This paper presents a method to identify landmines in various burial conditions. A ground penetration radar is used to generate data set, which is then processed to reduce the ground effect and noise to obtain landmine signals. Principal components and Fourier coefficients of the landmine signals are computed, which are used as features of each landmine for detection and identification. A database is constructed based on the features of various types of landmines and the ground conditions, including the different levels of moisture and types of ground and the burial depths of the landmines. Detection and identification is performed by searching for features in the database. For a robust decision, the counting method and the Mahalanobis distance-based likelihood ratio test method are employed. Four landmines, different in size and material, are considered as examples that demonstrate the efficiency of the proposed method for detecting and identifying landmines. 1. Introduction Landmine removal is a critical problem faced by many countries around the world, and the situation can be compounded by natural disasters or land development. Therefore, it is an urgent issue to detect landmines in the ground and remove them safely. The process of landmine removal starts with the detection of landmines in the ground. For safe detection, non-touch-based detection methods are required. These methods involve the detection of landmines in the signals obtained by non-touch-based sensors, such as metal detectors and radars. Among those sensors, ground penetrating radars, or GPRs, are an attractive choice for landmine detection due to their advantages over other sensors. The GPR can be used as a stand-alone sensor or as a complementary sensor to a metal detector [1, 2]. It can detect both metal and nonmetal landmines [3]. Moreover, its weight can be made light, so that it can be installed in a hand-held system or in a vehicle-mounted system in the form of an array of multiple antenna elements [4–6]. In the landmine detection step, the key factor is to obtain unique signatures of a landmine from the signal, which are used as tags of each landmine. The signal may be, however, contaminated by noise, surface reflections, and so forth. Therefore, minimizing such influences and extracting the unique signatures for each landmine is an active research topic. Many studies show various methods of computing signatures from GPR data [7–12]. Tantum et al. [13] compare different algorithms for landmine detection. Each approach, however, utilizes one feature for detection, which may not
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