KEYWORDS: Independent component analysis, Detection and tracking algorithms, Land mines, General packet radio service, Matrices, Antennas, Ground penetrating radar, Receivers, Signal detection, Signal processing
This paper deals with the detection of non-metallic anti-personnel (AP) land mines using stepped-frequency ground penetrating radar. A class of the so-called Independent Component Analysis (ICA) represents a powerful tool for such a detection. Various ICA algorithms have been introduces in the literature; therefore there is a need to compare these methods. In this contribution, four of the most common ICA methods are studied and compared to each other as regarding their ability to separate the target and clutter signals. These are the extended Infomax, the FastICA, the Joint Approximate Diagonalization of Eigenmatrices (JADE), and the Second Order Blind Identification (SOBI). The four algorithms have been applied to the same data set which has been collected using an SF-GPR. The area under the Receiver Operating Characteristic (ROC) curve has been used to compare the clutter removal efficiency of the different algorithms. All four methods have given approximately consistent results. However both JADE and SOBI methods have shown better performances over Infomax and FastICA.
KEYWORDS: General packet radio service, Wavelets, Land mines, Detection and tracking algorithms, Interference (communication), Wavelet transforms, Time-frequency analysis, Filtering (signal processing), Statistical analysis, Signal to noise ratio
This paper deals with the detection problem of non-metallic anti-personnel (AP) land mines by using ground penetrating radar (GPR). The clutter signal due to the reflection from the ground surface is often a performance limiting factor in GPR. An algorithm has been developed for this purpose, which combines two powerful tools: The wavelet packet analysis and the higher-order-statistics (HOS). The use of both techniques makes the detection of shallow AP land mines objects, which are obscured by the return from air-soil interface, possible. A de-noising procedure is performed on the wavelet coefficients and the classical method of detection is then applied to these coefficients. The used threshold is based on the higher order statistics. The algorithm has been tested using two source data, one-GHz pulse GPR data, and one-to-four-GHz stepped-frequency GPR data, from laboratory measurements.
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