Samuel Dilshan Somasundaram

Exploiting Spin Echo Decay in the Detection of Nuclear Quadrupole Resonance Signals

Nuclear quadrupole resonance (NQR) is a radio-frequency technique that can be used to detect the presence of quadrupolar nuclei, such as the 14N nucleus prevalent in many explosives and narcotics. In a typical application, one observes trains of decaying NQR echoes, in which the decay is governed by the spin echo decay time(s) of the resonant line(s). In most detection algorithms, these echoes are simply summed to produce a single echo with a higher signal-to-noise ratio, ignoring the decaying echo structure of the signal. In this paper, after reviewing current NQR signal models, we propose a novel NQR data model of the full echo train and detail why and how these echo trains are produced. Furthermore, we refine two recently proposed approximative maximum-likelihood detectors that enable the algorithms to optimally exploit the proposed echo train model. Extensive numerical evaluations based on both simulated and measured NQR data indicate that the proposed detectors offer a significant improvement as compared to current state-of-the-art detectors

Robust Detection of Stochastic Nuclear Quadrupole Resonance Signals

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) spectroscopic technique, allowing the detection of compounds containing quadrupolar nuclei, a requirement fulfilled by many high explosives and narcotics. The practical use of NQR is restricted by the inherently low signal-to-noise ratio (SNR) of the observed signals, a problem that is further exacerbated by the presence of strong RF interference (RFI). The current literature focuses on the use of conventional, multiple-pulsed NQR (cNQR) to obtain signals. Here, we investigate an alternative method called stochastic NQR (sNQR), having many advantages over cNQR, one of which is the availability of signal-of-interest free samples. In this paper, we exploit these samples forming a matched subspace-type detector and a detector employing a prewhitening approach, both of which are able to efficiently reduce the influence of RFI. Further, many of the ideas already developed for cNQR, including providing robustness to uncertainties in the assumed complex amplitudes and exploiting the temperature dependencies of the NQR spectral components, are recast for sNQR. The presented detectors are evaluated on both simulated and measured trinitro-toluene (TNT) data.

Countering Radio Frequency Interference in Single-Sensor Quadrupole Resonance

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) spectroscopic technique that allows for the detection of many narcotics and highly explosive substances. Unfortunately, the practical use of NQR is often restricted by the presence of strong RF interference (RFI). In this letter, extending our recent work on stochastic NQR (sNQR), we propose acquiring signal-of-interest free samples, containing only corrupting signals, and exploiting them to reduce the effects of RFI on conventional NQR (cNQR) measurements. Similar to the sNQR case, the presented detectors are able to substantially outperform previous cNQR detectors when RFI is present.

Robust subspace-based fundamental frequency estimation

The problem of fundamental frequency estimation is considered in the context of signals where the frequencies of the harmonics are not exact integer multiples of a fundamental frequency. This frequently occurs in audio signals produced by, for example, stiff-stringed musical instruments, and is sometimes referred to as inharmonicity. We derive a novel robust method based on the subspace orthogonality property of MUSIC and show how it may be used for analyzing audio signals. The proposed method is both more general and less complex than a straight-forward implementation of a parametric model of the inharmonicity derived from a physical instrument model. Additionally, it leads to more accurate estimates of the individual frequencies than the method based on the parametric inharmonicity model and a reduced bias of the fundamental frequency compared to the perfectly harmonic model.

Robust Nuclear Quadrupole Resonance Signal Detection Allowing for Amplitude Uncertainties

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency spectroscopic technique that can be used to detect compounds which contain quadrupolar nuclei, a requirement fulfilled by many high explosives and narcotics. Unfortunately, the low signal-to-noise ratio (SNR) of the observed signals currently inhibits the widespread use of the technique, thus highlighting the need for intelligent processing algorithms. In earlier work, we proposed a set of maximum likelihood-based algorithms enabling detection of even very weak NQR signals. These algorithms are based on derived realistic NQR data models, assuming that the (complex) amplitudes of the NQR signal components are known to within a multiplicative constant. However, these amplitudes, which are obtained from experimental measurements, are typically prone to some level of uncertainty. For such cases, these algorithms will experience a loss in performance. Herein, we develop a set of robust algorithms, allowing for uncertainties in the assumed amplitudes, showing that these offer a significant performance gain over the current state-of-the art techniques.

Analysis of nuclear quadrupole resonance signals from mixtures

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) technique, able to detect and identify compounds containing quadrupolar nuclei, including many high explosives, narcotics and pharmaceutical compounds. In addition to being able to identify entirely different compounds, the technique is also able to distinguish between different polymorphic forms of the same compound. Analysing the signals from mixtures, either of different compounds or of polymorphs, is important in several applications. Being able to exploit the signals from the different components of a mixture is important in the detection of explosives, whilst quantification of such components is important in several pharmaceutical applications. In this paper, we propose two hybrid detectors that can exploit the signals from multiple components, offering improved probability of detection, as compared to recently proposed detectors. The algorithms also provide estimates of the relative proportions of the components, as well as estimates of other important NQR signal parameters. The algorithms are evaluated on both real and simulated data. The former is measured from a sample of trinitrotoluene which contains at least two polymorphic forms with rather different NQR properties.

Frequency-selective robust detection and estimation of polymorphic QR signals

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.

Robust Multichannel Detection of Mixtures Using Nuclear Quadrupole Resonance

Nuclear quadrupole resonance (NQR) is a radio-frequency (RF) spectroscopic technique, able to detect the presence of many high explosives and narcotics. In practice, the weak NQR signal is often corrupted by strong RF interference (RFI); therefore, various multichannel detection algorithms have recently been proposed for NQR. However, these algorithms allow for only a single compound/polymorph, whose associated amplitudes are assumed to be known to within a scaling. Regrettably, these amplitudes are typically prone to some level of uncertainty; and, in several cases of interest, signals from a mixture of NQR compounds/polymorphs may be present, leading to performance degradation in the aforementioned algorithms. In this paper, we develop a robust multisensor hybrid algorithm able to process NQR signals from mixtures while also allowing for errors in the assumed amplitudes. Numerical investigations indicate that the proposed algorithm provides significant performance gains as compared to existing algorithms in the case when there are multiple polymorphs present and/or when uncertainties in the amplitudes exist.

Frequency Selective Detection of Nuclear Quadrupole Resonance (NQR) Spin Echoes

Nuclear Quadrupole Resonance (NQR) is a radio frequency (RF) technique that can be used to detect the presence of quadrupolar nuclei, such as the 14N nucleus prevalent in many explosives and narcotics. The technique has been hampered by low signal-to-noise ratios and is further aggravated by the presence of RF interference (RFI). To ensure accurate detection, proposed detectors should exploit the rich form of the NQR signal. Furthermore, the detectors should also be robust to any remaining residual interference, left after suitable RFI mitigation has been employed. In this paper, we propose a new NQR data model, particularly for the realistic case where multiple pulse sequences are used to generate trains of spin echoes. Furthermore, we refine two recently proposed approximative maximum likelihood (AML) detectors, enabling the algorithm to optimally exploit the data model of the entire echo train and also incorporate knowledge of the temperature dependent spin-echo decay time. The AML-based detectors ensure accurate detection and robustness against residual RFI, even when the temperature of the sample is not precisely known, by exploiting the dependencies of the NQR resonant lines on temperature. Further robustness against residual interference is gained as the proposed detector is frequency selective; exploiting only those regions of the spectrum where the NQR signal is expected. Extensive numerical evaluations based on both simulated and measured NQR data indicate that the proposed Frequency selective Echo Train AML (FETAML) detector offers a significant improvement as compared to other existing detectors.

Detection of Landmines Using Nuclear Quadrupole Resonance (NQR): Signal Processing to Aid Classification

Nuclear quadrupole resonance (NQR) is a sensor technology that measures a signature unique to the explosive contained in the mine, thus providing a means of efficiently detecting landmines. Unfortunately, the measured signals are inherently weak and therefore detection times are currently too long (especially for TNT-based landmines) to implement in a man-portable detection system. However, the NQR hardware is light enough to be integrated into a robot based system. This paper investigates several power spectrum estimation algorithms applied to NQR signals in order to distinguish between data containing signals from explosive and data that does not.