Position Estimation in Single-Frequency GPS Receivers Using Kalman Filter with Pseudo-Range and Carrier Phase Measurements

  • Authors:
  • M. R. Mosavi;M. Soltani Azad;I. Emamgholipour

  • Affiliations:
  • Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran 16846-13114;Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran 16846-13114;Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran 16846-13114

  • Venue:
  • Wireless Personal Communications: An International Journal
  • Year:
  • 2013

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Abstract

Today Global Positioning System (GPS) is the most important system of positioning in the world and is used in different industries. Basic positioning methods in GPS receivers are based on pseudo-range and carrier phase measurements types whilst each of which has its own advantages and disadvantages. Pseudo-range method is not very much accurate. Carrier phase has a substantial accuracy, but its main problem is that it is an indirect measurement which only computes the displacement. Carrier phase measurement can include some whole cycles plus a fraction of carrier phase. The number of whole cycles may change through time however this change is unknown for us. Code measurements (based on pseudo-range) and carrier phase are corrupted with the same error sources, but with main differences. Basically, code tracking with low accuracy makes unambiguous pseudo-ranges. Carrier phase measurements are highly accurate, but get limited with integer ambiguity. Integer is fixed until the time that carrier tracking loop is saved. Every kind of gap in tracking, no matter how short it is, changes the amount of integer which is the biggest problem in carrier phase utilization. In this paper, the corporation of pseudo-range capability and carrier phase in single-frequency GPS receivers will be discussed which makes a substitute for the pure pseudo-range observations and provides a high level of positioning accuracy. To achieve this aim, Kalman filter will be used.