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.
The probability hypothesis density (PHD) filter is widely used to solve multi-target tracking (MTT) problems. Although the Sequential Monte Carlo (SMC) implementation provides a tractable solution for PHD filter to handle the highly nonlinear and non-Gaussian MTT scenario, the high computational cost caused by a large number of particles limits the applications that need to be performed in real-time...
Quasi-Monte Carlo (QMC)-based particle filters can obtain more accurate estimation than the general particle filters, with formidable computational complexity, however. Spatial-domain multiresolutional particle filters are more efficient by reducing the number of particles, but unevenly samples may cause estimation error. Aiming at these, we combine QMC numerical technique and multiresolutional methodology...
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.