레이더 측정 잡음 추정을 통한 기동 표적 추적 성능 향상

국문 초록 (Abstract)

항공관제용 감시자료 처리시스템에 의한 기동 표적 추적에 있어서 레이더의 측정 잡음 분산은 상태 추정기의 압력으로서, 추적 정확도에 영향을 주는 주요한 요소 중 하나이다. 본 연구에서는 레이더의 측정 잡음 분산을 상수가 아닌 변수로 지정하여, 다중 IMM 필터의 우도함수를 통해 매 시간 측정 잡음 분산을 실시간으로 추정하는 알고리즘을 제시하였다. Monte Carlo 시뮬레이션 결과 측정 잡음 분산 값을 실제 값 대비 5% 이대 수준으로 예측함을 확인하였고, 이를 통해 기동 표적 추적 성능을 향상시킬 수 있음을 확인하였다.

다국어 초록 (Multilingual Abstract)

Measurement noise variance of the radar is one of the main inputs of a state estimator of surveillance data processing system for air traffic control and has influences on the accuracy performance of maneuvering target tracking. A method is presented of estimating measurement noise variances every frame of target tracking using likelihood functions of multiple IMM filter. The results by running of Monte Carlo simulation show that variances are estimated within 5% of errors compared with true values and the tracking accuracy performance is improved.

목차 (Table of Contents)

• ABSTRACT
• 초록
• Ⅰ. 서론
• Ⅱ. 측정 잡음 분산 추정 알고리즘
• Ⅲ. 시뮬레이션
• Ⅳ. 결론
• 후기
• 참고문헌

참고문헌 (Reference)

1 L. Campo, "State Estimation for Systems with Sojourn-Time-Dependent Markov Model Switching" 36 (36): 1991

2 R. W. Osborne, "Radar Measurement Noise Variance Estimation with Several Targets of Opportunity" 44 (44): 2008

3 H. Wang, "Precision Large Scale Air Traffic Surveillance Using IMM/Assignment Estimators" 35 (35): 1999

4 Y. Bar-Shalom, "Multitarget-Multisensor Tracking : Principles and Techniques" YBS Publishing 1995

5 W. Bolstad, "Introduction to Bayesian Statistics" A Wiley-Interscience Publication 2007

6 "Eurocontrol Standard Document for Radar Surveillance in En-Route Airspace and Major Terminal Areas"

7 Y. Bar-Shalom, "Estimation with Applications to Tracking and Navigation" A Wiley-Interscience Publication 2001

8 P. Vacher, "Design of a Tracking Algorithm for an Advanced ATC System, in Multitarget-Multisensor Tracking : Applications and Advances, Volume II" Artech House 1990

9 H. A. P. Blom, "Design of a Multisensor Tracking System for Advanced Air Traffic Control, in Multitarget-Multisensor Tracking : Applications and Advances, Volume II" Artech House 1990

10 X. Rong Li, "Design of Interacting Multiple Model Algorithm for Air Traffic Control Tracking" 1 (1): 1993

11 김현식, "Design of Fuzzy IMM Algorithm based on Basis Sub-models and Time-varying Mode Transition Probabilities" 제어·로봇·시스템학회 4 (4): 559-566, 2006

12 H. A. P. Blom, "An Efficient Filter for Abruptly Changing Systems" 1984

13 I. S. Hwang, "Air Traffic Surveillance and Control Using Hybrid Estimation and Protocol-Based Conflict Resolution" Stanford University 2003