A likelihood-based approach to joint target tracking and identification

O’Sullivan, Joseph A.; Jacobs, Steven P.; Miller, Michael I.; Snyder, Donald L.

doi:10.1109/acssc.1993.342519

Cited by 10 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Appropriate sampling schemes for the sphere are suggested in Ref. 36 (18) = -sJ2 + (t) -sy]2 + (t) -s]2 (19) …”

Section: Target Reflectancementioning

confidence: 99%

Tracking and recognition of airborne targets via commercial television and FM radio signals

Lanterman

1999

SPIE Proceedings

View full text Add to dashboard Cite

We formulate a Bayesian approach to the joint tracking and recognition of airborne targets via reflected commercial television and FM radio signals measured by an array of sensors. Such passive systems may remain covert, whereas traditional active systems must reveal their presence and location by their transmissions. Since the number of aircraft in the scene is not known a priori, and targets may enter and leave the scene at unknown times, the parameter space is a union of subspaces of varying dimension as well as varying target classes. Targets tracks are parameterized via both positions and orientations, with the orientations naturally represented as elements of the special orthogonal group SO(3). A prior on target tracks is constructed from Newtonian equations of motion. This prior results in a coupling between the position and orientation estimates, yielding a coupling between the tracking and recognition problems.A likelihood function is formulated which incorporates the sensor array geometry, the doppler shift associated with moving targets, the frequency and angle dependent complex reflectance of the particular target types, and the energy loss predicted by the radar equation. The likelihood and prior are combined to form a posterior distribution. Monte Carlo random sampling algorithms are proposed for exploring the posterior distribution; these methods avoid the need for linearizations associated with extended Kalman filtering or explicit representations of the density required by traditional nonlinear filters.

show abstract

“…Appropriate sampling schemes for the sphere are suggested in Ref. 36 (18) = -sJ2 + (t) -sy]2 + (t) -s]2 (19) …”

Section: Target Reflectancementioning

confidence: 99%

Tracking and recognition of airborne targets via commercial television and FM radio signals

Lanterman

1999

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…Following this work, Metron, applied the approach to shipboard HRR [9]. For the second method, Jacobs and O'Sullivan added tracking to their Bayesian HRR ATR algorithm and computed joint likelihood probabilities [10] with applications. Kastella has adapted the work of Jacobs and uses scatter-centering models for a nonlinear joint tracking and recognition algorithm based on joint probability density functions, but much of his work is simulated [11].…”

Section: Introductionmentioning

confidence: 99%

Set Theory Correlation Free Algorithm for HRRR Target Tracking

Blasch¹,

Liu

1999

View full text Add to dashboard Cite

One challenge of simultaneous tracking and identification of targets is the fusion of continuous and discrete information. Recently a few fusionists including Mahler [1] and Mori [2] are using a set theory approach for a unified data fusion theory which is a correlation free paradigm [3]. This paper uses the set theory approach as a basis for a method of fusing kinematic-continuous data and identification-discrete feature information. The set of features are high range resolution radar range-bin locations and amplitudes which are collected over a small aperture and a scrambled method is used to order a feature set. Once features are ordered, a recursive belief filter operates in feature space to combine track and identification measurements. The intersection of track and identification methods results in a simultaneous tracking and identification algorithm which accumulates evidence for belief in targets and rules out non-plausible targets Approved for public release; distribution is unlimited.

show abstract

Modelling 3D rigid-body object motion and structure estimation with HRR/GMTI measurements

Liu

2007

IET Control Theory Appl.

View full text Add to dashboard Cite

A likelihood-based approach to joint target tracking and identification

Cited by 10 publications

References 3 publications

Tracking and recognition of airborne targets via commercial television and FM radio signals

Tracking and recognition of airborne targets via commercial television and FM radio signals

Set Theory Correlation Free Algorithm for HRRR Target Tracking

Modelling 3D rigid-body object motion and structure estimation with HRR/GMTI measurements

Contact Info

Product

Resources

About