Ground Moving Target Extraction in a Multichannel Wide-Area Surveillance SAR/GMTI System via the Relaxed PCP

He, Yan; Wang, Robert; Li, Fēi; Deng, Yunkai; Liu, Yabo

doi:10.1109/lgrs.2012.2216248

Cited by 65 publications

(34 citation statements)

References 9 publications

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“…At time t, data collected by n channels are first pre-processed through a series of stages, then transformed to the range-Doppler domain for further processing. We refer to [23,24] for more details of data preprocessing.…”

Section: Ground Moving Targets Indication Problemmentioning

confidence: 99%

“…We will mainly focus on the mathematical models and the numerical algorithms. Before proposing our models, we first give a brief review of the RPCA model in [24].…”

Section: Gmti Problemmentioning

confidence: 99%

“…However, it suffers from a high computational burden, and the performance is not good under the nonhomogeneous circumstances, mainly due to the mis-estimation of the interference covariance matrix. In [24], the authors first introduced robust PCA to model the GMTI problem. Noting that data of echo information are complex matrices, the authors use APG [11,21] to solve the convex relaxation problem (4) for the real part and the imaginary part respectively, and unify the output matrices of APG into complex matrices as the final results.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation results demonstrate the advantages over STAP method. However, as mentioned in [24], the assumptions for the exact recovery of (4) are not satisfied due to the special structure of the sparse matrix (information matrix for moving targets), leaving the exact recovery of (4) a question. Due to the same reason, the RPCA model can not fully represent the GMTI problem.…”

Section: Introductionmentioning

confidence: 99%

“…5, with a comparison to the RPCA model in [24] to verify the efficiency of the two proposed models. It can be seen that the two models have great advantages over the one in [24]. In particular, the row-modulus RPCA model is overall better than the others.…”

Section: Introductionmentioning

confidence: 99%

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Robust PCA for Ground Moving Target Indication in Wide-Area Surveillance Radar System

et al. 2013

J. Oper. Res. Soc. China

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Robust PCA has found important applications in many areas, such as video surveillance, face recognition, latent semantic indexing and so on. In this paper, we study its application in ground moving target indication (GMTI) in wide-area surveillance radar system. MTI is the key task in wide-area surveillance radar system. Due to its great importance in future reconnaissance systems, it attracts great interest from scientists. In (Yan et al. in IEEE Geosci. Remote Sens. Lett., 10:617-621, 2013), the authors first introduced robust PCA to model the GMTI problem, and demonstrate promising simulation results to verify the advantages over other models. However, the robust PCA model can not fully describe the problem. As pointed out in (Yan et al. in IEEE Geosci. Remote Sens. Lett., 10:617-621, 2013), due to the special structure of the sparse matrix (which includes the moving target information), there will be difficulties for the exact extraction of moving targets. This motivates our work in this paper where we will detail the GMTI problem, explore the mathematical properties and discuss how to set up better models to solve the problem. We propose two models, the structured RPCA model and the row-modulus RPCA model, both of which will better fit the problem and take more use of the special structure of the sparse matrix. Simulation results confirm the improvement of the proposed models over the one in (Yan et al. in IEEE Geosci. Remote Sens. Lett., 10:617-621, 2013).

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Section: Ground Moving Targets Indication Problemmentioning

confidence: 99%

“…We will mainly focus on the mathematical models and the numerical algorithms. Before proposing our models, we first give a brief review of the RPCA model in [24].…”

Section: Gmti Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations