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Probabilistic reasoning applied to dynamic spectrum sharing systems enables them to characterize situational uncertainties and determine acceptable spectrum access behaviors. Spectrum sharing systems may use sensing data to reduce situational uncertainty and improve spectrum sharing potential. Probabilistic reasoning approaches enable risk-constrained spectrum access, a concept in which spectrum sharing...
The Sequential Probability Ratio Test (SPRT) is a classical detector for problems with an unfixed sample size. Though it is optimal under some conditions, SPRT can be directly used only for a binary hypothesis with exactly known distributions. In this paper, sequential detection problem with an uncertain hypothesis distribution is considered, in which the uncertain distribution is formulated in a...
Acoustic frequency tracking of a harmonic signal with continuously varying frequency is considered. The Rao-Blackwellized point mass filter (RBPMF), previously proposed by the authors for mechanical vibration tracking, is applied to the problem. The RBPMF is compared with two periodogram-based methods, and the similarities and differences between them are explained. Both experimental and simulation...
Detection with multiple distributions is considered. Rather than formulating the problem with multiple hypotheses, we formulate the problem in a binary hypothesis testing framework by a multiple model approach. Three classes of the Multi-Model Detection (MMD) problems are considered: simplex, compound, and mixture. Three concepts of optimality are given for these three problems, including Uniformly...
The question addressed in this paper is “what” is to be evaluated by the Uncertainty Representation and Reasoning Evaluation Framework (URREF) ontology. We thus identify the elements composing uncertainty representation and reasoning approaches, which constitute various subjects being assessed. We distinguish between primary evaluation subjects (Uncertainty Representation and Reasoning components...
This paper is the companion-paper of another paper presented in FUSION'17 concerning bearings-only target motion analysis (BOTMA). In this one, bearing data are replaced by range data: we study observability in range-only target motion analysis (ROTMA). When the observer is in constant turn motion, the target's trajectory is observable, as in BOTMA. If the observer is in constant acceleration motion,...
Moving route prediction offers important benefits for many emerging location-aware applications such as target advertising and urban traffic management. A common approach to route prediction is to match similar trace recordings from a larger volume of historical trajectories, and return the targeted recorded path as desired answer. However, due to privacy concerns, incentive mechanism and other reasons,...
The problem is target motion analysis (TMA) in situations where the variance (standard deviation) of additive white Gaussian measurement noise is unknown and time-varying. In particular, the paper examines a somewhat surprising result from the theoretical analysis based on the Cramer-Rao bound, which suggests that the best-achievable (second-order) error in target state estimation is unaffected by...
Despite many proposed solutions, multi-object tracking remains a challenging problem in complex situations involving partial occlusions and non-uniform and abrupt illumination changes. Considering modular systems, the tracking performance strongly depends on the consistency of the different blocks relatively to error features. In this work, using the Belief Function framework, we take into account...
Belief fusion consists of taking into account multiple sources of belief about a domain of interest. This paper describes cumulative and averaging multi-source belief fusion in the formalism of subjective logic, which represent generalisations of binary-source belief fusion operators previously described. The advantage of this approach is that we can model and analyse belief fusion situations involving...
An inertial navigation network (INN) is composed of a high-precision master inertial navigation system (MINS) and multiple low-precision slave inertial navigation systems (SINS). The MINS located at the center of a carrier provides accurate global navigation parameters, while the SINSs at different locations of a carrier provide local navigation parameters. The outputs of the MINS are used to improve...
The study of alternative probabilistic transformation (PT) in DS theory has emerged recently as an interesting topic, especially in decision making applications. These recent studies have mainly focused on investigating various schemes for assigning both the mass of compound focal elements to each singleton in order to obtain Bayesian belief function for real-world decision making problems. In this...
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...
The wide-sense auto-regressive moving-average (ARMA) model is widely applied into varieties of fields. The unknown bounded parameter estimation of an ARMA model is an extremely vital research subject. Up to recent, most research is conducted with the known disturbing environment noise or the model of the known noise with the unknown variance. Actually the disturbing noise in the modern control system...
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...
This work documents our investigation of multiple target tracking filters in proximity sensor networks when the target power levels are not known. The challenge is that when the targets are close, it is hard to determine if the sensor reports are the results of a loud target or multiple quiet targets. Given the binary measurements:1 for detection of targets and 0 for nondetection of targets, the works...
A fault detection, identification, estimation and state estimation (FDIESE) problem involves joint decision and estimation (JDE). Decision contains detection and identification, while estimation is for fault severeness and system state. Both detection and identification are highly coupled with estimation and a fault is identified after detection. To solve this problem, an approach named nested joint...
Information fusion aims to exploit truthful knowledge from various sources in a reliable and accurate way. Fusion of information can be conducted at three abstraction levels including feature level, score level and decision level. The feature fusion approaches have the advantages of preserving effective discriminative structure underlying various features. In this paper, we propose an effective feature...
In the complex pattern classification problem, the fusion of multiple classification results produced by different attributes is able to efficiently improve the accuracy. Evidence theory is good at representing and combining the uncertain information, and it is employed here. Each attribute (set) can be considered as one source of evidence (information). In some applications, the observation of target...
A loosely coupled INS/GPS integrated navigation system is a nonlinear dynamic system. A particle filter (PF) is a particular tool for the nonlinear and non-Gaussian problems. However typical bootstrap particle filters (BPFs) cannot solve the mismatch between the importance function and the likelihood function very well so that they are invalid to some extent in the application of the INS/GPS integrated...
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