding within Overlapping Gates
4.2 Target Range Track Detection Using Surveillance Radar Data
4.2.1 Main Operations under Target Range Track Detection
4.2.2 Statistical Analysis of “2m + 1/n” Algorithms under False Target Range Track Detection
4.2.3 Statistical Analysis of “2m + l/n” Algorithms under True Target Range Track Detection
4.3 Target Range Tracking Using Surveillance Radar Data
4.3.1 Target Range Autotracking Algorithm
4.3.2 United Algorithm of Detection and Target Range Tracking
Chapter 5 Filtering and Extrapolation of Target Track Parameters Based on Radar Measure
5.2 Process Representation in Filtering Subsystems
5.3 Statistical Approach to Solution of Filtering Problems of Stochastic (Unknown) Parameters
5.4 Algorithms of Linear Filtering and Extrapolation under Fixed Sample Size of Measurements
5.4.2 Algorithms of Optimal Estimation of Linear Target Track Parameters
5.4.3 Algorithm of Optimal Estimation of Second-Order Polynomial Target Track Parameters
5.4.4 Algorithm of Extrapolation of Target Track Parameters
5.4.5 Dynamic Errors of Target Track Parameter Estimation Using Polar Coordinate System
5.5 Recurrent Filtering Algorithms of Undistorted Polynomial Target Track Parameters
5.5.1 Optimal Filtering Algorithm Formula Flowchart
5.5.2 Filtering of Linear Target Track Parameters
5.5.3 Stabilization Methods for Linear Recurrent Filters
5.5.3.1 Introduction of Additional Term into Correlation Matrix of Extrapolation Errors
5.5.3.2 Introduction of Artificial Aging of Measuring Errors
5.6 Adaptive Filtering Algorithms of Maneuvering Target Track Parameters
5.6.1 Principles of Designing the Filtering Algorithms of Maneuvering Target Track Parameters
5.6.2 Implementation of Mixed Coordinate Systems under Adaptive Filtering
5.6.3 Adaptive Filtering Algorithm Version Based on Bayesian Approach in Maneuvering Target
5.7 Logical Flowchart of Complex Radar Signal Reprocessing Algorithm
6.1 Configuration and Flowchart of Radar Control Subsystem
6.2 Direct Control of Complex Radar Subsystem Parameters
6.2.2 Control under Directional Scan in Mode of Searched New Targets
6.2.3 Control Process under Refreshment of Target in Target Tracing Mode
6.3 Scan Control in New Target Searching Mode
6.3.1 Problem Statement and Criteria of Searching Control Optimality
6.3.2 Optimal Scanning Control under Detection of Single Target
6.3.3 Optimal Scanning Control under Detection of Unknown Number of Targets
6.4 Power Resource Control under Target Tracking
6.4.1 Control Problem Statement
6.4.2 Example of Control Algorithm under Target Tracking Mode
6.4.3 Control of Energy Expenditure under Accuracy Aligning
Chapter 7 Design Principles of Complex Algorithm Computational Process in Radar Systems
7.1.1 Parallel General-Purpose Computers
7.1.2 Custom-Designed Hardware
7.2 Complex Algorithm Assignment
7.2.1 Logical and Matrix Algorithm Flowcharts
7.2.2 Algorithm Graph Flowcharts
7.2.3 Use of Network Model for Complex Algorithm Analysis
7.3.1 Evaluation of Elementary Digital Signal Processing Algorithm Work Content
7.3.2 Definition of Complex Algorithm Work Content Using Network Model
7.3.3 Evaluation of Complex Digital Signal Reprocessing Algorithm Work Content in Radar System
7.4 Paralleling of Computational Process
7.4.1 Multilevel Graph of Complex Digital Signal Processing Algorithm
7.4.2 Paralleling of Linear Recurrent Filtering Algorithm. Macro-Operations
7.4.3 Paralleling Principles of Complex Digital Signal Processing Algorithm by Object Set
8.1 Structure and Main Engineering Data of Digital Signal Processing Subsystems
8.1.1 Single-Computer Subsystem
8.1.3 Multimicroprocessor Subsystems for Digital Signal Processing
8.1.4 Microprocessor Subsystems for Digital Signal Processing in Radar
8.2 Requirements for Effective Speed of Operation
8.2.1 Microprocessor Subsystem as a Queuing System
8.2.2 Functioning Analysis of Single-Microprocessor Control Subsystem as Queuing System
8.2.3 Specifications for Effective Speed of Microprocessor Subsystem Operation
8.3 Requirements for RAM Size and Structure
8.4 Selection of Microprocessor for Designing the Microprocessor Subsystems
Chapter 9 Digital Signal Processing Subsystem Design (Example)
9.2 Design of Digital Signal Processing and Control Subsystem Structure
9.2.2 Main Problems of Digital Signal Processing and Control Subsystem
9.2.3 Central Computer System Structure for Signal Processing and Control
9.3 Structure of Coherent Signal Preprocessing Microprocessor Subsystem
9.4 Structure of Noncoherent Signal Preprocessing Microprocessor Subsystem
9.4.1 Noncoherent Signal Preprocessing Problems
9.4.2 Noncoherent Signal Preprocessing Microprocessor Subsystem Requirements
9.5 Signal Reprocessing Microprocessor Subsystem Specifications
9.6 Structure of Digital Signal Processing Subsystem
Chapter 10 Global Digital Signal Processing System Analysis
10.1 Digital Signal Processing System Design
10.1.1 Structure of Digital Signal Processing System
10.1.2 Structure and Operation of Nontracking MTI
10.2 Analysis of “n – 1 – 1” MTI System
10.2.1 Required Number of Memory Channels
10.2.2 Performance Analysis of Detector–Selector
10.2.3 Analysis of MTI Characteristics
10.3 Analysis of “n – n – 1” MTI System
10.4 Analysis of “n – m– 1” MTI System
10.5 Comparative Analysis of Target Tracking Systems
Part III Stochastic Processes Measuring in radar Systems
Chapter 11 Main Statements of Statistical Estimation Theory
11.1 Main Definitions and Problem Statement
11.2 Point Estimate and Its Properties
11.4 Loss Function and Average Risk
11.5 Bayesian Estimates for Various Loss Functions
11.5.2 Linear Module Loss Function
11.5.3 Quadratic Loss Function
11.5.4 Rectangle Loss Function
Chapter 12 Estimation of Mathematical Expectation
12.2 Maximum Likelihood Estimate of Mathematical Expectation
12.3 Bayesian Estimate of Mathematical Expectation: Quadratic Loss Function
12.3.1 Low Signal-to-Noise Ratio (ρ2 ≪ 1)
12.3.2 High Signal-to-Noise Ratio (ρ2 ≫ 1)
12.4 Applied Approaches to Estimate the Mathematical Expectation
12.5 Estimate of Mathematical Expectation at Stochastic Process Sampling
12.6 Mathematical Expectation Estimate under Stochastic Process Amplitude Quantization
12.7 Optimal Estimate of Varying Mathematical Expectation of Gaussian Stochastic Process
12.8 Varying Mathematical Expectation Estimate under Stochastic Process Averaging in Time
12.9 Estimate of Mathematical Expectation by Iterative Methods
12.10 Estimate of Mathematical Expectation with Unknown Period
Chapter 13 Estimation of Stochastic Process Variance
13.1 Optimal Variance Estimate of Gaussian Stochastic Process
13.2 Stochastic Process Variance Estimate under Averaging in Time
13.3 Errors under Stochastic Process Variance Estimate
13.4 Estimate of Time-Varying Stochastic Process Variance
13.5 Measurement of Stochastic Process Variance in Noise
13.5.1 Compensation Method of Variance Measurement
13.5.3 Correlation Method of Variance Measurement
13.5.4 Modulation Method of Variance Measurement
Chapter 14 Estimation of Probability Distribution and Density Functions of Stochastic Process
14.1 Main Estimation Regularities
14.2 Characteristics of Probability Distribution Function Estimate
14.3 Variance of Probability Distribution Function Estimate
14.3.1 Gaussian Stochastic Process
14.3.2 Rayleigh Stochastic Process
14.4 Characteristics of the Probability Density Function Estimate
14.5 Probability Density Function Estimate Based on Expansion in Series Coefficient Estimations
14.6 Measurers of Probability Distribution and Density Functions: Design Principles
Chapter 15 Estimate of Stochastic Process Frequency-Time Parameters
15.1 Estimate of Correlation Function
15.2 Correlation Function Estimation Based on Its Expansion in Series
15.3 Optimal Estimation of Gaussian Stochastic Process Correlation Function Parameter
15.4 Correlation Function Estimation Methods Based on Other Principles
15.5 Spectral Density Estimate of Stationary Stochastic Process
15.6 Estimate of Stochastic Process Spike Parameters
15.6.1 Estimation of Spike Mean
15.6.2 Estimation of Average Spike Duration and Average Interval between Spikes