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Estimation of distribution algorithms (EDAs) are a special class of model-based evolutionary algorithms (EAs). To improve the performance of traditional EDAs, many remedies were suggested, which mainly focused on estimating a suitable probability distribution model with superior solutions. Different from existing research ideas, this paper tries to enhance EDA by exploiting the potential value of...
A two-stage method for off-grid coherent direction-of-arrival (DOA) estimation using atomic norm minimization based on the covariance matrix is proposed in this paper. In the first stage, by vectorizing the covariance matrix, a new off-grid model matched as a linear combination of two dimensional harmonic is presented, where the proposed denoising covariance matrix-based atomic norm minimization (DCMANM)...
An efficient sparsity-inducing method for direction-of-arrival (DOA) estimation is proposed to solve the challenging problem of computation cost and resolution. Element-space is firstly mapped to beamspace by using the beamforming matrix, and then the array covariance matrix is used for sparse representation. In doing so, the sparse Bayesian learning (SBL) technique is applied to enforce sparsity...
This paper presents a new covariance matrix estimate based on shrinkage method for breast tumor detection application. The parameters of shrinkage covariance matrix are obtained by solving a modified semi-definite programming (SDP) convex problem based on the mean-squared error (MSE) criterion. The reconstructed covariance matrix, using the determined shrinkage parameters, is then used to replace...
The small degrees of freedom (DOFs) and low SNR of incoming signal lead to direction of arrival (DOA) estimation performance degradation. A spacetime signal processing method for DOA estimation is presented. This method takes advantage of the spatial and temporal statistics of the incoming signal, expanding the dimensions of covariance matrix by increasing the time delay taps which can reduces the...
Over the decades, two major methods have been developed to deal with the underdetermined Direction-of-Arrival (DOA) estimation problem where there are more sources than sensors. One is based on the higher-order statistics of the received signals, which needs a plenty number of snapshots to guarantee the estimation accuracy and thus is computational complex. The other relies on the sparsity of sources,...
An algorithm for selecting nearest neighbors at a variable scale rather than a fixed search radius in point cloud neighbor search is proposed in this paper. We employ the concepts in differential geometry and divide the point cloud into different clusters according to their surface types. Not only the distance metric but also the clusters' surface type is taken into condition when we search the neighbors...
Calculation of the measurement error covariance matrix is an essential requirement in data reconciliation methods. Based on the practical measurement network, the measurement operator is proposed and is divided into three categories: basic operator, manual operator, compound operator. Then the estimation of measurement operator error variance-covariance is researched in mathematics statistics. Especially...
In order to meet the stereo vision navigation of the autonomous robot, stereo vision initial pose estimation based on unit quaternion is put forward and pose estimation accuracy is predicted and analysed. The research provides basis for further improving the pose estimation accuracy of robot. Gauss-distributed error covariance matrix propagation model was adopted to deduce the error propagation formulas...
Deployed high-latency anonymous communication systems conceal communication patterns using pool mixes as building blocks. These mixes are known to be vulnerable to Disclosure Attacks that uncover persistent relationships between users. In this paper we study the performance of the Least Squares Disclosure Attack (LSDA), an approach to disclosure rooted in Maximum Likelihood parameter estimation that...
Measuring risks in asset portfolios has been one of the central topics in the financial industry. Since the introduction of coherent risk measures, studies on risk measurement have flourished and measures beyond value-at-risk, such as expected shortfall, have been adopted by academics and practitioners. However, the complexity of financial products makes it very difficult and time consuming to perform...
In this paper, frequency-domain subspace-based algorithms are proposed to estimate discrete-time cross-power spectral density (cross-PSD) and auto-power spectral density (auto-PSD) matrices of vector auto-regressive moving-average and moving-average (ARMAMA) models from sampled values of the Welch cross-PSD and auto-PSD estimators on uniform grids of frequencies. The proposed algorithms are shown...
This paper presents a new approach of optimizing the performance of an Extended Kalman Filter (EKF) using particle swarm optimization (PSO) for speed estimation of an induction motor drive. The development of the EKF algorithm and selection of the filter covariance for the EKF based speed estimation are presented and discussed. The effectiveness of the optimization technique is verified through Matlab/Simulink...
Space debris trajectory estimation during atmospheric reentry is a complex problem. For such an object the ballistic coefficient, which characterizes the response of the object to aerodynamics braking, is usually a highly nonlinear function of time. This function may be unknown if no a priori information on the object type is available. It is therefore interesting to design a robust estimator that...
There is a growing need across GE businesses to push system operation performance closer to design entitlement through application of advanced controls and optimization. The performance requirements coupled with stringent operability boundaries necessitate systematic design of sensing networks for condition monitoring across applications such as power plants, aviation, etc. This paper addresses the...
A new method for globally optimal estimation in decentralized sensor-networks is applied to the decentralized control problem. The resulting approach is proven to be optimal when the nodes have access to all information in the network. More precisely, we utilize an algorithm for optimal distributed estimation in order to obtain local estimates whose combination yields the globally optimal estimate...
A guidance law of the missile interceptor with passive ranging system (e.g. image IR seeker) based on second-order sliding mode is proposed in this paper. The missile system equipped with the passive ranging system can provide good stealth ability during its course of tracking the trajectory of a maneuvering target, therefore enhancing the probability of successful interception. The passive ranging...
This paper presents a distributed moving horizon estimator (DMHE) based on dual decomposition. The DMHE is equivalent to a centralized Kalman filter and allows the distributed implementation of any centralized controller. This equivalence is achieved by formulating the estimation problem as a suitable convex optimization problem. The cost function is defined on a sliding window involving a finite...
In state estimation theory, two directions are mainly followed in order to model disturbances and errors. Either uncertainties are modeled as stochastic quantities or they are characterized by their membership to a set. Both approaches have distinct advantages and disadvantages making each one inherently better suited to model different sources of estimation uncertainty. This paper is dedicated to...
This paper presents an architecture for an embedded multi-antenna digital GNSS receiver. A two-stage adaptive beamformer for interference suppression and Line-of-Sight (LoS) signal amplification is presented and analyzed w.r.t. to an efficient implementation on embedded receivers. Jammer signals are mitigated at pre-correlation stage whereas the LoS signals are amplified at post-correlation stage...
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