Ground Radar Target Classification Using Singular Value Decomposition and Multilayer Perceptron

The paper deals with classification of ground radar targets. A received radar signal backscattered from a ground radar target was digitized and in the form of radar signal matrix utilized for a feature extraction based on Singular Value Decomposition. Furthermore, singular values of a backscattered radar signal matrix, as a target feature, were utilized for Radar Target Classification by multilayer perceptron. In the learning phase of a multilayer perceptron we used the learning target set and in the testing phase the testing target set was used. The learning and testing target sets were created on the basis of real ground radar targets

Direction detector for distributed targets in unknown noise and interference

Adaptive detection of distributed radar targets in homogeneous Gaussian noise plus subspace interference is addressed. It is assumed that the actual steering vectors lie along a fixed and unknown direction of a preassigned and known subspace, while interfering signals are supposed to belong to an unknown subspace, with directions possibly varying from one resolution cell to another. The resulting detection problem is formulated in the framework of statistical hypothesis testing and solved using an ad hoc algorithm strongly related to the generalised likelihood ratio test. A performance analysis, carried out also in comparison to natural benchmarks, is presented

Classification of Radar Targets Using Invariant Features

Automatic target recognition ATR using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit the comparison of targets modeled by different numbers of scattering centers. A metric distance metric is used that effectively identifies targets under real world conditions that include noise and obscuration

Radar target for remotely sensing hydrological phenomena

An apparatus for remotely measuring and accessing water status relative to snow and glacial melt, surface runoff, rainfall, evaporation, flow rate, and soil moisture is described. A radar target located at a selected location on the surface of the Earth is designed to collect water and render its cross sectional area variable as a function of the height of the water level within the target. The target is remotely monitored by an orbiting or airborne synthetic aperature radar. The target appears as a bright spot embedded within the radar image. The target brightness is indicative of the height of the water level within the ground located target

Representation of hypersonic glide vehicles as fluctuating radar targets

The monostatic RCS of a generic Hypersonic Glide Vehicle (HGV) is presented at S-band and is derived using a Physical Optics (PO) based simulation approach. Target RCS histograms are then produced and used to generate a custom statistical distribution which is subsequently compared against the conventional Swerling models associated with representation of target fluctuation when using the Radar Range Equation (RRE). The accuracy of the Swerling models for this particular target type and frequency band is subsequently discussed