Zeng Cao

Sum-difference Reduced-dimensional Processing in SAR Image Domain and Statistical Analysis for Ground Moving Target Indication

In this article, a new reduced-dimensional adaptive processing algorithm based on joint pixels sum-difference data for clutter rejection is proposed. The sum-difference data are obtained by orthogonal projection of the joint pixels data of different synthetic aperture radar (SAR) images generated by a multi-satellite radar system. In the sense of statistical expectation, the sum-differ- ence data contain the common and different information of the SAR images. Therefore, the objective of clutter cancellation can be achieved by adaptive processing. Moreover, based on the residual image after clutter rejection, statistical analysis of constant false-alarm rate (CFAR) detection of moving targets is also presented. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm even with heterogeneous clutter and image co-registration error.

Canonical Framework for ATI and DPCA

The topic of moving-target detection in clutter has been extensively studied, there are many methods, such as along-track interferometric (ATI) phase, displaced phase center antenna (DPCA) method, space-time adaptive processing (STAP), or some other metrics. A canonical framework is proposed that encompasses ATI and DPCA. The statistical test metric for ATI and DPCA is established in an simple form, via the definition of the complex central Wishart distribution, deduces the statistics of the test metric, the probability distribution of the test metric for ATI and DPCA have the complex central Wishart distribution of 1times1 case, namely the chi2- distribution. The theory foundation offers the possibility to construct the united multi-channel SAR GMTI detector, and derive the constant false-alarm rate (CFAR) detector tests for separating moving targets from clutter.

Wide-band frequency estimation with two different sub-Nyquist temporal sampling rates

A new method is presented for wide-band frequency estimation with sub-Nyquist sampling by using two independent sampling channels at the different rates. With low cost of hardware and moderate computation, the proposed method is not sensitive to the relative amplitude and phase errors of both channels. Simulations demonstrate the capabilities of the algorithm