依國際民航組織(International Civil Aviation Organization, ICAO)計畫，基於全球衛星導航系統(Global Navigation Satellite System，GNSS)的通訊、導航、監視與飛航管理系統(Communication, Navigation, Surveillance and Air Traffic Management, CNS/ATM)將取代傳統的航空管制系統。然而當微弱的GNSS無線電訊號容易遭到蓄意或非蓄意的干擾，導致CNS/ATM系統無法正常提供服務，危及生命財產之安全。美國民航局(Federal Aviation Administration, FAA)為了維持CNS/ATM系統持續的正常運作，展開了定位、導航與時間替代系統(Alternative Position, Navigation and Time, APNT)的相關研究，以幫助美國從國家空域系統(National Airspace System, NAS)轉換至新一代航空運輸系統(Next Generation Air Transportation System, NextGen)。而現有之民航雷達系統有望能為APNT系統中提供導航與監視的服務。
為了使雷達系統成為APNT的後備方案之一，其追蹤性能必須提升以滿足APNT的性能標準。本論文將使用一種改良式的追蹤演算法來提升雷達系統的定位精度。改良式的追蹤演算法為交互式多模型機率資料連結濾波器(Interacting Multiple Model and Probabilistic Data Association filter, IMMPDAF)，在追蹤目標在充滿雜訊(cluttered)的環境進行不同動態移動時仍能有較佳的定位精度。本篇論文將針對IMMPDAF進行詳細的介紹。並透過模擬結果，分析IMMPDAF相比於廣泛應用的Kalman濾波器有著大幅的性能增進。以及由台灣民航局提供的雷達資料，經過實驗結果來比較IMMPDAF與Kalman濾波器，同樣有著大幅的性能增進。

According to the International Civil Aviation Organization’s plan, the Communications, Navigation, Surveillance, and Air Traffic Management (CNS/ATM) system based on the Global Navigation Satellite System (GNSS) technology should be implemented to replace the traditional Air Traffic Control (ATC) system based on ground-based radar. The major concern of the CNS/ATM system may experience service interruption when the GNSS signal blocked by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the CNS/ATM system, the Federal Aviation Administration (FAA) is initiating an Alternative Position, Navigation and Timing (APNT) program to research various alternative strategies to support the US National Airspace System’s (NAS) transition to the Next Generation Air Transportation System (NextGen). A promising approach for the APNT is to use the existing ground-based radar system to provide navigation and surveillance services for the ATM system.

To utilize the existing radar system as a backup solution of the APNT, the tracking capability of the radar system has to be enhanced to be compatible with the standard APNT performance requirements. This study implements a tracking algorithm to improve the aircraft tracking performance of the radar system. The proposed tracking algorithm is called the Interacting Multiple Model and Probabilistic Data Association filter (IMMPDAF). The IMMPDAF can accurately track an aircraft in a cluttered environment under various maneuver modes. The procedure of implementing the IMMPDAF into a radar system is discussed. The tracking performance of the IMMPDAF is compared with that of a filter that uses the nearest neighbor method as well as the standard Kalman filter (NNKF). Additionally, the computation loads of these filters are evaluated. The simulation results are presented, Finally, real radar data collected by the Civil Aeronautics Administration of Taiwan are used to demonstrate the tracking performance improvement with the IMMPDAF. The experiment results are consistent with simulation results. The IMMPDAF outperforms the NNKF.

[1]International Civil Aviation Organization, doc 9750 Global Air Navigation Plan for CNS/ATM Systems, second edition 2002.
[2]Federal Aviation Administration, NextGen Implementation Plan, March 2012.
[3]Federal Aviation Administration, Concept of Operations for NextGen Alternative Positioning, Navigation, and Timing (APNT), 2012.
[4]Blom, H.A.P., Bar-Shalom, Y., The interacting multiple model algorithm for systems with Markovian switching coefficients, IEEE Trans. Automat. Contr., vol. AC-33, pp. 780-783, 1988.
[5]Blackman, S.S., Popoli, R., Design and analysis of modern tracking systems, Artech House radar library, 1999.
[6]Bar-Shalom, Y., Tse, E., Tracking in a Cluttered Environment With Probabilistic Data Association, Automatica, vol. 11, pp. 451-460, 1975.
[7]Bar-Shalom, Y., Daum, F., Huang, J., The Probabilistic Data Association Filter-Estimation In the Presence of Measurement Origin Uncertainty, IEEE Control Systems Magazine, December 2009.
[8]Gustafsson, F., Isaksson, A., Best Choice of Coordinate System for Tracking Coordinate Turns, 35th IEEE CDC, 1996.
[9]EUROCONTROL, Standard Document for Radar Surveillance in En-Route Airspace and Major Terminal Areas, EUROCONTROL, Brussels, Belgium, ed. 1.0, Mar. 1997.
[10]Li, X.R., Bar-Shalom, Y., Design of an Interacting Multiple Model algorithm for Air Traffic Control, IEEE Decision and Control, vol. 1, pp. 906-911, 1993.
[11]Li X.R., Jilkov V.P., A survey of maneuvering target tracking: dynamic models, SPIE Signal and Data Processing of Small Target, April 2000.
[12]Fitzgerald, R.F., Simple tracking Filters: steady state filtering and smoothing performance, IEEE Trans. Aerosp. Electron. Syst., vol. AES-16, pp. 860-864, 1980.
[13]Kang, J.G., You, B.J., A Run-time Estimate Method of Measurement Error Variance for Kalman Estimator. 16th IEEE RO-MAN, 2007.
[14]Narins, M., Eldredge, L., Enge, P., Harrison, M., Kenagy, R., Lo, S., Alternative Position, Navigation, and Timing – The Need for Robust Radionavigation, 2010