Signal processing of ground penetrating radar using super resolution technique

Abstract

Ground Penetrating Radar (GPR) using electromagnetic pulse required high resolution due to its narrow bandwidth nature. The rising requirement for high resolution lead to specific demands for improved prediction methods, all requiring many aspects to be examined. In this paper, the application possibility of super resolution method MUSIC (Multiple Signal Classification) algorithm and FFT (Fast Fourier Transform) are examined. Combined Processing Method (CPM) of time domain response of MUSIC and IFFT (Inverse Fast Fourier Transform) is proposed for signal processing of GPR for the first time. Simulation and experimental results show that CPM has both high resolution and high receiving signal level than other conventional signal processing methods.

Signal Processing of Ground Penetrating Radar Using Spectral Estimation Techniques to Estimate the Position of Buried Targets

“…This frequency-domain spectrum of radar data, shown in Figure 3a, undergoes IFFT processing. In the mean time, the same radar signal undergoes MUSIC processing and the results are comparatively studied [18,19]. The frequency-domain data is converted into the time-domain data in both cases.…”

“…where Z(t) is the time-domain data of CPM. In order to explain (18), mathematical analysis has been performed. Since combining the time-domain responses of IFFT and MUSIC are performed by calculating the slope and the position of the signal, the delay of the IFFT response signal and MUSIC response signal should be coincided, which is a required condition.…”

Remote Sensing of Soil Moisture Content by GPR Signal Processing in the Frequency Domain

“…We have searched through the literature and are not aware of prior work on soil moisture determination from GPR signal. Some researchers have investigated the use of spectral features [4], [5], particle filtering [9], "quasi-frequency-domain" [10], non-uniform fast Fourier transform (NUFFT) [11] and time-frequency features [12], for landmine detection and clutter discrimination; others have estimated temporal soil moisture variability under irrigation and drainage conditions by the fixed offset (FO) method using the direct ground wave [6] or using time-domain reflectometry (TDR)-based water content methods [13]; finally, further studies have put in evidence the need to use efficient techniques such as FFT and MUSIC algorithms, for improved prediction methods [14], [15]. Conversely, this paper presents an innovative, high-performance method that does not need calibration to estimate the moisture content of a porous medium.…”