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In this article, the recursive filtering problem is investigated for state-saturated complex networks (CNs) subject to uncertain coupling strengths (UCSs) and deception attacks. The measurement signals transmitted via the communication network may suffer from deception attacks, which are governed by Bernoulli-distributed random variables. The purpose of the problem under consideration is to design a minimum-variance filter for CNs with deception attacks, state saturations, and UCSs such that upper bounds on the resulting error covariances are guaranteed. Then, the expected filter gains are acquired via minimizing the traces of such upper bounds, and sufficient conditions are established to ensure the exponential mean-square boundedness of the filtering errors. Finally, two simulation examples (including a practical application) are exploited to validate the effectiveness of our designed approach. [ABSTRACT FROM AUTHOR]

In this article, a new outlier-resistant recursive filtering problem (RF) is studied for a class of multisensor multirate networked systems under the weighted try-once-discard (WTOD) protocol. The sensors are sampled with a period that is different from the state updating period of the system. In order to lighten the communication burden and alleviate the network congestions, the WTOD protocol is implemented in the sensor-to-filter channel to schedule the order of the data transmission of the sensors. In the case of the measurement outliers, a saturation function is employed in the filter structure to constrain the innovations contaminated by the measurement outliers, thereby maintaining satisfactory filtering performance. By resorting to the solution to a matrix difference equation, an upper bound is first obtained on the covariance of the filtering error, and the gain matrix of the filter is then characterized to minimize the derived upper bound. Furthermore, the exponential boundedness of the filtering error dynamics is analyzed in the mean square sense. Finally, the usefulness of the proposed outlier-resistant RF scheme is verified by simulation examples. [ABSTRACT FROM AUTHOR]

Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy in hyperspectral imagery (HSI) classification. In this article, we propose a novel HSI feature extraction based on joint adaptive structure density (JASD), which can make full use of the texture feature of feature level and effectively utilize the spatial structure feature of pixel level. Specifically, the proposed JASD method considers two weak assumptions when exploiting the structure information contained in the HSI: 1) close pixels in spectral space have the same label and 2) all the pixels in a subregion belong to the same class. The JASD method consists of the following steps. First, a principal component analysis method is applied to the original high-dimensional HSI to obtain low-dimensional features in order to promote the separability of pixels between different classes and improve the execution efficiency of the proposed method. Then, the local density peak-assisted $k$ nearest neighbor idea is introduced into oversegmentation-based regions to define the structural spatial information of the HSI and overcome the aforementioned weak assumptions. Meanwhile, recursive filtering in the transform domain is applied to the above low-dimensional HSI to preserve global edge details. Next, the global edge details and the local spatial structure feature are fused based on the stacking method. Finally, the composite hyperspectral feature is fed into the supervised classifier to obtain the classification results. Experimental results on three widely used real HSI data sets demonstrated that the proposed JASD method is superior to several comparative classification methods in terms of classification accuracy and computational burden. [ABSTRACT FROM AUTHOR]

Superpixel segmentation is a powerful tool that can effectively utilize spectral and spatial information for hyperspectral image (HSI) classification. In this paper, we propose a new HSI classification method via superpixel and correlation coefficient (CC) representation (SCCR), where CC is a powerful metric to assess similarity among pixels. Considering the correlation among pixels within each superpixel, the superpixel region is viewed as the neighborhood of a test sample that can better exploit the spatial structure information of HSI. In the proposed method, recursive filtering is first used to extract features for hyperspectral data analysis. Then, based on the $k$ -nearest neighbors theory, the CC between each test sample and each class of training samples can be jointly calculated by the $k$ most relevant pixels in the neighborhood, which effectively reduces the effect of the mixed samples from different classes on the classification accuracy. Finally, the class label of each test sample is determined by which class presents the maximum CC. The experimental results demonstrate that the proposed SCCR improves classification performance compared to other well-known classification methods. [ABSTRACT FROM AUTHOR]