Study of Kalman Filter Applied in Phase Unwrapping of InSAR
|School||Shandong University of Science and Technology|
|Course||Geodesy and Survey Engineering|
|Keywords||InSAR kalman filter phase unwrapping algorithm comparison topographic factors local fringe frequency estimation|
Synthetic aperture radar interferometry (InSAR) technology has many advantages, such as all time, all weather and high spatial resolution. It has become a great potential technology for Earth observation from space and has broad applications. However, a lot of the key technologies are still need for further exploration and research in the actual data processing.As one of them, phase unwrapping has also been more and more attention of scholars home and abroad.In this paper, a variety of advantages and disadvantages of many typical phase unwrapping algorithms are firstly researched. Based on this, the Kalman filter model is introduced in phase unwrapping, and the principle of the Kalman filter algorithm for phase unwrapping and its realization process are deeply discussed. While, Kalman filter phase unwrapping algorithm based on topographic factors is proposed in the situation that the unwrapping results have error propagation when in steep terrain or large steep. Simulated and real InSAR data show the feasibility and effectiveness of the algorithm. The main research work is summarized as follows:(1) The principle of phase unwrapping and some typical phase unwrapping algorithms are summarized, and the phase unwrapping algorithms used in four common radar image processing softwares are outlined, advantages and disadvantages of six typical phase unwrapping algorithms are evaluated and the application of them.are given through experimental analysis of real InSAR data.(2)The Kalman filter that applied in the phase unwrapping is researched, and the features of the phase unwrapping Kalman filter that phase noise reduction and phase unwrapping at the same time are summarized. The principle that the Kalman filter applied in phase unwrapping and the specific implementation process are analyzed in details, and specific procedures for the implementation are achieved. It is verified that Kalman filter phase unwrapping algorithm has effectiveness and feasibility in the respect of noise restraining and unwrapping effect using simulated data. Using real InSAR data to do experiment, it is comparatively analyzed with the results of other typical phase unwrapping algorithms from both visual and quantitative aspects, and comprehensively evaluated the performance of the algorithm.(3) Considering this situation that unwrapping result causes error transmission using the existing Kalman filter phase unwrapping algorithm when in steep terrain or larger slope, through the introduction of the input control variable associated with topographic factors to the state-space model of Kalman filter, this paper presents an improved Kalman filter phase unwrapping algorithm based on topographic factors. The implementation of algorithm is described in details. In the local frequency estimation, using two-dimensional Chirp-Z transform, better estimate of the results may be quickly got. It is verified that the proposed algorithm can effectively deal with the situation of steep terrain and larger slope through the experimental result of simulated data and real InSAR data.