Abstract
We present a reliable image reconstruction algorithm suitable for a microwave holographic vision system with several sensors coupled to the spin-diode based microwave detector and a single emission source. An objective is, by reconstructing the spatial microwave scattering density on the scene, to detect the presence and the nature of road obstacles impeding driving in the near vehicle zone. The idea of holographic visualization is to reconstruct the spatial microwave scattering density of an object by detecting an amplitude and phase of a reflected signal by lattice of sensors. We discuss versions of an algorithm, determine and analyse its resolution limits for various distances with different number of sensors for a one-dimensional test problem of detecting two walls (or posts) separated by a gap at a fixed distance. The maximal interval between sensors needed for a reliable reconstruction equals approximately Fresnel zone width. We show that maximal resolution achieved by our algorithm with an appropriate number of sensors was about 40% of Fresnel zone width for wall detection and about 30% of zone width for gap detection.
Highlights
The growing interest in automated and connected vehicle technology prompted the need for efficient traffic and obstacle detection techniques in large variety of settings
A promising technology direction in this area is the development of microwave-based detection techniques
Different densities indicate different materials – stone, flesh, metal, wood etc. At short distances this technology combines the advantages of radars and of LIDAR, adding an extra capacity for object type detection, based on accurate determination of object material
Summary
The growing interest in automated and connected vehicle technology prompted the need for efficient traffic and obstacle detection techniques in large variety of settings. For inner-city mobility, as well as for vehicle parking setting, critical issues are control of obstacles and object movement in the near-vehicle zone, about 0.3 to 4.5 meters distance to detection point. Different densities indicate different materials – stone, flesh, metal, wood etc. At short distances this technology combines the advantages of radars (can work at every weather conditions, low cost, can see behind obstacles) and of LIDAR (doing 3D scenario reconstruction), adding an extra capacity for object type detection, based on accurate determination of object material. We discuss the potential of that technique for accurate obstacle recognition in near-vehicle zone from the image reconstruction and algorithmic point of view. We show that there exists a capacity to use it as a component in an integrated realistic object recognition automotive system
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