Time reversal imaging of obscured targets from multistatic data

Devaney, Anthony J.

doi:10.1109/tap.2005.846723

Cited by 229 publications

(215 citation statements)

References 14 publications

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“…Multistatic data matrices (MDMs) corresponding to each of the indicated locations are recorded. Recall that a MDM is obtained by successively firing a short pulse by each array element and recording the received signal by all elements [44,45]. In our case, we use the first derivative of the Blackmann-Harris (BH) pulse [46] which has a center frequency f c = 400 MHz and time duration 1.55/f c .…”

Section: Problem Scenariomentioning

confidence: 99%

Imaging and tracking of targets in clutter using differential time-reversal techniques

Fouda

Teixeira

2012

Waves in Random and Complex Media

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Two time-reversal algorithms for identifying, imaging, and tracking moving targets in clutter are introduced. The first algorithm classifies existing scatterers into stationary vs. moving targets. Multistatic data matrices (MDMs) corresponding to successive radar acquisitions (snapshots) of the scene are recorded. Singular value decomposition of the (time-)averaged MDM provides information on stationary targets, whereas singular value decomposition of the differential MDM provides information on moving targets. The second algorithm yields real-time selective tracking of each moving target by means of differential time-reversal. It requires minimal processing and memory resources, and exploits distinctive features of timereversal such as statistical stability and superresolution. Numerical simulations are used to illustrate the capabilities of the proposed algorithms in different scenarios involving clutter from discrete secondary scatterers and from inhomogeneous random medium backgrounds.

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Section: Problem Scenariomentioning

confidence: 99%

Imaging and tracking of targets in clutter using differential time-reversal techniques

Fouda

Teixeira

2012

Waves in Random and Complex Media

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“…(11). In order for the analysis to hold, we make certain assumptions about the transmit waveforms and the scattering environment.…”

Section: The Preconditioning Operatormentioning

confidence: 99%

“…It has been used in many applications which involve wavepropagation, e.g., ultrasound imaging [4], [5], [6], [7], underwater acoustics [8], [9], radar imaging [10], [11], and microwave imaging [12]. Typically, time-reversal applications work well in a multiple-scattering medium and where explicit modeling of the medium is difficult due to its complexity or due to random perturbations [7], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Time-Reversal Waveform Preconditioning for Clutter Rejection

Varslot¹,

Cheney²,

Yarman³

et al. 2011

Principles of Waveform Diversity and Design

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Abstract-A time-reversal implementation of a transmit waveform preconditioning scheme for optimal clutter rejection in radar imaging is presented. Waveform preconditioning involves determining a map on the space of transmit waveforms, and then applying this map to the waveforms before transmission. Our work applies to antenna arrays with an arbitrary number of transmit-and receive elements, and makes no assumptions about the elements being co-located. Waveform preconditioning for clutter rejection achieves efficient use of power and computational resources by distributing power properly over a frequency band and by eliminating clutter filtering in receive processing. By our time-reversal implementation we avoid the need to obtain an explicit model for the environment in order to compute the preconditioning operator.

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“…Using the summation convention, the relation between the stress and the displacement given by (2) can by written simply as σ ij = C ijkl ε kl , where the summation is understood to be over the repeated indexes k and l. By defining the vectors s and e as s = σ 11 , σ 22 , σ 33 , σ 23 , σ 31 , σ 12 t , (4) we can write the following relationship between the six independent components of stress and strain tensors:…”

Section: Notation and Preliminariesmentioning

confidence: 99%

“…If the dimension of the signal space, s(= 9), is known or is estimated from the singular value decomposition of A, defined by A = VΣU * , then the MUSIC algorithm applies [12,10]. Furthermore, if v i denote the column vectors of the matrix V, then for any vector a ∈ C 9 \{0} and for any space point x within the search domain, a map of the estimator W (x) defined as the inverse of the squared Euclidean distance from the vector H(x) · a to the signal space by…”

Section: Decomposition Of the Msr Matrixmentioning

confidence: 99%

Direct Elastic Imaging of a Small Inclusion

Ammari¹,

Calmon²,

Яковлева³

2008

SIAM J. Imaging Sci.

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Abstract. In this paper we consider the problem of locating a small three-dimensional elastic inclusion, using arrays of elastic source transmitters and receivers. This procedure yields the multistatic response matrix that is characteristic of the elastic inclusion. We show how the eigenvalue structure of this matrix can be employed within the framework of a noniterative method of MUSIC (multiple signal classification) type in order to retrieve the elastic inclusion. We illustrate our reconstruction procedure with a variety of computational examples from synthetic, noiseless, and severely noisy data.

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Time reversal imaging of obscured targets from multistatic data

Cited by 229 publications

References 14 publications

Imaging and tracking of targets in clutter using differential time-reversal techniques

Imaging and tracking of targets in clutter using differential time-reversal techniques

Time-Reversal Waveform Preconditioning for Clutter Rejection

Direct Elastic Imaging of a Small Inclusion

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