Frequency Domain Filtering SAR Interferometric Phase Noise Using the Amended Matrix Pencil Model

Interferometric phase filtering is one of the key steps in interferometricsynthetic aperture radar (InSAR/SAR). However, the ideal filtering results are difficult toobtain due to dense fringe and low coherence regions. Moreover, the InSAR/SAR datarange is relatively large, so the efficiency of interferential phase filtering is one of themajor problems. In this letter, we proposed an interferometric phase filtering methodbased on an amended matrix pencil and linear window mean filter. The combination ofthe matrix pencil and the linear mean filter are introduced to the interferometric phasefiltering for the first time. First, the interferometric signal is analyzed, and theinterferometric phase filtering is transformed into a local frequency estimation problem.Then, the local frequency is estimated using an amended matrix pencil at a window. Thelocal frequency can represent terrain changes, thus suggesting that the frequency can beaccurately estimated even in dense fringe regions. Finally, the local frequency is filteredby using a linear window mean filter, and the filtered phase is recovered. The proposedmethod is calculated by some matrices. Therefore, the computational complexity isreduced, and the efficiency of the interferometric phase filtering is improved.Experiments are conducted with simulated and real InSAR data. The proposed methodexhibits a better filtering effect and an ideal efficiency as compared with the traditionalfiltering method.

A Robust Fault Detection Algorithm for the GNSS/INS Integrated Navigation Systems

Integrity is an important index for GNSS-based navigation and positioning, and the receiver autonomous integrity monitoring (RAIM) algorithm has been presented for integrity applications. In the integrated navigation systems of a global navigation satellite system (GNSS) and inertial navigation system (INS),the conventional RAIM algorithm has been developed to extended receiver autonomous integrity monitoring (ERAIM). However, the ERAIM algorithm may fail and a false alarm may generate once the measurements are contaminated by significant outliers, and this problem is rarely discussed in the existing literatures. In this paper, a robust fault detection and the corresponding data processing algorithm are proposed based on the ERAIM algorithm and the robust estimation. In the proposed algorithm, weights of the measurements are adjusted with the equivalent weight function, and the efficiency of the outlier detection and identification is improved, therefore, the estimates become more reliable, and the probability of the false alarm is decreased. Experiments with the data collected under actual environments are implemented, and results indicate that the proposed algorithm is more efficient than the conventional ERAIM algorithm for multiple outliers and a better filtering performance is achieved.

A state‑domain robust autonomous integrity monitoring with an extrapolation method for single receiver positioning in the presence of slowly growing fault

Single receiver positioning has been widely used as a standard and standalone positioning technique for about 25 years.To detect the slowly growing faults caused by satellite and receiver clocks in single receiver positioning,the Autonomous Integrity Monitoring with an Extrapolation method(AIME)was proposed based on the Kalman filter measurement domain.However,AIME was designed with the assumption of there is the same number of visible satellites at each epoch,which limits its application.To address this issue,this paper proposes a state-domain Robust Autonomous Integrity Monitoring with the Extrapolation Method(SRAIME).The slowly growing fault detection statistics is established based on the difference between the estimates of the state propagator and the posterior state estimation in Kalman filtering.Meanwhile,singular value decomposition is adopted to factor the covariance matrix of the difference to increase computational robustness.Besides,the relevant formulas of the proposed method are theoretically derived,and it is proven that the proposed method is suitable for any positioning model based on the Kalman filter.Additionally,the results of two experiments indicate that SRAIME can detect slowly growing faults in single receiver positioning earlier than AIME.