ICUAS'22 Paper Abstract

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Paper WeA4.5

Park, Junwoo (Korea Advanced Institute of Science and Technology), Bang, Hyochoong (Korea Advanced Institute of Science and Technology)

Evenly Weighted Particle Filter for Terrain-Referenced Navigation Using Gaussian Mixture Proposal Distribution

Scheduled for presentation during the Regular Session "UAS Perception" (WeA4), Wednesday, June 22, 2022, 11:50−12:10, Divona-2

2022 International Conference on Unmanned Aircraft Systems (ICUAS), June 21-24, 2022, Dubrovnik, Croatia

This information is tentative and subject to change. Compiled on March 28, 2024

Keywords Navigation, Sensor Fusion

Abstract

The irreversible problematic situation of bootstrap particle filter that it is subject to the weight collapse, is tackled with an evenly weighted setup especially in application to terrain-referenced navigation problems of unmanned aerial systems. The paper is featured with the Gaussian mixture proposal density taking multimodal noise characteristics of terrain clearance sensors into account. Each particle explores further towards the region of high likelihood in addition to its original motion model, while the amount of transition of the introduced proposal density is calculated from a superposition of a couple of optimal data assimilation methods. Numerical local terrain elevation gradient in conjunction with the parameters that describe the multimodality realize the calculation of transition gain by which the innovation is multiplied. The proposed approach significantly reduces the variance of particle weight and reinforces the diversity of particles by locating them exploiting both the terrain measurement and its noise characteristic.

 

 

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