Matching pursuits with a wave-based dictionary

McClure, Mark R.; Carin, Lawrence

doi:10.1109/78.650250

Cited by 96 publications

(66 citation statements)

References 42 publications

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“…The wave-based matching pursuits algorithm presented by McClure and Carin [7] was attempted with varying degrees of success for this application. If the impulse response can be calculated from a good understanding of the generating physics and more importantly these calculated effects can be observed in the striking experiments, then these customized basis decompositions show great promise.…”

Section: Discussionmentioning

confidence: 99%

Classification of Underwater Objects Via Impulse Excitation

2006

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Section: Discussionmentioning

confidence: 99%

Classification of Underwater Objects Via Impulse Excitation

2006

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“…Different from the dictionaries used in [20][21][22], the dictionary set in this paper is built up with the 2-D scatterer parameter of radar images. Let Γ (m) = {Φ (m) j , j = 1, 2, .…”

Section: Construction Of Dictionary Setmentioning

confidence: 99%

“…To implement the recognition, the image I(x, y) of the candidate target is decomposed with the MP algorithm in [22], where the inner product between the image I(x, y) and the atom ψ (m) ij (x, y) as…”

Section: Recognition Proceduresmentioning

confidence: 99%

Target Recognition With Radar Images via Parameterized Dictionary Sets

Wang¹,

Gu²,

Shang³

et al. 2014

PIER C

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Abstract-Target recognition through the processing of high-resolution radar images has been an active research area in past decades. In this paper, dictionary sets parameterized by the two-dimensional (2-D) location parameters of main high-energy scatterers are considered to recognize the candidate targets. For this purpose, the scatterer extraction and orientation estimation of radar image are firstly provided in this paper. Furthermore, the recognition method based on the parameterized dictionary sets is subsequently proposed. Different from the existed recognition methods, only the sampled images at the 2-D location parameters of main high-energy scatterers are used in the proposed method. Consequently, the noise or clutter outside the sampling locations can be filtered, which results in more robust performance. Moreover, the 2-D location parameters are proportional to the geometrical structure, and the proposed method is adaptive to the scale variation of the target images. Simulated results are provided to demonstrate the proposed method.

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“…There are fuzzy IF-THEN rules; each rule follows the format If is and is and is then (20) where th element of the vector under test, ; system output; defined above. is an exponential, or Gaussian, membership function given by (21) where is the th element of (i.e., the th rule premise), and is defined in the firing strength expression [ (13)]. The membership functions are completely defined by the cluster centers.…”

Section: Subtractive Fuzzy Clustering Algorithmmentioning

confidence: 99%

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

Collins

Zhang

et al. 2001

IEEE Trans. Fuzzy Syst.

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Abstract-In most field environments, unexploded ordnance (UXO) items are found among extensive surface and subsurface clutter and shrapnel from ordnance. Traditional algorithms for UXO remediation experience severe difficulty distinguishing buried targets from anthropic clutter. Furthermore, naturally occurring magnetic geologic noise often adds to the complexity of the discrimination task. These problems render site remediation a very slow, labor-intensive, and inefficient process. While sensors have improved significantly over the past several years in their ability to detect conducting and/or permeable targets, reduction of the false alarm rate has proven to be a significantly more challenging problem. Our work has focused on the development of signal processing algorithms that incorporate the underlying physics characteristic of the sensor and of the anticipated 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 has been developed by the U.S. Naval Research Laboratory (NRL), and includes both electromagnetic induction (EMI) and magnetometer sensors. We describe four signal processing techniques that incorporate features derived from simple physics-based sensor models: 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. We show that overall the subtractive fuzzy technique performs better than the alternative techniques when the training and testing data sets are separate. The results also allow us to quantify the utility of fusing the magnetometer and the EMI data, and we show that performance is improved when both EMI and magnetometer features are utilized. 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.

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Matching pursuits with a wave-based dictionary

Cited by 96 publications

References 42 publications

Classification of Underwater Objects Via Impulse Excitation

Classification of Underwater Objects Via Impulse Excitation

Target Recognition With Radar Images via Parameterized Dictionary Sets

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

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