Radar Data Processing With Applications

Wiley (Verlag)
  • erschienen am 1. August 2016
  • |
  • 560 Seiten
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-118-95688-5 (ISBN)
A systematic introduction to the theory, development and latest research results of radar data processing technology
* Presents both classical theory and development methods of radar data processing
* Provides state-of-the-art research results, including data processing for modern style radars, and tracking performance evaluation theory
* Includes coverage of performance evaluation, registration algorithm for Radar network, data processing of passive radar, pulse Doppler radar, and phased array radar
* Has applications for those engaged in information engineering, radar engineering, electronic countermeasures, infrared techniques, sonar techniques, and military command
weitere Ausgaben werden ermittelt
  • Intro
  • Title Page
  • Copyright
  • Contents
  • About the Authors
  • Preface
  • Chapter 1 Introduction
  • 1.1 Aim and Significance of Radar Data Processing
  • 1.2 Basic Concepts in Radar Data Processing
  • 1.2.1 Measurements
  • 1.2.2 Measurement Preprocessing
  • 1.2.3 Data Association
  • 1.2.4 Wave Gate
  • 1.2.5 Track Initiation and Termination
  • 1.2.6 Tracking
  • 1.2.7 Track
  • 1.3 Design Requirements and Main Technical Indexes of Radar Data Processors
  • 1.3.1 Basic Tasks of Data Processors
  • 1.3.2 The Engineering Design of Data Processors
  • 1.3.3 The Main Technical Indexes of Data Processors
  • 1.3.4 The Evaluation of Data Processors
  • 1.4 History and Present Situation of Research in Radar Data Processing Technology
  • 1.5 Scope and Outline of the Book
  • Chapter 2 Parameter Estimation
  • 2.1 Introduction
  • 2.2 The Concept of Parameter Estimation
  • 2.3 Four Basic Parameter Estimation Techniques
  • 2.3.1 Maximum A Posteriori Estimator
  • 2.3.2 Maximum Likelihood Estimator
  • 2.3.3 Minimum Mean Square Error Estimator
  • 2.3.4 Least Squares Estimator
  • 2.4 Properties of Estimators
  • 2.4.1 Unbiasedness
  • 2.4.2 The Variance of an Estimator
  • 2.4.3 Consistent Estimators
  • 2.4.4 Efficient Estimators
  • 2.5 Parameter Estimation of Static Vectors
  • 2.5.1 Least Squares Estimator
  • 2.5.2 Minimum Mean Square Error Estimator
  • 2.5.3 Linear Minimum Mean Square Error Estimator
  • 2.6 Summary
  • Chapter 3 Linear Filtering Approaches
  • 3.1 Introduction
  • 3.2 Kalman Filter
  • 3.2.1 System Model
  • 3.2.2 Filtering Model
  • 3.2.3 Initialization of Kalman Filters
  • 3.3 Steady-State Kalman Filter
  • 3.3.1 Mathematical Definition and Judgment Methods for Filter Stability
  • 3.3.2 Controllability and Observability of Random Linear System
  • 3.3.3 Steady-State Kalman Filter
  • 3.4 Summary
  • Chapter 4 Nonlinear Filtering Approaches
  • 4.1 Introduction
  • 4.2 Extended Kalman Filter
  • 4.2.1 Filter Model
  • 4.2.2 Some Problems in the Application of Extended Kalman Filters
  • 4.3 Unscented Kalman Filter
  • 4.3.1 Unscented Transformation
  • 4.3.2 Filtering Model
  • 4.3.3 Simulation Analysis
  • 4.4 Particle Filter
  • 4.4.1 Filtering Model
  • 4.4.2 Examples of the Application of EKF, UKF, and PF
  • 4.5 Summary
  • Chapter 5 Measurement Preprocessing Techniques
  • 5.1 Introduction
  • 5.2 Time Registration
  • 5.2.1 Interpolation/Extrapolation Method Using Velocity
  • 5.2.2 The Lagrange Interpolation Algorithm
  • 5.2.3 Least-Squares Curve-Fitting Algorithm
  • 5.3 Space Registration
  • 5.3.1 Coordinates
  • 5.3.2 Coordinate Transformation
  • 5.3.3 Transformation of Several Common Coordinate Systems
  • 5.3.4 Selection of Tracking Coordinate Systems and Filtering State Variables
  • 5.4 Radar Error Calibration Techniques
  • 5.5 Data Compression Techniques
  • 5.5.1 Data Compression in Monostatic Radar
  • 5.5.2 Data Compression in Multistatic Radar
  • 5.6 Summary
  • Chapter 6 Track Initiation in Multi-target Tracking
  • 6.1 Introduction
  • 6.2 The Shape and Size of Track Initiation Gates
  • 6.2.1 The Annular Gate
  • 6.2.2 The Elliptic/Ellipsoidal Gate
  • 6.2.3 The Rectangular Gate
  • 6.2.4 The Sector Gate
  • 6.3 Track Initiation Algorithms
  • 6.3.1 Logic-Based Method
  • 6.3.2 Modified Logic-Based Method
  • 6.3.3 Hough Transform-Based Method
  • 6.3.4 Modified Hough Transform-Based Method
  • 6.3.5 Hough Transform and Logic-Based Method
  • 6.3.6 Formation Target Method Based on Clustering and Hough Transform
  • 6.4 Comparison and Analysis of Track Initiation Algorithms
  • 6.5 Discussion of Some Issues in Track Initiation
  • 6.5.1 Main Indicators of Track Initiation Performance
  • 6.5.2 Demonstration of Track Initiation Scan Times
  • 6.6 Summary
  • Chapter 7 Maximum Likelihood Class Multi-target Data Association Methods
  • 7.1 Introduction
  • 7.2 Track-Splitting Algorithm
  • 7.2.1 Calculation of Likelihood Functions
  • 7.2.2 Threshold Setting
  • 7.2.3 Modified Likelihood Function
  • 7.2.4 Characteristics of Track-Splitting Algorithm
  • 7.3 Joint Maximum Likelihood Algorithm
  • 7.3.1 Establishment of Feasible Partitions
  • 7.3.2 Recursive Joint Maximum Likelihood Algorithm
  • 7.4 0-1 Integer Programming Algorithm
  • 7.4.1 Calculation of the Logarithm Likelihood Ratio
  • 7.4.2 0-1 Linear Integer Programming Algorithm
  • 7.4.3 Recursive 0-1 Integer Programming Algorithm
  • 7.4.4 Application of 0-1 Integer Programming Algorithm
  • 7.5 Generalized Correlation Algorithm
  • 7.5.1 Establishing the Score Function
  • 7.5.2 Application of the Generalized Correlation Algorithm
  • 7.6 Summary
  • Chapter 8 Bayesian Multi-target Data Association Approach
  • 8.1 Introduction
  • 8.2 Nearest-Neighbor Algorithm
  • 8.2.1 Nearest-Neighbor Standard Filter
  • 8.2.2 Probabilistic Nearest-Neighbor Filter Algorithm
  • 8.3 Probabilistic Data Association Algorithm
  • 8.3.1 State Update and Covariance Update
  • 8.3.2 Calculation of the Association Probability
  • 8.3.3 Modified PDAF Algorithm
  • 8.3.4 Performance Analysis
  • 8.4 Integrated Probabilistic Data Association Algorithm
  • 8.4.1 Judgment of Track Existence
  • 8.4.2 Data Association
  • 8.5 Joint Probabilistic Data Association Algorithm
  • 8.5.1 Basic Models of JPDA
  • 8.5.2 Calculation of the Probability of Joint Events
  • 8.5.3 Calculation of the State Estimation Covariance
  • 8.5.4 Simplified JPDA Model
  • 8.5.5 Performance Analysis
  • 8.6 Summary
  • Chapter 9 Tracking Maneuvering Targets
  • 9.1 Introduction
  • 9.2 Tracking Algorithm with Maneuver Detection
  • 9.2.1 White Noise Model with Adjustable Level
  • 9.2.2 Variable-Dimension Filtering Approach
  • 9.3 Adaptive Tracking Algorithm
  • 9.3.1 Modified-Input Estimation Algorithm
  • 9.3.2 Singer Model Tracking Algorithm
  • 9.3.3 Current Statistical Model Algorithm
  • 9.3.4 Jerk Model Tracking Algorithm
  • 9.3.5 Multiple Model Algorithm
  • 9.3.6 Interacting Multiple Model Algorithm
  • 9.4 Performance Comparison of Maneuvering Target Tracking Algorithms
  • 9.4.1 Simulation Environment and Parameter Setting
  • 9.4.2 Simulation Results and Analysis
  • 9.5 Summary
  • Chapter 10 Group Target Tracking
  • 10.1 Introduction
  • 10.2 Basic Methods for Track Initiation of the Group Target
  • 10.2.1 Group Definition
  • 10.2.2 Group Segmentation
  • 10.2.3 Group Correlation
  • 10.2.4 Group Velocity Estimation
  • 10.3 The Gray Fine Track Initiation Algorithm for Group Targets
  • 10.3.1 Gray Fine Association of Targets within the Group Based on the Relative Position Vector of the Measurement
  • 10.3.2 Confirmation of the Tracks within a Group
  • 10.3.3 Establishment of State Matrixes for Group Targets
  • 10.3.4 Simulation Verification and Analysis of the Algorithm
  • 10.3.5 Discussion
  • 10.4 Centroid Group Tracking
  • 10.4.1 Initiation, Confirmation, and Cancellation of Group Tracks
  • 10.4.2 Track Updating
  • 10.4.3 Other Questions
  • 10.5 Formation Group Tracking
  • 10.5.1 Overview of Formation Group Tracking
  • 10.5.2 Logic Description of Formation Group Tracking
  • 10.6 Performance Analysis of Tracking Algorithms for Group Targets
  • 10.6.1 Simulation Environment
  • 10.6.2 Simulation Results
  • 10.6.3 Simulation Analysis
  • 10.7 Summary
  • Chapter 11 Multi-target Track Termination Theory and Track Management
  • 11.1 Introduction
  • 11.2 Multi-target Track Termination Theory
  • 11.2.1 Sequential Probability Ratio Test Algorithm
  • 11.2.2 Tracking Gate Method
  • 11.2.3 Cost Function Method
  • 11.2.4 Bayesian Algorithm
  • 11.2.5 All-Neighbor Bayesian Algorithm
  • 11.2.6 Performance Analysis of Several Algorithms
  • 11.3 Track Management
  • 11.3.1 Track Batch Management
  • 11.3.2 Track Quality Management
  • 11.3.3 Track File Management in the Information Fusion System
  • 11.4 Summary
  • Chapter 12 Passive Radar Data Processing
  • 12.1 Introduction
  • 12.2 Advantages of Passive Radars
  • 12.3 Passive Radar Spatial Data Association
  • 12.3.1 Phase Changing Rate Method
  • 12.3.2 Doppler Changing Rate and Azimuth Joint Location
  • 12.3.3 Doppler Changing Rate and Azimuth, Elevation Joint Location
  • 12.3.4 Multiple-Model Method
  • 12.4 Optimal Deployment of Direction-Finding Location
  • 12.4.1 Area of the Position Concentration Ellipse
  • 12.4.2 Derivation of the Conditional Extremum Based on the Lagrange Multiplier Method
  • 12.4.3 Optimal Deployment by the Criterion that the Position Concentration Ellipse Area is Minimum
  • 12.5 Passive Location Based on TDOA Measurements
  • 12.5.1 Location Model
  • 12.5.2 Two-Dimensional Condition
  • 12.5.3 Three-Dimensional Condition
  • 12.6 Summary
  • Chapter 13 Pulse Doppler Radar Data Processing
  • 13.1 Introduction
  • 13.2 Overview of PD Radar Systems
  • 13.2.1 Characteristics of PD Radar
  • 13.2.2 PD Radar Tracking System
  • 13.3 Typical Algorithms of PD Radar Tracking
  • 13.3.1 Optimal Range-Velocity Mutual Coupling Tracking
  • 13.3.2 Multi-target Tracking
  • 13.3.3 Target Tracking with Doppler Measurements
  • 13.4 Performance Analysis on PD Radar Tracking Algorithms
  • 13.4.1 Simulation Environments and Parameter Settings
  • 13.4.2 Simulation Results and Analysis
  • 13.5 Summary
  • Chapter 14 Phased Array Radar Data Processing
  • 14.1 Introduction
  • 14.2 Characteristics and Major Indexes
  • 14.2.1 Characteristics
  • 14.2.2 Major Indexes
  • 14.3 Structure and Working Procedure
  • 14.3.1 Structure
  • 14.3.2 Working Procedure
  • 14.4 Data Processing
  • 14.4.1 Single-Target-in-Clutter Tracking Algorithms
  • 14.4.2 Multi-target-in-Clutter Tracking Algorithm
  • 14.4.3 Adaptive Sampling Period Algorithm
  • 14.4.4 Real-Time Task Scheduling Strategy
  • 14.5 Performance Analysis of the Adaptive Sampling Period Algorithm
  • 14.5.1 Simulation Environment and Parameter Settings
  • 14.5.2 Simulation Results and Analysis
  • 14.5.3 Comparison and Discussion
  • 14.6 Summary
  • Chapter 15 Radar Network Error Registration Algorithm
  • 15.1 Introduction
  • 15.2 The Composition and Influence of Systematic Errors
  • 15.2.1 The Composition of Systematic Errors
  • 15.2.2 The Influence of Systematic Errors
  • 15.3 Fixed Radar Registration Algorithm
  • 15.3.1 Radar Registration Algorithm Based on Cooperative Targets
  • 15.3.2 RTQC Algorithm
  • 15.3.3 LS Algorithm
  • 15.3.4 GLS Algorithm
  • 15.3.5 GLS Algorithm in ECEF Coordinate System
  • 15.3.6 Simulation Analysis
  • 15.4 Mobile Radar Registration Algorithm
  • 15.4.1 Modeling Method of Mobile Radar Systems
  • 15.4.2 Mobile Radar Registration Algorithm Based on Cooperative Targets
  • 15.4.3 Mobile Radar Maximum Likelihood Registration Algorithm
  • 15.4.4 ASR Algorithm
  • 15.4.5 Simulation Analysis
  • 15.5 Summary
  • Chapter 16 Radar Network Data Processing
  • 16.1 Introduction
  • 16.2 Performance Evaluation Indexes of Radar Networks
  • 16.2.1 Coverage Performance Indexes
  • 16.2.2 Target Capacity
  • 16.2.3 Anti-jamming Ability
  • 16.3 Data Processing of Monostatic Radar Networks
  • 16.3.1 The Process of Data Processing of the Monostatic Radar Network
  • 16.3.2 State Estimation of Monostatic Radar Networks
  • 16.4 Data Processing of Bistatic Radar Networks
  • 16.4.1 Basic Location Relation
  • 16.4.2 Combined Estimation
  • 16.4.3 An Analysis of the Feasibility of Combinational Estimation
  • 16.5 Data Processing of Multistatic Radar Networks
  • 16.5.1 Tracking Principle of Multistatic Radar Systems
  • 16.5.2 Observation Equation of Multistatic Radar Network Systems
  • 16.5.3 The Generic Data Processing Process of Multistatic Tracking Systems
  • 16.6 Track Association
  • 16.7 Summary
  • Chapter 17 Evaluation of Radar Data Processing Performance
  • 17.1 Introduction
  • 17.2 Basic Terms
  • 17.3 Data Association Performance Evaluation
  • 17.3.1 Average Track Initiation Time
  • 17.3.2 Accumulative Number of Track Interruptions
  • 17.3.3 Track Ambiguity
  • 17.3.4 Accumulative Number of Track Switches
  • 17.4 Performance Evaluation of Tracking
  • 17.4.1 Track Accuracy
  • 17.4.2 Maneuvering Target Tracking Capability
  • 17.4.3 False Track Ratio
  • 17.4.4 Divergence
  • 17.5 Evaluation of the Data Fusion Performance of Radar Networks
  • 17.5.1 Track Capacity
  • 17.5.2 Detection Probability of Radar Networks
  • 17.5.3 Response Time
  • 17.6 Methods of Evaluating Radar Data Processing Algorithms
  • 17.6.1 Monte Carlo Method
  • 17.6.2 Analytic Method
  • 17.6.3 Semi-physical Simulation Method
  • 17.6.4 Test Validation Method
  • 17.7 Summary
  • Chapter 18 Radar Data Processing Simulation Technology
  • 18.1 Introduction
  • 18.2 Basis of System Simulation Technology
  • 18.2.1 Basic Concept of System Simulation Technology
  • 18.2.2 Digital Simulation of Stochastic Noise
  • 18.3 Simulation of Radar Data Processing Algorithms
  • 18.3.1 Simulation of Target Motion Models
  • 18.3.2 Simulation of the Observation Process
  • 18.3.3 Tracking Filtering and Track Management
  • 18.4 Simulation Examples of Algorithms
  • 18.5 Summary
  • Chapter 19 Practical Application of Radar Data Processing
  • 19.1 Introduction
  • 19.2 Application in ATC Systems
  • 19.2.1 Application, Components, and Requirement
  • 19.2.2 Radar Data Processing Structure
  • 19.2.3 ATC Application
  • 19.3 Application in Shipboard Navigation Radar
  • 19.4 Application in Shipboard Radar Clutter Suppression
  • 19.4.1 Principle of Clutter Suppression in Data Processing
  • 19.4.2 Clutter Suppression Method through Shipboard Radar Data Processing
  • 19.5 Application in Ground-Based Radar
  • 19.5.1 Principle of Data Acquisition
  • 19.5.2 Data Processing Procedure
  • 19.6 Applications in Shipboard Monitoring System
  • 19.6.1 Application, Components, and Requirement
  • 19.6.2 Structure of the Marine Control System
  • 19.7 Application in the Fleet Air Defense System
  • 19.7.1 Components and Function of the Aegis Fleet Air Defense System
  • 19.7.2 Main Performance Indexes
  • 19.8 Applications in AEW Radar
  • 19.8.1 Features, Components, and Tasks
  • 19.8.2 Data Processing Technology
  • 19.8.3 Typical Working Mode
  • 19.9 Application in Air Warning Radar Network
  • 19.9.1 Structure of Radar Network Data Processing
  • 19.9.2 Key Technologies of Radar Network Data Processing
  • 19.10 Application in Phased Array Radar
  • 19.10.1 Functional Features
  • 19.10.2 Data Processing Procedure
  • 19.10.3 Test Examples
  • 19.11 Summary
  • Chapter 20 Review, Suggestions, and Outlook
  • 20.1 Introduction
  • 20.2 Review of Research Achievements
  • 20.2.1 The Basis of State Estimation
  • 20.2.2 Measurement Preprocessing Technology
  • 20.2.3 Track Initiation in Multi-target Tracking
  • 20.2.4 Multi-target Data Association Method
  • 20.2.5 Maneuvering Target and Group Tracking
  • 20.2.6 Multi-target Tracking Termination Theory and Track Management
  • 20.2.7 System Error Registration Issue
  • 20.2.8 Performance Evaluation of Radar Data Processors
  • 20.2.9 Simulation Technology of Radar Data Processing
  • 20.2.10 Applications of Radar Data Processing Techniques
  • 20.3 Issues and Suggestions
  • 20.3.1 The Application of Data Processing Technology in Other Sensors
  • 20.3.2 Track Initiation in Passive Sensor Tracking
  • 20.3.3 Non-Gaussian Noise
  • 20.3.4 Data Processing in Non-standard and Nonlinear Systems
  • 20.3.5 Data Processing in Multi-radar Networks
  • 20.3.6 Joint Optimization of Multi-target Tracking and Track Association
  • 20.3.7 Comprehensive Utilization of Target Features and Attributes in Multi-radar Tracking
  • 20.3.8 Comprehensive Optimization of Multi-radar Information Fusion Systems
  • 20.3.9 Tracking Multi-targets in Complex Electromagnetic Waves and Dense Clutter
  • 20.4 Outlook for Research Direction
  • 20.4.1 Information Fusion and Control Integration Technology of Multi-radar Networks
  • 20.4.2 Joint Optimization of Target Tracking and Identification
  • 20.4.3 Integration Technology of Search, Tracking, Guidance, and Command
  • 20.4.4 Multi-radar Resource Allocation and Management Technology
  • 20.4.5 Database and Knowledge Base Technology in Radar Data Processing
  • 20.4.6 Engineering Realization of Advanced Radar Data Processing Algorithms
  • 20.4.7 High-Speed Calculation and Parallel Processing Technology
  • 20.4.8 Establishment of System Performance Evaluation Methods and Test Platforms
  • 20.4.9 Common Theoretical Models for Variable Structure State Estimation
  • 20.4.10 Automatic Tracking of Targets in Complex Environments
  • 20.4.11 Tracking and Invulnerability of Multi-radar Network Systems
  • References
  • Index
  • EULA


1.1 Aim and Significance of Radar Data Processing

Generally, a modern radar system consists of two important components: a signal processor and a data processor. The signal processor is used for target detection (i.e., the suppression of undesirable signals produced by ground or sea surface clutter, meteorological factors, radio frequency interference, noise sources, and man-made interference) [1-3]. When the video output signal, after signal processing and constant false alarm rate (CFAR) detection fusion, exceeds a certain detection threshold, it can be determined that a target has been discovered. Then, the discovered target signal will be transmitted to the data recording device, where the space position, amplitude value, radial velocity, and other characteristic parameters of the target are recorded, usually by computers. The measurement output from the data recording device needs to be processed in the data processor, which associates, tracks, filters, smooths, and predicts the obtained measurement data - such as the target position (radial distance, azimuth, and pitch angle) and the motion parameters [4-6] - for the effective suppression of random errors occurring during the measurement, estimation of the trajectory and related motion parameters (velocity and acceleration, etc.) of the target in the control area, prediction of the target's position at the next moment, and formation of a steady target track, so that highly accurate real-time tracking is realized [7-9].

In terms of the level at which radar echo signals are processed, radar signal processing is usually viewed as the primary processing of the information detected by the radar unit. It is done at each radar station, with information obtained from the same radar and the same scanning period and distance unit, with the aim of extracting useful target information from clutter, noise, and various active and passive jamming backgrounds. Radar data processing is usually viewed as secondary processing of the radar information [10-13]. Making use of information from the same radar, but with different scanning periods and distance units, it can be done both at each independent radar station and at the information processing center or system command center of the radar network. Data fusion of multiple radars can be viewed as a third or tertiary processing of the radar information, which is usually done at the information processing center. Specifically, the information the processing center receives is the measurement from the primary processing or the track from the secondary processing (usually called the local track) by multiple radars, and the track after fusion (called the global track or system track). The function of the secondary processing of radar information, based on the primary processing, is to filter and track several targets, and estimate the targets' motion parameters and characteristic parameters. Secondary processing is done strictly after primary processing, while there is no strict time limit between secondary and tertiary processing. The third level of processing is the expansion and extension of secondary processing, which is mainly reflected in space and dimension.

1.2 Basic Concepts in Radar Data Processing

The input to the radar data processing unit is the measurement from the front, which is the object of data processing, while the output is the track formed after data processing is conducted. Generally, functional modules of radar data processing include measurement pretreatment, track initiation and termination, and data association and tracking. A wave gate must be set up between the association and the tracking process, and their relationship is shown in the block diagram in Figure 1.1. The content and related concepts of the functional modules of radar data processing are briefly discussed as follows.

Figure 1.1 Radar data processing relation diagram

1.2.1 Measurements

Measurements, also called observations, refer to noise-corrupted observations related to the state of a target [14]. The measurements are not usually raw data points, but the output from the data recording device after signal processing. Measurements can be divided, according to whether they are associated with the known target track, into free measurements and correlated measurements. Free measurements are spots that are not correlated with the known target track, while correlated measurements are spots that are correlated with the known target track.

1.2.2 Measurement Preprocessing

Although modern radar adopts many signal processing technologies, there will always be a small proportion of clutter/interference signals left out. To relieve the computers doing the follow-up processing job from a heavy burden, prevent computers from saturation, and improve system performance, the measurement given by the primary processing needs to be preprocessed, which is called "measurement preprocessing": the preprocessing of secondary processing of radar information. The preprocessing is a precondition of correct processing of radar data, since an effective measurement data processing method can actually help yield twice the result with half the effort, with the target tracking accuracy improved while the computational complexity of the target tracking is reduced. The measurement preprocessing technology mainly involves system error registration, time synchronization, space alignment, outlier rejection, and saturation prevention. System Error Registration

The measurement data from radars contains two types of error. One is random error, resulting from the interior noise of the measurement system. Random error may vary with each measurement, and may be eliminated to some extent by increasing the frequency of measurement and minimizing its variance in the statistical sense by means of methods like filtering. The other is system error, resulting from measurement environments, antennas, servo systems, and such non-calibration factors in the data correction process as the position error of radar stations and the zero deviation of altimeters. System error is complex, slowly varying, and non-random, and can be viewed as an unknown variable in a relatively long period of time. As indicated by the findings in Ref. [15], when the ratio of system errors to random errors is greater than or equal to 1, the effect of distributed track fusion and centralized measurement fusion deteriorates markedly, and at this point system errors must be corrected. Time Synchronization

Owing to the possible difference in each radar's power-on time and sampling rate, the target measurement data recorded by data recording devices may be asynchronous. Therefore, these observation data must be synchronized in multiple-radar data processing. Usually, the sampling moment of a radar is set as the benchmark for the time of other radars. Space Alignment

Space alignment is the process of unifying the coordinate origin, coordinate axis direction, etc. of the data from the radar stations in different places, so as to bring the measurement data from several radars into a unified reference framework, paving the way for the follow-up radar data processing. Outlier Rejection

Outlier rejection is the process of removing the obviously abnormal values from radar measurement data. Saturation Prevention

Saturation prevention mainly deals with saturation in the following two cases.

  1. In the design of a data processing system, there is a limit to the number of target data. However, in a real system, saturation occurs when the data to be processed exceed the processing capacity.
  2. The time used to process data is limited. Saturation occurs when the number of measurements, or batches of targets, reaches a certain extent. In this case, the processing of the data from one observation has to be interrupted before the processor starts to deal with the next batch of data.

1.2.3 Data Association

In the single-target, clutter-free environment, where there is only one measurement in the target-related wave gate, only tracking is involved. Under multi-target circumstances, where a single measurement falls in the intersection area of several wave gates or several measurements fall in the related wave gate of a single target, data association is involved. For instance, suppose two target tracks have been established before the radar's nth scanning, and two echoes are detected in the nth scanning, are the echoes from two new targets or from the two established tracks at that time? If they are from the two established tracks at that time, then in what way can the echoes resulting from the two scans and the two tracks be correctly paired? The answer involves data association, the establishment of the relationship between the radar measurements at a given moment and the measurements (or tracks) at other moments, to check whether these measurements originate from the processing of the same target (or to ensure a correct process of measurement-and-track pairing).

Data association, also called "data correlation" or "measurement correlation," is a crucial issue in radar data processing. False data association could pair the target with a false velocity, which could result in the...

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