Phase Unwrapping Algorithm Based on Extended Particle Filter for SAR Interferometry

This study aims to design a robust particle filter using artificial intelligence algorithms to enhance estimation performance using a low-grade interferometric radar altimeter (IRA). Based on the synthetic aperture radar (SAR) interferometry technology, the IRA can extract three-dimensional ground coordinates with at least two antennas. However, some IRA uncertainties caused by geometric factors and IRA-inherent measurement errors have proven to be difficult to eliminate by signal processing. These uncertainties contaminate IRA outputs, crucially impacting the navigation performance of low-grade IRA sensors in particular. To deal with such uncertainties, an ant-mutated immune particle filter (AMIPF) is proposed. The proposed filter combines the ant colony optimization (ACO) algorithm with the immune auxiliary particle filter (IAPF) to bring individual mutation intensity. The immune system indicates the stochastic parameters of the ACO, which conducts the mutation process in one step for the purpose of computational efficiency. The ant mutation then moves particles into the most desirable position using parameters from the immune system to obtain optimal particle diversity. To verify the performance of the proposed filter, a terrain referenced navigation (TRN) simulation was conducted on an unmanned aerial vehicle (UAV). The Monte Carlo simulation results show that the proposed filter is not only more computationally efficient than the IAPF but also outperforms both the IAPF and the auxiliary particle filter (APF) in navigation performance and robustness.

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Phase Unwrapping for Large-Scale P-Band UWB SAR Interferometry

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2015.2451991 ◽

2015 ◽

Vol 12 (10) ◽

pp. 2120-2124 ◽

Cited By ~ 6

Author(s):

Junyi Xu ◽

Daoxiang An ◽

Xiaotao Huang ◽

Guangxue Wang

Keyword(s):

Large Scale ◽

Phase Unwrapping ◽

Sar Interferometry

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Quantitavive comparison of phase unwrapping algorithms for SAR interferometry

2008 Asia-Pacific Microwave Conference ◽

10.1109/apmc.2008.4958433 ◽

2008 ◽

Author(s):

Xiaojin Shi ◽

Yunhua Zhang

Keyword(s):

Phase Unwrapping ◽

Sar Interferometry

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Extended Phase Unwrapping Max-Flow/Min-Cut Algorithm for Multibaseline SAR Interferograms Using a Two-Stage Programming Approach

Sensors ◽

10.3390/s20020375 ◽

2020 ◽

Vol 20 (2) ◽

pp. 375 ◽

Cited By ~ 1

Author(s):

Lifan Zhou ◽

Yang Lan ◽

Yu Xia ◽

Shengrong Gong

Keyword(s):

Chinese Remainder Theorem ◽

Phase Unwrapping ◽

Sar Interferometry ◽

Noise Robustness ◽

Programming Approach ◽

Two Stage ◽

Extended Phase ◽

Contextual Dependence ◽

Stage 1 ◽

Min Cut

Multi-baseline (MB) phase unwrapping (PU) is a key step of MB synthetic aperture radar (SAR) interferometry (InSAR). Compared with the traditional single-baseline (SB) PU, MB PU is applicable to the area where topography varies violently without obeying the phase continuity assumption. A two-stage programming MB PU approach (TSPA) proposed by H. Yu. builds the link between SB and MB PUs, so many existing classical SB PU methods can be transplanted into the MB domain. In this paper, an extended PU max-flow/min-cut (PUMA) algorithm for MB InSAR using the TSPA, referred to as TSPA-PUMA, is proposed, consisting of a two-stage programming procedure. In stage 1, phase gradients are estimated based on Chinese remainder theorem (CRT). In stage 2, a Markov random field (MRF) model of PUMA is designed for modeling local contextual dependence based on the phase gradients obtained by stage 1. Subsequently, the energy of the MRF model is minimized by graph cuts techniques. The experiment results illustrate that the TSPA-PUMA method can drastically enhance the accuracy of the original PUMA method in the rugged area, and is more efficient than the original TSPA method. In addition, the noise robustness of TSPA-PUMA can be improved through adding more interferograms with different baseline lengths.

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