arXiv subjects

Di Song

Publications and source records attributed to Di Song.

3 recordsLinked to original sources

arXiv2026

Fundamental Limits of Adaptive Beamforming Under Finite Training

Finite training reduces the output signal-to-interference-plus-noise ratio (SINR) of an adaptive beamformer, and a natural question is how much of this loss is unavoidable. This paper determines this question, providing a beamforming counterpart of the Cram\'er--Rao bound in spectral estimation. An exact identity expresses the SINR loss as a bounded function of the error in the clairvoyant minimum-variance distortionless-response (MVDR) weight. It yields a local asymptotic minimax lower bound over all measurable data-dependent beamforming rules, including biased and irregular rules. The first-order coefficient is $\tr(\Mb\Jb_{\rm eff}^{-1})$, where $\Jb_{\rm eff}$ describes the information in the training data and $\Mb$ measures the sensitivity of the output SINR. Matching constructions determine this coefficient in two complex-Gaussian models. For an $N$-sensor uniform linear array with $K$ distinct point interferers and $2K+1\le N$, a data-driven split one-step beamformer attains the coefficient $C_\theta\le K$ at every interior scene of a fixed compact regular parameter set. For unrestricted covariance matrices, sample matrix inversion (SMI) attains the coefficient $N-1$ through the classical Reed--Mallett--Brennan law. The difference quantifies the first-order value of finite-source structure. Geometric formulas and numerical results describe the dependence on interference power and array geometry, and the finite-sample departure near a weak-source boundary.

cs.IT↗

arXiv2022

Space-Time Adaptive Processing Using Random Matrix Theory Under Limited Training Samples

Space-time adaptive processing (STAP) is one of the most effective approaches to suppressing ground clutters in airborne radar systems. It basically takes two forms, i.e., full-dimension STAP (FD-STAP) and reduced-dimension STAP (RD-STAP). When the numbers of clutter training samples are less than two times their respective system degrees-of-freedom (DOF), the performances of both FD-STAP and RD-STAP degrade severely due to inaccurate clutter estimation. To enhance STAP performance under the limited training samples, this paper develops a STAP theory with random matrix theory (RMT). By minimizing the output clutter-plus-noise power, the estimate of the inversion of clutter plus noise covariance matrix (CNCM) can be obtained through optimally manipulating its eigenvalues, and thus producing the optimal STAP weight vector. Two STAP algorithms, FD-STAP using RMT (RMT-FD-STAP) and RD-STAP using RMT (RMT-RD-STAP), are proposed. It is found that both RMT-FD-STAP and RMT-RD-STAP greatly outperform other-related STAP algorithms when the numbers of training samples are larger than their respective clutter DOFs, which are much less than the corresponding system DOFs. Theoretical analyses and simulation demonstrate the effectiveness and the performance advantages of the proposed STAP algorithms.

cs.IT↗

arXiv2018

A new approach for rain gush formation associated with ionic wind

Based upon experimental observation in the laboratory, we propose that ionic wind from corona discharge inside a thundercloud would play an important role in producing a rain gush. A cyclic chain of events inside a super-saturated environment in a thundercloud is proposed, each event enhancing the successive ones until lightning occurs. These successive events are collision between snowflakes and rimers, charge separation, corona discharge, avalanche ionization, ionic wind originating from the positively and negatively charged masses of cloud, vortex motion and turbulence when mixed with the updraft, more collision, more charge separation, stronger corona discharge, and so on. Meanwhile, avalanche ionization would produce more CCN (cloud condensation nuclei) resulting in more precipitation and hence rimers formation in the super-saturated environment. More collision in the buoyant turbulence would lead to more fusion of droplets and the formation of larger rimers. The cyclic processes would repeat themselves until the electric field between the two oppositely charged masses of cloud was strong enough to induce a breakdown. The latter would create a sudden short circuit between the two charged masses of cloud neutralizing the charges. There would be no more ionic wind, hence, much less buoyant turbulence. The updraft alone would not be sufficiently strong to support larger rimers which would fall down 'suddenly' to the earth surface as a rain gush.

physics.ao-ph↗