Aspect-dependent efficient multipath ghost suppression in TWRI with sparse reconstruction

Muqaibel, Ali H.; Abdalla, Abdi T.; Alkhodary, Mohammad T.; Al-Dharrab, Suhail

doi:10.1017/s1759078717000666

Cited by 9 publications

(16 citation statements)

References 42 publications

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“…The received signal at n th location when the m th frequency f m is transmitted for R multipath returns comprises four main contributions: reflection from the front wall, target‐to‐side wall reflection, target‐to‐target reflection, and ambient noise, as given in , by

\begin{matrix} y [m, n] & = {false\sum}_{r = 0}^{R - 1} {false\sum}_{p = 0}^{N_{x} N_{y} - 1} σ_{p}^{(r)} exp (- j 2 π f_{m} t_{pn}^{(r)}) \\ + {false\sum}_{r_{normalw} = 0}^{R_{normalw} - 1} σ_{w}^{()(, {normalr}_{normalw})} exp (- j 2 π f_{m} t_{w}^{({normalr}_{normalw})}) \\ + {false\sum}_{r = 0}^{R - 1} {false\sum}_{p, q = 0 p \neq q}^{N_{x} N_{y} - 1} σ_{pq}^{(r)} exp (- j 2 π f_{m} t_{pqn}^{(r)}) \\ + v (m, n), \end{matrix}

where

t_{italicpn}^{false(r false)}

represents the round‐trip delay between the p th target and the n th receiver due to the r th return,

t_{italicpnq}^{false(r false)}

is the round‐trip delay between the p th and q th targets with the n th transceiver, and

t_{normalw}^{false(r_{}}

…”

Section: Twri Scene and Signal Modelsmentioning

confidence: 99%

“…Since the contributions from the target interactions in (5) is nonlinear, it was suggested in that the overall signal reflectivity due to the target interactions,

σ_{italicpq}^{false(r false)}

, is dictated by the second target, and the first target was taken as a perfect reflector.…”

Section: Twri Scene and Signal Modelsmentioning

confidence: 99%

“…The received signal at nth location when the mth frequency f m is transmitted for R multipath returns comprises four main contributions: reflection from the front wall, target-to-side wall reflection, target-to-target reflection, and ambient noise, as given in [11], [12] by…”

Section: Twri Scene and Signal Modelsmentioning

confidence: 99%

“…Moreover, r ðrÞ p and r r w w are the target and wall pixel reflectivities, respectively, with respect to the rth return, and v m; n ð Þis the noise sample. Since the contributions from the target interactions in (5) is nonlinear, it was suggested in [11] that the overall signal reflectivity due to the target interactions, r ðrÞ pq , is dictated by the second target, and the first target was taken as a perfect reflector.…”

Section: Twri Scene and Signal Modelsmentioning

confidence: 99%

“…Basically, D is a Bernoulli matrix that is obtained by randomly selecting J rows from an MN 9 MN identity matrix. Downsampling (11) gives…”

Section: Multipath Exploitation With Sparse-reconstruction-based Ghosmentioning

confidence: 99%

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Multipath Ghosts in Through-the-Wall Radar Imaging: Challenges and Solutions

Abdalla¹,

Alkhodary²,

Muqaibel³

2018

ETRI Journal

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In through‐the‐wall radar imaging (TWRI), the presence of front and side walls causes multipath propagation, which creates fake targets called multipath ghosts. They populate the scene and reduce the probability of correct target detection, classification, and localization. In modern TWRI, specular multipath exploitation has received considerable attention for reducing the effects of multipath ghosts. However, this exploitation is challenged by the requirements of the reflecting geometry, which is not always available. Currently, the demand for a high radar image resolution dictates the use of a large aperture and wide bandwidth. This results in a large amount of data. To tackle this problem, compressive sensing (CS) is applied to TWRI. With CS, only a fraction of the data are used to produce a high‐quality image, provided that the scene is sparse. However, owing to multipath ghosts, the scene sparsity is highly deteriorated; hence, the performance of the CS algorithms is compromised. This paper presents and discusses the adverse effects of multipath ghosts in TWRI. It describes the physical formation of ghosts, their challenges, and existing suppression techniques.

show abstract

\begin{matrix} y [m, n] & = {false\sum}_{r = 0}^{R - 1} {false\sum}_{p = 0}^{N_{x} N_{y} - 1} σ_{p}^{(r)} exp (- j 2 π f_{m} t_{pn}^{(r)}) \\ + {false\sum}_{r_{normalw} = 0}^{R_{normalw} - 1} σ_{w}^{()(, {normalr}_{normalw})} exp (- j 2 π f_{m} t_{w}^{({normalr}_{normalw})}) \\ + {false\sum}_{r = 0}^{R - 1} {false\sum}_{p, q = 0 p \neq q}^{N_{x} N_{y} - 1} σ_{pq}^{(r)} exp (- j 2 π f_{m} t_{pqn}^{(r)}) \\ + v (m, n), \end{matrix}

where

t_{italicpn}^{false(r false)}

represents the round‐trip delay between the p th target and the n th receiver due to the r th return,

t_{italicpnq}^{false(r false)}

is the round‐trip delay between the p th and q th targets with the n th transceiver, and

t_{normalw}^{false(r_{}}

…”

Section: Twri Scene and Signal Modelsmentioning

confidence: 99%

“…Since the contributions from the target interactions in (5) is nonlinear, it was suggested in that the overall signal reflectivity due to the target interactions,

σ_{italicpq}^{false(r false)}

, is dictated by the second target, and the first target was taken as a perfect reflector.…”

Section: Twri Scene and Signal Modelsmentioning

confidence: 99%

“…The received signal at nth location when the mth frequency f m is transmitted for R multipath returns comprises four main contributions: reflection from the front wall, target-to-side wall reflection, target-to-target reflection, and ambient noise, as given in [11], [12] by…”

Section: Twri Scene and Signal Modelsmentioning

confidence: 99%

Section: Twri Scene and Signal Modelsmentioning

confidence: 99%

“…Basically, D is a Bernoulli matrix that is obtained by randomly selecting J rows from an MN 9 MN identity matrix. Downsampling (11) gives…”

Section: Multipath Exploitation With Sparse-reconstruction-based Ghosmentioning

confidence: 99%

See 3 more Smart Citations

Multipath Ghosts in Through-the-Wall Radar Imaging: Challenges and Solutions

Abdalla¹,

Alkhodary²,

Muqaibel³

2018

ETRI Journal

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show abstract

Fast sparse image reconstruction method in through-the-wall radars using limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm

Mwisomba

Abdalla

Amour

et al. 2021

Int. J. Microw. Wireless Technol.

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Compressed sensing allows recovery of image signals using a portion of data – a technique that has drastically revolutionized the field of through-the-wall radar imaging (TWRI). This technique can be accomplished through nonlinear methods, including convex programming and greedy iterative algorithms. However, such (nonlinear) methods increase the computational cost at the sensing and reconstruction stages, thus limiting the application of TWRI in delicate practical tasks (e.g. military operations and rescue missions) that demand fast response times. Motivated by this limitation, the current work introduces the use of a numerical optimization algorithm, called Limited Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS), to the TWRI framework to lower image reconstruction time. LBFGS, a well-known Quasi-Newton algorithm, has traditionally been applied to solve large scale optimization problems. Despite its potential applications, this algorithm has not been extensively applied in TWRI. Therefore, guided by LBFGS and using the Euclidean norm, we employed the regularized least square method to solve the cost function of the TWRI problem. Simulation results show that our method reduces the computational time by 87% relative to the classical method, even under situations of increased number of targets or large data volume. Moreover, the results show that the proposed method remains robust when applied to noisy environment.

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Through‐the‐wall radar imaging algorithm for moving target under wall parameter uncertainties

Sun

Chen

2019

IET Image Processing

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Aspect-dependent efficient multipath ghost suppression in TWRI with sparse reconstruction

Cited by 9 publications

References 42 publications

Multipath Ghosts in Through-the-Wall Radar Imaging: Challenges and Solutions

Multipath Ghosts in Through-the-Wall Radar Imaging: Challenges and Solutions

Fast sparse image reconstruction method in through-the-wall radars using limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm

Through‐the‐wall radar imaging algorithm for moving target under wall parameter uncertainties

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