Continuously Optimizing Radar Placement with Model Predictive Path Integrals
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918022509232128 |
|---|---|
| author | Potter, Michael Tang, Shuo Ghanem, Paul Stojanovic, Milica Closas, Pau Akcakaya, Murat Wright, Ben Necsoiu, Marius Erdogmus, Deniz Everett, Michael Imbiriba, Tales |
| author_facet | Potter, Michael Tang, Shuo Ghanem, Paul Stojanovic, Milica Closas, Pau Akcakaya, Murat Wright, Ben Necsoiu, Marius Erdogmus, Deniz Everett, Michael Imbiriba, Tales |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18999 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Continuously Optimizing Radar Placement with Model Predictive Path Integrals Potter, Michael Tang, Shuo Ghanem, Paul Stojanovic, Milica Closas, Pau Akcakaya, Murat Wright, Ben Necsoiu, Marius Erdogmus, Deniz Everett, Michael Imbiriba, Tales Applications Artificial Intelligence Robotics 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. |
| title | Continuously Optimizing Radar Placement with Model Predictive Path Integrals |
| topic | Applications Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2405.18999 |