Ground Penetrating Radar (GPR) is seen as one of several promising technologies aimed to help mine detection. GPR is sensitive to any inhomogeneity in the ground. Therefore any APM regardless of the metal content can be detected. On the other hand, all the inhomogeneities, which do not represent mines, show up as a clutter in GPR images. Moreover, it is known that reflectivity of APM is often weaker than that of stones, pieces of shrapnel and barbed wire, etc. Altogether these factors cause GPR to produce unacceptably high false alarm rate whilst it reaches the 99.6% detection rate which is prescribed by an UN resolution as a standard for humanitarian demining. The main goal of the work presented in the thesis is reduction of the false alarm rate while keeping the 99.6% detection rate intact. To reach this goal a set of data processing algorithms is developed and organized into an unsupervised target detection scheme. These algorithms are dedicated to clutter suppression and simultaneous detection of APM signatures in both GPR raw data and images resulting from them. The developed algorithms constitute together the following achievements: - An unsupervised generalized likelihood ratio test-based feature fusion framework; - A waveform based target detection/clutter suppression; - Advanced methods for construction of GPR maps The unsupervised generalized likelihood ratio test based feature fusion framework, which has been suggested in this thesis, takes as input an arbitrary amount of confidence maps corresponding to training and testing sites. The output of the framework is a list of target locations. The framework uses training data which can come from independent and non-coincident measurements with different radars and even sensors. The data from each of the sensors are processed independently to result in several detection lists. Every detection in these lists is accompanied with one or several features each represented by a scalar number. A decision level fusion is applied to reconcile the lists i.e. to associate the detections in them with the appropriate physical locations. Then the binary hypothesis testing is executed for the reconciled locations separating them on clutter and target lists. The generalized likelihood ratio test is employed to this end. The feature pre-normalization via Johnson's transform in suggested by the author to be used prior the testing. It is shown in the thesis that such approach outperforms the direct generalized likelihood ratio testing ad. hoc. based fusion techniques. The waveform based target detection/clutter suppression algorithm, which detects disc-shaped APM in heavy clutter with low false alarm rate, has been developed by the author. The algorithm detects a class of low-metal APM with a cylindrical shape (such as PMN2, M14, and NR22 etc.) using just a single reference target return. It suppresses clutter responses from friendly objects while marking the presence of targets with sharp monopulses and preserving the spatial pattern inherent to localized objects. The algorithm is insensitive to the reflectivity and physical diameter of the target and also tolerates certain volatility in the properties of the hosting soil. This algorithm is superimposed with a focusing technique to further improve the mine detectability. A number of improved projection techniques, which allow better detection of APM in focused GPR images is also developed by the author. These utilize the prior knowledge on the character of the spatial correlation properties of target images and allow detection of the burial depth of the target. The algorithms suggested in the thesis were tested on the data acquired during two separate measurement campaigns held at the special facilities for testing of mine detection systems. It has been shown, that the fused multi-feature detection that uses the algorithms reported in this thesis, significantly decreases the false alarm rate in comparison to the previously published studies for the same minefields.