MLAMBDA: a modified LAMBDA method for integer least-squares estimation

By Xiao-Wen Chang, X. Yang, T. Zhou. 2005

387 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Oceanography

View Paper: https://www.semanticscholar.org/paper/e63b1ccb4c4b6ef11710ed440496730cda675343

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Yuanxi Yang
2. Hao Li
3. Xiaolong Yang
4. Haibo He
5. Hongya Wang
6. Junyi Xu
7. Renlan Cai
8. Shiyu Song

In Artificial Intelligence

1. Yuanxi Yang
2. Hao Li
3. Xiaolong Yang
4. Haibo He
5. Hongya Wang
6. Renlan Cai
7. Shiyu Song
8. Guowei Wan

In Oceanography

1. Yuanxi Yang
2. Xiaohong Zhang

Citations in the corpus

• Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes (2017, methodology): “With the least squares, we can get the singledifference (SD) float ambiguities and their covariance, then apply a transformation matrix to convert them into the double-differenced (DD) ambiguities with the integer nature, after which, MLAMBDA [28] is utilized to resolve the ambiguities.”
• Instantaneous Real-Time Kinematic Decimeter-Level Positioning with BeiDou Triple-Frequency Signals over Medium Baselines (2015, methodology): “When using geometry-based model, the Integer Least Squares (ILS) method is always used, which was investigated by many researchers [34–36].”
• GNSS multi-carrier fast partial ambiguity resolution strategy tested with real BDS/GPS dual- and triple-frequency observations (2013, methodology): “The MLAMBDA algorithm (Chang et al. 2005) implemented with C language was applied to solve realvalued ambiguities to their integer values in both two schemes, and the solved integer ambiguities were validated by the ratio test with the fixed threshold value of 2.”