Sparse recovery using an SVD approach to interference removal and parameter estimation

Hayes, Charles Ethan; McClellan, J.H.; Scott, Waymond R.

doi:10.1109/dsp-spe.2015.7369553

Cited by 4 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, Section 3.1 demonstrated that target signals show a frequency dependence which can be modeled as a sparse collection of atoms in a DSRF dictionary, A. Hayes showed that careful combination of A and G can be exploited to project the data into a space which is mostly isolated from the soil. 29 The creation of P G is completed in two steps. First, a projection matrix into the soil subspace is developed as P GG = Ψ(Ψ T Ψ) −1 Ψ T .…”

Section: Soil Interference Removalmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of hand-held WEMI target detection algorithms

McCurley

Bocinsky

Zare

2019

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV

View full text Add to dashboard Cite

Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards. While WEMI sensors have proven effective at localizing objects exhibiting large magnetic responses, detecting objects lacking or containing very low amounts of conductive materials can be challenging. In this paper, we compare a number of target detection algorithms in the literature in terms of detection performance. In the comparison, methods are tested on two real-world data sets: one containing relatively low amounts of ground noise pollution, and the other demonstrating highly-magnetic soil interference. Results are quantitatively evaluated through receiver-operator characteristic (ROC) curves and are used to highlight the strengths and weaknesses of the compared approaches in hand-held explosive hazard detection.rely on observing high-magnitude responses to flag potential threats, 6 Scott, et al. proposed that the relaxation energy emitted by conductive objects can be modeled as a function of operating frequency and used to created a dictionary of expected target responses. 7-9 Elaboration on the discrete spectrum of relaxation frequencies (DSRF) target dictionary and its exploitation for use in WEMI hazard detection is provided in Section 3.1.While there have been many successes in algorithm development for detection of high-metal targets, EHD is certainly not a solved problem. The first obstacle making EHD difficult is that there exists a wide assortment of targets to be detected, ranging from small anti-personal to large anti-tank. Additionally, metal content can range from high to low, or even even be absent in the case of plastic targets. While it has long been assumed that the inherent properties exhibited by metal detectors make discovery of lowly and non-conductive objects difficult to achieve, recent work has shown that the utilization of a novel WEMI processing procedure may provide discriminative information for unmasking difficult targets. Further description of this processing as well an initial investigation to the use of its generated features for hazard discovery is presented in Sections 3.6 and 4.4.3, respectively. In addition to the range of targets, non-target objects (clutter) and natural variations in the soil's electromagnetic properties can bring rise to false alarms (FA) where nontarget objects are labeled as potential threats. A principal challenge in EHD is mitigating false alarms while not masking true detections, or true positives (TP).In this paper, the authors compare an assortment of WEMI prescreeners which have shown considerable success as subsurface object detectors on alternative WEMI hazard discovery systems. Experiments were conducted to 1.) gauge the performance of target detectors across a variety of WEMI sensors, 2.) compare prescreeners utilizing pre-defined versus learned target concepts, and 3.) evaluate two pre-processing methods for interference removal. Quantitative evaluation of performance is given by receiver-operating characte...

show abstract

Section: Soil Interference Removalmentioning

confidence: 99%

“…An additional SVD was performed on each data window 29) and the left singular vectors U S (corresponding to frequency) were scaled by the square root of the ratio between the corresponding and first singular values. 10,11 The scaled representations were concatenated to create feature vectors.…”

Section: Soil Interference Removalmentioning

confidence: 99%