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For multi-model multisensor system with uncertain variance linearly correlated white noises, the problems of designing robust weighted fusion Kalman estimators (predictor, filter, smoother) are addressed. According to the minimax robust estimation principle, applying Lyapunov equation approach, a unified design approach to obtain the local and three weighted fusion robust Kalman estimators of the...
In many practical problems, measurement noise data are often skewed, heterogeneous, or containing outliers. The Gaussian or even Student-t measurement noise settings are often not fit to the request of system. A novel EKF filtering algorithm for nonlinear discrete state-space models with skewed Student-t measurement noise is presented. The algorithm makes use of the method of Variational Bayes to...
Many inverse problems in science and engineering are formulated as recovery of piecewise finite-dimensional continuous (PFC) signals. Although the higher-order total variation (HTV) is known to be particularly effective for the sparsity-aware recovery of piecewise polynomials, it remains unclear so far whether the HTV can be extended to other signal models. In this paper, we present a convex regularizer...
For highly nonlinear problems, the linear minimum mean-square error (LMMSE) estimation using a nonlinearly converted measurement can outperform the one using the original measurement. For a function space of measurement conversions, every function in the space can be represented as a linear combination of a basis of the space. Then the LMMSE estimator using a vector with its entries forming a basis...
For nonlinear estimation, the Gaussian sum filter (GSF) provides a flexible and effective framework. It approximates the posterior probability density function (pdf) by a Gaussian mixture in which each Gaussian component is obtained using a linear minimum mean squared error (LMMSE) estimator. However, for a highly nonlinear problem with large measurement noise, the estimation performance of the LMMSE...
We develop an Expectation-Maximization (EM) algorithm for the simultaneous tracking and shape estimation of a star-convex object based on multiple spatially distributed measurements. In order to formulate the problem within the EM framework, the unknown measurement sources on the object are modeled as hidden variables. As the measurement sources are continuous quantities, we develop a suitable discretization...
The Simultaneous Localization And Mapping (SLAM) estimation problem is a nonlinear problem, due to the nature of the range and bearing measurements. In latter years it has been demonstrated that if the nonlinearities from the attitude are handled by a separate nonlinear observer, the SLAM dynamics can be represented as a linear time varying (LTV) system, by introducing these nonlinearities and nonlinear...
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...
This paper studies and formulates the problem of distributed filtering with a diffusion strategy for state estimation of a dynamic system by using observations from sensors in a network. The sensor-nodes have estimation ability and work in a collaborative manner. The information transmission across the network abides by the diffusion strategy that each node communicates only with its neighbors. First,...
The set-membership information fusion problem is investigated for general multisensor nonlinear dynamic systems. Compared with linear dynamic systems and point estimation fusion in mean squared error sense, it is a more challenging nonconvex optimization problem. Usually, to solve this problem, people try to find an efficient or heuristic fusion algorithm. It is no doubt that an analytical fusion...
A bias-compensated normalized least mean absolute deviation (NLMAD) algorithm is developed for system identification under impulsive output measurement noise and noisy input environment, which takes the advantage of the NLMAD to resist impulsive output noises. Considering biased estimation caused by the noisy input, we employ an unbiasedness criterion to obtain a bias-compensated vector for NLMAD...
Within the complex driving environment, progress in autonomous vehicles is supported by advances in sensing and data fusion. Safe and robust autonomous driving can only be guaranteed provided that vehicles and infrastructure are fully aware of the driving scenario. This paper proposes a methodology for feature uncertainty prediction for sensor fusion by generating neural network surrogate models directly...
In this paper, a novel image moment-based model for extended object shape estimation and tracking is presented. A method to represent and estimate an elliptical shape using its image moments is first developed. The model of representing the shape of an object falls under the category of random hypersurface model (RHM) for extended object tracking. The moments are estimated using an unscented Kalman...
This paper focuses on addressing the data fusion problems in asynchronous sensor networks using distribute particle filter (DPF). Generally, the type of the local information communicated between sensors and the time synchronization of the local information are two major issues for DPF algorithms, which have significant influence on fusion accuracy and communication requirements. To address these...
The identification of a 1D heterogenous network with unmeasurable interconnections between neighboring systems is studied in this paper. For a large-scale networked system, it is usually computationally prohibitive to identify the global system in a centralized manner. To cope with this problem, the local identification of a network using local input-output data is considered in this paper, and a...
A Cubature Kalman Filter with noise estimator is proposed to solve the problem that the selection of the statistic property parameter is not reasonable, which leads to filtering algorithm declining in accuracy and even diverging, when the noise statistic property is unknown in the bearing only target tracking of Unmanned Underwater Vehicle. This algorithm can estimate the noise statistic property...
Noise estimation is crucial in many image processing algorithms such as image denoising. Conventionally, the noise is assumed as signal-independent additive white Gaussian process. However, for the real raw-data of imaging sensors, the present noise is better modeled as signal-dependent noise. In this work, we propose an efficient image sensor noise estimation method based on iterative re-weighted...
For an accurate and precise periodic scanning motion of a galvanometer scanner, this paper presents iterative learning control (ILC) that is designed and implemented in the frequency domain to compensate for system nonlinearities, such as static friction. For a case that system identification in advance is difficult due to the nonlinearities, the frequency-domain ILC itself incorporates and performs...
Signal adaptive multiple-clock-cycle, but also completely pipelined hardware implementation of the optimal (Wiener) time-frequency filter is proposed in this paper. The verification of the proposed design is provided, as well as the most implementation details and the extensive comparative analysis. All significant characteristics of the corresponding recently proposed signal adaptive filtering solution...
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...
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