Ground penetrating radar (GPR) has been proposed as an effective sensing modality for reducing the excessively high false alarm rates often encountered in landmine detection applications. Ground penetrating radar is sensitive to discontinuities in the interrogated medium, rather than the presence of metal, and thus exploits a different phenomenology than electromagnetic induction (EMI) sensors. Thus, unique signals that are dependent on the composition of the targets can be obtained from buried objects. Consequently, the detection of low metal content targets is improved since the radar responds to non-metallic objects, such as wood, plastic, and stone, as well as metallic objects. When the GPR sensor is mounted on a moving platform, the target signatures are hyperbolas in a time-domain data record. Furthermore, the hyperbolas from different targets often exhibit different characteristics. The goal of this work is to develop robust signal processing algorithms which exploit this knowledge to improve target detection and discrimination. Among the algorithms considered are a Bayesian approach and an approach similar to the Hough transform. The algorithms are evaluated using real data collected with fielded GPR sensors, and are compared in terms of their computational requirements as well as their detection and discrimination performance.