Abstract
In this article, we propose a novel phase unwrapping method termed the square-root cubature Kalman filter phase unwrapping (SCKFPU) method. Specifically, as the system model of the interferometric synthetic aperture (InSAR) phase unwrapping can be formulated as the discrete Markovian jump system for tracking a maneuvering target, a phase unwrapping method is proposed by combining the SCKF algorithm with a phase gradient estimator. Moreover, we design a phase quality estimate function, and present an optimal path-following strategy. The strategy ensures that the SCKFPU method simultaneously performs noise suppression and unwraps InSAR images along the pixels with high-reliance to the pixels with low-reliance. Simulation results based on synthetic data, true InSAR data, and measured (real) InSAR data show that the proposed InSAR phase unwrapping method can achieve better performance compared with other existing phase unwrapping methods.
Highlights
I NTERFEROMETRIC synthetic aperture (InSAR) has been widely used in many applications, such as creating digital elevation models (DEMs) and monitoring surface deformation [1]
Thereby, the CKFPU method can achieve a better performance in InSAR phase unwrapping
We present a novel phase unwrapping method based on a square-root cubature Kalman filter (SCKF)
Summary
I NTERFEROMETRIC synthetic aperture (InSAR) has been widely used in many applications, such as creating digital elevation models (DEMs) and monitoring surface deformation [1]. Thereby, the optimal following path is selected by minimizing the designed phase estimate function, which ensures the proposed method simultaneously performs noise suppression and unwraps InSAR images along the pixels with high-reliance (the smaller values of the phase estimate function) to the pixels with low-reliance (the larger values of the phase estimate function). In this way, the SCKFPU method can achieve a better unwrapping performance compared with the one guided only by the coherence or by the phase estimate function in [21], which can be proved by our simulations.
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