本研究的目的是要開發及運用”回歸型最簡類化型小腦模型控制器”(Recurrent S_CMAC_GBF)來增進差分式全球衛星定位系統(Differential-Global Positioning System)之精準度,並使其以FPGA晶片來實現及測試。本文呈現以Recurrent S_CMAC_GBF 增進GPS更精準的定位,我們使用低成本的商業模組(Trimble Lassen)改善其DGPS定位誤差,並且讓昂貴的商業模組(Trimble 5700)接收器的定位精準度做進一步的提昇。本文所提出的方法是搜集參考點與接收器之間的誤差資訊,運用Recurrent S_CMAC_GBF預測未來的誤差趨勢,並回饋至接收器本身做誤差校正,藉以改善並提升衛星接收器定位的準確度。最後使用以Recurrent S_CMAC_GBF為架構之FPGA硬體測試結果,並由RS-232將資料回傳至電腦驗證之。
This study is to design the structure of Recurrent S_CMAC_GBF use for improve in DGPS (Differential-Global Positioning System) accuracy, and to implement and test the hardware structure by using FPGA chip. This paper present the Recurrent S_CMAC_GBF in DGPS to increase for accurate positioning. We used a low cost commercial module (Trimble Lassen) to improvement the DGPS system and used the expensive commercial module (Trimble 5700) receiver to further. The proposed project is collects erroneous information and use Recurrent S_CMAC_GBF to predicted future erroneous tendency. This predicted information will be employed to improve the accuracy of DGPS. Finally, the FPGA chip is used implement Recurrent S_CMAC_GBF hardware structure, the result will be translated to computer by RS-232 to verify.
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