This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian mod...This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.展开更多
To monitor the stress state of prestressed reinforcement in large reinforcement prestressed structure, two sensing structures, namely the direct spiral-winding structure and sawtooth modulated structure, were designed...To monitor the stress state of prestressed reinforcement in large reinforcement prestressed structure, two sensing structures, namely the direct spiral-winding structure and sawtooth modulated structure, were designed based on the ordinary communication optical fiber. The sensing theories were analyzed, and the experimental studies were also carried out. The quasi-distributed sensing system based on optical time domain reflective technology was established. The detection wavelength and spatial resolution were analyzed, and the estimation formula of maximal number of sensing point was also given. The results show that the system can realize the quasi-distributed test of measurand with single fiber, which helps to simplify the in-out wires. Moreover it can take on the important task of long-term and continuous monitoring of prestress, which helps to realize the life cycle detection of prestress, and play an important role in the estimating of bridge health state.展开更多
This paper deals with subspace detection for rangespread target in non-homogeneous clutter with unknown covariance matrix where structured interference is presented in the received data.Through exploiting the persymme...This paper deals with subspace detection for rangespread target in non-homogeneous clutter with unknown covariance matrix where structured interference is presented in the received data.Through exploiting the persymmetry of the clutter covariance matrix,we propose two adaptive target detectors,which are referred to as persymmetric subspace Rao to suppress interference and persymmetric subspace Wald to suppress interference("PS-Rao-I"and"PS-Wald-I"),respectively.The persymmetry-based design brings in the advantage of easy implementation for small training sample support.The signal flow analysis of the two detectors shows that the PS-Rao-I rejects interference and integrates signals successively through separated matrix projection,while the PS-Wald-I jointly achieves interference elimination and signal combination via oblique projection.In addition,both detectors are shown to be constant false alarm rate detectors,significantly improving the detection performance with other competing detectors under the condition of limited training.展开更多
Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the hea...Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.展开更多
As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decompos...As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.展开更多
基金supported by the National Natural Science Foundation of China(62371382,62071346)the Science,Technology&Innovation Project of Xiong’an New Area(2022XAGG0181)the Special Funds for Creative Research(2022C61540)。
文摘This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.
文摘To monitor the stress state of prestressed reinforcement in large reinforcement prestressed structure, two sensing structures, namely the direct spiral-winding structure and sawtooth modulated structure, were designed based on the ordinary communication optical fiber. The sensing theories were analyzed, and the experimental studies were also carried out. The quasi-distributed sensing system based on optical time domain reflective technology was established. The detection wavelength and spatial resolution were analyzed, and the estimation formula of maximal number of sensing point was also given. The results show that the system can realize the quasi-distributed test of measurand with single fiber, which helps to simplify the in-out wires. Moreover it can take on the important task of long-term and continuous monitoring of prestress, which helps to realize the life cycle detection of prestress, and play an important role in the estimating of bridge health state.
基金supported by the National Natural Science Foundation of China(61901467,61701370)the Aeronautical Foundation of China(20180181001)+2 种基金China Postdoctoral Science Foundation(2019M653561,2020T130493)the Aerospace Science and Technology Fund(SAST2018-098)the National Defense Science and Technology Foundation of China(2019-JCJQ-JJ-060)。
文摘This paper deals with subspace detection for rangespread target in non-homogeneous clutter with unknown covariance matrix where structured interference is presented in the received data.Through exploiting the persymmetry of the clutter covariance matrix,we propose two adaptive target detectors,which are referred to as persymmetric subspace Rao to suppress interference and persymmetric subspace Wald to suppress interference("PS-Rao-I"and"PS-Wald-I"),respectively.The persymmetry-based design brings in the advantage of easy implementation for small training sample support.The signal flow analysis of the two detectors shows that the PS-Rao-I rejects interference and integrates signals successively through separated matrix projection,while the PS-Wald-I jointly achieves interference elimination and signal combination via oblique projection.In addition,both detectors are shown to be constant false alarm rate detectors,significantly improving the detection performance with other competing detectors under the condition of limited training.
文摘Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.
基金supported by the National Natural Science Foundation of China(62273354,61673387,61833016).
文摘As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.
