A Nonlinear Tracker Using Attitude Measurements

Andrisani, Dominick; Kuhl, Frank P.; Gleason, Daniel

doi:10.1109/taes.1986.310718

Cited by 67 publications

(41 citation statements)

References 6 publications

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“…It is important to know the limits of each sensor and know when to make the call, such as for airborne targets. [1,13,15] Target type and kinematic state may be considered jointly for group tracking and for determining other tactical information such as who come from where (source) and head toward where (sink) using which route (line of communication). Among the list of possible couplings between the target tracking and target identification systems described above, we concentrate below on the filtering aspect in greater details.…”

Section: J Call For Better Imaging Sensorsmentioning

confidence: 99%

<title>Modeling intent for a target tracking and identification scenario</title>

Blasch

2004

SPIE Proceedings

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The tracking goal is to reduce positional uncertainty. There are many ways to reduce tracking uncertainty: including classification data, using trafficability maps, and employing behavior information. We seek to extend tracking and identification modeling by incorporating intent to update prediction velocity vectors. A hybrid state space approach is formulated to deal with continuous-valued kinematics and discrete-valued target type, pose (inherently continuous but quantized), and intent behavior. The coupled tracker design is illustrated within the context of using ground moving target indicator (GMTI) and high range-resolution (HRRR) measurements as well as digital terrain elevation data (DTED), road map, and estimated goal states. The resulting Intent Coupled Tracking and Identification (ICTI) system is expected to outperform separately designed systems particularly during target maneuvers and recovering from temporary data dropout.

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Section: J Call For Better Imaging Sensorsmentioning

confidence: 99%

<title>Modeling intent for a target tracking and identification scenario</title>

Blasch

2004

SPIE Proceedings

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“…Numerous investigators have shown the importance of attitude data in the tracking and prediction of aircraft trajectory [1,2,3,27,30,31]. This work builds upon the type of tracker proposed by Andrisani [1,2,3].…”

Section: Conclusion 15mentioning

confidence: 99%

“…This work builds upon the type of tracker proposed by Andrisani [1,2,3]. This approach exploits the relationship between vehicle attitude and acceleration.…”

Section: Conclusion 15mentioning

confidence: 99%

Aircraft Trajectory Tracking and Prediction

Cattani¹,

Eagle²,

Lin³

et al. 1992

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“…Specifically, an extended nonlinear kalman filter has been designed that uses r".dar position and velocity information and optical attitude measurements of the aircraft being tracked. Background material on the new tracker has been published in the paper "A Nonlinear Tracker Using Attitude Information," by Andrisani, Kuhl, and Gleason [10].…”

Section: E Tracking Of Maneuvering Targets Using Attitude Measurementsmentioning

confidence: 99%