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New targets number estimation method under colored noise background

Journal of Systems Engineering and Electronics 2012年第6期23卷 831-837页

作者： Jing Wang Jianguo Huang Jing Han Zhenhua Xu College of Marine Engineering Northwestern Polytechnical University Xi'an 710072 E R. China

A multiple targets detection method based on spatial smoothing （MTDSS） is proposed to solve the problem of the source number estimation under the colored noise background. The forward and backward smoothing based on auxiliary vectors which are received data on some specific elements is computed. By the spatial smoothing with auxiliary vectors, the correlated signals are decorrelated, and the colored noise is partially alleviated. The correlation matrix formed from the cross correlations between subarray data and auxiliary vectors is computed. By exploring the second-order statistics property of the covariance matrix, a threshold based on Gerschgorin radii of the smoothing correlation matrix is set to estimate the number of sources. Simulations and experimental results validate that MTDSS has an effective performance under the condition of the colored noise background and coherent sources, and MTDSS is robust with the correlated factor of signals and noise.

关键词： colored noise auxiliary vector multiple targets detec-tion spatial smoothing Gerschgorin radii.

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A State Estimator of Stochastic Delayed Neural Networks

A State Estimator of Stochastic Delayed Neural Networks

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第24届中国控制与决策会议

作者： Chunxiao Zhang 1 , Yun Chen 1 , Junhong Wang 1 1. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

The problem of state estimation for stochastic Hopfield neural networks with time-varying delay is investi- gated in this paper. Based on an auxiliary vector and free-weighting matrix technique, a delay-dependent Luenberger- type state estimator, which ensures mean-square asymptotic stability of the resulting filtering error state system, is de- signed. In this paper, the model transformations and cross terms bounding techniques are avoided. A numerical example is proposed to show the validity of the method.

关键词： Stochastic neural networks time-varying delay auxiliary vector Lyapunov-Krasovskii functional

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