  The use of autonomous vehicles for target localization in modern applications
has emphasized their superior efficiency, improved safety, and cost advantages
over human-operated methods. For localization tasks, autonomous vehicles can be
used to increase efficiency and ensure that the target is localized as quickly
and precisely as possible. However, devising a motion planning scheme to
achieve these objectives in a computationally efficient manner suitable for
real-time implementation is not straightforward. In this paper, we introduce a
motion planning solution for enhanced target localization, leveraging Bernstein
polynomial basis functions to approximate the probability distribution of the
target's trajectory. This allows us to derive estimation performance criteria
which are used by the motion planner to enhance the estimator efficacy. To
conclude, we present simulation results that validate the effectiveness of the
suggested algorithm.
