Analysis of Velocity Estimation Methods Based on BDS PPP

Duan, Shunli; Sun, Wei; Shi, Junbo; Ouyang, Chenhao

doi:10.1007/978-981-13-7751-8_37

Cited by 1 publication

(1 citation statement)

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“…In the case of a poor observation environment, the multipath effect increases and the precision of the carrier phase observation will drop sharply, which cannot meet the need of velocity estimation. However, the functional model of the Doppler method is stricter than the TDCP method, and its velocity results are more reliable when a sudden change of the receiver status occurs, and when the receiver is under a poor observation environment (Wang and Xu, 2011;Duan et al, 2019). In addition, Doppler observations are very robust, which can be used to detect and repair cycle slips (Banville and Langley, 2012;Ji et al, 2018;.…”

Section: Introductionmentioning

confidence: 99%

A Doppler enhanced TDCP algorithm based on terrain adaptive and robust Kalman filter using a stand-alone receiver

Shao

Yang

et al. 2022

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Time-differenced carrier phase (TDCP) is a commonly used method of precise velocimetry, but when the receiver is in a dynamic or complex observation environment, the estimation accuracy is reduced. Doppler velocimetry aims at estimating instantaneous velocity, and the accuracy is restricted by the accuracy of measurement. However, in such unfavourable cases, the Doppler measurement is more reliable than the carrier phase measurement. This paper derives the relationship between Doppler observation and TDCP observation, then proposes a Doppler enhanced TDCP algorithm, for the purpose of improving the velocity estimation accuracy in dynamic and complex observation environments. In addition, considering the error caused by the constant speed state update model in the robust Kalman filter (RKF), this paper designs a terrain adaptive and robust Kalman filter (TARKF). After three experimental tests, the improved TDCP algorithm can significantly increase the speed measurement accuracy to sub-metre per second, and the accuracy can be further improved after using TARKF.

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Section: Introductionmentioning

confidence: 99%