Continuously Optimizing Radar Placement with Model Predictive Path Integrals

2024-05-29

IEEE Transactions on Aerospace and Electronic Systems

AI Summary

This work addresses the real-time optimization of mobile radar deployment in dynamic environments. Methodologically, it introduces an information-driven trajectory planning framework that uniquely couples Model Predictive Path Integral (MPPI) control with a physically grounded radar range measurement model incorporating actual radar parameters. The framework integrates Cubature Kalman Filter-based state estimation, information-geometric modeling, and Monte Carlo uncertainty quantification to enable kinematically feasible, dynamically consistent, and obstacle-aware online trajectory optimization. Experimental evaluation across 500 simulations demonstrates that the proposed approach reduces average root-mean-square error (RMSE) by 38–74% compared to static deployment and simplified models, while shrinking the upper tail of the 90% highest-density interval by 33–79%. These improvements significantly enhance both localization accuracy and robustness for dynamic targets.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Optimizing radar placement for precise target localization
• Addressing oversimplified sensor models and dynamic constraints
• Improving target localization accuracy with advanced control methods

Innovation

• Uses Model Predictive Path Integral control
• Incorporates radar parameters and distance
• Outperforms stationary radars significantly