Detection of explosives has the flavor of those mathematical problems that are not invertible. It is easier to hide explosives than to find them. Many approaches have been proposed and executed for the remote detection of explosives, contraband materials, weapons of mass destruction, currency, etc. Most detection technologies suffer from a common problem: the features they look for, such as discontinuities in electrical conductivity, are not unique properties of the target but are contained, to some degree, in the more benign surroundings. Such a degeneracy leads to "clutter" in the response. For example, resolving the false alarms generated by this clutter can determine the rate of advance of a conventional electromagnetic metal detector employed as a landmine detector. One approach that provides a "unique" signature is nuclear quadrupole resonance (NQR) (the technique is also called QR, to avoid confusion with strictly nuclear techniques). This paper outlines the important physical principles behind the use of NQR for remote detection, indicates areas of applicability, and presents results of field trials of a prototype landmine detection system.

Remote sensing by nuclear quadrupole resonance

Landmines are affecting the lives and livelihoods of millions of people around the world. A number of detection techniques, developed for use with impulse ground penetrating radar, are described, with emphasis on a Kalman filter based approach. Comparison of results from real data show that the Kalman filter algorithm provides the best detection performance, although its computational burden is also the highest.

Signal processing techniques for landmine detection using impulse ground penetrating radar

The amplitude estimation of a signal that is known only up to an unknown scaling factor, with interference and noise present, is of interest in several applications, including using the emerging quadrupole resonance (QR) technology for explosive detection. In such applications, a sensor array is often deployed for interference suppression. This paper considers the complex amplitude estimation of a known waveform signal whose array response is also known a priori. Two approaches, viz., the Capon and the maximum likelihood (ML) methods, are considered for the signal amplitude estimation in the presence of temporally white but spatially colored interference and noise. We derive closed-form expressions for the expected values and mean-squared errors (MSEs) of the two estimators. A comparative study shows that the ML estimate is unbiased, whereas the Capon estimate is biased downwards for finite data sample lengths. We show that both methods are asymptotically statistically efficient when the number of data samples is large but not when the signal-to-noise ratio (SNR) is high. Furthermore, we consider a more general scenario where the interference and noise are both spatially and temporally correlated. We model the interference and noise vector as a multichannel autoregressive (AR) random process. An alternating least squares (ALS) method for parameter estimation is presented. We show that in most cases, the ALS method is superior to the model-mismatched ML (M/sup 3/L) method, which ignores the temporal correlation of the interference and noise.

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Array signal Processing in the known waveform and steering vector case

As described by Zadeh, recognition technology refers to systems that incorporate new sensors, novel signal processing, and soft computing. In this paper, we discuss these principles applied to the problem of land mine detection. We describe a complex recognition system that is evolving from basic research into a fielded system. Some components of this system have been field tested with excellent results, whereas other components have achieved such results in the laboratory. Fuzzy set-based information fusion algorithms are central to the excellent results obtained. Multiple-detection algorithms are applied to signals acquired from an innovative ground penetrating radar that produces volumetric sub-surface imagery. The outputs of the detection algorithms are combined using the fuzzy logic and Sugeno and Choquet fuzzy integrals to produce overall detection scores. Experimental results are provided on training data and on completely blind test data collected in the field and scored by the US Army.

Recognition technology for the detection of buried land mines

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

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

Nuclear quadrupole resonance (NQR) 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 presence of explosives, since this effect is not seen in most clutter, both natural and anthropic. However, this problem is complicated by several issues. First, the late-time radiation if often very weak, particularly for TNT, and therefore the signal-to-noise ratio must be high for extracting the NQR response. Further, the frequency at which the explosive radiates is often a strong function of the background environment, and therefore in practice the NQR radiation frequency is not known a priori. Finally, at the frequencies of interest, there is a significant amount of background radiation, which induces radio frequency interference (RFI). In this paper we discuss several signal processing tools we have developed to enhance the utility of NQR explosives detection. In particular, with regard to the RFI, we exposure least-mean-squares algorithms which have proven well suited to extracting background interference. Algorithm performance is assessed through consideration of actual measured data. With regard to the detection of the NQR electromagnetic echo, we consider a Bayesian discrimination algorithm. The performance of the Bayesian algorithm is presented, again using measured NQR data.

Signal processing for NQR discrimination of buried land mines

Nuclear Quadrupole Resonance (NQR) combines the compound specific detection capability offered by chemical offered by chemical detection techniques with the spatial coating capability and convenience of an induction coil metal detector. In this paper we present the first results of the detection of TNT by NQR with sufficient for detection of many antipersonnel mines and essentially all antitank mines. In addition, we review the result of a blind in-field demonstration of the system in detecting RDX in which 28 out of 31 RDX-only targets were found with 1 false alarm in a 110 m test lane, and a second test in which 21 out of 21 RDX mines were found with zero false alarms at a clearance rate of 1.1 m2 per minute.

Detection of TNT and RDX landmines by standoff nuclear quadrupole resonance

We report on field test results conducted during 1999 in Bosnia and at the Army Mine Training School, Fort Leonard Wood, MO, on a ne prototype landmine detection system. In all test, non-metallic, anti-personnel (AP) and anti-tank (AT) landmines were detected via the NQR explosive signature with a probability of detection of 100 percent. The initial false alarm rate for the AP mine test was < 5 percent and was reduced to zero by a subsequent remeasurement. The test included typical burial depths and a variety of ground and weather conditions. In addition, the system can tolerate very high levels of metallic clutter and has repeatedly achieved zero false alarm rate when scanning for buried explosives at an EOD test range.

Field test results of a nuclear quadrupole resonance land mine detection system

Quadrupole Resonance (QR) has recently been shown to be a feasible method for the non-contact measurement of strain in polymeric fiberglass-reinforced composites. Tiny crystals of a QR active additive are embedded into the composite or are applied as part of a surface coating. Strains in the composite are transferred to the additive crystals. These crystals can be interrogated via radio frequency pulses provided by a single-sided radio frequency coil. Thus, the additive crystals give rise to a strain dependent QR frequency response. The QR frequency and line width from composites containing additive are found to be sensitive parameters for the measurement of tensile strain. The QR active additive that was embedded in the composite matrix was found to be inert and non-intrusive.

David M. Gregory

Papers

Signal processing for NQR discrimination of buried land mines

Detection of TNT and RDX landmines by standoff nuclear quadrupole resonance

Field test results of a nuclear quadrupole resonance land mine detection system

NDE of composites via quadrupole resonance spectroscopy