KEYWORDS: Polarization, Signal detection, Magnetometers, Magnetism, Sensors, Magnetic sensors, Signal to noise ratio, Resonance enhancement, Explosives, Nitrogen
Nuclear Quadrupole Resonance (NQR) has been demonstrated for the detection of 14-N in explosive compounds. Application of a material specific radio-frequency (RF) pulse excites a response typically detected with a wire- wound antenna. NQR is non-contact and material specific, however fields produced by NQR are typically very weak, making demonstration of practical utility challenging. For certain materials, the NQR signal can be increased by transferring polarization from hydrogen nuclei to nitrogen nuclei using external magnetic fields. This polarization enhancement (PE) can enhance the NQR signal by an order of magnitude or more. Atomic magnetometers (AM) have been shown to improve detection sensitivity beyond a conventional antenna by a similar amount. AM sensors are immune to piezo-electric effects that hamper conventional NQR, and can be combined to form a gradiometer for effective RF noise cancellation. In principle, combining polarization enhancement with atomic magnetometer detection should yield improvement in signal-to-noise ratio that is the product of the two methods, 100-fold or more over conventional NQR. However both methods are even more exotic than traditional NQR, and have never been combined due to challenges in operating a large magnetic field and ultra-sensitive magnetic field sensor in proximity. Here we present NQR with and without PE with an atomic magnetometer, demonstrating signal enhancement greater than 20-fold for ammonium nitrate. We also demonstrate PE for PETN using a traditional coil for detection with an enhancement factor of 10. Experimental methods and future applications are discussed.
Nuclear Quadrupole Resonance (NQR) is a highly selective spectroscopic method that can be used to detect and
identify a number of chemicals of interest to the defense, national security, and law enforcement community. In the
past, there have been several documented attempts to utilize NQR to detect nitrogen bearing explosives using
induction sensors to detect the NQR RF signatures. We present here our work on the NQR detection of explosive
simulants using optically pumped RF atomic magnetometers. RF atomic magnetometers can provide an order of
magnitude (or more) improvement in sensitivity versus induction sensors and can enable mitigation of RF
interference, which has classically has been a problem for conventional NQR using induction sensors. We present
the theory of operation of optically pumped RF atomic magnetometers along with the result of laboratory work on
the detection of explosive simulant material. An outline of ongoing work will also be presented along with a path
for a fieldable detection system.
KEYWORDS: Statistical modeling, Data modeling, General packet radio service, Sensors, Land mines, Detection and tracking algorithms, Ground penetrating radar, Metals, Systems modeling, Target detection
Prony's Method with a Polynomial Model (PMPM) is a novel way of doing classification. Given a number
of training samples with features and labels, it assumes a Gaussian mixture model for each feature, and
uses Prony's method to determine a method of moments solution for the means and priors of the
Gaussian distributions in the Gaussian mixture model. The features are then sorted in descending order
by their relative performance. Based on the Gaussian mixture model of the first feature, training
samples are partitioned into clusters by determining which Gaussian distribution each training sample is
most likely from. Then with the training samples in each cluster, a new Gaussian mixture model is built
for the next most powerful feature. This process repeats until a Gaussian mixture model is built for each
feature, and a tree is thus grown with the training data partitioned into several final clusters. A "leaf"
model for each final cluster is the weighted least squares solution (regression) for approximating a
polynomial function of the features to the truth labels. Testing consists of determining for each testing
sample a likelihood that the testing sample belongs to each cluster, and then regressions are weighted
by their likelihoods and averaged to produce the test confidence. Evaluation of PMPM is done by
extracting features from data collected by both Ground Penetrating Radar and Metal Detector of a
robot-mounted land-mine detection system, training PMPM models, and testing in a cross-validation
fashion.
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