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Tracking And Data Association Bar Shalom Pdf

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In today battlefield multi-sensors installed on naval ship are acquiring too much information. Information is used through naval combat system to improve reaction capability to threat more quickly and precise.

This paper is to survey and put in perspective the working methods of multi-target tracking in clutter. This paper includes theories and practices for data association and related filter structures and is motivated by increasing interest in the area of target tracking, security, surveillance, and multi-sensor data fusion. It is hoped that it will be useful in view of taking into consideration a full understanding of existing techniques before using them in practice.

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Multisensor Fusion pp Cite as. Multitarget tracking MTT here after deals with state estimation of an unknown number of moving targets. Clutter is generally considered as a model describing false alarms. Its spatio-temporal statistical properties are quite different from target ones which render possible the separation of target tracks on the one hand and clutter model on the second. To perform multitarget tracking the observer has at its disposal a huge amount of data, possibly collected on multiple sensors [2]. Elementary measurements are receiver outputs; e. But the main difficulty comes from the assignment of a given measurement to a target model.

Tracking in a cluttered environment with probabilistic data association

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Probabilistic data association techniques for target tracking in clutter Abstract: In tracking targets with less-than-unity probability of detection in the presence of false alarms FAs , data association-deciding which of the received multiple measurements to use to update each track-is crucial. Most algorithms that make a hard decision on the origin of the true measurement begin to fail as the FA rate increases or with low observable low probability of target detection maneuvering targets.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Bar-Shalom and E. Bar-Shalom , E. Tse Published Mathematics.

Data Association and Multitarget Tracking

The multiple hypothesis tracker MHT and multiple model MM algorithm are two well-known methods dealing with these two problems, respectively. Different ways of combinations of MHT and MM for this purpose were available in previous studies, but in heuristic manners. The GMR is adopted because it provides a theoretically appealing way to reduce the exponentially increasing numbers of measurement association possibilities and target model trajectories.

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Data Association and Multitarget Tracking

Performance indexes obtained in idealized simulated scenarios are the primary source of data for evaluating different target tracking algorithms in most researches presented in the literature. Despite the convenience of simulation, ultimate evaluation of a tracking algorithm must be made in real scenarios. Unfortunately, real radar measurements as well as accurate aircraft position, necessary for calculating tracking errors, are not easily available. In this paper, we present an evaluation of the well-known Interacting Multiple-Model with Probabilistic Data Association Filtering algorithm using data obtained from a flight inspection of a Brazilian Air Force ground-based long-range surveillance radar. The presented results show that, in this scenario the Interacting Multiple-Model with Probabilistic Data Association Filtering algorithm performance using real data is worse compared to simulation. Statistical properties of the real radar measurements are also investigated, and some evidence is found that embedded noise is not well modeled as perfectly white.

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Probabilistic data association filter

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[email protected],). Y. Bar-Shalom is with the Information, Communication and Decision. Systems Group, Electrical and Computer.

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