Modelling of extended objects using sparse multi-aspect High Range Resolution radar data set

Abstract

The two-dimensional (2D) geometry of extended objects is modelled as a discrete constellation of a small number of scattering centres. Each scattering centre is characterised by its 2D location and extension, as well as by its observability from aspect angles spanning a wide angular interval. The considered input information of the modelling consists of high range resolution (HRR) data sets, which are measured by a network of scanning surveillance radars from a limited set of aspect angles of the object. During the short time-on-target of the antennas within each radar scan, inverse synthetic aperture radar (ISAR) imaging is performed. The Expectation-Maximisation (ER) algorithm is applied in order to fit each ISAR image to a Gaussian mixture model (GMM), pertaining to the aspect angle applicable to the respective radar. A merging algorithm for clustering all the partial GMMs into a unique 2D model of the extended object is designed. Cluster validation criteria are applied for correct alignment of the multi-radar multi-scan object model estimates. A Mixture of Gaussians classifier (MOGC), which is based on representation of each object by the estimated 2D model, is discussed. Results from application of the algorithms to two measured HRR radar data sets are presented.