원문정보
초록
영어
Conventional threshold-based Built-In Test (BIT) schemes in monopulse radar systems are limited in detecting gradual performance degradation caused by Σ/Δ channel imbalance and tracking loop parameter variations. The objective of this study is to develop a performance-oriented BIT framework capable of diagnosing system-level degradation beyond component-level fault detection. To achieve this, a physics-based digital twin of a monopulse radar system is constructed by modeling Σ/Δ channel characteristics, automatic gain control, and range and angle tracking loops. Residuals between digital twin predictions and measured system responses are extracted and analyzed using machine learning techniques for anomaly detection and fault classification.The proposed approach enables unified application across PBIT, IBIT, and CBIT, providing enhanced diagnostic sensitivity and reliability compared to conventional BIT methods.
한국어
기존 모노펄스 레이더 시스템의 임계치 기반 BIT 기법은 Σ/Δ 채널 불균형이나 추적 루프 파라미터 변화 로 인한 점진적 성능 저하를 조기에 탐지하는 데 한계를 가진다. 본 논문의 목적은 시스템 동작 거동을 기반으로 성능 열화를 진단할 수 있는 성능 지향 BIT 프레임워크를 제안하는 것이다. 이를 위해 Σ/Δ 채널, 자동이득제어 및 거리·각도 추적 루프를 포함하는 물리 기반 디지털 트윈을 구성하고, 디지털 트윈 예측 결과와 실제 계측 신호 간의 잔차를 머신러닝 기법으로 분석하였다. 제안된 기법은 PBIT, IBIT, CBIT 전 주기에 적용 가능하며, 기존 BIT 대비 향상된 진단 민감도와 신뢰성을 제공한다.
목차
요약
1. Introduction
2. Monopulse Radar System Theory and Performance Degradation Factors
2.1 Basic Principle of Monopulse Radar
2.2 Amplitude Comparison Monopulse and Performance Metrics
2.3 Control Loops and Propagation of Performance Degradation
3. Monopulse Radar BIT Architecture and Analysis of Conventional Diagnostic Methods
3.1 Concept and Purpose of Built-In Test
3.2 Functional Classification and Limitations of PBIT, IBIT, and CBIT
3.3 Structural Limitations of Rule-Based BIT
3.4 Necessity of Extension to Performance-Based BIT
3.5 Background for Introduction of Digital Twin and Data-Driven Diagnostics
4. Digital Twin–Based Modeling of Monopulse Radar Systems
4.1 Concept of Digital Twin and Necessity for Radar System Application
4.2 Physics-Based Monopulse Radar Signal Model
4.3 Modeling of Σ/Δ Channel Imbalance and Calibration Parameters
4.4 Dynamic Modeling of Range and Angle Tracking Loops
4.5 Modeling of Automatic Gain Control
4.6 Digital Twin Synchronization and State Variable Definition
5. ML-Based Digital Twin BIT Algorithm Design
5.1 Overall Structure of Digital Twin–Based BIT Algorithm
5.2 Residual-Based Feature Definition
5.3 ML-Based Anomaly Detection and Fault Classification
5.4 Application Scenarios for PBIT, IBIT, and CBIT
5.5 Fault Isolation and Improvement of Diagnostic Reliability
5.6 Discussion on Practical Validation and Robustness
6. Conclusions and Future Work
REFERENCES