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Quadrupole resonance (QR) is a technique that discriminates mines from clutter by exploiting unique properties of explosives, rather than the attributes of the mine that exist in many forms of anthropic clutter. After exciting the explosive with a properly designed electromagnetic-induction (EMI) system, one attempts to sense late-time spin echoes, which are characterized by radiation at particular frequencies. It is this narrow-band radiation that indicates the present of explosives, since this effect is not seen in most clutter, both natural and anthropic. However, explosives detection via QR is complicated by several practical issues. First, the late-time radiation is often very weak, particularly for TNT, and therefore the signal- to-noise ratio must be high for extracting the QR response. Further, the frequency at which the radiation occurs is often a strong function of the background environment, and therefore in practice the QR radiation frequency is not known a priori. Also, at frequencies of interest, there is a significant amount of background radiation, which induces radio frequency interference (RFI). In addition, the response properties of the system are sensitive to the height of the sensor above the ground, and the QR sensor effectively becomes 'de-tuned'. Finally, present QR systems cannot detect the explosive in metal-cased mines, thus the system and associated signal processing must be extended to also operate as a metal detector. Previously, we have shown that adaptive noise cancellation techniques, in particular, the least-mean-square algorithm, provide an effective means of RFI mitigation and can dramatically improve QR detection. In this paper we discuss several signal processing tools we have developed to further enhance the utility of QR explosives detection. In particular, with regard to the uncertainties concerning the background environment and sensor height, we explore statistical signal processing strategies to rigorously account for the inherent variability in these parameters.

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