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How to quantify the uncertainty information consisted in the body of evidence (BOE) in the framework of Dempster-Shafer evidence theory is still an open issue. A few uncertainty measures have been proposed in Dempster-Shafer evidence theory framework, but these studies mainly focused on the mass function itself and the scale of the frame of discernment (FOD) is totally ignored. Since the existing...
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...
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...
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...
A practical method for full-dimension attitude determination based on the combination of two-antenna global positioning system (GPS) and strapdown inertial navigation system (SINS) is presented. In view of the fact that two-antenna GPS can only provide two attitude angles using carrier phase difference measurements, not all of the SINS attitude errors can be directly corrected by the integrated navigation...
This paper investigates the box-particle filter for multi-target tracking, and proposes a clustering based box-particle implementation of PHD filter. A subdivision step is added before the estimation of states. Each box is divided into several sub-box based on the estimated number of targets. An equivalent set of particles can be extracted from the set of subdivided boxes. Then, clustering technique...
Different estimators have different optimization criteria according to the concrete application considered. Most existing metrics on estimation performance are some averages of estimation errors, which usually give “big” or “small” results to show the “bad” or “good” performance of the evaluated estimators. However, these metrics are only appropriate for measuring minimum mean-square error (MMSE),...
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...
This paper presents a systematic approach to evaluate the tracking performance limits for different sensor modalities (lidar, radar and vision) and for combination of these sensors modalities. The Cramer-Rao lower bound (CRLB) is used to predict the tracking performance limits for state of the art sensors such as the Continental ARS408 radar, Velodyne HDL-64E lidar and a state of the art monocular/stereo...
Uncertainty measures in evidence theory can supply a new criterion to rate the quality of information carried by belie structures. It can also be used to measure the quantity of knowledge conveyed by belief structures. Following the work of Klir and Yuan, several uncertainty measures for belief structures have been developed. Among them, aggregate uncertainty AU, the total uncertainty TU and the ambiguity...
The estimation fusion problem and posterior Cramer-Rao bound (PCRB) are presented for multi-sensor nonlinear systems with uncertain observations. In order to effectively deal with the difficulties caused by uncertainty, a novel method is proposed by introducing 0–1 latent variables. It has two nice properties. Firstly, the derived estimation fusion method can take full advantage of the character of...
In this paper, a new entropy based uncertainty measure is introduced for evaluating the significance of subsets of attributes in incomplete decision tables. Some properties of rough conditional entropy are derived. And three attribute reduction algorithms are provided, including an algorithm using exhaustive search, an algorithm using heuristic search and an algorithm using probabilistic search for...
This paper addresses the design problem of robust weighted fusion white noise deconvolution estimators for a class of uncertain multisensor systems with missing measurements, uncertain noise variances and linearly correlated white noises. By introducing the fictitious noise, the considered system is converted into one with only uncertain noise variances. According to the minimax robust estimation...
The probability hypothesis density (PHD) filter is a promising filter for multi-target tracking which propagates the posterior intensity of the multi-target state. In this paper, a Gaussian mixture particle flow PHD (GMPF-PHD) filter is proposed which uses a bank of particles to represent the Gaussian components in the Gaussian mixture PHD (GM-PHD) filter. Then a particle flow is implemented to migrate...
This paper is concerned with the guaranteed cost robust weighted fusion prediction problem for discrete-time systems with multiplicative noises, colored measurements noises and uncertain noise variances. Applying the augmented state approach and a fictitious noise technique, the original system is converted into a system only with uncertain noise variances. Two classes of guaranteed cost robust weighted...
This paper presents the generalized optimal sub-pattern assignment (GOSPA) metric on the space of finite sets of targets. Compared to the well-established optimal sub-pattern assignment (OSPA) metric, GOSPA is not normalised by the cardinality of the largest set and it penalizes cardinality errors differently, which enables us to express it as an optimisation over assignments instead of permutations...
For distributed estimation, algorithms have to be specifically crafted to minimize communication between the sensor nodes. As an adjusted version of the regular Kalman filter, the distributed Kalman filter (DKF) allows for deriving optimal results while not requiring regular communication. To achieve this, the DKF requires that each node has full knowledge about the system model and measurement models...
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