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Dempster-Shafer evidence theory (DST) is a theoretical framework for uncertainty modeling and reasoning. The determination of basic belief assignment (BBA) is crucial in DST, however, there is no general theoretical method for BBA determination. In this paper, a method of generating BBA using fuzzy numbers is proposed. First, the training data are modeled as fuzzy numbers. Then, the dissimilarities...
In this paper, we report on a body state and ground profile estimator for a snake-like robot executing a rolling gait to travel from flat ground to a slope. With the help of the estimator, the snake-like robot can adaptively adjust the body shape and locomotion speed by changing the gait parameters for the purpose of tackling a steep slope. Specifically, we propose a repeating sequence of continuous...
Dempster-Shafer theory (DST) is an important theory for information fusion. However, in DST how to determinate the basic belief assignment (BBA) is still an open issue. The interval number based BBA determination method is simple and effective, where the features of different classes' samples are modeled using the interval numbers, i.e., an interval number model is constructed for each focal element...
Infrared and visible image fusion is an active area in digital image processing. Many methods in spatial or transform domains have been proposed, but there are still several complex challenges. In this paper, we introduce a three-scale image transformation, which possesses multi-scale, translation-invariance and spatial-localization characteristics that are very important for image fusion. The decomposition...
Advances in sensor systems have resulted in the availability of high resolution sensors, capable of generating massive amounts of data. For complex systems to run online, the primary focus is on computationally efficient filters for the estimation of latent states related to the data. In this paper a novel method for efficient state estimation with the unscented Kalman Filter is proposed. The focus...
This paper considers the robust filtering problem for a class of nonlinear discrete-time systems, and a conjugate unscented transform (CUT) based strong tracking H∞ filter is proposed. Firstly, an extended strong tracking H∞ filter is presented based on the fusion of the extended H∞ filter and strong tracking filter. By online estimating the time-varying noises, the fading factor in the strong tracking...
Dempster-Shafer theory of evidence is widely applied to uncertainty modelling and knowledge reasoning because of its advantages in dealing with uncertain information. But some conditions or requirements, such as exclusiveness hypothesis and completeness constraint, limit the development and application of that theory to a large extend. To overcome the shortcomings and enhance its capability of representing...
To embed ensemble techniques into belief decision trees for performance improvement, the bagging algorithm is explored. Simple belief decision trees based on entropy intervals extracted from evidential likelihood are constructed as the base classifiers, and a combination of individual trees promises to lead to a better classification accuracy. Requiring no extra querying cost, bagging belief decision...
A real-time multi-mode robust filtering method is proposed for the large thrust and slow spin characteristics of the rocket above the flight stage. The rocket upper stage in the sliding section of the existence of slow spin characteristics, which will lead to ground measurement equipment tracking instability, poor measurement data quality. In order to solve this problem, the robustness theory and...
In this paper, we consider an adaptive node and power simultaneous scheduling (ANPSS) strategy for target tracking in distributed multiple radar systems. For all of the available nodes, with full resources allocation, minimizing estimation mean-square error (MSE) may exceed the predetermined system tracking performance goal and cause unnecessary resources consumption. Therefore, tracking performance...
This paper investigates the passive localization of a mobile source based on time difference of arrival (TDOA) measurements when the sensor positions suffer from random uncertainties. In the formulation of the dynamic system, the nonlinear measurement function contains random parameters, so the classical high-degree cubature Kalman filtering (CKF) method is unrealizable. We develop an augmented high-degree...
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...
Direction-of-arrival (DOA) estimation and tracking of signals using passive sensor arrays is a classic problem that becomes challenging when the number of sources varies over time and the signal-to-noise ratio is low. In this paper, we pose this problem as minimum mean OSPA (MMOSPA) estimation, which minimizes the the optimal sub-pattern assignment (OSPA) metric of the posterior random finite set...
This paper considers the state estimation of Markovian jump linear systems with random parameters and estimate feedback. The state estimate at the previous epoch is introduced into the dynamical model to depict some phenomena that the system evolvement may depend on the most recent estimate. Then, the linear minimum mean square error estimator is derived for the considered system. A filtering framework...
Hough voting based methods for object detection work by means of allowing local image patches to vote for the center of the object according to the trained visual words. They are effective for object with small local varieties, but incapable of solving multi-view detection problem. The traditional way is training visual words for each subcategory that has similar view. However, limited training data...
Situation information and sensor information are differentiated and a method for computing the situation information expected value (SIEV) is presented for use in Information Based Sensor Management (IBSM). Nine case pairs are evaluated in which the sensor capabilities vary among poor, average, and good sensors, and the goal lattice values vary among attack, defend, and stealth modes showing that...
This paper establishes a risk assessment index system for oil and gas resource countries, with 46 screened indicators across 5 dimensions, namely political risk, economic risk, investment risk, oil and gas resources risk, and the Chinese factor. Furthermore, by using the principal component analysis method, the 46 risk indicators were narrowed down to 16 principal components. Subsequently, a support...
In this paper, we consider a scenario where sensors are deployed over a large geographical area for tracking a target with circular nonlinear constraints on its motion dynamics. The sensor state estimates are sent over long-haul networks to a remote fusion center for fusion. We are interested in different ways to incorporate the constraints into the estimation and fusion process in the presence of...
Tracking hypersonic glide reentry vehicles (HGRVs) is considered in the paper. Firstly, justified by an analysis of dynamic models of HGRVs, we proposed a more accurate motion model with less computation burden. Secondly, fixed-interval Gaussian mixture approximation smoother for non-linear Markov jump systems (NLMJSs) is presented in the paper. The Gaussian mixture filter can effectively approximate...
Periocular characteristics has gained substantial importance in recent times to supplement the performance of facial biometrics or as a stand-alone characteristics. While most of the current biometric systems for authentication or surveillance operate either in NIR spectrum or visible spectrum, the ocular information can be well utilized if a comparison of images from different spectra has to be conducted...
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