Stacy L. Tantum

A comparison of algorithms for subsurface target detection and identification using time-domain electromagnetic induction data

The performance of subsurface target identification algorithms using data from time-domain electromagnetic induction (EMI) sensors is investigated. The response of time-domain EMI sensors to the presence of a conducting object may be modeled as a weighted sum of decaying exponential signals. Although the weights associated with each of the modes are dependent on the target/sensor orientation, the decay rates are a function of the targets composition and geometry and therefore are intrinsic to the target. Since the decay rates are not dependent on target/sensor orientation or other unobservable parameters, decay rate estimation has previously been proposed as a viable method for target identification. The performance attained with Bayesian target identification algorithms operating on the entire time-domain signal and decay rate estimates is compared through both numerical simulations and application to experimental data. The decay rate estimates utilized in the numerical simulations are assumed to achieve the Cramer-Rao lower bound (CRLB), which provides a lower bound on the variance of an unbiased parameter estimate. The simulations as well as results obtained with experimental data show that processing the entire time-domain signal provides better target identification and discrimination performance than processing decay rate estimates.

Cramer-Rao lower bound for estimating quadrupole resonance signals in non-Gaussian noise

Quadrupole resonance (QR) technology for the detection of explosives is of crucial importance in an increasing number of applications. For landmine detection, where the detection system cannot be shielded, QR has proven to be highly effective if the QR sensor is not exposed to radio-frequency interference (RFI). However, strong non-Gaussian RFI in the field is unavoidable. A statistical model of such non-Gaussian RFI noise is given in this letter. In addition, the asymptotic Cramer-Rao lower bound for estimating a deterministic QR signal in this non-Gaussian noise is presented. The performance of several convenient estimators is compared to this bound.

Landmine detection with nuclear quadrupole resonance

Nuclear quadrupole resonance (NQR) technology for the detection of explosives is of crucial importance in an increasing number of applications. For landmine detection, NQR has proven to be highly effective if the NQR sensor is not exposed to radio frequency interference (RFI). Since strong nonstationary RFI in the field is unavoidable, a robust detection method is required. With the aid of reference antennas, a frequency domain LMS algorithm is applied to cancel the RFI in field data. An average power detector based on power spectral estimation algorithms is proposed and performance using both the periodogram and MUSIC algorithms is evaluated. The detection performance has been compared with that of a non-adaptive Bayesian detector. The experimental results show that, unlike the non-adaptive Bayesian detector, the average power detector provides perfect detection capability if the data segments involved in the collection process are sufficiently long.

Tracking and localizing a moving source in an uncertain shallow water environment

An optimal approach to tracking a moving source in the presence of environmental variability is presented. This tracking algorithm, called the optimum uncertain field tracking algorithm (OUFTA), incorporates a model for the source motion as well as the uncertain ocean environment. The performance of the OUFTA is evaluated over a range of signal-to-noise ratios (SNRs) as a function of the number of observations by examining its ability to correctly estimate both the current source position and the entire source track. The improvement in performance provided by the OUFTA is illustrated by comparison to two suboptimal tracking algorithms. Results show that incorporating a priori knowledge of both the source dynamics and the ocean model provides the most accurate estimates of the source track.

Bayesian Mitigation of Sensor Position Errors to Improve Unexploded Ordnance Detection

Phenomenological modeling coupled with statistical signal processing has been shown to significantly improve capabilities for discriminating unexploded ordnance (UXO) from benign clutter using electromagnetic induction (EMI) sensor data. The general premise underlying the majority of these coupled approaches is that a phenomenological model is fit to the measured data, and the parameters estimated from this model inversion, which characterize the interrogated target, are utilized in subsequent statistical signal processing algorithms to classify the target as either UXO or clutter. A potential limitation of this coupled approach is that the inversion has been shown to be sensitive to uncertainty associated with the sensor positions. When the measurement positions are uncertain, the inversion results are more variable, and consequently, discrimination performance degrades. In this letter, a Bayesian methodology is applied to estimate the desired features from the measured data. This method explicitly acknowledges that uncertainty in the sensor positions exists and incorporates this knowledge to find the maximum-likelihood feature estimates by integrating over the uncertain measurement positions. Due to the high dimensionality of the integration, Monte Carlo integration, a statistical technique to estimate the value of an integral, is employed. Simulation results show that this Bayesian approach in mitigating sensor position uncertainty produces features with lower variability and, therefore, provides improved discrimination performance.

EMI-based classification of multiple closely spaced subsurface objects via independent component analysis

Previous work in subsurface object discrimination using electromagnetic induction data has shown that discrimination algorithms based on statistical signal processing techniques are effective for classifying data from objects that occur in isolation. However, for multiple closely spaced subsurface objects, the raw (unprocessed) measurement is a mixture of the responses from several objects and as such cannot be used directly to determine the identity of each of the individual objects. Thus, we propose to separate individual signatures from the mixture by posing the problem as a blind source separation (BSS) problem and effecting signature separation using independent component analysis. We propose to apply BSS to separate the mixed signatures and then follow the separation process with a Bayesian classifier. This approach is evaluated using both simulated data and data from unexploded ordnance items. The results show that this approach can be used to effectively classify multiple closely spaced objects.

Signal processing for NQR discrimination of buried land mines

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.

Kalman filtering for enhanced landmine detection using quadrupole resonance

Quadrupole resonance (QR) is a novel technology recently applied to landmine detection. The detection process is specific to the chemistry of the explosive, and therefore is less susceptible to the types of false alarms experienced by metal detectors and ground-penetrating radars. Although QR is vulnerable to radio-frequency interference (RFI) when the sensor is deployed in the field, adaptive RFI mitigation can remove most of the RFI. In this paper, advanced signal processing algorithms applied to the postmitigation signal are studied to enhance explosive detection. A new Kalman filtering strategy is proposed to estimate and detect the QR signal in the postmitigation signal. The results using both simulated data and experimental data show that the proposed algorithm can provide robust landmine detection performance.

On array design for matched-field processing

Conventional plane-wave beamforming array design guidelines are motivated by the desire to obtain particular beampattern characteristics, such as main lobe width and side lobe levels. These design guidelines are appropriate for arrays employed for beamforming, where a plane-wave signal model is utilized to derive both the array design parameters and the beamforming algorithm. However, matched-field processing utilizes full-field acoustic propagation models to exploit the complexities of ocean acoustic propagation. As a result, there may be more appropriate design guidelines for arrays employed for matched-field processing. In this paper, general guidelines for matched-field processing array design utilizing a normal mode propagation model are proposed. Various line array configurations are evaluated with respect to source localization performance, and the results suggest that arrays designed for matched-field processing should provide a unique representation of each propagating mode along the extent of the array. Further, the empirical analyses support the guidelines suggested by the theoretical analyses and show that arrays which are far from meeting conventional beamforming array design requirements may be more than sufficient for matched-field processing.

Single-sensor processing and sensor fusion of GPR and EMI data for land mine detection

In our previous work, we have shown theoretically that model-based Bayesian approach to the detection of landmines affords significant performance gains over standard thresholding techniques. These performance gains hold for both time- and frequency-domain electromagnetic induction (EMI) sensors. Our methodology merges physical models of the evoked target response with a probabilistic description of the clutter. Under a specific set of assumptions, our technique provides both an optimal detection algorithm and performance evaluation measures expressed as probability of detection and probability of false alarm. This approach also provides a formal framework for incorporating target and/or environmental uncertainties into the processing algorithms. The significant performance improvements observed theoretically have been verified on both time-domain and frequency-domain EMI data collected in the field. In this paper, we review our previous theoretical work, and we use actual data collected in the field to illustrate the improvement obtained by appropriately accounting for environmental uncertainties. We present new results in which a suboptimal processor provides nearly identical performance to that of the optimal processor but with much greater computational efficiency. We also present result that indicate that such an approach can be applied successfully to ground penetrating radar data. Specifically, we consider data taken by the BRTRC/Wichmannn system. In addition to processing the data from each type of sensor individually, as well as the combination of sensor, will be discussed.