Recursive Calculation Model for a Special Multivariate Normal Probability of First-Order Stationary Sequence

Jietao Xie () and Juan Wu ()
Additional contact information
Jietao Xie: Baicheng Ordnance Test Center of China, Baicheng 137001, Jilin, China
Juan Wu: Baicheng Ordnance Test Center of China, Baicheng 137001, Jilin, China

INFORMS Journal on Computing, 2020, vol. 32, issue 1, 164-171

Abstract: The consecutive hit probability of antiaircraft artillery corresponds to the multivariate normal probability distribution. The computational complexity depends on the length of the firing error sequence (i.e., the integral dimension may exceed 100). The traditional numerical integration and the Monte Carlo method are too slow for this calculation. This paper established the state equation of the firing error sequence, which was the bridge between the multivariate normal probability distribution and stochastic process theory. The recursive calculation model was given after the rigorous derivation process. The accuracy and computational complexity of the model were quantified by theoretical analysis and expressed intuitively by examples. The model shows the upper bound for the absolute error, and the computational efficiency is significantly improved.

Keywords: multivariate normal probability; recursive calculation; first-order; antiaircraft; hit probability (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1287/ijoc.2018.0852 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:32:y:2020:i:1:p:164-171

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().