This paper deals with models of ballistic target (BT) motion during the boost phase for target tracking. Different
options to improve the accuracy of modeling are discussed and several enhanced models are proposed. They include
simple kinematic models of the so-called gravity turn (GT) target motion and more sophisticated models, accounting for
the BT flight dynamics during boost, as well. Tracking simulations are presented.
This paper proposes a multiple-model (MM) hypothesis testing approach for detection of unknown target maneuvers that may have several possible prior distributions. An MMmaneuver detector based on sequential hypothesis testing is developed. Simulation results that compare the performance of the proposed MM detector to that of traditional maneuver detectors are presented. They demonstrate that the new sequential MM detector outperforms traditional multiple hypothesis testing based detectors when the prior acceleration distributions are unknown.
In this paper a novel approach for detecting unknown target maneuver
using range rate information is proposed based on the generalized
Page's test with the estimated target acceleration magnitude. Due to
the high nonlinearity between the range rate measurement and the
target state, a measurement conversion technique is used to treat
range rate as a linear measurement in Cartesian coordinates so that
a standard Kalman filter can be applied. The detection performance
of the proposed algorithm is compared with that of existing maneuver
detectors over various target maneuver motions. In addition, a model
switching tracker based on the proposed maneuver detector is
compared with the state-of-the-art IMM estimator. The results
indicate the effectiveness of the maneuver detection scheme which
simplifies the tracker design. The tracking performance is also
evaluated using a steady state analysis.
This paper compares six different algorithms for target maneuver
detection in a number of typical maneuvering target tracking
scenarios. Measurement residual based chi-square test, input
estimate based chi-square test, input estimate based significance
test, generalized likelihood ratio, cumulative sum, and
marginalized likelihood ratio detectors are examined. Maneuver
onset detection times and ROC curves are presented and performance
measures are discussed through simulations. Further, the effect of
different window sizes on detection performance is evaluated.