The height variation of ground surface and incorrect velocity will affect imaging processing of landmine. To eliminate these effects, ground surface topography and velocity model are needed. For effective detection of landmines, a stepped-frequency continuous-wave array antenna ground penetrating radar system, called SAR-GPR, was developed. Based on multi-offset common middle point (CMP) data acquired by SAR-GPR, we describe a velocity model estimation method using velocity spectrum technique. Also after pre-stack migration, the ground surface can be identified clearly. To compensate landmine imaging for the effect created by height variation, the ground surface displacement, a kind of static correction technique, is used based on the information of ground surface topography and velocity model. To solve the problem of incorrect velocity, we present a continuous variable root-mean-square velocity based on the velocity model. The velocity is used in normal moveout correction (NMO) to adjust the time delay of multi-offset data, and also applied to migration for reconstruction of landmine image. After the application of ground surface topography and velocity model to data processing, we could obtain good landmine images in experiment.
Proc. SPIE. 5794, Detection and Remediation Technologies for Mines and Minelike Targets X
KEYWORDS: Target detection, Linear filtering, Feature extraction, Antennas, Target acquisition, Filtering (signal processing), Time-frequency analysis, Mahalanobis distance, Land mines, General packet radio service
In this work we investigate which bandwidth of a ground penetrating radar (GPR) is optimal for time-frequency landmine discrimination. We extracted three time-frequency features of the early-time target response from the Wigner distribution. The features were found to be relatively invariant to target depth for a data acquired with a stepped-frequency ultra-wideband GPR. The frequency sweep was from 0.3 GHz up to 6 GHz. The features allowed discrimination of two different low-metal landmines from a mine-like stone. The results were visualized in the three-dimensional feature space where each point related to a certain target represents a certain GPR scenario. For a number of scenarios we obtained two separated clusters for the landmines and the stone respectively. Numerically the quality of target discrimination can be evaluated with the Mahalanobis distance which estimates the separation between such feature clusters accounting for their size. Here we use the Mahalanobis distance as a criterion of optimality for the GPR bandwidth. Having obtained good results for the large data bandwidth, we reduce it by digital filtering with a small step in changing the cut-off frequencies, then extract the features and compute the Mahalanobis distance between the landmines and the stone. Its maximal value defines the optimal GPR lower and upper frequencies.
SAR-GPR is a sensor system composed of a GPR and a metal detector for landmine detection. The GPR employs an array antenna for advanced signal processing for better subsurface imaging. This system combined with synthetic aperture radar algorithm, can suppress clutter and can image buried objects in strongly inhomogeneous material. SAR-GPR is a stepped frequency radar system, whose RF component is a newly developed compact vector network analyzers. The size of the system is 30cm x 30cm x 30cm, composed from 6 Vivaldi antennas and 3 vector network analyzers. The weight of the system is less than 30kg, and it can be mounted on a robotic arm on a small unmanned vehicle. The field test of this system was carried out in March 2005 in Japan, and some results on this test are reported.
Currently there are a few projects for landmine detection in Afghanistan, which is supported by the Japanese government. Some field test for landmine detection sensors have been carried out in Afghanistan and Japan. We introduce in this paper about the plan of these projects, and its evaluation tests. JST (Japan Science and Technology Agency) which is under the Ministry of Education, Culture, Sports, Science and Technology (MEXT)) is developing unmanned vehicles which are mounted sensors for AP landmine detection. The prototype of the sensors and equipments will be ready by February 2005 and will be tested in a test site in Japan by March 2005. Then, it is planned to be evaluated in Afghanistan in summer 2005. JICS (Japan International Cooperation System) which is under the Ministry of Foreign affairs (MOFA) has a project on "Developing Mine Clearance related equipment in Afghanistan". In this project, we plan to evaluate mine detectors in Afghanistan until March 2005. The evaluation test of JICS project has already started in August-Deecember 2004, in Afghanistan,. In the evaluation the both projects, we are preparing test lanes. Most of the sensors to be evaluated is a combination of a metal detector and GPR, and as for GPR, there has been not many examples of such evaluation tests. In this paper, we introduce the outline of the evaluation test, and also discuss some technical aspects of the evaluation test for the combination sensors of a metal detector and a GPR.
Proc. SPIE. 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX
KEYWORDS: Detection and tracking algorithms, Interference (communication), Deconvolution, Antennas, Target recognition, Algorithm development, Time-frequency analysis, Continuous wavelet transforms, Land mines, General packet radio service
In this work we developed target recognition algorithms for landmine detection with ultra-wideband ground penetrating radar (UWB GPR). Due to non-stationarity of UWB signals their processing requires advanced techniques, namely regularized deconvolution, time-frequency or time-scale analysis. We use deconvolution to remove GPR and soil characteristics from the received signals. An efficient algorithm of deconvolution, based on a regularized Wiener inverse filter with wavelet noise level estimation, has been developed. Criteria of efficiency were stability of the signal after deconvolution, difference between the received signal and the convolved back signal, and computational speed. The novelty of the algorithm is noise level estimation with wavelet decomposition, which defines the noise level separately for any signal, independently of its statistics. The algorithm was compared with an iterative time-domain deconvolution algorithm based on regularization. For target recognition we apply singular value decomposition (SVD) to a time-frequency signal distribution. Here we compare the Wigner transform and continuous wavelet transform (CWT) for discriminant feature selection. The developed algorithms have been checked on the data acquired with a stepped-frequency GPR.
This work deals with the processing of GPR (ground penetrating radar) signals for AP (anti-personnel) mine detection. It focuses on two steps in this processing, namely the deconvolution of the system impulse response, and the extraction of target features for classification. The objective of the work is to find discriminant and robust target features by means of time-frequency analysis. Deconvolution is an ill-posed inverse problem, which can be solved with regularization methods. In this paper a deconvolution algorithm, based on the iterative v-method, is proposed. For discriminant feature selection the Wigner distribution (WD) is considered. Singular value decomposition (SVD) along with the concept of the center of mass as the most robust feature are used for feature extraction from the WD. The proposed normalized time-frequency-energetic features have a good discriminant power, which doesn't degrade with increasing object depth.