A multi-baseline data fusion algorithm for distributed satellites SAR interferometry by combining iterative and maximum-likelihood methods

In distributed satellites interferometric SAR (InSAR) system, short baselines usually make higher height ambiguity but the interferometric phase can be unwrapped much more easily, while the long baselines usually cause more complicated phase unwrapping problems but have less height ambiguity. It´s of obvious significance if we can combine the data from baselines of different length. In this paper, a multi-baseline InSAR data fusion method based on iterative and maximum-likelihood methods is proposed to combine the information from different baselines and obtain more accurate digital elevation model (DEM) compared with single baseline. Our simulation shows that the proposed algorithm is more effective and accurate than iterative or maximum-likelihood method in multi-baseline InSAR system, and it is especially appropriate for the rugged terrain height retrieving or processing highly ambiguous data, for which commonly used phase unwrapping algorithms may fail.