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精确的目标定位至关重要。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，我们采用了一种综合考虑雷达参数与雷达-目标距离的测距测量模型，并结合模型预测路径积分（MPPI）控制来处理复杂环境障碍与动态约束。我们通过目标状态的容积卡尔曼滤波器（CKF）估计器的均方根误差（RMSE），将所提方法与固定雷达或基于简化测距模型的方案进行比较。此外，我们可视化呈现雷达与目标随时间的几何构型演变，突出显示测量信息增益最高的区域，从而验证该方法的优势。所提策略在目标定位任务中显著优于固定雷达与简化测距模型，在全部时间步上进行的500次蒙特卡洛（MC）试验中，平均RMSE降低38-74%，90%最高密度区间（HDI）的上尾概率降低33-79%。代码将在论文录用后公开。