為了因應日益增加的航空交通流量，而發展新一代飛航管理並且重新整合通訊、導航、監視的概念。相較現有的雷達系統，廣播式自動回報監視（ADS-B）系統更精確、全面及可靠，但此系統有一定的誤差。目前有許多增加系統準確度的方法，但大部分的方法需要改善硬體設備，而本研究所採用不需要增加硬體設備的濾波器演算法。本研究設計的解碼器來處理在台北飛航情報區所接收到的ADS-B數據。ADS-B的數據經過分析之後，可分為三種不同類型的誤差，包括斷訊、跨越CPR區域邊界時產生的位置跳動以及差錯位元產生的位置跳動。為了改善這些誤差模式，本論文採用搭配自適應率的連續-離散卡爾曼濾波器，該濾波器算法的目標是設計一個即時系統。經過測試後發現，如果信號能夠完整的被接收，則本論文所提出的濾器演算法能改善位置誤差至可接受的範圍。

Air traffic density is expected to significantly increase in the future. The Automatic dependent surveillance-broadcast (ADS-B) is the next generation surveillance system to increase airspace capacity. However, the ADS-B signal has some defects. In order to increase the stability of ADS-B system. There has many methods to improve the ADS-B signal. While the most of the solutions need to be improved by hardware development. The filter algorithm is a key which can improve ADS-B signal without enhancing equipment. This research design the decoder to process the ADS-B data which collected in Taipei Flight Information Region (FIR). After analyze the amount of ADS-B data, many errors can be found. These errors can be sorted into three types including loss of signal, cross the boundary of CPR zone and receive error signal. In order to mitigate these failure modes, this thesis design algorithm applying continuous-discrete Kalman filter with adaptive law. This filter algorithm is aim to design for real time system. After tested by the error status which identified in our research. If the signals received completed, the performance of the proposed filter algorithm can mitigate the impact of the position jump to the acceptable level.

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