tailieunhanh - Báo cáo hóa học: " Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models | EURASIP Journal on Applied Signal Processing 2005 12 1867-1885 2005 Hindawi Publishing Corporation Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models Hichem Frigui Department of Computer Engineering and Computer Science University of Louisville Louisville KY 40292 USA Email K. C. Ho Department of Electrical and Computer Engineering University of Missouri-Columbia Columbia MO 65211 USA Email hod@ Paul Gader Department of Computer and Information Science and Engineering University of Florida Gainesville FL 32611 USA Email pgader@ Received 25 October 2004 Revised 3 March 2005 Recommended for Publication by Fulvio Gini We propose a real-time software system for landmine detection using ground-penetrating radar GPR . The system includes an efficient and adaptive preprocessing component a hidden Markov model- HMM- based detector a corrective training component and an incremental update of the background model. The preprocessing is based on frequency-domain processing and performs ground-level alignment and background removal. The HMM detector is an improvement of a previously proposed system baseline . It includes additional pre- and postprocessing steps to improve the time efficiency and enable real-time application. The corrective training component is used to adjust the initial model parameters to minimize the number of misclassification sequences. This component could be used offline or online through feedback to adapt an initial model to specific sites and environments. The background update component adjusts the parameters of the background model to adapt it to each lane during testing. The proposed software system is applied to data acquired from three outdoor test sites at different geographic locations using a state-of-the-art array GPR prototype. The first collection was used as training and the other two contain data from more than 1200 m2 of .