Subjects: Geosciences >> Space Physics submitted time 2017-03-10
Abstract： In this paper a three component model-based decomposition with adaptive selection of unitary transformations for polarimetric synthetic aperture radar (POLSAR) data processing is proposed. Singh et al implemented two unitary transformations on the coherency matrix to minimize the power of cross-polarization, and as a result the T23element of the coherency matrix becomes zero. Another two unitary transformations are proposed by us to carry out on the coherency matrix also to minimize the power of crosspolarization, and the T13element of the coherency matrix becomes zero. Here, we first implement Singh's two unitary transformations and the proposed two unitary transformations on the coherency matrix separately. Then we select the one which leads to the smaller T33. At last, we carry out the three component model-based decomposition proposed by Freeman and Durden based on the obtained coherency matrix. The smaller T33is obtained, the better the over-estimation of volume scattering in model-based decomposition can be suppressed. The RADARSAT-2 POLSAR data of San Francisco area is used to validate the improvement of the proposed method over the three component decomposition only with Singh's two unitary transformations.
Peer Review Status:
Awaiting Review
Subjects: Geosciences >> Space Physics submitted time 2016-05-12
Abstract： In this paper, we propose a method based on signal processing techniques to improve the performance of aperture antennas, such as a parabolic reflector, used to estimate the Direction of Arrival (DOA) of a signal. Three signal processing algorithms are investigated, including the correlation method, which is used to make an initial estimate of the incidence angle within a certain range. Then inverse matrix method and singular value decomposition method are subsequently utilized to refine the estimate within this range.
Peer Review Status:Awaiting Review
Subjects: Geosciences >> Space Physics submitted time 2016-05-12
Abstract： In this work, we employ a novel basis matrix method, applied in conjunction with the singular value decomposition (SVD) algorithm to achieve sub-wavelength resolution in the images derived by using phase conjugation (PC) scheme in the microwave imaging problem. Illuminative examples of different bar-code type of distributions are presented, together with the images recovered by using the proposed method.
Peer Review Status:Awaiting Review
Subjects: Geosciences >> Space Physics submitted time 2016-05-03
Abstract： This paper proposes a burst model of chaotic noise signals with randomly stepped carrier frequencies for velocity estimation and high-resolution range imaging of high-speed moving targets. The random stepping of carrier frequencies is controlled by a combination chaotic map (CCM), which is generated by embedding a Logistic map into a Bernoulli map. The baseband noise signal adopts the CCM based frequency-modulation (CCM-FM) signal, which has good randomness and a thumbtack ambiguity function as well. The velocity estimation includes a coarse search and a precise search, where the coarse search is conducted with a fixed step to makes the velocity deviation less than the velocity resolution, while the precise search adopts the Golden Section Search (GSS) algorithm to get an accurate estimation of velocity. What should be emphasized is that the velocity estimation process can be completed with just a burst of subpulses. Then the spectra are coherently synthesized to obtain ultra-wide bandwidth and high-resolution range imaging. Finally, numerical simulations demonstrate a good performance of the proposed signal model and the processing algorithm.
Peer Review Status:Awaiting Review
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