The Experts below are selected from a list of 89796 Experts worldwide ranked by ideXlab platform
Wen Hong - One of the best experts on this subject based on the ideXlab platform.
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Measurement Matrix optimization and mismatch problem compensation for dlsla 3 d sar cross track reconstruction
Sensors, 2016Co-Authors: Qian Bao, Chenglong Jiang, Yun Lin, Weixian Tan, Zhirui Wang, Wen HongAbstract:With a short linear array configured in the cross-track direction, downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain the 3-D image of an imaging scene. To improve the cross-track resolution, sparse recovery methods have been investigated in recent years. In the compressive sensing (CS) framework, the reconstruction performance depends on the property of Measurement Matrix. This paper concerns the technique to optimize the Measurement Matrix and deal with the mismatch problem of Measurement Matrix caused by the off-grid scatterers. In the model of cross-track reconstruction, the Measurement Matrix is mainly affected by the configuration of antenna phase centers (APC), thus, two mutual coherence based criteria are proposed to optimize the configuration of APCs. On the other hand, to compensate the mismatch problem of the Measurement Matrix, the sparse Bayesian inference based method is introduced into the cross-track reconstruction by jointly estimate the scatterers and the off-grid error. Experiments demonstrate the performance of the proposed APCs’ configuration schemes and the proposed cross-track reconstruction method.
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Measurement Matrix optimization schemes for dlsla 3 d sar cross track reconstruction based on mutual coherence criterions
Progress in Electromagnetic Research Symposium, 2016Co-Authors: Wen HongAbstract:Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain the 3-D image of the true scene, and compressive sensing (CS) provide the solution for cross-track super-resolution reconstruction. This paper concerns the issue of optimizing the Measurement Matrix, which affects the reconstruction performance. Two mutual coherence based criteria are proposed to optimize the Measurement Matrix, and the experiments verify the proposed algorithms.
H V Poor - One of the best experts on this subject based on the ideXlab platform.
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Measurement Matrix design for compressive sensing based mimo radar
IEEE Transactions on Signal Processing, 2011Co-Authors: Yao Yu, Athina P Petropulu, H V PoorAbstract:In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as a Measurement Matrix. The samples are subsequently forwarded to a fusion center, where an l1-optimization problem is formulated and solved for target information. CS-based MIMO radar exploits target sparsity in the angle-Doppler-range space and thus achieves the high localization performance of traditional MIMO radar but with significantly fewer Measurements. The Measurement Matrix affects the recovery performance. A random Gaussian Measurement Matrix, typically used in CS problems, does not necessarily result in the best possible detection performance for the basis Matrix corresponding to the MIMO radar scenario. This paper considers optimal Measurement Matrix design with the optimality criterion depending on the coherence of the sensing Matrix (CSM) and/or signal-to-interference ratio (SIR). Two approaches are proposed: the first one minimizes a linear combination of CSM and the inverse SIR, and the second one imposes a structure on the Measurement Matrix and determines the parameters involved so that the SIR is enhanced. Depending on the transmit waveforms, the second approach can significantly improve the SIR, while maintaining a CSM comparable to that of the Gaussian random Measurement Matrix (GRMM). Simulations indicate that the proposed Measurement matrices can improve detection accuracy as compared to a GRMM.
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Measurement Matrix Design for Compressive Sensing–Based MIMO Radar
IEEE Transactions on Signal Processing, 2011Co-Authors: Athina P Petropulu, H V PoorAbstract:In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as a Measurement Matrix. The samples are subsequently forwarded to a fusion center, where an l1-optimization problem is formulated and solved for target information. CS-based MIMO radar exploits target sparsity in the angle-Doppler-range space and thus achieves the high localization performance of traditional MIMO radar but with significantly fewer Measurements. The Measurement Matrix affects the recovery performance. A random Gaussian Measurement Matrix, typically used in CS problems, does not necessarily result in the best possible detection performance for the basis Matrix corresponding to the MIMO radar scenario. This paper considers optimal Measurement Matrix design with the optimality criterion depending on the coherence of the sensing Matrix (CSM) and/or signal-to-interference ratio (SIR). Two approaches are proposed: the first one minimizes a linear combination of CSM and the inverse SIR, and the second one imposes a structure on the Measurement Matrix and determines the parameters involved so that the SIR is enhanced. Depending on the transmit waveforms, the second approach can significantly improve the SIR, while maintaining a CSM comparable to that of the Gaussian random Measurement Matrix (GRMM). Simulations indicate that the proposed Measurement matrices can improve detection accuracy as compared to a GRMM.
Qian Bao - One of the best experts on this subject based on the ideXlab platform.
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Measurement Matrix optimization and mismatch problem compensation for dlsla 3 d sar cross track reconstruction
Sensors, 2016Co-Authors: Qian Bao, Chenglong Jiang, Yun Lin, Weixian Tan, Zhirui Wang, Wen HongAbstract:With a short linear array configured in the cross-track direction, downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain the 3-D image of an imaging scene. To improve the cross-track resolution, sparse recovery methods have been investigated in recent years. In the compressive sensing (CS) framework, the reconstruction performance depends on the property of Measurement Matrix. This paper concerns the technique to optimize the Measurement Matrix and deal with the mismatch problem of Measurement Matrix caused by the off-grid scatterers. In the model of cross-track reconstruction, the Measurement Matrix is mainly affected by the configuration of antenna phase centers (APC), thus, two mutual coherence based criteria are proposed to optimize the configuration of APCs. On the other hand, to compensate the mismatch problem of the Measurement Matrix, the sparse Bayesian inference based method is introduced into the cross-track reconstruction by jointly estimate the scatterers and the off-grid error. Experiments demonstrate the performance of the proposed APCs’ configuration schemes and the proposed cross-track reconstruction method.
Shen Jingshi - One of the best experts on this subject based on the ideXlab platform.
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An improved Hadamard Measurement Matrix based on Walsh code for compressive sensing
2013 9th International Conference on Information Communications & Signal Processing, 2013Co-Authors: Cai Zhuoran, Zhao Honglin, Jia Min, Wang Gang, Shen JingshiAbstract:Measurement Matrix design is a very significant part in the whole process of compressive sensing, the ratio of the signal compression and the receiver reconstruction accuracy of the original signal are all determined by it. An improved Hadamard Measurement Matrix based on Walsh code for compressive sensing is proposed in this paper. Simulation results demonstrate that the measure performance of the proposed Measurement Matrix is better than original Hadamard Measurement Matrix.
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ICICS - An improved Hadamard Measurement Matrix based on Walsh code for compressive sensing
2013 9th International Conference on Information Communications & Signal Processing, 2013Co-Authors: Cai Zhuoran, Jia Min, Wang Gang, Zhao Hong-lin, Shen JingshiAbstract:Measurement Matrix design is a very significant part in the whole process of compressive sensing, the ratio of the signal compression and the receiver reconstruction accuracy of the original signal are all determined by it. An improved Hadamard Measurement Matrix based on Walsh code for compressive sensing is proposed in this paper. Simulation results demonstrate that the measure performance of the proposed Measurement Matrix is better than original Hadamard Measurement Matrix.
Athina P Petropulu - One of the best experts on this subject based on the ideXlab platform.
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Measurement Matrix design for compressive sensing based mimo radar
IEEE Transactions on Signal Processing, 2011Co-Authors: Yao Yu, Athina P Petropulu, H V PoorAbstract:In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as a Measurement Matrix. The samples are subsequently forwarded to a fusion center, where an l1-optimization problem is formulated and solved for target information. CS-based MIMO radar exploits target sparsity in the angle-Doppler-range space and thus achieves the high localization performance of traditional MIMO radar but with significantly fewer Measurements. The Measurement Matrix affects the recovery performance. A random Gaussian Measurement Matrix, typically used in CS problems, does not necessarily result in the best possible detection performance for the basis Matrix corresponding to the MIMO radar scenario. This paper considers optimal Measurement Matrix design with the optimality criterion depending on the coherence of the sensing Matrix (CSM) and/or signal-to-interference ratio (SIR). Two approaches are proposed: the first one minimizes a linear combination of CSM and the inverse SIR, and the second one imposes a structure on the Measurement Matrix and determines the parameters involved so that the SIR is enhanced. Depending on the transmit waveforms, the second approach can significantly improve the SIR, while maintaining a CSM comparable to that of the Gaussian random Measurement Matrix (GRMM). Simulations indicate that the proposed Measurement matrices can improve detection accuracy as compared to a GRMM.
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Measurement Matrix Design for Compressive Sensing–Based MIMO Radar
IEEE Transactions on Signal Processing, 2011Co-Authors: Athina P Petropulu, H V PoorAbstract:In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as a Measurement Matrix. The samples are subsequently forwarded to a fusion center, where an l1-optimization problem is formulated and solved for target information. CS-based MIMO radar exploits target sparsity in the angle-Doppler-range space and thus achieves the high localization performance of traditional MIMO radar but with significantly fewer Measurements. The Measurement Matrix affects the recovery performance. A random Gaussian Measurement Matrix, typically used in CS problems, does not necessarily result in the best possible detection performance for the basis Matrix corresponding to the MIMO radar scenario. This paper considers optimal Measurement Matrix design with the optimality criterion depending on the coherence of the sensing Matrix (CSM) and/or signal-to-interference ratio (SIR). Two approaches are proposed: the first one minimizes a linear combination of CSM and the inverse SIR, and the second one imposes a structure on the Measurement Matrix and determines the parameters involved so that the SIR is enhanced. Depending on the transmit waveforms, the second approach can significantly improve the SIR, while maintaining a CSM comparable to that of the Gaussian random Measurement Matrix (GRMM). Simulations indicate that the proposed Measurement matrices can improve detection accuracy as compared to a GRMM.
