Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Analysis of nuclear quadrupole resonance signals from mixtures
Signal Processing
Exploiting temperature dependency in the detection of NQR signals
IEEE Transactions on Signal Processing
Array signal Processing in the known waveform and steering vector case
IEEE Transactions on Signal Processing
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Nuclear quadrupole resonance (NQR) is a non-invasive, solid state, radio frequency (RF) technique, able to distinguish between polymorphic forms of certain compounds. Exploiting the signals from multiple polymorphs is important in explosives detection, whilst quantifying these polymorphs is important in pharmaceutical applications. Recently proposed hybrid algorithms, able to process the signals from multiple polymorphs, assume that the amplitudes associated with each polymorph are known to be within a scaling. Any error in this a priori information will lead to performance degradation in these algorithms. In this paper, we develop a robust hybrid algorithm allowing for uncertainties in the assumed amplitudes, extending a recently proposed robust algorithm, formulated for single polymorphs, to process signals from multiple polymorphs. In the proposed robust algorithm, the amplitudes are allowed to vary within an uncertainty hyper-sphere whose radius is evaluated using analytical expressions derived herein. Extensive numerical investigations indicate that the proposed algorithm provides significant performance gains as compared to both the existing hybrid algorithms, when uncertainties in the amplitudes exist, and the existing robust algorithm, when there are multiple polymorphs. Finally, the Cramer-Rao lower bound is derived for the uncertain data case as a reference for the quantification problem.