Dilyan Damyanov

Super-resolution feature extraction imaging algorithm for complex objects

For the goal of an Object Recognition (OR) Radar system, a feature extraction algorithm is proposed in this paper. Conventional radar imaging method based on Kirchhoff Migration and the revised range point migration method are known to obtain fast and accurate images. However, these methods are not suitable for feature extraction as the Kirchhoff Migration method processes the whole evolution of the Radar data and the revised range point migration method extracts the coordinates of the target contour not features. Furthermore, the new proposed feature extraction algorithm is designed for a circular scanning trajectory, or a rotating target and a bi-static antenna configuration. The proposed algorithm calculates the target points from the a priori extracted wavefronts of the Object Under Test (OUT). A Polarimetric Dynamic Correlation Method (PDCM) is employed in the proposed algorithm for the extraction of the wavefronts. Experimental validations are performed with two complex OUT, a M-sequence Radar device (4.5 GHz-13.5 GHz) and compact dual-polarized Ultra-Wideband antennas.

A super-resolution polarimetric wavefront extraction algorithm for UWB-radar under massive interference conditions

To provide short-range super-resolution UWB Radar under multi-scattering conditions, a superior wavefront extraction algorithm is proposed in this paper. Conventional correlation based pulse separation methods based on SAGE, CLEAN or the previously introduced superior Dynamic Correlation Method (DCM) are revised, validated and compared. In this paper the DCM is improved significantly by applying the Pauli scattering matrix decomposition onto the Radar data. This novel wavefront extraction algorithm is called polarimetric DCM (PDCM) and is suitable to resolve several overlapping pulses which consist of both strong echoes and weak echoes which are masked by the strong ones. The performance of the PDCM and the comparison with alternative algorithms is carried out by a subsequent feature extraction algorithm for visual verification. Experimental validations are performed with two complex test objects, an M-sequence Radar device (4.5 GHz-13.5 GHz) and compact dual-polarized Ultra Wideband antennas.

Active contour extraction method for objects with a rough surface using single-chip FMCW radars

In this paper a method for contour extraction of objects with a high degree of surface roughness in the range of 60 GHz Radar is presented. The proposed algorithm is based on an active contour model (snake) with external forces called component normalized gradient vector flow (CN-GVF). The snake algorithm extracts the contour of the object under test (OUT) based on wavefront Radar imaging methods. Experimental validations were performed with two fully integrated wideband frequency modulated continuous-wave (FMCW) single-chip Radar transceivers with an operational band from 57 GHz to 64 GHz, a corner cube retroreflector and a target object with a high degree of surface roughness.

A super-resolution polarimetric direct mapping imaging algorithm for a bistatic UWB-radar

In this paper a real time capable 2D imaging method for ultra-wideband Radar is presented. A well known wavefront localization method is adapted and improved using a super-resolution wavefront extraction method and exploring the polarimetric information of the target under test. The imaging algorithm is real time capable and directly maps an extracted wavefront to the target contour in contrast to classical popular migration algorithms. Furthermore, the new proposed imaging algorithm is designed for a circular scanning trajectory, or a rotating target and a bi-static antenna configuration with two receiver antennas. Experimental validations are performed with a geometrically complex object, a M-sequence UWB Radar device (4.5 GHz - 13.5 GHz) and compact Vivaldi Ultra-Wideband antennas.

An enhanced super-resolution wavefront extraction algorithm for wideband FMCW radar systems

In this paper a novel super-resolution wavefront extraction algorithm is introduced that merges the advantages of the efficiency correlation techniques based on [1] and super-resolution ability of DCM techniques [2]. The introduced ADCM algorithm is based on the correlation method DCM, but in contrast this algorithm uses a set of weighted differently shifted reference pulses. Due to the clustering of wavefronts the computational effort of the wavefront extraction is optimized. In the herein used set-up a low-cost, single chip radar operating from 57 GHz to 64 GHz is applied. The low-cost and low-weight radar is ideal for autonomous security robots where real-time conditions are necessary.

Feature based ultra-wideband object recognition

For the goal of an Object Recognition (OR) in emergency situations, an OR Ultra-Wideband (UWB) Radar system is proposed in this paper. Conventional OR Radar systems based on vector machines or neural networks result in a high recognition rates, but are not suitable for OR in a real time scenario, due to the vast computational load. Hence the OR Radar system proposed in this paper is based on a minimum mean square error detector and seven Object Recognition features with low mathematical and computational complexity. Furthermore, the proposed OR features are extracted from polarimetric images Radar acquired by two imaging methods. Experimental validations are performed with an alphabet of twelve complex objects, a M-sequence UWB Radar device (4.5 GHz - 13.5 GHz) and compact dual-polarized Ultra-Wideband antennas.

Accurate 3D UWB radar super-resolution imaging for a bi-static antenna configuration

This paper deals with a real-time capable accurate 3D ultra-wideband radar imaging algorithm for complex shaped 3D objects including edges and corners. A well known wavefront based imaging algorithm is adapted to the bi-static 3D scenario which is, in contrast to the popular migration based algorithms, real-time capable and directly gathers the object contour coordinates. In order to reconstruct a accurate 3D object contour, the commonly proposed planar scan track (i.e. the planar aperture) of the antennas is modified and extended to a spatial scanning track with a circumnavigation of the object. To provide a more diverse radar signature the monostatic antenna configuration is extended to a bistatic configuration. Hence, the shape of the radiating wavefront is no longer spherical but ellipsoidal. Consequently, to ensure the super-resolution accuracy, the intersection point of 3 arbitrarily oriented and shifted ellipsoids in the 3 dimensional Euclidean space has to be determined. An iterative solution will be presented which utilizes the Gauss-Newton method to obtain a fast converging estimation with negligible error in the least-square sense. An experimental validation is carried out based on complex test objects with small shape variations relative to the used wavelength, an pseudo noise radar device (from 4.5 GHz to 13.5 GHz) and two tapered slot line Vivaldi antennas.