@inproceedings{4f046d15f09d4ca4b5092534de35b949,
title = "Multi-Sensor Adaptive Birth for Autonomous Driving Labeled RFS Filters using Doppler Measurements",
abstract = "Alternative proposal distributions for Monte Carlo importance sampling-based, multi-sensor, measurement adaptive track initiation unrestricted to invertible measurement functions have been established to achieve scalable track initialization for labeled random finite set filters. Such proposal distributions provide an efficient means to expand the observable state space in track initialization by way of uninvertible measurements of relative object velocity. This paper proposes an augmented proposal distribution for Monte Carlo importance sampling-based, multi-sensor measurement adaptive track initiation that improves joint measurement pseudolikelihood approximation using augmented measurements of object velocity. The solution for a Doppler-only measurement function is provided and autonomous driving simulation results are shown to verify the resultant tracking performance increase.",
author = "Dale, {Terell L.} and Dimitri Cugini and Schuck, {Tod M.} and Narayanan, {Ram M.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 76th Annual IEEE National Aerospace and Electronics Conference, NAECON 2024 ; Conference date: 15-07-2024 Through 18-07-2024",
year = "2024",
doi = "10.1109/NAECON61878.2024.10670621",
language = "English (US)",
series = "Proceedings of the IEEE National Aerospace Electronics Conference, NAECON",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "68--73",
booktitle = "NAECON 2024 - IEEE National Aerospace and Electronics Conference",
address = "United States",
}