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The convolutive blind source separation (BSS) problem can be solved in frequency domain. To solve the permutation ambiguity problem in frequency domain, this paper presents an improved permutation alignment algorithm. According to features of radar signals, first the frequency domain is divided to some region segmentations. Then the permutation alignment is performed in each region independently....
For the multisensor linear discrete time-invariant stochastic system with unknown noise variances, the new measurement system is constructed by using matrix pseudo-inverse method, which can yield many groups of new measurement sequences by cooperating work. Furthermore, the statistics characteristics of the new measurement sequences are analyzed to determine whether the sensors are faulting or not...
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...
This paper studies the optimal-fuel control problem of spacecraft rendezvous with collision avoidance constraint. A nonlinear spacecraft rendezvous model is established. Based on this model, an optimal control problem with state constraint is formulated. An exact penalty function method is used to transform the constrained optimal control problem into a sequence of approximate unconstrained optimization...
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...
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