Abstract
This paper introduces a modified random sample consensus (M-RANSAC) and short-time fractional Fourier transform (STFRFT)-based algorithm for feature extraction of synthetic aperture radar (SAR) scattering centers. In this algorithm, the range migration curve (RMC) of a scattering center is formulated as a parametric model. By estimating these parameters, the backscattering envelope of scattering center, corresponding to the backscattering variation in synthetic aperture time, is extracted directly from a time-domain range-compressed signal. The estimated parameters can also reconstruct the geographical location and along-track velocity of scattering centers. Thus, even without knowing explicit knowledge of platform velocity and forming a SAR image, this algorithm is capable of realizing feature extraction. To estimate parameters scatter by scatter, M-RANSAC approach is proposed as an implementary method with iterative procedure. In the iterations, fitting precision indicator (FPI) works cooperatively with construction fitness coefficient (CFC) to determine the optimal parameters of different scattering centers. Adapting this method to more general cases, STFRFT is introduced to separate the overlapped trajectories of RMCs of scattering centers. The root mean squared errors (RMSEs) of parameter estimation are close to their Cramer-Rao lower bounds (CRLB). The effectiveness of feature extraction based on the devised algorithm is validated by both simulated and real SAR data.
Highlights
1 Introduction Feature extraction has confirmed its usage in synthetic aperture radar (SAR) target recognition and classification, where a given target is classified as a specific target type by feature matching over the known database [1,2,3,4,5]
M-RANSAC-based algorithm is proposed to classify the groups of points corresponding to different scattering centers, get rid of existing noise, and realize parameter estimation of these range migration curve (RMC) simultaneously
We generate raw data of a single target when the broadside airborne SAR system operates. This raw data is added with various Gaussian white noise and taken as the input of M-RANSAC algorithm to estimate the location and velocity of dominant scattering center
Summary
Feature extraction has confirmed its usage in synthetic aperture radar (SAR) target recognition and classification, where a given target is classified as a specific target type by feature matching over the known database [1,2,3,4,5]. Interested attributes for each scattering center generally include backscattering envelope, geographical location, To extract the attributes of scattering centers, a family of time-frequency analysis (TFA) approaches has been devised. They use Wigner-Ville decomposition [9], Sheng et al EURASIP Journal on Advances in Signal Processing (2016) 2016:46 wavelet transforms [10], and Fourier transform [8, 11] to realize feature extraction. Free from SAR image formation, another group of approaches can directly extract the feature from the spectrum of raw data These methods rely on spectral estimation and include parametric [12,13,14], nonparametric [15,16,17], and semi-parametric approaches [18]. Since the aforementioned methods start with the spectrum, it may degrade the effectiveness of feature extraction
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