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.
In real world multiple extended target tracking problems, the presence of merged measurements is a frequently occurring phenomenon, however, most existing tracking algorithms in the literature assume that each target generates independent measurements. When this measurement merging phenomenon occurs, it increases the computational complexity of the tracking algorithms. Recently, the conditional joint...
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...
The iterated-corrector PHD (IC-PHD) filter, which is the most commonly used multi-sensor PHD filter, is affected by the sensor order and the probability of detection. To address this problem, the product multi-sensor PHD (PM-PHD) filter, a modified version of the IC-PHD filter, is proposed. The update formulation of the PM-PHD filter consists of a likelihood function and a modified coefficient. Although...
A novel approach called box-particle cardinalized probability hypothesis density (BP-CPHD) filter for multi-target tracking is proposed in this paper. A box particle is a random sample that occupies a small and controllable rectangular region of nonzero volume in the target state space. Box-particle filter is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data...
Many filter algorithms based on the probability hypothesis density (PHD) filter have been proposed to solve the multi-target tracking (MTT) problem. Most of them are applied to single-sensor case. As a simple and feasible multi-sensor filter algorithm, the Iterated-PHD filter is influenced by the order of the sensor updates and the probability of detection. In this paper, an improved algorithm with...
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.