Shichao Chen

A New Look at Loffeld's Bistatic Formula in Tandem Configuration

A new way of looking at Loffelds bistatic formula (LBF) is presented for tandem configuration in this letter. The factors that affect the precision of the spectrum are obtained through the comparison with another analytical one. It has been proved that the cosine of the half bistatic angle plays a more important role respect to the other factor, which is whether the baseline to range ratio is equal to the tangent of the half bistatic angle or not. As long as the cosine of the half bistatic angle is very close to one, the LBF spectrum is of high quality, and it does not have a direct influence by the length of the baseline (baseline-to-range ratio) or the size of the squint angle. The factors that affect the precision of the spectrum are discussed in detail through simulated experiments.

SAR Target Configuration Recognition via Two-Stage Sparse Structure Representation

A two-stage sparse structure representation algorithm which can preserve the manifold structure of the data is proposed for synthetic aperture radar target configuration recognition in this paper. Manifold structure of the data is preserved by two stages. In the training stage, taking advantage of both the sparse representation (SR) and manifold learning, local structure of the data is preserved in the reconstruction space, where SR-based recognition is realized. In the testing stage, two structure preserving factors based on the testing samples are embedded into the SR model to enhance structure preserving performance. The first one is constructed to preserve the local structure of the testing samples, which can guarantee the samples that are close to each other in the original space will also be close to each other in the sparse space. And the second one is established to preserve the distant structure of the testing samples, which can ensure the samples that are far from each other in the original space will also be far from each other in the sparse space. Manifold structure of the data is well captured and preserved by two stages. Experimental results on the moving and stationary target acquisition and recognition database demonstrate the effectiveness of the proposed algorithm.

Focusing of tandem bistatic SAR data using the chirp-scaling algorithm

Based on an exact analytical bistatic point target spectrum, an efficient chirp-scaling algorithm is proposed to correct the range cell migration of different range gates to the one of the reference range for tandem bistatic synthetic aperture radar data processing. The length of the baseline (baseline to range ratio) does not give a direct influence to the proposed algorithm, which can be applied to the processing of tandem bistatic data with a large baseline even when the baseline is equal to the range. No interpolation is needed during the entire processing, only fast Fourier transforms and phase multiplications are needed, which result in efficiency. The validity of the proposed algorithm has been verified by simulated experiments.

A nonlinear chirp scaling algorithm for tandem bistatic SAR

A nonlinear chirp scaling algorithm (NCSA) is proposed for bistatic SAR data processing with high squint angles in tandem configuration. Besides the dependence of the azimuth frequency, the dependence of range is considered for the Doppler chirp rate of tandem bistatic SAR. The proposed algorithm can compensate the range dependence of both the range cell migration (RCM) and the second range compression (SRC) terms well by appropriately setting the coefficients of the phase term, which is obtained after the nonlinear chirp scaling process in the two-dimensional wavenumber domain. Only fast Fourier transforms (FFTs) and phase multiplies are required, and fast imaging is implemented in the frequency domain. Satisfying focusing qualities verify the effectiveness of the proposed algorithm by simulations.

Range-time backprojection for 3D shape estimation of small space debris

A coherent backprojection based approach is presented for 3D shape estimation of small debris in space. The approach utilizes the fact that space debris is in high-speed spinning motion. Making use of the modulated range envelope and Doppler spreading, a backprojection maps the data into a three-dimensional parameter domain, and the spatial coordinates of scatterers can be extracted to reconstruct 3D shape. In most of recent papers on the imaging of space debris or rotating targets, the rotation from the translational motion is assumed to be neglectable, while in our approach the backprojection is combined with a Fourier transform to deal with the rotation of translational motion and full coherent accumulation can be achieved. The approach is robust in the occurrence of serious profile overlapping and strong noise. Simulations confirm its validity and good performance.

Sar target configuration recognition using class-dependent locality preserving projections

Locality preserving projections (LPP) can preserve the local structure of the datasets effectively. However, it is not capable of separating the samples that are close to each other in the high-dimensional space but belong to different classes. Focusing on the problem, a class-dependent locality preserving projections (CDLPP) algorithm is proposed in this paper. The class information is embedded into the LPP model, and the similarity matrix and the difference matrix are constructed according to the class information. The similarity matrix is utilized to preserve the local structure of the samples belong to the same class, whereas the difference matrix is utilized to separate the samples that are close to each other in the high-dimensional space but belong to different classes. Experiments are conducted using the moving and stationary target acquisition and recognition (MSTAR) database, the results verify the effectiveness of the proposed algorithm.