&lt;title&gt;Stochastic models and performance bounds for pose estimation using high-resolution radar data&lt;/title&gt;

O'Sullivan, Joseph A.; Jacobs, Steven P.; Kedia, Vikas

doi:10.1117/12.321860

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have studied in detail the use of both deterministic and conditionally Gaussian models for high range resolution (HRR) radar data. These results, discussed in [17,18], show that the deterministic model works very well when all possible HRR range profiles are described by the database. That is, in the situation where the database is complete, the deterministic model is ideal.…”

Section: A Deterministic and Stochastic Modelsmentioning

confidence: 60%

“…However, the deterministic model is not very robust. For example, as shown in [18], if a deterministic model is trained with a set of range profiles which does not intersect the set of range profiles used for testing, then significant performance degradation results. The conditionally Gaussian model, on the other hand, is robust in the sense that range profiles not used in the training data may still be modeled well.…”

Section: A Deterministic and Stochastic Modelsmentioning

confidence: 99%

See 1 more Smart Citation

SAR ATR performance using a conditionally Gaussian model

O'Sullivan

DeVore

Kedia

et al. 2001

IEEE Trans. Aerosp. Electron. Syst.

156

View full text Add to dashboard Cite

A family of conditionally Gaussian signal models for synthetic aperture radar (SAR) imagery is presented, extending a related class of models developed for high resolution radar range profiles. This signal model is robust with respect to the variations of the complex-valued radar signals due to the coherent combination of returns from scatterers as those scatterers move through relative distances on the order of a wavelength of the transmitted signal (target speckle). The target type and the relative orientations of the sensor, target, and ground plane parameterize the conditionally Gaussian model. Based upon this model, algorithms to jointly estimate both the target type and pose are developed. Performance results for both target pose estimation and target recognition are presented for publicly released data from the MSTAR program.

show abstract

Section: A Deterministic and Stochastic Modelsmentioning

confidence: 60%

Section: A Deterministic and Stochastic Modelsmentioning

confidence: 99%

SAR ATR performance using a conditionally Gaussian model

O'Sullivan

DeVore

Kedia

et al. 2001

IEEE Trans. Aerosp. Electron. Syst.

156

View full text Add to dashboard Cite

show abstract

<title>Dependence of ATR system performance on size of training sets</title>

O’Sullivan¹,

Schmid²

1999

Algorithms for Synthetic Aperture Radar Imagery VI

Self Cite

View full text Add to dashboard Cite

Automatic target recognition systems often have parameters that are estimated using training data. These parameters are then used in an implementation of the system as if they are the true parameters. The training sets consist of independent and identically distributed copies of the data given the target type. In an ideal case, we analyze the degradation in performance of such systems as a function of the size of the training sets. The training sets consist of independent and identically distributed copies of the data given the target type. The ideal performance is determined by the true parameters and is characterized in terms of a receiver operating characteristic (ROC) for a two-target problem. For a finite-sized training set the ROC curves fall below the ideal and converge to the ideal as the size of the training sets grows. Since in practical systems we have only a very limited amount of training data, it is desirable to quantify the degradation based on the size of the training sets. This will allow a prediction of the difference between performance obtained empirically and the optimal performance. Laplace approximations for the performance are explored. We study a Gaussian model in detail.

show abstract

<title>Stochastic models and performance bounds for pose estimation using high-resolution radar data</title>

Cited by 2 publications

References 0 publications

SAR ATR performance using a conditionally Gaussian model

SAR ATR performance using a conditionally Gaussian model

<title>Dependence of ATR system performance on size of training sets</title>

Contact Info

Product

Resources

About