The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Cost-reference particle filtering (CRPF) allows for tracking of nonlinear dynamic states without a prior knowledge of the probability distributions of the noises in the state-space representation of the system. In this paper we consider a setup where the system unknowns consist of linear and nonlinear states. We propose an efficient scheme for estimation of the states by combining CRPF with the recursive...
This paper focuses on particle filtering techniques for tracking a single target using bearings-only measurements. The problem is formulated as fusing information collected from two or more sensors in the presence of additive noise and multiplicative/additive biases. Assuming the biases are nuisance parameters and marginalizing them out from the estimation problem, we propose an algorithm that combines...
In this paper we introduce a simplified marginalized particle filtering method for dynamic systems with nonlinear and conditionally linear states with the marginal posteriors of the nonlinear states being multimodal. We propose a particle filter that employs Rao-Blackwellization by only one Kalman filter per mode for marginalizing the unknown linear states of the system. The validity of the method...
Sensors that measure received signal strength from moving targets may have bias that need to be accounted for if accurate tracking of targets in time is needed. When the bias is unknown, it has to be estimated together with the other unknowns of the system model. If the applied methodology for tracking is particle filtering and if the number of sensors is large, the performance of the used particle...
In this paper we address the problem of tracking of multiple targets in a wireless sensor network using particle filtering. This methodology approximates the probability distributions of the objects of interest by using random measures composed of particles and associated weights. An important challenge of the resulting algorithms is the need for very large number of particles when the dimensions...
In this paper we address the problem of tracking by using bearings-only data obtained by more than one sensor. We apply the generalized particle filtering methodology which does not require any probabilistic assumptions, including prior probabilities and noise distributions in the state and observation equations. As a result, the proposed approach is much more robust in performance than standard particle...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.