太空載具仰賴姿態感測與控制次系統來確保其正常運作，其中姿態感測器的性能至關重要。常見的姿態感測器有太陽感測器、地球感測器、陀螺儀和星體追蹤器。由於星體追蹤器具有體積小、重量輕、抗磁干擾、運作角度範圍廣泛、不受軌道限制及角秒級精度等優勢，近年來在太空載具上的應用越來越受到關注。姿態感測的準確度和星點辨識的成功率是評估星體追蹤器性能的關鍵指標。高性能的星體追蹤器不僅需要在一般狀況下提供角秒級的準確度，還需能夠在多樣或嚴峻的環境中穩定運行，承受各種干擾如雜訊、偽星點和雜散光。本研究著重於為星體追蹤器發展高性能的軟體演算法，該演算法準確且高效，並且具備在干擾情況下的穩健性以及面對各種環境的適應性。
星體追蹤器透過搭配的星資料庫來辨識視野中的星點，以估測姿態。本研究對演算法的兩種模式分別進行開發。在迷航模式中，星點辨識採用基於金字塔法的改良方法，使用更佳幾何性質的基本圖樣，進一步提升了演算法在大量偽星點狀況下的穩健性，提供極高可靠度的匹配結果，並提高了計算效率。在追蹤模式中，使用擴展式卡爾曼濾波器，結合區域背景濾除功能，抑制來自雜散光的干擾，使演算法亦能適應於雜散光環境。
本研究將開發的星體追蹤器演算法進行兩階段的驗證與評估。第一階段透過軟體模擬出各式環境下的星圖，包含任意姿態、連續姿態、含有雜訊、大量偽星點或雜散光的情況，分別對兩種模式進行性能分析。第二階段為實際夜空實驗，使用地面拍攝的真實影像對整體演算法進行分析。實驗地點選於台南郊區，影像中含有來自城市光源及薄雲層的干擾。由於涉及硬體與環境，須修正天文因素及底座誤差，以量測出開發星體追蹤器的絕對準確度。
第一階段模擬結果顯示，改良的星點識別方法避免了錯誤辨識並過濾了大偏移星點，提升辨識成功率和姿態準確度。雖然姿態輸出率下降，其增強了對雜訊和偽星點的穩健性，並提高匹配效率。此外，追蹤演算法在雜散光和雜訊影響下，透過結合動態背景濾除功能持續追蹤星點。雖然運算時間增加，其提高姿態準確度並增加了指認率。第二階段夜空實驗顯示，演算法有效地處理了背景不均勻的實際影像，修正天文因素及底座誤差後，滾動軸的姿態感測誤差分別減少了91%和83%。總體而言，所開發的演算法在實際環境下提供了連續且穩定的姿態感測，可達角分等級的姿態準確度。

Spacecraft operation is based on an attitude determination and control system. Attitude sensors play a crucial role in this subsystem, and common attitude sensors include Sun sensors, Earth sensors, gyroscopes, and star trackers. Because of their advantages such as small size, light weight, immunity to magnetic interference, wide operational angle range, lack of orbit constraints, and arcsecond accuracy, star trackers have been increasingly used in spacecraft. The accuracy of attitude estimation and the success rate of star identification are key indicators for evaluating the performance of star trackers. A high-performance star tracker should not only provide arcsecond accuracy under normal conditions but also operate stably in complex environments. Moreover, it should account for different types of interference, such as noise, spurious stars, and stray light. The present thesis developed an accurate and efficient algorithm to enhance the robustness and adaptability of star trackers.
A star tracker uses onboard star catalogs to identify stars within its field of view to estimate spacecraft attitude. The proposed algorithm functions as follows. In the lost-in-space mode of a star tracker, the proposed algorithm uses a modified pyramid-based method to identify stars, employing a basic pattern with improved geometry to enhance robustness against numerous spurious stars. This approach ensures highly reliable matching results and increases computational efficiency. In the tracking mode, the algorithm employs an extended Kalman filter and a local background removal function to suppress interference from stray light, thus enabling the star tracker to adapt to environments with stray light.
The proposed algorithm was subjected to two-stage verification and evaluation. In the first stage, stellar images were generated using a simulator for environments with features such as random attitudes, continuous attitudes, noise, a considerable number of spurious stars, or stray light. The performance of the proposed algorithm in the two star tracker modes was analyzed for these simulated environments. In the second stage, real star images were captured under the night sky at a suburban area in Tainan, Taiwan. The images included interference from urban lights and thin clouds. Because of hardware and environmental factors, corrections for astronomical factors and mounting offsets were made to measure the absolute accuracy of the developed algorithm.
Simulation results showed that the modified star identification method improved success rates and attitude estimation accuracy by avoiding star mismatches and filtering out large centroid offsets. Despite the method reduced the output rate, it enhanced robustness to noise and spurious stars and achieved higher matching efficiency. Moreover, combining an extended Kalman filter with dynamic background removal maintained continuous star tracking, even with stray light and noise. Despite the method increased tracking time, it improved attitude estimation accuracy and increased the mapping rate. The night-sky experiment showed that the developed algorithm, using a box filter for background filtering, effectively managed an unevenly bright image background. Corrections for factors affecting star position and mounting offsets reduced attitude estimation errors in the rolling axis by 91% and 83%, respectively. Overall, the algorithm provided continuous and stable attitude estimates with arcminute-level accuracy.

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