Least Squares Ambiguity Decorrelation Adjustment Algorithm This is a Pythonic translation of Dr P.J. Teunissen's LAMBDA algorithm for fixing integer ambiguities. The original LAMBDA was written in MATLAB by Dr Sandra Verhagan and Dr Bofeng Li, TU Delft / Curtin University. This Pythonic translation employs the default integer least-squares (ILS) with search-and-shrink method. It decorrelates float ambiguities based on the covariance matrix search-and-shrink, and then fixes ambiguities. Although it was intended for GNSS ambiguity fixing, it can fix integers for other applications like InSAR. Credits to PJ Teunissen, Jonge, J Tiberius, S. Verhagen, and Bofeng Li.
INPUTS:
- ahat : numpy array of float ambiguities
- Qahat : numpy covariance matrix for float ambiguities
- ncands : number of candidates (optional parameter, default = 2)
OUTPUT:
- afixed1 : Best estimated integer candidates
- afixed2 : 2nd best estimated integer candidates
- sqnorm : Distance between integer candidate and float ambiguity vectors in the metric of the variance-covariance matrix