This paper studies the problem of tracking a ballistic object in
the reentry phase by processing radar measurements. A suitable
(highly nonlinear) model of target motion is developed and the
theoretical CRAMER—Rao lower bounds (CRLB) of estimation
error are derived. The estimation performance (error mean and
This paper studies the problem of tracking a ballistic object in
the reentry phase by processing radar measurements. A suitable
(highly nonlinear) model of target motion is developed and the
theoretical CRAMER—Rao lower bounds (CRLB) of estimation
error are derived. The estimation performance (error mean and
This paper studies the problem of tracking a ballistic object in
the reentry phase by processing radar measurements. A suitable
(highly nonlinear) model of target motion is developed and the
theoretical CRAMER—Rao lower bounds (CRLB) of estimation
error are derived. The estimation performance (error mean and
We consider the problem of target localization by a
network of passive sensors. When an unknown target emits an
acoustic or a radio signal, its position can be localized with multiple
sensors using the time difference of arrival (TDOA) information.
In this paper, we consider the maximum likelihood formulation
of this target localization problem and provide efficient convex
relaxations for this nonconvex optimization problem.We also propose
a formulation for robust target localization in the presence of
sensor location errors. Two CRAMER-Rao bounds are derived corresponding
to situations with and without sensor node location errors.
Simulation results confirm the efficiency and superior performance
of the convex relaxation approach as compared to the
existing least squares based approach when large sensor node location
errors are present.
