A New Method For DGPS Ambiguity Resolution?

Teunissen, Peter

doi:10.1017/s0373463310000536

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Consequently, for high-precision localization purposes, the carrier phase double difference model is commonly employed. The carrier phase double difference observation equation [43] can be expressed as follows:…”

Section: Mathematical Model Of Carrier Phase Double Differencementioning

confidence: 99%

Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm

Guo,

Zhang,

et al. 2023

Sensors

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Carrier phase measurements currently play a crucial role in achieving rapid and highly accurate positioning of global navigation satellite systems (GNSS). Resolving the integer ambiguity correctly is one of the key steps in this process. To address the inefficiency and slow search problem during ambiguity solving, we propose a single-frequency GNSS integer ambiguity solving based on an adaptive genetic particle swarm optimization (AGPSO) algorithm. Initially, we solve for the floating-point solution and its corresponding covariance matrix using the carrier-phase double difference equation. Subsequently, we decorrelate it using the inverse integer Cholesky algorithm. Furthermore, we introduce an improved fitness function to enhance convergence and search performance. Finally, we combine a particle swarm optimization algorithm with adaptive weights to conduct an integer ambiguity search, where each generation selectively undergoes half-random crossover and mutation operations to facilitate escaping local optima. Comparative studies against traditional algorithms and other intelligent algorithms demonstrate that the AGPSO algorithm exhibits faster convergence rates, improved stability in integer ambiguity search results, and in practical experiments the baseline accuracy of the solution is within 0.02 m, which has some application value in the practical situation of short baselines.

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Section: Mathematical Model Of Carrier Phase Double Differencementioning

confidence: 99%

Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm

Guo,

Zhang,

et al. 2023

Sensors

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“…The standard GNSS Baseline Model is the mixed integer model (Teunissen, 2003, 2011a). It is defined as:where: Y is the given GNSS data vector. a and b are the unknown parameter vectors of order n and p respectively.E(·) and D(·) denote the expectation and dispersion operators, respectively. A and B are the given design matrices that link the data vector to the unknown parameters.…”

Section: Gnss Baseline Modelmentioning

confidence: 99%

Assessment of GNSS Orthogonal Transformation Model

Chen

Zhang

2012

J. Navigation

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GNSS relative positioning technique is an important field of study, in which the standard 'GNSS Baseline Model' is often used. Differencing between observation equations is used to construct the mathematical model, since this method can eliminate some common errors in the GNSS signal measurements. The 'Orthogonal Transformation' method can also construct the GNSS Baseline Model. However, as is described by some scholars, this model may avoid some drawbacks of Double Differencing (DD) while maintaining all the advantages. For comparison purposes, this model is evaluated and the theoretical equivalence of both approaches is proved for the short baseline from two aspects: the Integer Ambiguity Resolution and the conditional least-squares baseline vector. K E Y

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A New Method For DGPS Ambiguity Resolution?

Cited by 2 publications

References 19 publications

Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm

Single-Frequency GNSS Integer Ambiguity Solving Based on Adaptive Genetic Particle Swarm Optimization Algorithm

Assessment of GNSS Orthogonal Transformation Model

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