Enhanced multi-baseline unscented Kalman filtering phase unwrapping algorithm

This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optimal path-following strategy based on phase quality estimate function. The enhanced joint phase gradient estimator can accurately and effectively extract the phase gradient information of wrapped pixels from noisy interferograms, which greatly increases the performances of the proposed method. The optimal path-following strategy ensures that the proposed algorithm simultaneously performs noise suppression and phase unwrapping along the pixels with high-reliance to the pixels with low-reliance. Accordingly, the proposed algorithm can be predicted to obtain better results, with respect to some other algorithms, as will be demonstrated by the results obtained from synthetic data.

Multi-baseline extended particle filtering phase unwrapping algorithm based on amended matrix pencil model and quantized path-following strategy

This paper proposes a new multi-baseline extended particle filtering phase unwrapping algorithm which combines an extended particle filter with an amended matrix pencil model and a quantized path-following strategy. The contributions to multibaseline synthetic aperture radar(SAR) interferometry are as follows: a new recursive multi-baseline phase unwrapping model based on an extended particle filter is built, and the amended matrix pencil model is used to acquire phase gradient information with a higher precision and lower computational cost, and the quantized path-following strategy is introduced to guide the proposed phase unwrapping procedure to efficiently unwrap wrapped phase image along the paths routed by a phase derivative variance map.