Continuously Optimizing Radar Placement with Model Predictive Path Integrals

TL;DR

This paper introduces a dynamic radar placement strategy using Model Predictive Path Integral control that significantly improves target localization accuracy by optimizing sensor positions in complex environments.

Contribution

It presents a novel approach combining detailed radar measurement models with MPPI control for continuous sensor placement optimization, outperforming static methods.

Findings

Achieves 38-74% reduction in RMSE for target localization.

Outperforms stationary radars and simplified models across all trials.

Effectively manages environmental obstacles and dynamic constraints.

Abstract

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.