Improved Enumeration of Scatterers Using Multifrequency Data Fusion in MDL for Microwave Imaging Applications

Fazli, Roohallah; Owlia, Hadi; Pourahmadi, Majid

doi:10.2528/pierc20091803

Cited by 1 publication

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the simulations, the geometric arrangement of the sensors is similar to the real Fresnel data [24], but the geometric shapes of the receivers are different for the two cases. Also, the scattering data matrix K is generated by (11), and additive white Gaussian noise W is added to this simulated data ( K = K+W). The signal-to-noise value (SNR) in dB is defined as SNR = 20 log 10 ∥K∥ ∥W∥ (19) where ∥.∥ denotes the Euclidean norm value.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In another simulation, the efficiency of the proposed algorithm to estimate the number of scatterers has been compared with the methods given in our published paper [11]. To perform this experiment, the Monte-Carlo technique was used 500 times, and the results were averaged.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…However, the MUSIC algorithm for guaranteeing good imaging performance requires a priori knowledge of the number of scatterers. This problem is addressed as source number estimation in the literature, and several attempts have been made to solve it [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have been conducted to develop algorithms for number detection and location estimation of small scatterers (tumors) in microwave tomographic imaging [10][11][12][13]. One group of these algorithms, namely the nonlinear inversion methods, is based on the Newton-type iteration technique that can successfully characterize the number and location of small scatterers within the imaging domain.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], Pourahmadi et al illustrated that the scattering from the small scatterers can be, mathematically, modeled as the MDL model, and they applied the MDL to enumerate the targets required for the MUSIC algorithm. In [11], we have proposed a method, named FMDL, which combines the multifrequency data in MDL to improve the MDL performance for the source number estimation. However, the accuracy of the MDL and FMDL for number detection is decreased when the number of snapshots (transmitters) is small, and/or the signal-to-noise ratio (SNR) is low.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Microwave Imaging of Small Scatterers by MUSIC Algorithm Using a Novel Source Number Detection Method

Fazli¹,

Momeni²

2022

PIER C

View full text Add to dashboard Cite

Microwave imaging of small scatterers is an inverse scattering problem, and recently, the MUSIC algorithm has been proposed to solve this type of problem. The MUSIC algorithm, by assuming that the number of targets is a priori known, can locate the scatterers from the peaks of the well-known pseudospectrum. The noise and multiple scattering create ambiguity to detect the number of targets. Usually, information-based algorithms such as Akaike information criterion (AIC) and minimum description length (MDL) are employed for source number estimation. However, in the cases of low signal-to-noise ratio (SNR) and close targets, the performance of these methods is seriously degraded. In the present work, we propose a two-step approach to enumerate the scatterers in microwave imaging applications for cases where traditional methods fail. Firstly, the MUSIC algorithm is applied to locate all possible targets by assuming the maximum number of targets, and secondly, we can discriminate between the real and unreal targets by using a novel formula that acts as a spatial filter. The efficiency of the proposed method has been examined through various simulation tests using numerical and experimental datasets, and the results verify that the method can accurately specify the location and number of scatterers in 2D microwave imaging applications.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations