Abstract It is well understood that the key to high accuracy differential global navigation satellite system (DGNSS) relative positioning is the resolution of the integer ambiguities within the carrier phase measurements and the continuous tracking of them. The resolution process is usually converted into an integer least square optimization problem, e.g., among them is the most notable Least-squares Ambiguity Decorrelation Adjustment (LAMBDA) algorithm. This paper adds additional statistical constraints to the existing LAMBDA approach to improve the performance of ambiguity resolution process when the LAMBDA ratio is below a certain predefined threshold. In order to fix the integer ambiguity as soon as possible, a novel ambiguity search algorithm is proposed, which is like the threshold based algorithm but explicitly exploits the correlation structure of the double difference covariance model and the measurement accuracy difference. To do that, the relationship between global navigation satellite system (GNSS) pseudorange measurement accuracy and the resolution of the integer ambiguity of carrier phase measurements is analyzed based on the GNSS distance measurement equations. Analytic statistical models of the single and double differences of the distance measurements are presented. Based on the analysis, it is found that the conventional choice of the highest elevation satellite as the reference satellite may not be a superior selection in single epoch algorithms. In fact, code phase measurements are used for ambiguity resolution, and the covariance of baseline vector is independent with reference satellite selection when the least square problem is optimally weighted. The novel ambiguity search algorithm is presented to fix the ambiguity as soon as possible. Simulation and field test data validate the analysis and the ambiguity search algorithm.
[1]
Alexander Graham,et al.
Kronecker Products and Matrix Calculus: With Applications
,
1981
.
[2]
De-feng Gu,et al.
Spaceborne GPS receiver antenna phase center offset and variation estimation for the Shiyan 3 satellite
,
2016
.
[3]
Elliott D. Kaplan.
Understanding GPS : principles and applications
,
1996
.
[4]
Ding,et al.
RESEARCH ON THE METHODS OF IMPROVING DGPS POSITIONING PRECISION
,
2000
.
[5]
Peter Teunissen,et al.
Penalized GNSS Ambiguity Resolution
,
2004
.
[6]
Xiaogang Gu,et al.
DGPS positioning using carrier phase for precision navigation
,
1994,
Proceedings of 1994 IEEE Position, Location and Navigation Symposium - PLANS'94.
[7]
Gene H. Golub,et al.
Matrix computations
,
1983
.
[8]
P. Teunissen.
Success probability of integer GPS ambiguity rounding and bootstrapping
,
1998
.
[9]
Peter Teunissen,et al.
Integer aperture bootstrapping: a new GNSS ambiguity estimator with controllable fail-rate
,
2005
.
[10]
P. Teunissen.
An optimality property of the integer least-squares estimator
,
1999
.
[11]
Gerhard Beutler,et al.
Rapid static positioning based on the fast ambiguity resolution approach
,
1990
.
[12]
Ron Hatch,et al.
Instantaneous Ambiguity Resolution
,
1991
.
[13]
P. Teunissen,et al.
The least-squares ambiguity decorrelation adjustment: its performance on short GPS baselines and short observation spans
,
1997
.
[14]
Xiao-Wen Chang,et al.
MLAMBDA: A Modified LAMBDA Method for Integer Ambiguity Determination
,
2005
.
[15]
Peter Teunissen,et al.
The success rate and precision of GPS ambiguities
,
2000
.