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In this study, we utilize the least squares formulation to solve the real-time buried object detection problem. Least squares estimation is used to estimate the next ground penetrating radar (GPR) signal from previous samples, where there is no underground object. If the measured GPR signal is considerably different than the estimated signal, presence of an underground object is concluded. In order to attain real-time performance, Cholesky factorization is used when solving the linear systems. The proposed approach is tested on an extensive data set of different surrogate mines and other objects that are commonly encountered under the ground. The data are collected from three different terrains with different soil types to reveal the true performance of the method. It is demonstrated that our approach achieves almost 100% performance with a false alarm rate of approximately 10% on real GPR data collected with a handheld GPR system.