張帆人臺灣大學:電機工程學研究所姜光遠Chiang, Kuang-YuanKuang-YuanChiang2010-07-012018-07-062010-07-012018-07-062008U0001-2907200814420900http://ntur.lib.ntu.edu.tw//handle/246246/187950當GPS技術整合到手機或手持裝置時，在大部分的情況下，可能都不是熱開機的狀態(甚至可能都是在冷開機狀態下使用)，我們需要一個更有效之訊號擷取方法，以降低剛開機時定位所需要的時間。 本論文利用粗估星曆(Almanac)解算衛星位置及速度，進而預測接收機的可視衛星群，及各別衛星的都卜勒頻移，再根據上次的定位紀錄，統計出該台接收機合理的平均鐘飄頻移，其中粗估星曆的來源，或由該接收機先前之儲存，或由手機之無線通訊服務系統提供。利用上述之輔助資訊，當GPS接收機需要冷開機時，我們即可減少訊號擷取所需搜尋的次數，進而降低冷開機所需的時間。上述之方法亦可應用到微弱訊號擷取演算法，此微弱訊號演算法對10ms的資料做同調積分，而10ms的資料其相對應之頻率解析度為100Hz。 接著，利用SE4110L Front-end接收衛星訊號，並透過實驗證實提出的想法。由實驗結果可發現，頻率預測法可使得訊號擷取時所需的頻率搜尋範圍由原本的±5KHz縮小為±250Hz，而頻率搜尋間隔也可由500Hz縮小至250Hz，搜尋次數原本需要21次減少為3次，因此執行速度也更快。應用到微弱訊號搜尋演算法後，若為靜態之接收機，此方法能有效的將頻移範圍縮小至± 150 Hz 內，若為低速載具，也可利用微幅調整頻率搜尋範圍，在最快的執行速度下，達到對微弱訊號擷取的目標。最後，根據定位紀錄利用最小平方法統計出接收機的平均鐘飄頻移，並討論了鐘飄頻移對微弱訊號擷取的影響。When GPS technique is going to be integrated with cell phone or hand-held devices, it is expected that in most situations the receiver will be in a cold-start condition. In view of this, a signal acquisition method that can decrease TTFT (Time to First Fix) efficiently is well worth a further study. In this thesis, we propose a method that is able to efficiently reduce the time to acquire the GPS signal by utilizing the almanac data of the satellites. If a user''s rough location is known, the aforementioned information (satellite’s almanac data) can be used to determine the visible satellites and their corresponding Doppler frequency shifts. The almanac data can be either obtained from last received (complete) navigation data or transmitted from the cell phone wireless service provider. Another factor that may influence the frequency shift is caused by the drift rate of the receiver clock. In this thesis, we also propose a method to estimate the receiver’s clock drift rate to improve the overall performance of our algorithm, where the quantity of the drift rate can be obtained from the previous navigation records. These two procedures can be used to reduce the number of required searches in a GPS signal acquisition algorithm, and therefore the execution time can be decreased. Since the time needed for acquisition is saved, we can spend much more effort to acquire a relative weak signal. We also apply the proposed algorithm to acquire a weak signal for 10ms coherent integration time. In this case, the down-converted frequency will be processed with a bandwidth of ±50 Hz because 10 ms of data would have a corresponding frequency resolution of 100Hz (1/10 ms). Several experiments are conducted to prove the proposed method. In those experiments, the used data is actual raw GPS signal received by using an SE4110L ASIC-based Front-end. From the experimental results, the follow statements may be concluded. Firstly, the frequency search interval is reduced from ±5KHz to ±250Hz when the frequency shift prediction is applied. In other words, the number of frequency searches is reduced from 21 to 3. Secondly, for a static GPS receiver, the interval of frequency search in the frequency shift prediction algorithm is only ±150 Hz. In view of this, by utilizing the frequency shift prediction algorithm, weak signal acquisition may provide better performance and spend less execution time even in the case of a moving vehicle. Finally, the frequency shift caused by the drift rate is obtained from the previous navigation records by applying least-squares method and the effect of this quantity in weak signal acquisition is also discussed.摘要 ibstract iii目錄 vii目錄 ix一章 緒論 1.1 研究背景 1.2 研究方向 2.3 論文架構 4二章 GPS的時間系統與空間系統 5.1 GPS時間系統 5.1.1 世界時系統 6.1.2 原子時系統 8.1.3 世界協調時 (Coordinate Universal Time, UTC) 10.1.4 GPS時 (GPS Time, GPST) 10.2 座標系統 11.2.1 地心慣量座標系統 11.2.2 地心地固座標系統 11.2.3 衛星軌道座標系 (Satellite Orbit) 12.2.4 WGS84座標系統 (大地基準) 13.2.5 當地水平座標系統 (Local-Level System) 15.3 座標系統間之座標轉換 16.4 衛星星曆 (Ephemeris)和粗估星曆 (Almanac) 18.4.1 衛星星曆 18.4.2 粗估星曆 18.5 旋轉矩陣導數之推導 20三章 GPS訊號及訊號擷取演算法 25.1 GPS訊號 25.2 傳統GPS接收機的架構 27.3 軟體接收機的架構 29.4 擷取的重要性及其演算法 30.4.1 串接搜尋擷取法 (Serial Search) 31.4.2 並列頻率空間搜尋擷取法 (Parallel Frequency Space Search) 32.4.3 並列碼空間搜尋擷取法 (Parallel Code Space Search) 33.4.4 混合搜尋擷取演算法 34四章 GPS衛星頻率偏移預估法 35.1 基本構想 35.2 NTP 網路時間校正 36.3 振盪器頻率性能對載波頻率的影響 37.3.1 時鐘偏移變化率對頻率造成的影響 37.3.2 估計鐘飄頻移的方法 39.4 都卜勒效應 (Doppler Effect) 41.5 利用粗估星曆預測都卜勒頻移 44.5.1 粗估星曆的各項參數定義 44.5.2 預測都卜勒頻移之計算過程 47.6 微弱訊號擷取演算法的改善 52五章 實驗方法與結果數據 53.1 實驗設備與方法 53.2 利用粗估星曆預測可視衛星群 57.3 頻率偏移預估演算法對訊號擷取速度上的改善 60.4 微弱訊號演算法的效能及改善 63.5 探討時鐘漂移對頻率誤差所造成的影響 69.6 只考慮衛星與接收機相對運動造成的頻率誤差 73六章 結論與未來展望 75.1 結論 75.2 未來展望 76考文獻 772972804 bytesapplication/pdfen-US訊號擷取粗估星曆都卜勒頻率時鐘漂移GPSAlmanacTTFFDoppler EffectClock BiasAcquisitionthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/187950/1/ntu-97-R95921064-1.pdf