A Cycle Slip Detection Framework for Reliable Single Frequency RTK Positioning

Abstract

Single frequency real-time kinematic (RTK) positioning is expected to be the leading implementation platform for a variety of emerging GNSS mass-market applications. During RTK positioning, the most common source of measurement errors is carrier-phase cycle slips (CS). The presence of CS in carrier-phase measurements is tested by a CS detection technique and correspondingly taken care of. While using CS prone measurement data, positioning reliability is an area of concern for RTK users. Reliability can be linked with the CS detection scheme through a least squares (LS) adjustment process. This paper proposes a CS detection framework for reliable RTK positioning using single-frequency GNSS receivers. The scheme uses double differenced measurements for CS detection via LS adjustment using a detection, identification, and adaptation approach. For reliable positioning, the procedure to link the detection and identification stages is described. Through tests conducted on kinematic data, internal and external reliability are theoretically determined by calculating minimal detectable bias (MDB) and marginally detectable errors, respectively. After introducing CS, the actual values of MDB are found to be four cycles, which are higher than the theoretically obtained values of one and two cycles. Although CS detection for reliable positioning is implemented for single-frequency RTK users, the proposed procedure is generic and can be used whenever CS are detected through statistical tests during LS adjustment.

Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

“…The well-known least-squares (LS) has been widely used as a standard method of model parameters estimation in geodetic applications and many others branches of modern science [29][30][31][32][33][34][35][36][37][38][39][40][41][42]. This is due to the flexibility of the LS, since no concepts from probability theory are used in formulating the least-squares minimisation problem.…”