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VC-LAMBDA: a baseline vector constrained LAMBDA method for integer least-squares estimation
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AI
DOI：10.1007/s00190-022-01644-7.png)
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摘要
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Global navigation satellite system (GNSS) ambiguity resolution is the key to realize high-precision navigation and positioning applications. For some dynamic application scenes such as moving-baseline-based positioning and attitude determination, the baseline information of GNSS antennas can be measured in advance; such a priori information can contribute to integer ambiguity resolution, especially when it is in weak GNSS models. In this research, a baseline vector constrained ambiguity resolution method (VC-LAMBDA) is demonstrated theoretically and verified experimentally. The objective function of VC-LAMBDA method is derived firstly, and then the corresponding search algorithm is designed, with an adaptive search space strategy adopted to speed up the search efficiency. The VC-LAMBDA method extends the scalar baseline length constrained LAMBDA method (C-LAMBDA) to the three-dimensional vector case, and simplifies the nonlinearly constrained integer least-squares (ILS) problem to a linearly constrained ILS problem. Two groups of real-measured data with one vehicle platform and one aircraft platform are tested to evaluate the practical performance of VC-LAMBDA, also compared with standard LAMBDA and C-LAMBDA methods. The two experiments show consistent results that VC-LAMBDA performs better than C-LMABDA method in ambiguity fixed success rate, robustness and time consumption, and both the two baseline constraint methods are significantly superior than standard LAMBDA method when it is in weak GNSS models.
Keyword:
GNSS
Integer ambiguity resolution
Weak model
C-LAMBDA
VC-LAMBDA
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IF:
4
论文数:
2.5K
被引数:
8.8K
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