ESTIMATION OF DOPPLER CENTROID FREQUENCY IN SPACEBORNE SCANSAR

Doppler centroid frequency is an essential parameter in the imaging processing of the Scanning mode Synthetic Aperture Radar (ScanSAR). Inaccurate Doppler centroid frequency will result in ghost images in imaging result. In this letter, the principle and algorithms of Doppler centroid frequency estimation are introduced. Then the echo data of ScanSAR system is analyzed. Based on the algorithms of energy balancing and correlation Doppler estimator in the estimation of Doppler centroid frequency in strip mode SAR, an improved method for Doppler centroid frequency estimation in ScanSAR is proposed. The method has improved the accuracy of Doppler centroid frequency estimation in ScanSAR by zero padding between burst data. Finally, the proposed method is validated with the processing of ENVIronment SATellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) wide swath raw data.

UWB SAR RFI SUPPRESSION BASED ON REGION OF SUPPORT IN 2-D WAVENUMBER DOMAIN

Radio Frequency Interference (RFI) degrades the quality of focused Ultra-WideBand Syn- thetic Aperture Radar (UWB SAR) images. From both the theoretical analysis and real data valida- tion, it is concluded that target echo and RFI have different Region Of Support (ROS) in 2-D fast- time wavenumber and aperture wavenumber domain. Consequently, a novel adaptive filter is pro- posed according to the Wiener optimum criterion on the distinct ROS characteristics of target echo and RFI. Compared with the notch filter and the Least Mean Square (LMS) adaptive filter in previ- ous literatures, the proposed method is more computationally efficient with satisfactory suppression results. In terms of Signal-to-Interference Ratio Improvement (SIRI) and processing time, the per- formance of the proposed adaptive filter is verified with the field data collected with a UWB SAR system.

A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.

A HIGH RESOLUTION WIDE SWATH SAR METHOD BASED ON INTRA-PULSE NULL STEERING AND MIMO

High Resolution Wide Swath (HRWS) Synthetic Aperture Radar (SAR) often suffers from low Signal-to-Noise Ratio (SNR) due to small transmitting antenna, especially in phased array antenna systems. Digital Beam Forming (DBF) based on Single Input and Multiple Output (SIMO) achieves receiving array gain at the cost of increasing data rate. This letter proposes a new HRWS SAR method, which employs intra-pulse null steering to get receiving gain in elevation and decrease the data rate, and Multiple Input and Multiple Output (MIMO) using Space-Time Block Coding (STBC) in azimuth to get transmitting gain and receiving array gain simultaneously. The feasibility is verified by deduction and simulations.

THE ANGLE ERROR ESTIMATE METHOD IN THE WIDE SWATH SAR BASING ON MULTI-RECEIVER

The wide-swath method based on multi-receiver is a novel and highly accurate wide-swath method, which requires a very precise view angle. The estimated angle has error because of the atmosphere refraction, angle error of view and target height. A method is proposed in this paper to estimate the angle error from the return signal. The method makes use of the relationship between the view angle error and the signal correlation of the subswaths to estimate the angle error. The precision of this method is analyzed by the law of great number and it turns out to be in direct proportion to the root square number of averaging. The simulation result is given and the angle precision is 0.025°.

SAR STEREO IMAGING ALGORITHM BASED ON CAPON SPECTRUM ESTIMATION

This letter presents a novel approach for the Synthetic Aperture Radar (SAR) stereo imaging based on the Capon spectrum estimation technique. In order to deal with nonuniform sampling space and lead to super resolution in the elevation direction, Capon approach is used to focus the SAR data on a certain height. Results obtained on simulated data demonstrate the feasibility of the Capon based algorithm. Compared with the classical Fast Fourier Transform (FFT), the Capon based algo-rithm shows better resolution quality.

NEW RANGE ALIGNMENT ALGORITHM FOR ISAR BASED ON HIGH ORDER MOMENT

A new technique is proposed for range alignment in Inverse Synthetic Aperture Radar (ISAR). The basic idea is to perform range alignment using a maximum kurtosis (fourth-order central moment) criterion. After maximizing the kurtosis of the combined range profile of two adjacent echoes, the amount of range shift between them can be automatically tracked out. The combined range profile is constructed by a max operation, which only reserves the larger elements of the two echoes, and the echoes’ amplitudes are limited before they are combined. This algorithm has been used to process real ISAR data and the results demonstrate the effectiveness of the method. Compared with the correlation method and the minimum entropy method, the proposed algorithm obtains much better results in both examples in this paper. Its computation complexity has the same order of magnitude as the minimum entropy method.

ADAPTIVE KURTOSIS OPTIMIZATION AUTOFOCUS ALGORITHM

This paper proposes an autofocus algorithm used for Synthetic Aperture Radar (SAR) images, called Adaptive Kurtosis Optimization Autofocus Algorithm (AKOAA). The AKOAA can reduce the differ-ence between initial value and real value in focusing by adaptively adjusting the initial value, therefore effec-tively improve the local extremum problem in the Contrast Optimization Autofocus Algorithm (COAA) and speed up the convergence velocity. The principle and realization method of AKOAA are thoroughly investi-gated, and experimental results using real L-band SAR data show that the focus speed of AKOAA is nearly doubled compared with that of the COAA, and the image contrast of AKOAA is improved as well.

NOISE AMPLIFICATION ANALYSIS AND COMPARISON OF TWO PERIODIC NONUNIFORM SAMPLING RECONSTRUCTION METHODS USED IN DPCA SAR

General Sampling Expansion Reconstruction Method (GSERM) and Digital Spectrum Reconstruction Method (DSRM), which prove effective to reconstruct azimuth signal of Displaced Phase Center Apertures (DPCA) Synthetic Aperture Radar (SAR) system from its Periodic Non-Uniform Sampling (PNUS) data sequences, would amplify the noise and sidelobe clutter simultaneously in the reconstruction. This paper formulates the relation of the system transfer matrixes of the above two methods, gives the properties, such as periodicity, symmetry, and time-shift property, of their Noise and Sidelobe Clutter Amplification Factor (NSCAF), and discovers that DSRM is more sensitive than GSERM in the white noise environment. In addition, criteria based on initial sampling point analysis for the robust PRF selection are suggested. Computer simulation results support these con-clusions.