2-D phase unwrapping and instantaneous frequency estimation
نویسنده
چکیده
منابع مشابه
Velocity measurement using phase fitting of analytic spatiotemporal images
In this paper, the problem of one-dimensional (1-D) velocity estimation is addressed, using two-dimensional (2D) spatiotemporal orientation estimation. A new frequency estimator based on a least square plane fitting of the estimated autocorrelation phase has been previously developed and is applied here to random textured images for the problem of velocity estimation. This algorithm requires th...
متن کاملTwo-dimensional frequency estimation using autocorrelation phase fitting
In this paper, the problem of two-dimensional (2-D) frequency estimation of a complex sinusoid embedded in a white Gaussian additive noise is addressed. A new frequency estimator based on a least square plane fitting of the estimated autocorrelation phase of the signal is derived. This algorithm requires a 2-D phase unwrapping step which can be easily done. This algorithm is shown to be unbiase...
متن کاملModel based phase unwrapping of 2-D signals
A parametric model and a corresponding parameter estimation algorithm for unwrapping two-dimensional phase functions, are presented. The proposed algorithm performs global analysis of the observed signal. Since this analysis is based on parametric model tting, the proposed phase unwrapping algorithm has low sensitivity to phase aliasing due to low sampling rates and noise, as well as to local e...
متن کاملMulti-frequency Phase Unwrapping from Noisy Data: Adaptive Local Maximum Likelihood Approach
The paper introduces a new approach to absolute phase estimation from frequency diverse wrapped observations. We adopt a discontinuity preserving nonparametric regression technique, where the phase is reconstructed based on a local maximum likelihood criterion. It is shown that this criterion, applied to the multifrequency data, besides filtering the noise, yields a 2πQ-periodic solution, where...
متن کاملA Statistical-cost Approach to Unwrapping the Phase of Insar Time Series
Fully 3-D phase-unwrapping algorithms are commonly based on the central assumption that the phase difference between neighbouring sample points in any dimension is generally less than half a phase cycle (the Nyquist criteria). In the case of InSAR time series, however, signals are correlated spatially but uncorrelated over the repeatpass time, due chiefly to changes in atmospheric delay. Here I...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- IEEE Trans. Geoscience and Remote Sensing
دوره 33 شماره
صفحات -
تاریخ انتشار 1995