IEEE - Aerospace and Electronic Systems - February 2023 - 7

Yang et al.
Figure 2.
Schematic diagram of algorithm framework.
with the largest numerator is the one that A belongs to,
and it can be expressed as
arg max fX1;X2;;Xn Yj ðx1;x2;;xn yijÞfYðyiÞ
the expression is
fYðyiÞ¼ PYiðÞ ¼
no
(8)
where fYðyiÞ is the class parameter estimation, and
NYiðÞ
N
;i ¼ 1; 2; ... ;m
(9)
where NðYiÞ is the number of the samples belonging to
class Yi, and N is the total number of samples. In particular,
ifn features are independent of each other, then
arg max fX1;X2;;Xn Yj ðx1;x2;;xn yij ÞfYðyiÞ
¼ arg max fX1 Yj ðx1 yij ÞfX2 Yj ðx2 yij Þ fXn Yj ðxn yij ÞfYðyiÞ
no
no
¼ arg max
fXj Yj ðxj yij ÞfYðyiÞ
()
Yn
j¼1
(10)
where fX1jY:ðx1jyi:Þ, fX2jY:ðx2jyi:Þ; ... ;fXnjY:ðxnjyi:Þ are
the probability density functions ofX1;X2; ... ;Xn,
respectively.
OBJECT RECOGNITION ALGORITHM BASED ON
CONVOLUTIONAL FEATURE INFERENCE NETWORK
The algorithm framework ofthis article is shown in Figure 2.
In the Bayesian recognition architecture designed in
this article, 2DPCANet [22] is introduced as a feature
extraction network to improve the characterization
ability of objects and artificial interference. Meanwhile,
a Bayesian object recognition algorithm integrating a
2-D principal component analysis network is proposed.
The 2DPCANet establishes the mapping relationship
between the bottom-level data and high-level features
through a hierarchical structure, exhibiting a powerful
abstract representation capability.
DATASET CONSTRUCTION AND LABELING
To train the Bayesian network, an image dataset including a
huge number of objects and interferences with multiple
angles is needed. However, there is a lack of images of actually
flying air-to-air missiles and objects. Also, the real air
combat confrontation images are difficult to obtain. Hence,
Figure 3.
Schematic diagram of object sample labeling.
Figure 4.
Schematic diagram of interference sample labeling.
FEBRUARY 2023
IEEE A&E SYSTEMS MAGAZINE
7

IEEE - Aerospace and Electronic Systems - February 2023

Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - February 2023

Contents
IEEE - Aerospace and Electronic Systems - February 2023 - Cover1
IEEE - Aerospace and Electronic Systems - February 2023 - Cover2
IEEE - Aerospace and Electronic Systems - February 2023 - Contents
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