تعيين موقعيت نقطه اي استاندارد (SPP) كينماتيكي در شرايط طوفان ژئو مغناطيسي شديد با استفاده از فيلتر كالمن و مشاهدات كد C/A

Kinematic Standard Point Positioning (SPP) based on Kalman Filter and C/A Code Observations Under Intense Geomagnetic Storm Conditions

ﺗﻌﻴﻴﻦ ﻣﻮﻗﻌﻴﺖ ﻧﻘﻄﻪاي اﺳﺘﺎﻧﺪارد )SPP ( ﺑﺎ اﺳﺘﻔﺎده از ﻳﻚ ﮔﻴﺮﻧﺪه ﺗﻚ ﻓﺮﻛﺎﻧﺴﻪGPS ﺑﺎ ﻳﻚ ﭼﺎﻟﺶ اﺻﻠﻲ ﺑﻪ ﺧﺎﻃﺮ ﺑﺎﻳﺎسﻫﺎي ﻣﺤﻴﻄﻲ )ﺑﺨﺼﻮص ﺗﺎﺛﻴﺮات ﻳﻮﻧﺴﻔﺮي( ﻣﻮاﺟﻪ اﺳﺖ. ﻫﺪف اﺻﻠﻲ ﻣﻘﺎﻟﻪ ﺣﺎﺿﺮ، آن اﺳﺖ ﻛﻪ ﺗﺎﺛﻴﺮات ﻳﻮﻧﺴﻔﺮﻳﻚ را ﺑﺎ اﺳﺘﻔﺎده از ﻳﻚ ﻣﺪل ﻳﻮﻧﺴﻔﺮي ﻏﻴﺮ دﻗﻴﻖ ﻣﺎﻧﻨﺪ ﻣﺪل ﻛﻼﺑﭽﺎر ﻣﺪﻟﺴﺎزي ﻧﻤﺎﻳﺪ و ﺳﭙﺲ آن را ﺑﺎ ﻣﺸﺎﻫﺪات ﻏﻴﺮ دﻗﻴﻖ ﻛﺪ )C/A ( در ﺷﺮاﻳﻂ ﻃﻮﻓﺎن ژﺋﻮﻣﻐﻨﺎﻃﻴﺴﻲ ﺷﺪﻳﺪ ﺗﻠﻔﻴﻖ ﻧﻤﺎﻳﺪ و ﺑﺎ ﺑﻜﺎرﮔﻴﺮي اﻟﮕﻮرﻳﺘﻢ ﻓﻴﻠﺘﺮ ﻛﺎﻟﻤﻦ ﺑﻪ دﻗﺖﻫﺎﻳﻲ در ﺳﻄﺢ دﺳﻲﻣﺘﺮ ﺑﺮﺳﺪ. ﺑﺮاي اﻳﻦ ﻣﻨﻈﻮر ﻣﺸﺎﻫﺪات ﻛﺪ C/A در روزﻫﺎي 26 ﻓﻮرﻳﻪ 2018 و 20 دﺳﺎﻣﺒﺮ 2015 در اﻳﺴﺘﮕﺎه داﺋﻤﻲ ﺗﻬﺮان ﺟﻤﻊآوري ﺷﺪه اﺳﺖ. ﻧﺘﺎﻳﺞ ﺣﺎﺻﻞ از ﻓﻴﻠﺘﺮﻳﻨﮓ ﻛﺎﻟﻤﻦ، ﻧﺸﺎن دﻫﻨﺪه دﺳﺖﻳﺎﺑﻲ ﺑﻪ دﻗﺖﻫﺎﻳﻲ در ﺳﻄﺢ ﭼﻨﺪ دﺳﻲﻣﺘﺮ ﺑﺮاي ﺗﻤﺎم ﻣﻮﻟﻔﻪﻫﺎي ﻣﺨﺘﺼﺎﺗﻲ در ﺷﺮاﻳﻂ ﻃﻮﻓﺎن ژﺋﻮﻣﻐﻨﺎﻃﻴﺴﻲ ﺷﺪﻳﺪ اﺳﺖ ﻛﻪ در ﺑﺴﻴﺎري از ﻛﺎرﺑﺮدﻫﺎي ﻛﻴﻨﻤﺎﺗﻴﻜﻲ ﻣﻲﺗﻮاﻧﺪ ﭘﺮاﻫﻤﻴﺖ ﺑﺎﺷﺪ. ﻫﻤﭽﻨﻴﻦ، آﻧﺎﻟﻴﺰ ﻧﺘﺎﻳﺞ ﻧﺸﺎن ﻣﻲدﻫﺪ ﻛﻪ ﺑﺎ ﺑﻜﺎرﮔﻴﺮي ﻣﺪل ﻛﻼﺑﭽﺎر، دﻗﺖ ﺗﻌﻴﻴﻦ ﻣﻮﻗﻌﻴﺖ ﻧﻘﻄﻪاي SPP ﺣﺪود 20% در روز 26 ﻓﻮري 2018 و ﺣﺪود 21% در روز 20 دﺳﺎﻣﺒﺮ2015 ﺑﻬﺒﻮد ﻣﻲﻳﺎﺑﺪ.

In this paper, we discussed standard point positioning technique based on the single frequency code-based (C/A) receivers. Then, we presented its performance by means of different measures. However, the use of one single frequency GPS receiver to obtain high-precision positioning make a major challenge due to the environmental biases, in particular the ionospheric effects are handled. The main objective of the present study is to integrate a inospheric model such as Klobuchar Inospheric Model (KIM) with imprecise code (C/A) observations under intense geomagnetic storm conditions, then, to obtain dm level positioning accuracy using Kalman filter. For this purpose we used code (C/A) observations on two different days (February 26, 2018 and December 20, 2015) at Tehran station. The results show that we could obtain multi-dm level positioning accuracy under geomagnetic storm condition by using Kalman filter that will be important in the field of kinematic applications.