Quantifying safety of laser-based navigation

Mathieu Joerger; Boris Pervan

doi:10.1109/TAES.2018.2850381

Quantifying safety of laser-based navigation

Mathieu Joerger, Boris Pervan

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

In this paper, a new safety risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). First, at FE, we establish a probabilistic measure of separation between features to quantify the sensor's ability to distinguish landmarks. Then, an innovation-based DA process is designed to evaluate the impact on integrity risk of incorrect associations, while considering all potential measurement permutations. The algorithm is analyzed and tested in a structured environment.

Original language	English (US)
Article number	8395344
Pages (from-to)	273-288
Number of pages	16
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	55
Issue number	1
DOIs	https://doi.org/10.1109/TAES.2018.2850381
State	Published - Feb 2019
Externally published	Yes

Keywords

Autonomous vehicle
data association (DA)
integrity
navigation
safety

ASJC Scopus subject areas

Aerospace Engineering
Electrical and Electronic Engineering

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10.1109/TAES.2018.2850381

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title = "Quantifying safety of laser-based navigation",

abstract = "In this paper, a new safety risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). First, at FE, we establish a probabilistic measure of separation between features to quantify the sensor's ability to distinguish landmarks. Then, an innovation-based DA process is designed to evaluate the impact on integrity risk of incorrect associations, while considering all potential measurement permutations. The algorithm is analyzed and tested in a structured environment.",

keywords = "Autonomous vehicle, data association (DA), integrity, navigation, safety",

author = "Mathieu Joerger and Boris Pervan",

note = "Funding Information: This work was supported in part by the Federal Aviation Administration, and in part by the National Science Foundation under FAA Grant #14-G-018, NSF award #1637899. Publisher Copyright: {\textcopyright} 1965-2011 IEEE.",

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N2 - In this paper, a new safety risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). First, at FE, we establish a probabilistic measure of separation between features to quantify the sensor's ability to distinguish landmarks. Then, an innovation-based DA process is designed to evaluate the impact on integrity risk of incorrect associations, while considering all potential measurement permutations. The algorithm is analyzed and tested in a structured environment.

AB - In this paper, a new safety risk evaluation method is developed, simulated, and tested for laser-based navigation algorithms using feature extraction (FE) and data association (DA). First, at FE, we establish a probabilistic measure of separation between features to quantify the sensor's ability to distinguish landmarks. Then, an innovation-based DA process is designed to evaluate the impact on integrity risk of incorrect associations, while considering all potential measurement permutations. The algorithm is analyzed and tested in a structured environment.

KW - Autonomous vehicle

KW - data association (DA)

KW - integrity

KW - navigation

KW - safety

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Quantifying safety of laser-based navigation

Abstract

Keywords

ASJC Scopus subject areas

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