A method of attitude determination based on counter solved integer ambiguity

Li, Yuhai; Xia, Na; Tang, Mei Huei; Qian, Haowei

doi:10.3724/sp.j.1187.2009.07035

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…The following double difference carrier phase observation equation ( 3) can be derived by calculating the difference of the single difference carrier phase observation equation of two satellites at the same time. In equation ( 3), the instantaneous clock difference between the two antennas will be eliminated [15]: Since the distance between the satellite and the two antennas is much longer than b, the carrier signal can be regarded as a plane wave [16], so the altitude and azimuth angles of the same satellite at the antennas A and B are the same. Substituting equation ( 2) into equation ( 3), we can get:…”

Section: Single Epoch Fitness Function Model Of Attitude Anglementioning

confidence: 99%

Adaptive weighted particle swarm optimization (AWPSO) attitude determination algorithm based on Chi-square test

Liao¹,

Sun²,

Ji³

et al. 2022

Meas. Sci. Technol.

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The optimal search algorithm based on double-difference carrier phase is one of the methods to solve the attitude angle. An adaptive weighted particle swarm optimization (AWPSO) algorithm based on the Chi-square test is proposed to solve the attitude angle of ultra-short baseline. We establish the fitness function by introducing the relationship between attitude angle and baseline vector into the observation equations of double-difference carrier phase. Searching the attitude according to the fitness model instead of calculating the angle directly, which avoids solving the integer ambiguity. Using adaptive inertial weights and synchronous adaptive learning factors to speed up the convergence of attitude search. Constructing the candidate solution sequence to save the search result and calculating the Mahalanobis distance of the candidate solution. The local optimal solution is eliminated by the Chi-square test, and the attitude angle is gained by weighting the filtered sequence. Various static experimental results show that the new algorithm performs better than the direct solution method, the least square estimation method, and the PSO-based attitude solution method. The RMS error of yaw angle and pitch angle is 0.245° and 0.236° directly at 0.575 m baseline.

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Section: Single Epoch Fitness Function Model Of Attitude Anglementioning

confidence: 99%