Online-sequential extreme learning machine-based satellite signal cycle slip detection and restoration method

Abstract

The invention discloses an online-sequential extreme learning machine-based satellite signal cycle slip detection and restoration method, which is characterized in that the method is implemented by the following steps of performing high-order differentiation processing on the carrier phase of a satellite signal, forming a cycle slip-free training sample set by utilizing cycle slip-free carrier phase values, training an initial online-sequential extreme learning machine model, constructing cycle slip detection statistic by utilizing a model predicted value, detecting and restoring cycle slips, and updating the online-sequential extreme learning machine model by using the cycle slip-free carrier phase values. According to the online-sequential extreme learning machine-based satellite signal cycle slip detection and restoration method, small cycle slips (of three cycles and more) can be effectively detected without additional auxiliary information, and the method is applied to a single-frequency receiver, can be widely applied to the processing of GPS (global positioning system), GLONASS (global navigation satellite system), Galileo and Beidou navigation satellite signals, and has broad application prospect.

Description

technical field [0001] The invention relates to a method for detecting and repairing the whole-cycle jump of satellite signals. Background technique [0002] High-precision satellite navigation applications (high-precision positioning, direction finding, attitude, etc.) all use the carrier phase measurement method, and the entire cycle jump of the carrier phase (abbreviated as cycle jump) will have a great impact on the accuracy and stability of the measurement results, so The detection and repair of cycle slips is a key problem that must be solved in this field. [0003] The cycle slip of the carrier phase refers to the phenomenon that when the carrier phase is observed, there is a systematic deviation in the counting of the whole cycle, but the part of less than one cycle is still correct. Three types of reasons can lead to cycle slip: the first type is that the satellite signal is blocked and temporarily interrupted; the second type is the bad ionospheric condition, seri...

AI Technical Summary

Problems solved by technology

Three types of reasons can lead to cycle slip: the first type is that the satellite signal is blocked and temporarily interrupted; the second type is the bad ionospheric condition, serious multipath effect, or the satellite elevation angle is too low, resulting in a low signal-to-noise ratio of the satellite signal; The third category is errors caused by incomplete design of the built-in software of the receiver

[0006] The high-order difference method and polynomial fitting method are suitable for single-frequency receivers, but they can only detect large cycle slips of more than 5 cycles, and cannot detect small cycle slips; the ability of the pseudo-range phase combination method to detect cycle slips depends on the pseudo-range measurement. accuracy, so it is not suitable for single-frequency receivers; the ionospheric residual method requires dual-frequency carrier phase values, and is not suitable for single-frequency receivers; High; the wavelet method requires two or more stations to obtain double-difference observations, and the complexity is high

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