IMM/MHT solution to radar benchmark tracking problem

Blackman, Samuel S.; Dempster, Robert J.; Busch, Melanie; Popoli, Robert

doi:10.1109/7.766953

Cited by 118 publications

(110 citation statements)

References 21 publications

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“…This model was successfully used as a building block of a multiple-model algorithm for aircraft tracking application in an air defense system [77,78]. The above model uses (65) for the turn rate per se.…”

Section: B Ct Models With Unknown Turn Ratementioning

confidence: 99%

Survey of maneuvering targettracking . part I: dynamic models

Jilkov

2003

IEEE Trans. Aerosp. Electron. Syst.

1,800

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This is the first part of a comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. It surveys various mathematical models of target motion/dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among models and insight to the pros and cons of models are provided. Some material presented here has not appeared elsewhere. CONTENTS

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Section: B Ct Models With Unknown Turn Ratementioning

confidence: 99%

Survey of maneuvering targettracking . part I: dynamic models

Jilkov

2003

IEEE Trans. Aerosp. Electron. Syst.

1,800

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“…A later track-to-track fusion stage might be then carried out at centralized level. 3,5 Situation Assessment, which is typically a JDL Level-2 functionality, follows both stages and receives as input the fused set of track reports.…”

Section: Introductionmentioning

confidence: 99%

“…This set of measurements is filtered in order to provide the estimate of the target state vector over time, which is arranged into the corresponding track report. 3 From an architectural point of view, we assume that detection and tracking stages -i.e., Level-1 of the JDL Data Fusion model 4 -are implemented at sensor level. A later track-to-track fusion stage might be then carried out at centralized level.…”

Section: Introductionmentioning

confidence: 99%

Air route selection for improved air-to-ground situation assessment

2013

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Air-to-Ground Situation Assessment (SA) requires gathering information on the entities evolving on the ground (e.g., people, vehicles), and inferring the relations among them and their final intent. Several airborne sensor data might concur in the compilation of such high-level picture, which is aimed at identifying threats and promptly raising alarms. However, this process is intrinsically prone to errors: as the evidence - provided to the SA algorithm - originates from noisy sensor observations, the final outcome is also affected by uncertainty. High-level inferred variables, such as \Situation and \Threat Level, can be seen as error-affected, incomplete estimates of the ground truth, hence they are subject to improvement by properly steering the data acquisition process. In this paper we address the problem of optimizing the air route of the sensing platform, in order to reduce the number of false declarations or the delay in threat declaration in the SA stage. Specifically, we consider the problem of detecting a hostile behavior between pairs of ground targets by exploiting track data generated from airborne bearings-only measurements. We model the optimization problem with a sequential Markov Decision Process (MDP): the platform sequentially selects the best maneuver (i.e., its acceleration vector) in order to maximize the total reward over an infinite horizon. We define the potential contribution of an action as a function of the expected environmental conditions (e.g., obscurations of the line-of-sight) and the improvement of the localization accuracy achievable for the tracked objects. We demonstrate that following the optimized trajectory the delay in the declaration of a hostile behavior between two targets is reduced at the same erroneous declaration rate

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“…terrain information) [57], sensor management [58], and processing algorithms [59] from which to assess objects in the environment. Various techniques have incorporated grouping object movements [60], road information [61,62], and updating the object states based on environmental constraints [63].…”

Section: Information Fusion Decision Supportmentioning

confidence: 99%

Information fusion measures of effectiveness (MOE) for decision support

2011

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For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE) and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for decision support systems. In this paper, we overview some information fusion methods in decision support and address the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an information fusion system. Decision support implies that information methods augment user decision making as opposed to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain, and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy, confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality (time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.

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IMM/MHT solution to radar benchmark tracking problem

Cited by 118 publications

References 21 publications

Survey of maneuvering targettracking . part I: dynamic models

Survey of maneuvering targettracking . part I: dynamic models

Air route selection for improved air-to-ground situation assessment

Information fusion measures of effectiveness (MOE) for decision support

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