Multi-sensor Towed Array Detection System Research Articles

Performance Analysis of Parameter Estimation in Electromagnetic Induction Data

We derive the Cramer–Rao lower bound (CRB) for target tensor amplitudes and target location parameters that can be estimated from electromagnetic induction (EMI) measurements of a target. In deriving the bound, no restrictions are placed on the target type, target orientation, quantity, or location of the measurement positions, nor the geometry of the EMI sensor or sensor array. The analysis is applicable to both a scanned sensor and a stationary array, as well as both time- and frequency-domain sensors. We show how the bound varies as a function of target type, orientation, depth, and signal-to-noise ratio. In addition, we illustrate the ways to use the CRB and the Fisher information matrix to analyze the relationships between the tensor amplitudes and location parameters. We show results of the CRB analysis applied to an experimental frequency-domain Georgia Tech sensor for a few target types for a nominal 2-D scan geometry as well as the Time-domain Electromagnetic Multi-sensor Towed Array Detection System. We also provide an algorithm for estimating the target location and tensor that achieves the CRB.

The Application of Magnetometers and Electromagnetic Induction Sensors in UXO Detection

This paper introduces the physical model of unexploded ordnance (UXO) in the magnetization process and the formula derivation of the magnetic dipole model. The application of magnetometers and electromagnetic induction sensors in the detection of UXO is introduced. The magnetometers introduce the total field measurements technology of the CS optical-pump marine magnetometer and the MagSTAR (Magnetic Scalar Triangulation and Ranging) gradient detection technology. The electromagnetic induction sensors introduce the working theories and the popular product models of Geophex company and Geonics Ltd. The method of detecting UXO by MTADS of United States Naval Research Laboratory (The multisensor towed array detection system) compares the difference between the United States Naval Research Laboratory for target recognition.

The Orthonormalized Volume Magnetic Source Model for Discrimination of Unexploded Ordnance

We introduce a fast and accurate numerical technique for the solution of electromagnetic induction sensing problems called the orthonormalized volume magnetic source model. The model assumes that the secondary magnetic field measured by a sensor originates from a set of magnetic dipole sources distributed over a volume that coincides with the interrogated area. The Green functions associated with the responding sources are turned into an orthonormal basis using a generalization of the Gram-Schmidt method, enabling one to determine the sources' strengths directly from measured data without having to invert large and potentially ill-conditioned matrices. The method treats multitarget cases naturally and robustly. Several examples are presented to illustrate the applicability of the method in the discrimination of unexploded ordnance (UXO). In particular, we analyze data taken by the Time-Domain Electromagnetic Multisensor Towed Array Detection System sensor array at a test stand and during a blind test administered at a UXO live site. The method is highly successful in distinguishing UXO from among other UXO and from accompanying clutter.

Validation of Advanced EM Models for UXO Discrimination

The work reported here details basic validation of our advanced physics-based electromagnetic induction forward and inverse models against data collected by the Naval Research Laboratory Time-Domain Electromagnetic Multisensor Towed Array Detection System platform. The data were collected under laboratory-type conditions using both artificial spheroidal targets and real unexploded ordnance (UXO). The artificial target models are essentially exact and enable detailed comparison of theory and data in support of measurement platform characterization and target identification. Real UXO targets cannot be treated exactly, but it is demonstrated that quantitative comparisons of the data with the spheroid models nevertheless aid in extracting key target discrimination information, such as target geometry and hollow target shell thickness.

Joint diagonalization applied to the detection and discrimination of unexploded ordnance

Efforts to discriminate buried unexploded ordnance from harmless surrounding clutter are often hampered by the uncertainty in the number of buried targets that produce a given detected signal. We present a technique that helps determine that number with no need for data inversion. The procedure is based on the joint diagonalization of a set of multistatic response (MSR) matrices measured at different time gates by a time-domain electromagnetic induction sensor. In particular, we consider the Naval Research Laboratory’s Time-Domain Electromagnetic Multisensor Towed Array Detection System (TEMTADS), which consists of a [Formula: see text] square grid of concentric transmitter/receiver pairs. The diagonalization process itself generalizes one of the standard procedures for extracting the eigenvalues of a single matrix; in terms of execution time, it is comparable to diagonalizing the matrices one by one. We present the method, discuss and illustrate its mathematical basis and physical meaning, and apply it to several actual measurements carried out with TEMTADS at a test stand and in the field at the former Camp Butner in North Carolina. We find that each target in a measurement is associated with a set of nonzero time-dependent MSR eigenvalues (usually three), which enables estimation of the number of targets interrogated. These eigenvalues have a characteristic shape as a function of time that does not change with the location and orientation of the target relative to the sensor. We justify analytically and empirically that symmetric targets have pairs of eigenvalues with constant ratios between them.

Multisensor towed array detection system for UXO detection

The multisensor towed array detection system (MTADS) was designed to be an efficient, sensitive tool for the detection and characterization of buried unexploded ordnance. It comprises arrays of total-field magnetometers and time-domain electromagnetic induction (EMI) sensors, associated navigation and data acquisition hardware, and a custom data analysis system. The MTADS has conducted eleven demonstrations and surveys. The system has shown the ability to detect ordnance at its likely self-penetration depths with a probability of detection of 0.95 or better The model-derived positions and depths of the detected ordnance items are generally well within the physical size of the targets, making remediation much quicker and less costly than with standard techniques. Data sets corresponding to many of the MTADS surveys are available to others in the field.

Model-based characterization of electromagnetic induction signatures obtained with the MTADS electromagnetic array

A sensor response model based on empirically determined orthogonal response coefficients /spl beta/ is presented for the analysis of electromagnetic induction (EMI) sensor data. The model is applied to high-quality survey data from the multi-sensor towed array detection system (MTADS) program. Data from both a prepared test range and a live-site survey are analyzed. At the test field, the authors show that the model successfully reproduces the measured signatures of ordnance, ordnance simulants, flat plates, and clutter. At the live site, fit statistics were collected showing that for the most abundant ordnance target, 81-mm mortars, the three beta response parameters are distributed log-normally. This allows for a simple method of discrimination. A receiver operating characteristic (ROC) analysis based on this discrimination method is performed and the results discussed.

A comparison of the performance of statistical and fuzzy algorithms for unexploded ordnance detection

We focus on the development of signal processing algorithms that incorporate the underlying physics characteristic of the sensor and of the anticipated unexploded ordnance (UXO) target, in order to address the false alarm issue. In this paper, we describe several algorithms for discriminating targets from clutter that have been applied to data obtained with the multisensor towed array detection system (MTADS). This sensor suite includes both electromagnetic induction (EMI) and magnetometer sensors. We describe four signal processing techniques: a generalized likelihood ratio technique, a maximum likelihood estimation-based clustering algorithm, a probabilistic neural network, and a subtractive fuzzy clustering technique. These algorithms have been applied to the data measured by MTADS in a magnetically clean test pit and at a field demonstration. The results indicate that the application of advanced signal processing algorithms could provide up to a factor of two reduction in false alarm probability for the UXO detection problem.

Collection and Analysis of Multi‐Sensor Ordnance Signatures withMTADS

The Multi‐sensor Towed Array Detection System (MTADS) has been used to record the total‐field magnetometer and pulsed‐induction signatures of a range of ordnance under conditions of depth and orientation expected to be encountered at typical ranges. The MTADS Data Analysis System has been used to perform analyses of the measured signatures. In all cases, the magnetic anomalies are well fit by a model based on a magnetic dipole. Accurate positions and depths are obtained from this model although a range of sizes result depending on the orientation of the ordnance. The modified pulsed‐induction sensors used in the MTADS array have the sensitivity to detect small and intermediate ordnance at depths below the detection limit of the magnetometer array. The EM anomaly fitting algorithm, which is based on a sphere model, is not as successful at estimating the depth and size of the ordnance. We have developed a preliminary, prolate spheroid model that better represents the measured ordnance signatures. We are dev...