A method for classifying targets using a low-rank representation of broadband electromagnetic induction data is presented. The method does not require position data, a sensor model, or a complex inversion so it is applicable to hand-held EMI systems or a simple vehicle-based system. The low-rank representation is very straightforward to compute and does not require position significant computational resources. The method will be shown for data from a cart-based Georgia Tech EMI sensor that operates in the frequency domain and collects data at 15 logarithmically spaced frequencies from 1 kHz to 90 kHz. The data for several will be presented in the low-rank form to show that they are consistent within a target type and distinct for different targets. An example using the low-rank data to classify targets will be presented.
Recent work with Wideband Electromagnetic Induction (WEMI) sensors has shown that a low-rank model can be used to exploit the measurements. The low-rank model has led to a new filterless processing framework for frequency-domain WEMI sensors, where projection operators can be used in both the frequency and spatial dimensions of the data. Previous work has used a single subspace from the projected measurements to perform target detection, classification, and localization. This work investigates the eight remaining measurement subspaces created by the projection operators and how they can be exploited to extract more information for WEMI processing.
Proc. SPIE. 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII
KEYWORDS: Target detection, Mathematical modeling, Signal to noise ratio, Data modeling, Sensors, Matrices, Remote sensing, Magnetism, Interference (communication), Signal processing, Magnetic sensors, Electromagnetic coupling, Signal detection, Electromagnetism, Land mines
Wideband electromagnetic induction (WEMI) sensors can be used to detect, classify, and locate metallic targets buried underground. By examining the WEMI model from a new perspective, a low-rank equivalence for the WEMI data is obtained. The low-rank physical WEMI model directly leads to a new “filterless” processing framework that differs radically from traditional approaches to WEMI processing. The new framework enables target signature recovery independent of location estimation. Processing of field data with the new filterless WEMI framework is presented to show classification results based on the low-rank target signature.
This work describes a target location estimation procedure for single channel 2-D electromagnetic induction (EMI) scan data for both a rotating and non-rotating sensor head. The location estimation technique is based on a new low-rank model for electromagnetic induction data and and addresses difficulties in how to efficiently construct the dictionary for inverting the single channel EMI data. The location estimation technique is tested on simulated data for a single z-directed wire loop for both a rotating and non-rotating sensor head, and on non-rotating sensor head laboratory measurements for four different mine types. The technique is shown to accurately estimate the target location for simulated data and experimental data, and is able to estimate the location of strong mine targets at depths of 20 – 30 cm with ±2 cm error.
This work introduces two advances in wide-band electromagnetic induction (EMI) processing: a novel adaptive matched filter (AMF) and matched subspace detection methods. Both advances make use of recent work with a subspace SVD approach to separating the signal, soil, and noise subspaces of the frequency measurements The proposed AMF provides a direct approach to removing the EMI self-response while improving the signal to noise ratio of the data. Unlike previous EMI adaptive downtrack filters, this new filter will not erroneously optimize the EMI soil response instead of the EMI target response because these two responses are projected into separate frequency subspaces. The EMI detection methods in this work elaborate on how the signal and noise subspaces in the frequency measurements are ideal for creating the matched subspace detection (MSD) and constant false alarm rate matched subspace detection (CFAR) metrics developed by Scharf The CFAR detection metric has been shown to be the uniformly most
powerful invariant detector.
Experimental data measured at a field test site with a broadband electromagnetic induction (EMI) sensor are presented. The system is an improved version of the Georgia Tech EMI system developed over the past several years. The system operates over a 300 to one bandwidth and is more sensitive while being more power efficient than earlier systems. Data measured with the system will be presented with an emphasis on features in the data that can be used to separate metallic targets from the soil response and to discriminate between certain classes of metallic targets.