Abstract The quantity of grain in silos has commercial and crucial importance. That’s why many researches have been implemented to detect the quantity of the grain. Although the traditional methods can measure the level of the grain from one measurement point, there has not yet been an effective method regarding to 3 dimensional (3D) volume measurement. Available thru-air radar (TAR) based systems can be adapted to 3D perception by increasing the beamwidth of the illumination. But, to achieve pure grain reflections from cluttered noisy signal (containing multi-path (MP) scatterings and mirror scatterings that suppressed the grain reflections) is a challenging problem. In this study, a new wide-beamwidth radar based level measurement method is firstly proposed to determine the amount of grain in silos. Based on the proposed CS-based method, the back-scattering information of the grain surface is obtained accurately. In this way, Cartesian coordinates of the powerful scattering points of grain surfaces, illuminated electromagnetically by three antennas, are identified and 3D height information belongs to the surface are obtained. According to the dominant scattering point’s coordinates and the probable smooth conical stack of the grain, a heuristic volume expression is derived and the volume of the stack grain is estimated with high accuracy. The success of the developed measurement method is confirmed through a real data of a commercial test silo.
[1]
Enes Yiğit,et al.
Compressed Sensing for Millimeter-wave Ground Based SAR/ISAR Imaging
,
2014
.
[2]
Sevket Demirci,et al.
Wide-field circular SAR imaging: An empirical assessment of layover effects
,
2015
.
[3]
David L Donoho,et al.
Compressed sensing
,
2006,
IEEE Transactions on Information Theory.
[4]
Hakan Isiker,et al.
Concept for a novel grain level measurement method in silos
,
2009
.
[5]
Sevket Demirci,et al.
A synthetic aperture radar‐based focusing algorithm for B‐scan ground penetrating radar imagery
,
2007
.
[6]
Emmanuel J. Candès,et al.
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
,
2004,
IEEE Transactions on Information Theory.