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