Most operating radar systems don′t have sufficient frequency bandwidth to produce high range resolution(HRR) profile of a target. But we can use stepped frequency waveform in a narrow band coherent radar to obtai...Most operating radar systems don′t have sufficient frequency bandwidth to produce high range resolution(HRR) profile of a target. But we can use stepped frequency waveform in a narrow band coherent radar to obtain the HRR profile of a target. For moving targets which are of great importance in practical radar usage, autofocusing,i.e. phase correction, is a necessary and critical step of the synthetic HRR processing. The purpose of autofocusing is to remove the radial motion effect of the target from radar echoes, and only reserve the stepped frequency effect which is the basis of synthetic HRR capability. We investigate two autofocusing approaches for synthetic HRR radars using stepped frequency waveform in this paper. The first is motion fitting method. This method depends on a certain parametric model, and is computationally expensive. Then we propose the iterative dominant scatterer method. It is robust, non parametric and simple in computation in comparison with the motion fitting method. Experimental results based on data acquired by using a metallised scale model B 52 in a microwave anechoic chamber reveal the validity and effectiveness of the method.展开更多
An important problem constraining the practical implementation of robust watermarking technology is the low robustness of existing algorithms against geometrical distortions. An adaptive blind watermarking scheme util...An important problem constraining the practical implementation of robust watermarking technology is the low robustness of existing algorithms against geometrical distortions. An adaptive blind watermarking scheme utilizing neural network for synchronization is proposed in this paper,which allows to recover watermark even if the image has been subjected to generalized geometrical transforms. Through classification of image’s brightness, texture and contrast sensitivity utilizing fuzzy clustering theory and human visual system, more robust watermark is adaptively embedded in DWT domain. In order to register rotation, scaling and translation parameters, feedforward neural network is utilized to learn image geometric pattern represented by six combined low order image moments. The distortion can be inverted after determining the affine distortion applied to the image and watermark can be extracted in a standard way without original image. It only needs a trained neural network. Experimental results demonstrate its advantages over previous method in terms of computational effectiveness and parameter estimation accuracy. It can embed more robust watermark under certain visual distance, and effectively resist JPEG compression, noise and geometric attacks.展开更多
文摘Most operating radar systems don′t have sufficient frequency bandwidth to produce high range resolution(HRR) profile of a target. But we can use stepped frequency waveform in a narrow band coherent radar to obtain the HRR profile of a target. For moving targets which are of great importance in practical radar usage, autofocusing,i.e. phase correction, is a necessary and critical step of the synthetic HRR processing. The purpose of autofocusing is to remove the radial motion effect of the target from radar echoes, and only reserve the stepped frequency effect which is the basis of synthetic HRR capability. We investigate two autofocusing approaches for synthetic HRR radars using stepped frequency waveform in this paper. The first is motion fitting method. This method depends on a certain parametric model, and is computationally expensive. Then we propose the iterative dominant scatterer method. It is robust, non parametric and simple in computation in comparison with the motion fitting method. Experimental results based on data acquired by using a metallised scale model B 52 in a microwave anechoic chamber reveal the validity and effectiveness of the method.
基金the National High Technology Research and Development Program of China(Grant No. 2001AA422420-02).
文摘An important problem constraining the practical implementation of robust watermarking technology is the low robustness of existing algorithms against geometrical distortions. An adaptive blind watermarking scheme utilizing neural network for synchronization is proposed in this paper,which allows to recover watermark even if the image has been subjected to generalized geometrical transforms. Through classification of image’s brightness, texture and contrast sensitivity utilizing fuzzy clustering theory and human visual system, more robust watermark is adaptively embedded in DWT domain. In order to register rotation, scaling and translation parameters, feedforward neural network is utilized to learn image geometric pattern represented by six combined low order image moments. The distortion can be inverted after determining the affine distortion applied to the image and watermark can be extracted in a standard way without original image. It only needs a trained neural network. Experimental results demonstrate its advantages over previous method in terms of computational effectiveness and parameter estimation accuracy. It can embed more robust watermark under certain visual distance, and effectively resist JPEG compression, noise and geometric attacks.
