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.
This paper aims at illustrating some applications of finite random set (FRS) theory to the design and analysis of wireless communication receivers, and at pointing out similarities and differences between this scenario and that pertaining to multi-target tracking, where the use of FRS has been traditionally advocated. Two case studies are considered, i.e., multiuser detection in a dynamic environment,...
Feature aided tracking can often yield improved tracking performance over the standard radar tracking with positional measurements alone. However, the complexity of the tracker may dramatically increase due to the inclusion of the target feature state. In this paper, we study the situation where the target feature is a constant or slowly varying parameter with respect to the target state and can be...
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In the present work, it is shown that a missed detection in one part of the field of view has a significant effect on the probability hypothesis density (PHD) arbitrarily far apart from the missed detection. In the case of zero...
In this paper the multitarget tracking (MTT) under a cluttered environment is considered. The proposed approach contains two steps: The first step is based on clustering algorithm of finite mixture models (FMM). The second step first obtain equivalent measurement (EQM) and then the EQM is used to estimate state of target. In fact, The first step is the parametric estimation of the FMM and the second...
In theory, a good joint particle filter allows to approximate the exact Bayesian filter solution arbitrarily well. This has motivated a strong and successful development of single target tracking particle filters. Nevertheless, for tracking multiple closely spaced maneuvering targets, there is evidence in literature which seems to contradict the theoretical expectation. The mystery of this apparent...
Within the area of target tracking particle filters are the subject of consistent research and continuous improvement. The purpose of this paper is to present a novel method of fusing the information from multiple particle filters tracking in a multisensor multitarget scenario. Data considered for fusion is under the form of labeled particle clouds, obtained in the simulation from two probability...
The effective fusion and tracking of multistatic active sonar contacts is challenging, due to high levels of false alarm clutter present on all sonar nodes. Exploiting the occurrence of high strength detections generated by the specular geometric condition, a cueing approach can be used to selectively extract further data stored locally on the individual sonar nodes for ingestion into the multi-sensor,...
In this paper we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake. When the detections are fed to a tracking system like the Probabilistic Data Association Filter, the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even more severe in multitarget environments where...
In this paper, a two-tier hierarchical architecture is proposed to address the multi-target tracking problem using a particle probability hypothesis density filtering algorithm. According to a proposed cluster scheduling method, the base station selects active clusters at each time step and determines their order for the sequential data fusion in the second level of hierarchy. Within each active cluster,...
The multitarget intensity filter is derived from a Bayesian first principles approach using a Poisson point process approximation at one step. The prior multitarget model is assumed to be a Poisson point process. The Bayes multitarget posterior probability density function is first defined on the Poisson event space, and then reformulated in terms of the intensity functions that characterize all Poisson...
A multisensor multitarget intensity filter is derived for N sensors. The multitarget process is assumed to be a Poisson point process, as are the sensor measurement sets. The sensor data are pooled, but sensor labels are retained. The likelihood function of the pooled data is obtained via the Poisson point process models. The Bayes information updated point process is not Poisson, but it is shown...
This paper presents Monte Carlo (MC) methods for multi-target tracking and data association. We focus on comparing different estimation methods based on joint and non-joint state particle filters (PF) and joint probabilistic data association (JPDA) techniques. A novel data association algorithm for PF, founded on a combination of PDA and nearest neighbour (NN) techniques, is also developed. In this...
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.