Abstract
In this article, a statistical model of human motion as observed by a network of radar sensors is presented where knowledge on the position and heading of the target provides information on the observation conditions of each sensor node. Sequences of motions are estimated from measurements of instantaneous Doppler frequency, which captures informative micromotions exhibited by the human target. A closed-form Bayesian estimation algorithm is presented that jointly estimates the state of the target and its exhibited motion class which are described by a hidden Markov model. To correct errors in the estimated motion class distribution introduced by faulty modeling assumptions, calibration of the probability distribution and measurement likelihood is performed by isotonic regression. It is shown, by modeling sensor observation conditions and by isotonic calibration of the measurement likelihood that a cognitive resource management system is able to increase classification accuracy by 5%-10% while utilizing sensor resources in accordance with defined mission objectives.
Original language | English |
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Article number | 9780548 |
Pages (from-to) | 5661-5674 |
Number of pages | 14 |
Journal | IEEE Transactions on Aerospace and Electronic Systems |
Volume | 58 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2022 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- cognitive radar
- dynamic Bayesian network
- micro-Doppler signature
- radar network
- radar resource management
- radar target classification