EventsEuropean Navigation Conference 2024
Published
This submission belongs to the session Topic 2. Multi-Sensor and Autonomous Navigation of the event European Navigation Conference 2024
Published date
12 Nov, 2024
Academic Editor
author-avatarRuneeta Rai
Citation
Tomas Vaispacher, Radek Baranek, Pavol Malinak, Vibhor Bageshwar, Daniel Bertrand, Radar Altimeter Inertial Vertical Loop - Multisensor Estimation Of Vertical Parameters for Autonomous Vertical Landing, in Proceedings of European Navigation Conference 2024, Noordwijk, Zuid/Holland, 22 May–24 May 2024, MDPI: Basel, Switzerland
Share
Email
Facebook
Twitter
LinkedIn

Radar Altimeter Inertial Vertical Loop - Multisensor Estimation Of Vertical Parameters for Autonomous Vertical Landing

Pavol Malinak 1
Daniel Bertrand 2
1. Honeywell International, Czech Republic
2. Honeywell International, USA
Abstract

Design, key functionalities, and performance requirements placed on modern aircraft navigation systems must adhere to the needs imposed by the progressively growing UAS/UAM and eVTOL markets. Especially for the terminal phases of flight (autonomous landing) and high-accuracy applications in urban and airport areas, performance levels required for safe operations can be even more strict than those for the today’s commercial aircraft.

In the paper, design, implementation, and real-time validation of Honeywell’s new Kalman filter-based Radar Altimeter Inertial Vertical Loop (RIVL) prototype is addressed. The system aims to provide high accuracy and integrity estimates of vertical parameters (Altitude above ground and vertical velocity). Inspired by the legacy BIVL technology, the prototype benefits from the dedicated Kalman filter and Honeywell’s proprietary method to address issues related to unknown terrain.

In Kalman filter, vertical acceleration estimate provided by AHRS (based on inertial sensor (IMU) measurements) and measurements from the radar altimeter aiding are fused. The part of the system is patent pending technology addressing issue of unknow terrain profile which provides required stability of the system output together with required accuracy in the final landing phase.

Once tested in simulation environment (proof-of-concept), the RIVL algorithm was ported to a rapid prototyping platform. Subsequently, data collection has been performed via both crane-test and flight-test onboard the CS-23 category aircraft (representative flight environment). Experimental results (in terms of accuracy) from both data collection phases will be included in the paper as well.

Our preliminary results indicate that the RIVL prototype provides reliable estimates of aircraft’s vertical height (above the terrain) and vertical velocity at required performance levels mandatory for UAS/UAM/eVTOL high-accuracy operations in urban and airport areas, including autonomous landing.

Keywords
radar altimeter
Kalman filter
altitude/height above ground
vertical velocity
estimation
UAM
UAS
terrain detection
Optimised Signal Selection Algorithm for Acquisition and Re-acquisition in Multi-Constellation, Multi-Frequency GNSS receivers
Terrain Based Parameter Optimization for Zero-Velocity Update Inertial Based Navigation Solutions