  Continuously optimizing sensor placement is essential for precise target
localization in various military and civilian applications. While information
theory has shown promise in optimizing sensor placement, many studies
oversimplify sensor measurement models or neglect dynamic constraints of mobile
sensors. To address these challenges, we employ a range measurement model that
incorporates radar parameters and radar-target distance, coupled with Model
Predictive Path Integral (MPPI) control to manage complex environmental
obstacles and dynamic constraints. We compare the proposed approach against
stationary radars or simplified range measurement models based on the root mean
squared error (RMSE) of the Cubature Kalman Filter (CKF) estimator for the
targets' state. Additionally, we visualize the evolving geometry of radars and
targets over time, highlighting areas of highest measurement information gain,
demonstrating the strengths of the approach. The proposed strategy outperforms
stationary radars and simplified range measurement models in target
localization, achieving a 38-74% reduction in mean RMSE and a 33-79% reduction
in the upper tail of the 90% Highest Density Interval (HDI) over 500 Monte Carl
(MC) trials across all time steps.
  Code will be made publicly available upon acceptance.
