Bayesian statistical analysis of ground-clutter for the relative calibration of dual polarization weather radars

A new data processing methodology, based on the statistical analysis of ground-clutter echoes and aimed at investigating the stability of the weather radar relative calibration, is presented. A Bayesian classification scheme has been used to identify meteorological and/or ground-clutter echoes. The outcome is evaluated on a training dataset using statistical score indexes through the comparison with a deterministic clutter map. After discriminating the ground clutter areas, we have focused on the spatial analysis of robust and stable returns by using an automated region-merging algorithm. The temporal series of the ground-clutter statistical parameters, extracted from the spatial analysis and expressed in terms of percentile and mean values, have been used to estimate the relative clutter calibration and its uncertainty for both co-polar and differential reflectivity. The proposed methodology has been applied to a dataset collected by a C-band weather radar in southern Italy

Knowledge-Aided STAP Using Low Rank and Geometry Properties

This paper presents knowledge-aided space-time adaptive processing (KA-STAP) algorithms that exploit the low-rank dominant clutter and the array geometry properties (LRGP) for airborne radar applications. The core idea is to exploit the fact that the clutter subspace is only determined by the space-time steering vectors, {red}{where the Gram-Schmidt orthogonalization approach is employed to compute the clutter subspace. Specifically, for a side-looking uniformly spaced linear array, the} algorithm firstly selects a group of linearly independent space-time steering vectors using LRGP that can represent the clutter subspace. By performing the Gram-Schmidt orthogonalization procedure, the orthogonal bases of the clutter subspace are obtained, followed by two approaches to compute the STAP filter weights. To overcome the performance degradation caused by the non-ideal effects, a KA-STAP algorithm that combines the covariance matrix taper (CMT) is proposed. For practical applications, a reduced-dimension version of the proposed KA-STAP algorithm is also developed. The simulation results illustrate the effectiveness of our proposed algorithms, and show that the proposed algorithms converge rapidly and provide a SINR improvement over existing methods when using a very small number of snapshots.Comment: 16 figures, 12 pages. IEEE Transactions on Aerospace and Electronic Systems, 201

Patent Clutter

Patent claims are supposed to clearly and succinctly describe the patented invention, and only the patented invention. This Article hypothesizes that a substantial amount of language in patent claims is in fact not about the core invention, which may contribute to well-documented problems with patent claims. I analyze the claims of 40,000 patents and applications, and document the proliferation of “clutter”—language in patent claims that is not about the invention. Although claims are supposed to be exclusively about the invention, clutter appears across industries and makes up approximately 25% of claim language. Patent clutter may contribute several major problems in patent law. Extensive clutter makes patent claims harder to search. Excessive language in patent claims may be the result of over-claiming—when patentees describe potential corollaries they do not possess—thereby making the patent so broad in scope as to be invalid. More generally, it strains the comprehensibility of patents and burdens the resources of patent examiners. After arguing that patent clutter may contribute to these various problems, this Article turns to reforms. Rejections based on prolix, lack of enablement, and lack of written description can be crafted to dispose of the worst offenders, and better algorithms and different litigation rules can allow the patent system to adapt (and even benefit from) the remaining uses of excess language. The Article additionally generates important theoretical insights. Claims are often thought of as entirely synonymous with the invention and all elements of the claim are thought to relate equally strongly to the invention. This Article suggests empirically that these assumptions do not hold in practice, and offers a framework for restructuring conceptions of the relationship between claims and the invention

Synthetic aperture radar imagery of airports and surrounding areas: Study of clutter at grazing angles and their polarimetric properties

The statistical description of ground clutter at an airport and in the surrounding area is addressed. These data are being utilized in a program to detect microbursts. Synthetic aperture radar data were collected at the Denver Stapleton Airport. Mountain terrain data were examined to determine if they may potentially contribute to range ambiguity problems and degrade microburst detection. Results suggest that mountain clutter may not present a special problem source. The examination of clutter at small grazing angles was continued by examining data collected at especially low altitudes. Cultural objects such as buildings produce strong sources of backscatter at angles of about 85 deg, with responses of 30 dB to 60 dB above the background. Otherwise there are a few sources which produce significant scatter. The polarization properties of hydrospheres and clutter were examined with the intent of determining the optimum polarization. This polarization was determined to be dependent upon the ratio of VV and HH polarizations of both rain and ground clutter

Von Neumann-Morgenstern Clutters

A clutter on a set X is a simple hypergraph with pairwise not-comparable hyperedges, hence in particular any set of Von Neumann-Morgenstern (VNM) -stable sets of an irreflexive simple digraph is a clutter. A clutter (X,E) is representable by VNM-stable sets or VNM if there exists an irreflexive simple digraph (X, ?) such that E is a set of VNM-stable sets of (X, ?). The class of VNM clutters on a set X is characterizedVNM-stable sets, kernels, clutters, Sperner systems