Vision Guidance Update: Synthetic Aperture Radar (SAR) Multiple Image Exploitation For Position And Velocity Determination

Kenton, Arthur C.; Wright, James A.; Nelander, J.C.

doi:10.1117/12.960522

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This observation matrix is obtained by computing a first order Taylor series approximation of the filter's estimate of the measurement equations. For the monopulse radar measurements presented in this paper, the filter's observation matrix is similar to those presented in 6,7,8,9,10,11,12,13 The components that make up the jr" filter's observation matrix are summarized in Table 2 where Ep and v are the position and velocity errors, respectively, for the jfilter. All components of H are computed based on the estimates from the jr" filter.…”

Section: Computation Of the Filter's Observation Matrixmentioning

confidence: 98%

<title>Monopulse radar tracking using an adaptive interacting multiple-model method with extended Kalman filters</title>

Layne

1998

SPIE Proceedings

View full text Add to dashboard Cite

In this paper we investigate an adaptive interacting multiple model (AIIVIM) tracker using the extended Kalman filter. This adaptive algorithm is based on the interacting multiple model (1MM) tracking technique with the addition of an adaptive acceleration model to track behavior that falls in between the fixed model dynamics. In previous research, we found that the adaptive model matches more closely the true system dynamics when the target kinematics lie in between the fixed models, thus improving the overall performance of the tracking system. We also showed that the AIMM outperforms other existing adaptive approaches while reducing computational complexity. In this paper, we further investigate these superior qualities of the AIMM by considering a more realistic radar-tracking scenario where monopulse radar range, azimuth, and elevation measurements are processed using extended Kalman filters in the AIMM.Here a more complex three-dimensional simulation is implemented instead of the simplified two dimensional problem considered in our previous research. Again, the results show that the AIMM outperforms the classical 1MM when the target is maneuvering.

show abstract

Section: Computation Of the Filter's Observation Matrixmentioning

confidence: 98%