The Experts below are selected from a list of 21099 Experts worldwide ranked by ideXlab platform
Jonathon A Chambers - One of the best experts on this subject based on the ideXlab platform.
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robust adaptive beamforming for multiple input multiple output radar with spatial filtering techniques
Signal Processing, 2018Co-Authors: Junhui Qian, Wei Zhang, Yulong Huang, Jonathon A ChambersAbstract:Abstract In this paper, we consider robust adaptive beamformer design for multiple-input multiple-output (MIMO) radar systems. The desired transmit-receive steering Vector is estimated through maximizing the output power subject to constraints upon correlation coefficient and steering Vector Norm. The original nonconvex problem is reformulated as two reduced dimension semi-definite programming (SDP) problems. An iterative procedure is devised to tackle the two SDP problems, whose convergence is analytically proven. Based on the estimated desired signal, we are then able to obtain the interference covariance matrix via the matrix rank-constrained minimization method. Compared to other robust adaptive beamforming methods for MIMO radar, the proposed approach has the advantages of high efficiency and accuracy. Simulation results are presented to confirm the effectiveness and robustness of the proposed approach.
Huazhong Yang - One of the best experts on this subject based on the ideXlab platform.
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a blind audio watermarking algorithm by logarithmic quantization index modulation
Multimedia Tools and Applications, 2014Co-Authors: Xinkai Wang, Shuzheng Xu, Pengjun Wang, Peng Zhang, Huazhong YangAbstract:In this paper we present a blind audio watermarking algorithm based on the Vector Norm and the logarithmic quantization index modulation (LQIM) in the wavelet domain, integrating the robustness of the Vector Norm with the imperceptibility of the logarithmic quantization index modulation based on μ-Law (or mu-Law) companding. Firstly μ-Law companding is adopted to transform the Vector Norm of the segmented wavelet approximation components of the original audio signal. And then a binary image scrambled by the chaotic sequence as watermark is embedded in the transformed domain with a uniform quantization scheme. Experimental results demonstrate that even if the capacity of the proposed algorithm is high, up to 102.4 bps, this algorithm can still maintain a high quality of the audio signal, and achieve a better performance, such as imperceptibility, robustness and complexity in comparison with the uniform quantization based algorithms against common attacks. What's more, it can resist amplitude scaling attack effectively.
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a Norm space adaptive and blind audio watermarking algorithm by discrete wavelet transform
Signal Processing, 2013Co-Authors: Xinkai Wang, Pengjun Wang, Peng Zhang, Huazhong YangAbstract:In this paper, combining the robustness of Vector Norm with that of the approximation components after the discrete wavelet transform (DWT), a blind and adaptive audio watermarking algorithm is proposed. In order to improve the robustness and imperceptibility, a binary image encrypted by Arnold transform as watermark is embedded in the Vector Norm of the segmented approximation components, the count of which depends on the size of the watermark image, after DWT of the original audio signal through quantization index modulation (QIM) with an adaptive quantization step selection scheme. Moreover, a detailed method has been designed to search the suitable quantization step parameters. Experimental results indicate that even though the capacity of the proposed algorithm is high, up to 102.4bps, this algorithm is still able to maintain good quality of the audio signal and tolerate a wide class of common attacks such as additive white Gaussian noise (AWGN), Gaussian Low-pass filter, Kaiser Low-pass filter, resampling, requantizing, cutting, MP3 compression and echo.
Junhui Qian - One of the best experts on this subject based on the ideXlab platform.
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robust adaptive beamforming for multiple input multiple output radar with spatial filtering techniques
Signal Processing, 2018Co-Authors: Junhui Qian, Wei Zhang, Yulong Huang, Jonathon A ChambersAbstract:Abstract In this paper, we consider robust adaptive beamformer design for multiple-input multiple-output (MIMO) radar systems. The desired transmit-receive steering Vector is estimated through maximizing the output power subject to constraints upon correlation coefficient and steering Vector Norm. The original nonconvex problem is reformulated as two reduced dimension semi-definite programming (SDP) problems. An iterative procedure is devised to tackle the two SDP problems, whose convergence is analytically proven. Based on the estimated desired signal, we are then able to obtain the interference covariance matrix via the matrix rank-constrained minimization method. Compared to other robust adaptive beamforming methods for MIMO radar, the proposed approach has the advantages of high efficiency and accuracy. Simulation results are presented to confirm the effectiveness and robustness of the proposed approach.
H A Elmikati - One of the best experts on this subject based on the ideXlab platform.
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further study on robust adaptive beamforming with optimum diagonal loading
IEEE Transactions on Antennas and Propagation, 2006Co-Authors: Ayman Elnashar, Said Elnoubi, H A ElmikatiAbstract:Significant effort has gone into designing robust adaptive beamforming algorithms to improve robustness against uncertainties in array manifold. These uncertainties may be caused by uncertainty in direction-of-arrival (DOA), imperfect array calibration, near-far effect, mutual coupling, and other mismatch and modeling errors. A diagonal loading technique is obligatory to fulfil the uncertainty constraint where the diagonal loading level is amended to satisfy the constrained value. The major drawback of diagonal loading techniques is that it is not clear how to get the optimum value of diagonal loading level based on the recognized level of uncertainty constraint. In this paper, an alternative realization of the robust adaptive linearly constrained minimum variance beamforming with ellipsoidal uncertainty constraint on the steering Vector is developed. The diagonal loading technique is integrated into the adaptive update schemes by means of optimum variable loading technique which provides loading-on-demand mechanism rather than fixed, continuous or ad hoc loading. We additionally enrich the proposed robust adaptive beamformers by imposing a cooperative quadratic constraint on the weight Vector Norm to overcome noise enhancement at low SNR. Several numerical simulations with DOA mismatch, moving jamming, and mutual coupling are carried out to explore the performance of the proposed schemes and compare their performance with other traditional and robust beamformers
Xinkai Wang - One of the best experts on this subject based on the ideXlab platform.
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a blind audio watermarking algorithm by logarithmic quantization index modulation
Multimedia Tools and Applications, 2014Co-Authors: Xinkai Wang, Shuzheng Xu, Pengjun Wang, Peng Zhang, Huazhong YangAbstract:In this paper we present a blind audio watermarking algorithm based on the Vector Norm and the logarithmic quantization index modulation (LQIM) in the wavelet domain, integrating the robustness of the Vector Norm with the imperceptibility of the logarithmic quantization index modulation based on μ-Law (or mu-Law) companding. Firstly μ-Law companding is adopted to transform the Vector Norm of the segmented wavelet approximation components of the original audio signal. And then a binary image scrambled by the chaotic sequence as watermark is embedded in the transformed domain with a uniform quantization scheme. Experimental results demonstrate that even if the capacity of the proposed algorithm is high, up to 102.4 bps, this algorithm can still maintain a high quality of the audio signal, and achieve a better performance, such as imperceptibility, robustness and complexity in comparison with the uniform quantization based algorithms against common attacks. What's more, it can resist amplitude scaling attack effectively.
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a Norm space adaptive and blind audio watermarking algorithm by discrete wavelet transform
Signal Processing, 2013Co-Authors: Xinkai Wang, Pengjun Wang, Peng Zhang, Huazhong YangAbstract:In this paper, combining the robustness of Vector Norm with that of the approximation components after the discrete wavelet transform (DWT), a blind and adaptive audio watermarking algorithm is proposed. In order to improve the robustness and imperceptibility, a binary image encrypted by Arnold transform as watermark is embedded in the Vector Norm of the segmented approximation components, the count of which depends on the size of the watermark image, after DWT of the original audio signal through quantization index modulation (QIM) with an adaptive quantization step selection scheme. Moreover, a detailed method has been designed to search the suitable quantization step parameters. Experimental results indicate that even though the capacity of the proposed algorithm is high, up to 102.4bps, this algorithm is still able to maintain good quality of the audio signal and tolerate a wide class of common attacks such as additive white Gaussian noise (AWGN), Gaussian Low-pass filter, Kaiser Low-pass filter, resampling, requantizing, cutting, MP3 compression and echo.
