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.

翻译：连续优化传感器部署对于军事与民用领域中实现精确目标定位至关重要。尽管信息论在优化传感器布局方面展现出潜力，但许多研究过度简化了传感器测量模型或忽略了移动传感器的动态约束。为应对这些挑战，我们采用包含雷达参数与雷达-目标距离的测距测量模型，并结合模型预测路径积分控制来处理复杂环境障碍与动态约束。通过比较目标状态的容积卡尔曼滤波器估计器的均方根误差，我们将所提方法与固定雷达或简化测距模型进行对比。此外，我们可视化雷达与目标随时间的几何构型演化，突出测量信息增益最高的区域，从而展示该方法的优势。所提策略在目标定位任务中优于固定雷达与简化测距模型，在500次蒙特卡洛试验的所有时间步上，实现了均方根误差均值降低38-74%，90%最高密度区间上尾部误差降低33-79%。代码将在论文录用后公开。