Enes Yigit

CS-based radar measurement of silos level

The amount of the grain in bulk silos is the most important issue in commercial care. Therefore many level measurement methods have been used to measure the level of solids in silos. Existing methods, however, are generally based on one-point measurement which makes the three dimensional (3D) level measurement impractical. Microwave radar based systems can be used to 3D perception but the multiple scatterings occurred from metallic walls of the silo, makes it impossible. In this study we present the preliminary results of our compressive sensing based reconstruction algorithm to enhance backscattering signals inside a grain silo. The method proposed here eliminates the effect of multiple scattering form silo wall and gives the accurate reading of the grain level. The effectiveness of the recommend CS-based reconstruction method, which will be able to extend to 3D level perception, was verified through a real data of bulk silo.

A novel compressed sensing based quantity measurement method for grain silos

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.

An analysis for the use of compressed sensing method in microwave imaging

One of the most important problems encountered in microwave imaging methods is intensive data processing traffic that occurs when high resolution and real time tracking is desired. Radar signals can be recovered without loss of data with a randomly selected subset of the measurement data by compression sensing (CS) method which has been popular in recent years. For this reason, in this study, the use and capabilities of the CS method were investigated for tracking moving human, and the target information was correctly determined for the data obtained much below the Nyquist sampling criterion. In this study, it was revealed that the CS method can be developed for target detection and tracking.

Millimetre wave isar imaging technique based on sparse aperture data collection

The millimetre wave (MW) applications has become very popular in recent years due to the high-resolution requirement in inverse synthetic aperture radar (ISAR) imaging. The most important problem encountered in MW imaging method is the high data collection requirement. Compressed sensing (CS) is often used in MW applications because it allows processing of signals with a sampling number below the Nyquist rate. However, since existing techniques used in CS take random samples from all spatial-frequency ISAR data, too many data collection probes are needed. In this study, CS based ISAR image is reconstructed by taking random samples from only synthetic aperture data instead of all spatial-frequency ISAR data. So, this type of data collection mechanism offers a much more practical application area for CS based ISAR imaging. The proposed method was verified by simulation results and the quality of the images were evaluated calculating ISLR.

A generalized formula in calculation of the resonant frequency of notch antenna

Notch antenna is constructed by slotting an edge of rectangular patch placed on a substrate over ground plane. Analysis of the notch antenna is complicated and very difficult due to having non-uniform shape. In this work, a novel formulation is proposed for calculating the resonant frequency of the notch antennas. The formulation regarding the resonant length of the antenna reflecting the impact of the slot is derived using Particle Swarm Optimization (PSO). Data vector of 96 notch antennas consisting of seven geometrical and electrical parameters is acquired by simulations. A resonant length formula enclosing those parameters accompanying with optimization variables is constituted in conformity with simulation data. The variables are then optimally determined by fitting the calculated resonant frequency to the simulated one by PSO algorithm. The proposed formulation is verified with simulated/measured data and validated with a test notch antenna fabricated in this study. The results demonstrate that the resonant frequency of the notch antenna can be simply calculated using the proposed formulation without dealing with sophisticated mathematics and performing simulations or measurement.