# Continuously Optimizing Radar Placement with Model Predictive Path Integrals

arXiv: https://arxiv.org/abs/2405.18999 · HTML: https://mlsanity.com/paper/2405.18999/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 65 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 6 of 6)

- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association](https://mlsanity.com/paper/2202.12100/) (2022)
- [Derivation of a Constant Velocity Motion Model for Visual Tracking](https://mlsanity.com/paper/2005.00844/) (2020)
- [Model Predictive Optimized Path Integral Strategies](https://mlsanity.com/paper/2203.16633/) (2022)
- [Non-Monotone Energy-Aware Information Gathering for Heterogeneous Robot Teams](https://mlsanity.com/paper/2101.11093/) (2021)
- [Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks](https://mlsanity.com/paper/2209.02890/) (2022)

## Built on by (most cited first, 1 of 1)

- [Recursive Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2504.13241/) (2025)

## Abstract (arXiv)

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.