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In order to obtain more accurate state estimation from multisensor system, the state estimation with two-level fusion structure is presented. By means of measurement fusion algorithm, the local first-level fusion centers can obtain the globally optimal fused measurement information, and then the local state estimation can be got by classical Kalman filtering. At the second-level fusion center, the...
For the multisensor time-invariant uncertain system with uncertainties of both parameters and noise variances, by introducing a fictitious white noise to compensate the uncertain parameters, the uncertain system can be converted into the system with known parameters and uncertain noise variances. Using the minimax robust estimation principle, and weighted least squares method, a robust weighted measurement...
For the linear discrete time multisensor system with uncertain model parameters and noise variances, the centralized fusion robust steady-state Kalman filter is presented by a new approach of compensating the parameter uncertainties by a fictitious noise. Based on the minimax robust estimation principle, a robust centralized fusion Kalman filter is presented based on the worst-case conservative systems...
Text detection and recognition in natural scene images plays an important role in content analysis of images. In this paper, based on the characteristics of scene text, we propose a robust text detection and recognition method using Maximally Stable Extremal Regions (MSER) and Support Vector Machine (SVM). Different from the end to end text recognition, we split the recognition problem into detection...
In this work, we develop a minimum mean square error (MMSE) estimator for the underdetermined systems when the signal of interest is sparse. To address the uncertainty issue introduced in the measurement system, robust approaches are developed based on stochastic and worst case optimization techniques under the minimax framework. To solve the optimization problem, different constraints on the unknown...
Pedestrian detection and recognition has become the basic research in various social fields. Convolutional neural networks have excellent learning ability and can recognize various patterns with robustness to some extent distortions and transformations. Yet, they need much more intermediate hidden units and cannot learning from unlabeled samples. In this paper, we purpose a latent training model based...
This paper mainly considered the Satellite components sensor location method considering time delay of the system information transmission under diagnostic criteria. A method of transferring the dynamic signed directed graph (SDG) to the static SDG was proposed. After that, technique on sensor location was researched using the static SDG. Means of defining the root nodes that represented the diagnostic...
Target tracking is one of the most important applications for wireless sensor networks (WSNs). It is usually assumed that the knowledge of the sensor nodes' position is known precisely. However, practically nodes are randomly deployed without prior knowledge about their own positions. In this situation, simultaneous localization and tracking (SLAT) is necessary and is receiving more and more research...
In this paper, the problem of designing robust steady-state Kalman filter is considered for linear discrete-time system with uncertain model parameters and noise variances. By the new approach of compensating the parameter uncertainties by a fictitious noise, the system model is converted into that with uncertain noise variances only. Using the minimax robust estimation principle, based on the worst-case...
This paper proposes a fast algorithm for multiple targets tracking in complex environment of industrial workshop, which integrates the background modeling and the motion information. First, the probability density image is calculated based on histogram of color from each target object. Second, these probability density images are filtered according to background image obtained from previous background...
Robust pole assignment problem for linear systems with uncertainty is studied in this paper. The proposed gradient flow optimization algorithm is used to solve the Sylvester equations, in order that the close-loop control systems have the desired robust poles, namely, the uniformly asymptotically stable performance. The feedback gain matrix of the synthesized system can be derived from the gradient...
This paper is concerned with the problem of robust non-fragile fault-tolerant H∞ control for a type of uncertain linear systems with state-delay. We design a fault-tolerant state feedback controller with parameter perturbation such that the fault closed-loop system is asymptotically stable and satisfies the H∞ performance index. Based on Lyapunov function method the sufficient condition of the problem...
This paper studies an enhanced robust kernel least mean square (KLMS) adaptive filtering algorithm for nonlinear acoustic echo cancellation (NLAEC) in impulsive noise environment. Robust KLMS algorithm based on M-estimate theory shows robustness to simulated, Contaminated Gaussian (CG) impulsive noise. However, it fails to combat real-world impulsive noise which normally consists of a few consecutive...
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