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In this paper, under the situation of multiple interference regions, an optimal antenna placement problem for a distributed Multi-Input Multi-Output (MIMO) radar is studied. Considering multiple interference regions, we solve the antenna placement problem by utilizing antenna placement method based on Multi-Objective Particle Swarm Optimization (MOPSO). However, it is not clear when to stop the iteration...
In this paper, a joint revisit and dwell time management (JRDTM) strategy for single target tracking based on the predicted Bayesian Cramer-Rao lower bound (BCRLB) in phased array radar system is addressed. We achieve the time resources management by formulating and solving an optimization problem, which is to minimize the resource amount used for tracking with the tracking accuracy of the target...
In this paper, we will investigate a joint beam and dwell time allocation strategy for multiple targets tracking based on the phased array radar system. We achieve the resources allocation by formulating and solving an optimization problem, which is to minimize the total dwell time on all targets with the tracking accuracy of each target satisfying a pre-designed requirement. Since the Bayesian Cramer-Rao...
Multiple view data with different feature representations have widely arisen in various practical applications. Due to the information diversity, fusing multiview features is very valuable for classification purpose. In this paper, we propose a new multifeature fusion method called fractional-order discriminative multiview correlation projection (FDMCP), which is based on fractional-order scatter...
Feature fusion plays an important role in target recognition, especially when single sensor's recognition capability is limited under severe situations. In view of shortcomings of Multi-set Canonical Correlation Analysis (MCCA) and its supervised modified methods in using category information in fusion projection rule learning, a generalized discriminative learning version of MCCA, termed as GDMCCA,...
We consider the problem of choosing the best subset of sensors that results in a prescribed error probability Pe in Bayesian setting. Since minimizing the error probability is often difficult to evaluate and manipulate, conventional methods adopt Bhattacharyya distance instead of it. In fact, Chernoff distance is the best achievable exponent in the Bayesian error probability and it is more accurate...
This paper addresses the problem of joint detection and estimation fusion when sensor quantized data are correlated in the distributed system. The traditional methods to handle this joint problem tend to treat the detection and estimation tasks separately, which put more emphasis on the detection part but treat the estimation part sub-optimally. In this work, the joint detection and estimation fusion...
Support vector machine (SVM) is a popular machine learning method and has been widely applied in many real-world applications. Since SVM is sensitive to noises, fuzzy SVM (FSVM) has been proposed to relieve the over-fitting problem caused by noises through assigning a fuzzy membership to each sample. Then, different samples make different contributions to the learning of classification hyperplane...
Aiming at the radiation control problem for sensor scheduling, a sensor scheduling algorithm based on partially observable Markov decision process (POMDP) is proposed. The target model is set up in the three-dimensional space, and the tracking task requirement is given by fuzzy logic theory. Then the radiation risk model is formulated as a POMDP, and the sensor radiation risk is dynamic updated by...
This paper considers the sensor selection problem for target tracking in large-scale sensor networks. We propose a new sensor selection strategy based on dual-criterion optimization. Both the bias change detection and information gain maximization are considered as criteria in our proposed sensor selection strategy. This new approach extends the sensor selection problem from single criterion optimization...
In this paper, the problem of robust minimax testing of binary composite hypothesis is considered, while the actual probability densities are located in neighborhoods characterized by the Itakura-Saito divergence. And then the existence of a saddle value condition is proved under Sion's minimax theorem. Moreover, we derive the least favorable distributions and the robust decision rule involved four...
In this paper, passive source localization of time-difference-of-arrival (TDOA) is investigated using a swarm of UAVs. First, the measurement model with a parameter dependent variance is introduced. The Cramer-Rao low bound(CRLB) is calculated with parameter dependent of the incoming measurements. Then a method for optimizing UAVs trajectories based on CRLB is proposed. The Dryden model is applied...
In this paper, Multi-Task Linear Dependency Modeling is proposed to distinguish drug-related webpages that contain lots of images and text. Linear Dependency Modeling exploits semantic relations between images features and text features, and Multi-Task Learning takes advantage of metadata of webpages. Meaningful information of webpages can be made use of fully to improve classification accuracy. Experimental...
Single-image blind deblurring could be considered as an important preprocessing step in imaging information fusion. Its purpose is to simultaneously estimate blur kernel and latent sharp image from only one observed blurred image. Blind deblurring has been attracting increasing attention in the fields of image processing, computer vision, computational photography, etc. However, it is a typically...
Dynamic construction of optimal portfolio is investigated. Multiple assets are allocated and rebalanced periodically based on different principles. We develop several dynamic allocation strategies to maximize long-term portfolio value based on Kelly's approach related to mutual information. We show that the resulting asset allocation strategy outperforms the traditional approaches and produces an...
We propose a dynamic portfolio rebalancing approach within the mean-risk framework, in which the riskaversion coefficient is adjusted according to market trend information captured by a technical indicator. We employ Gini's Mean Difference as the risk measure and the moving average as the technical indicator. We conduct a thorough empirical evaluation with a rolling horizon approach using the S&P...
This paper proposes a new approach for constrained multiple model (MM) maximum a posteriori (MAP) estimation through the expectation-maximization (EM) method by using our previously developed constrained sequential list Viterbi algorithm (CSLVA). The approach is general and applicable for any type of constraints provided they are verifiable. Specific algorithms for implementation are designed, and...
This paper presents three iterative methods for orientation estimation. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF. The third method is based on nonlinear least squares (NLS) estimation of the...
This paper addresses the shrinkage estimation problem of high-dimensional covariance matrices with low sample size data. A class of structured target matrices that include banding, thresholding, diagonal and block diagonal matrices is proposed, and an optimal oracle shrinkage coefficient is derived. To approximate the oracle estimator, an iterative method is presented and proved to be convergent....
In this paper, sparsity-promoting sensor selection algorithms for target tracking with quantized data are developed. We formulate sensor selection as an optimization problem that aims to strike a balance between estimation accuracy and the number of selected sensors. To cope with sensor selection problems in large-scale wireless sensor networks (WSNs), we propose a fast centralized optimization algorithm...
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