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Forward Looking Radar Imaging by Truncated Singular Value Decomposition and Its Application for Adverse Weather Aircraft Landing.

Huang Y, Zha Y, Wang Y, Yang J - Sensors (Basel) (2015)

Bottom Line: The forward looking radar imaging task is a practical and challenging problem for adverse weather aircraft landing industry.We first present the theoretical background of forward looking radar imaging task and its application for aircraft landing.Simulation and experimental results demonstrate that the proposed method is effective in achieving angular resolution enhancement with suppressing the noise amplification in forward looking radar imaging.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Road, Gaoxin Western District, Chengdu 611731, China. yulinhuang@uestc.edu.cn.

ABSTRACT
The forward looking radar imaging task is a practical and challenging problem for adverse weather aircraft landing industry. Deconvolution method can realize the forward looking imaging but it often leads to the noise amplification in the radar image. In this paper, a forward looking radar imaging based on deconvolution method is presented for adverse weather aircraft landing. We first present the theoretical background of forward looking radar imaging task and its application for aircraft landing. Then, we convert the forward looking radar imaging task into a corresponding deconvolution problem, which is solved in the framework of algebraic theory using truncated singular decomposition method. The key issue regarding the selecting of the truncated parameter is addressed using generalized cross validation approach. Simulation and experimental results demonstrate that the proposed method is effective in achieving angular resolution enhancement with suppressing the noise amplification in forward looking radar imaging.

No MeSH data available.


Related in: MedlinePlus

The definition of peak to valley point difference in dB.
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f5-sensors-15-14397: The definition of peak to valley point difference in dB.

Mentions: In order to quantify and compare the angular super-resolution performance of the TSVDmethod on the simulation data, relative error (ReErr), structure similarity (SSIM)[46] andthe peak to valley point difference in dB are used in this section. They are definedas follows: (17)ReErr=‖f^−f‖2‖f‖2;SSIM=2ρ(f^,f)⋅(2μf^⋅μf)(μf^2+μf2)(σf^2+σf2)where μ,σ, and ρ are the mean, standarddeviation of the vectors, and the correlations correspond to the vectorf, f̂, f̂ andf̂ represent the obtained angular super-resolution resultand the original targets, respectively. The SSIM is a quality measurement between thesuper-resolution result and the original The peak to valley point difference in dB isdefined in Figure 5 andquantifies the ability of an angular super-resolution algorithm to separate twoclosely spaced targets. The difference of peak to valley point in dB is between 0 and−∞, where 0 means the angular super-resolution algorithm can fullyseparate two closely spaced targets.


Forward Looking Radar Imaging by Truncated Singular Value Decomposition and Its Application for Adverse Weather Aircraft Landing.

Huang Y, Zha Y, Wang Y, Yang J - Sensors (Basel) (2015)

The definition of peak to valley point difference in dB.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507589&req=5

f5-sensors-15-14397: The definition of peak to valley point difference in dB.
Mentions: In order to quantify and compare the angular super-resolution performance of the TSVDmethod on the simulation data, relative error (ReErr), structure similarity (SSIM)[46] andthe peak to valley point difference in dB are used in this section. They are definedas follows: (17)ReErr=‖f^−f‖2‖f‖2;SSIM=2ρ(f^,f)⋅(2μf^⋅μf)(μf^2+μf2)(σf^2+σf2)where μ,σ, and ρ are the mean, standarddeviation of the vectors, and the correlations correspond to the vectorf, f̂, f̂ andf̂ represent the obtained angular super-resolution resultand the original targets, respectively. The SSIM is a quality measurement between thesuper-resolution result and the original The peak to valley point difference in dB isdefined in Figure 5 andquantifies the ability of an angular super-resolution algorithm to separate twoclosely spaced targets. The difference of peak to valley point in dB is between 0 and−∞, where 0 means the angular super-resolution algorithm can fullyseparate two closely spaced targets.

Bottom Line: The forward looking radar imaging task is a practical and challenging problem for adverse weather aircraft landing industry.We first present the theoretical background of forward looking radar imaging task and its application for aircraft landing.Simulation and experimental results demonstrate that the proposed method is effective in achieving angular resolution enhancement with suppressing the noise amplification in forward looking radar imaging.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic Engineering, University of Electronic Science and Technology of China, 2006 Xiyuan Road, Gaoxin Western District, Chengdu 611731, China. yulinhuang@uestc.edu.cn.

ABSTRACT
The forward looking radar imaging task is a practical and challenging problem for adverse weather aircraft landing industry. Deconvolution method can realize the forward looking imaging but it often leads to the noise amplification in the radar image. In this paper, a forward looking radar imaging based on deconvolution method is presented for adverse weather aircraft landing. We first present the theoretical background of forward looking radar imaging task and its application for aircraft landing. Then, we convert the forward looking radar imaging task into a corresponding deconvolution problem, which is solved in the framework of algebraic theory using truncated singular decomposition method. The key issue regarding the selecting of the truncated parameter is addressed using generalized cross validation approach. Simulation and experimental results demonstrate that the proposed method is effective in achieving angular resolution enhancement with suppressing the noise amplification in forward looking radar imaging.

No MeSH data available.


Related in: MedlinePlus