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We proposed a novel blind polarization de-multiplexing technique for higher order modulation signals based on unscented Kalman filter (UKF) in 3D Stokes space. Simulation results show that UKF has better performance of de-multiplexing and convergence speed compared to extended Kalman filters (EKF).
This paper presents a kind of self-tuning filter in the colored noise environment when the noise variance is unknown. The main method of this filter is whitening the colored noise, and the correlation function is used to get the estimations of the variance of the noise. An example for the target tracking system is presented to design the self-tuning filter for the position and velocity, the simulating...
In this paper, distributed Nash equilibrium seeking for multi-agent games, particularly for games where the players' payoff functions are partially coupled, is investigated. To model the (partial, explicit) dependence of the players' payoff functions on the players' actions, an interference graph is introduced. Besides, the players are supposed to be equipped with a communication graph to achieve...
Traditional particle swarm optimization(PSO) will be failed because of falling into local optimum solutions and converging too slowly when being used to optimize planar array pattern. So a new method is presented to improve the traditional PSO convergence by means of efficient estimation of the optimum particle's initial values. A desired pattern is first constructed, and then the corresponding aperture...
This paper considers a central estimator design problem for stationary targets or moving targets based on weights assignment by a group of agents. A general form of estimator is first proposed. We then introduce the definitions of the converging central estimator. Then the central estimator is constructed by a new information assignment function and the information feedback function. Furthermore,...
This paper presents a novel nonlinear adaptive filter method, namely, Hammerstein adaptive filter with single feedback under minimum mean square error (HAF-SF-MMSE). A single delayed output is incorporated into the estimation of the current output based on minimum mean square error criterion, and therefore the history information of output is considered. Moreover, hybrid learning rates and adaptive...
This paper presents a preemptive job scheduler based on a 3-layer Backpropagation Neural Network (BPNN) and a greedy task alignment procedure. The BPNN estimates priority values of jobs based on the attributes of their subtasks and the given job selection criteria of the scheduler. The scheduler is formulated in such a way that, at each time interval, the most priority job will be selected from the...
In this paper, we attack the estimation problem in Kalman filtering when the measurements are contaminated by outliers. We employ the Laplace distribution to model the underlying non-Gaussian measurement process. The maximum posterior estimation is solved by the majorization minimization (MM) approach. This yields an MM based robust filter, where the intractable ℓ1 norm problem is converted into an...
This paper investigates stochastic containment control problems of multi-agent systems with measured noise in mean square sense. First, based on the Kalman-Bucy filtering theory, we design a proposed protocol for stochastic containment control problems based on the neighbors' information, and give a proof to check that Kalman-Bucy filtering estimation is an asymptotically unbiased estimation. Second,...
We study the problem of distributed state estimation over adaptive networks, where agents collaborate to estimate a common state parameter vector. If the sensing target area is too large or we want to improve the convergence speed of a large adaptive network, single-level diffusion algorithms do not have better performance, so we study the multi-level diffusion Kalman filter algorithm where a network...
Distributed algorithms are proposed to solve distributed optimization problems for a network of strongly connected agents in this paper. The proposed algorithms are based on a combination of a leader-following consensus protocol and the gradient descent method/primal-dual dynamics. In the leader-following consensus protocol, each agent acts as a virtual leader that provides its local measurements...
This paper deals with the real-time steady-state optimization of slow dynamic processes under plant-model mismatch. A novel scheme that iteratively adapts the process set-points (or operating parameters) to attain an economic optimum is proposed. Based on computing the next steady state from the process transient response to the current set-point change, this scheme can significantly reduce the time...
This paper proposes a method for estimating the surface of transparent objects based on light field convergency. The light field convergency represents the degree of convergence of the light field at each point. The proposed method utilizes local photo consistency, which is one of characteristics of the light field convergency. Around a boundary contour, a point that is visible from viewpoints with...
In this work an estimation based iterative learning control (ILC) approach for a vehicle suspension rig is presented. Slow convergence rates and non-monotonic learning transients of the typically cross-coupled multi-axial system require a high number of system measurements. This significantly damages the specimen before the actual endurance test. In combination with inverse model ILC the presented...
Motivated by applications in adaptive control, this article compares two recursive estimation algorithms for sparse estimation of linear dynamical (ARX) models. In most practical situations an accurate mathematical model estimation of a real system using the least number of parameters is highly desirable. The expectation of sparsity is imposed through minimization of an objective function that includes...
This paper proposes a diffusion proportionate affine projection sign algorithm for distributed estimation of sparse vector over network. The algorithm is derived by minimizing l1-norm intermediate error vector subject to a weighted constraint on the filter coefficients, where the positive definite weighting matrix is designed to accelerate the convergence of the nonzero coefficients for sparse vector...
This paper proposes new variable regularized (VR) partial update (PU) affine projection algorithms (APAs) for distributed estimation over adaptive networks. They extend the conventional diffuse PU-APAs (Diff-PU-APAs) by imposing a regularization parameter to mitigate possible impairments, such as modeling uncertainties and lacking of excitation, and to deal with sparse channel estimation problems...
We propose a direct estimation method for Rényi and f-divergence measures based on a new graph theoretical interpretation. Suppose that we are given two sample sets X and Y, respectively with N and M samples, where η := M/N is a constant value. Considering the k-nearest neighbor (k-NN) graph of Y in the joint data set (X, Y), we show that the average powered ratio of the number of X points to the...
We derive the mean squared error convergence rates of kernel density-based plug-in estimators of mutual information measures between two multidimensional random variables X and Y for two cases: 1) X and Y are both continuous; 2) X is continuous and Y is discrete. Using the derived rates, we propose an ensemble estimator of these information measures for the second case by taking a weighted sum of...
A new clutter-map constant false alarm rate (CFAR) detection algorithm for log-normal distribution is proposed. A combination of two recursive estimators (dual estimators) is considered for the estimation of the mean and standard deviation of the log-normal interference. Compared with the estimation method based on digital exponential filtering technique, the dual estimators converge faster while...
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