Dynamic assessment method of air target threat based on improved GIFSS

doi:10.21629/JSEE.2019.03.10

Abstract

Abstract:

As the air combat environment becomes more complicated and changeable, accurate threat assessment of air target has a significant impact on air defense operations. This paper proposes an improved generalized intuitionistic fuzzy soft set (GIFSS) method for dynamic assessment of air target threat. Firstly, the threat assessment index is reasonably determined by analyzing the typical characteristics of air targets. Secondly, after the GIFSS at different time is obtained, the index weight is determined by the intuitionistic fuzzy set entropy and the relative entropy theory. Then, the inverse Poisson distribution method is used to determine the weight of time series, and then the time-weighted GIFSS is obtained. Finally, threat assessment of five air targets is carried out by using the improved GIFSS (I-GIFSS) and comparison methods. The validity and superiority of the proposed method are verified by calculation and comparison.

Key words: threat dynamic assessment, generalized intuitionistic fuzzy soft set (GIFSS), intuitionistic fuzzy entropy, relative entropy, inverse Poisson distribution method

Jinfu FENG, Qiang ZHANG, Junhua HU, An LIU. Dynamic assessment method of air target threat based on improved GIFSS[J]. Journal of Systems Engineering and Electronics, 2019, 30(3): 525-534.

Figures/Tables 11

Fig 1

Table 1

Table 2

Table 3

IFS matrix of the air target threat at different time"

Time	Target	$C_{1}$	$C_{2}$	$C_{3}$	$C_{4}$	$C_{5}$	$C_{6}$	$C_{7}$
$t_{1}$	$T_{1}$	(0.9, 0.05, 0.05)	(0.1, 0.45, 0.45)	(1, 0, 0)	(0.909, 0.045, 0.125)	(0.750, 0.125, 0.125)	(1, 0, 0)	(0.9, 0.05, 0.05)
	$T_{2}$	(0.7, 0.15, 0.15)	(0.7, 0.15, 0.15)	(0.829, 0.086, 0.086)	(0.122, 0.439, 0.439)	(0.125, 0.438, 0.438)	(0.737, 0.132, 0.132)	(0.7, 0.15, 0.15)
	$T_{3}$	(0.5, 0.25, 0.25)	(0.5, 0.25, 0.25)	(0.629, 0.186, 0.186)	(0, 0.500, 0.500)	(0, 0.500, 0.50)	(0.395, 0.303, 0.303)	(0.5, 0.25, 0.25)
	$T_{4}$	(0.1, 0.45, 0.45)	(0.9, 0.05, 0.05)	(0, 0.500, 0.500)	(0.879, 0.061, 0.061)	(0.625, 0.188, 0.188)	(0.132, 0.434, 0.434)	(0.3, 0.35, 0.35)
	$T_{5}$	(0.3, 0.35, 0.35)	(0.5, 0.25, 0.25)	(0.986, 0.007, 0.007)	(1, 0, 0)	(1, 0, 0)	(0, 0.500, 0.500)	(0.7, 0.15, 0.15)
$t_{2}$	$T_{1}$	(0.9, 0.05, 0.05)	(0.1, 0.45, 0.45)	(1, 0, 0)	(0.927, 0.027, 0.046)	(0.818, 0.065, 0.117)	(1, 0, 0)	(0.9, 0.05, 0.05)
	$T_{2}$	(0.7, 0.15, 0.15)	(0.7, 0.15, 0.15)	(0.807, 0.070, 0.123)	(0, 0.738, 0.262)	(0, 0.750, 0.250)	(0.507, 0.213, 0.281)	(0.7, 0.15, 0.150
	$T_{3}$	(0.5, 0.25, 0.25)	(0.5, 0.25, 0.25)	(0.544, 0.203, 0.253)	(0.390, 0.172, 0.438)	(0.364, 0.220, 0.416)	(0.481, 0.156, 0.364)	(0.5, 0.25, 0.25)
	$T_{4}$	(0.1, 0.45, 0.45)	(0.9, 0.05, 0.05)	(0, 0.313, 0.688)	(0.780, 0.149, 0.071)	(0.546, 0.284, 0.170)	(0.208, 0.158, 0.634)	(0.3, 0.35, 0.35)
	$T_{5}$	(0.3, 0.35, 0.35)	(0.3, 0.63, 0.07)	(0.965, 0.014, 0.021)	(1, 0, 0)	(1, 0, 0)	(0, 0.350, 0.650)	(0.7, 0.15, 0.15)
$t_{3}$	$T_{1}$	(0.9, 0.05, 0.05)	(0.1, 0.45, 0.45)	(0.987, 0.004, 0.009)	(0.926, 0.027, 0.048)	(0.917, 0.028, 0.056)	(1, 0, 0)	(0.9, 0.05, 0.05)
	$T_{2}$	(0.7, 0.15, 0.15)	(0.9, 0.02, 0.08)	(0.677, 0.111, 0.213)	(0, 0.596, 0.404)	(0, 0.567, 0.433)	(0.494, 0.237, 0.270)	(0.5, 0.39, 0.11)
	$T_{3}$	(0.5, 0.25, 0.25)	(0.5, 0.25, 0.25)	(0.220, 0.298, 0.482)	(0.597, 0.112, 0.291)	(0.500, 0.205, 0.296)	(0.468, 0.249, 0.284)	(0.5, 0.25, 0.25)
	$T_{4}$	(0.1, 0.45, 0.45)	(0.7, 0.22, 0.08)	(0, 0.065, 0.935)	(0.691, 0.207, 0.102)	(0.583, 0.208, 0.208)	(0.143, 0.473, 0.385)	(0.30.35, 0.35)
	$T_{5}$	(0.3, 0.35, 0.35)	(0.3, 0.35, 0.35)	(1, 0, 0)	(1, 0, 0)	(1, 0, 0)	(0, 0.370, 0.630)	(0.7, 0.15, 0.15)

Table 3

Table 4

Table 5

Table 6

Fig 2

Fig 3

Fig 4

Table 7

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Index	Calculation method of membership degree
Target type	Missile 0.9; Battleplane 0.7; Bomber 0.5; Armed helicopter 0.3; Early warning aircraft 0.1
Jamming ability	Strongest 0.9; Strong 0.7; Average 0.5; Weak 0.3; Weakest 0.1
Target height	$\mu _h = (\max \{h_i\} - h_i) /(\max \{h_i\} - \min \{h_i\})$
Target distance	$\mu _l = (\max \{l_i\} - l_i)/(\max \{l_i\} - \min \{l_i\})$
Route shortcut	$\mu _s = (\max \{s_i\} - s_i)/ (\max \{s_i\} - \min \{s_i\})$
Target velocity	$\mu _v = (v_i - \min \{v_i\})/(\max \{v_i\} - \min \{v_i\})$
Maneuvering ability	Strongest 0.9; Strong 0.7; Average 0.5; Weak 0.3; Weakest 0.1

Time	Target	Type	Jamming ability	Height	Distance/km	Route shortcut/km	Velocity/(m/s)	Maneuvering ability
$t_{1}$	$T_{1}$	Missile	Weakest	1 000	80	7	480	Strongest
	$T_{2}$	Battleplane	Strong	2 200	210	12	290	Strong
	$T_{3}$	Bomber	Average	3 600	230	13	250	Average
	$T_{4}$	Early warning aircraft	Strongest	8 000	85	8	150	Weak
	$T_{5}$	Armed helicopter	Average	1 100	65	5	100	Strong
$t_{2}$	$T_{1}$	Missile	Weakest	800	70	6	500	Strongest
	$T_{2}$	Battleplane	Strong	1 900	260	15	310	Strong
	$T_{3}$	Bomber	Average	3 400	180	11	300	Average
	$T_{4}$	Early warning aircraft	Strongest	6 500	100	9	195	Weak
	$T_{5}$	Armed helicopter	Weak	1 000	55	4	115	Strongest
$t_{3}$	$T_{1}$	Missile	Weakest	650	60	5	515	Strongest
	$T_{2}$	Battleplane	Strongest	1 600	285	16	320	Average
	$T_{3}$	Bomber	Average	3 000	140	10	310	Average
	$T_{4}$	Early warning aircraft	Strong	3 673	117	9	185	Weak
	$T_{5}$	Armed helicopter	Weak	609	42	4	130	Strong

Target	$t_{1}$	$t_{2}$	$t_{3}$
$T_{1}$	(0.9, 0.07, 0.03)	(0.9, 0.05, 0.05)	(0.9, 0.01, 0.09)
$T_{2}$	(0.8, 0.12, 0.08)	(0.8, 0.16, 0.04)	(0.8, 0.05, 0.15)
$T_{3}$	(0.7, 0.10, 0.20)	(0.7, 0.13, 0.17)	(0.9, 0.04, 0.06)
$T_{4}$	(0.8, 0.04, 0.16)	(0.7, 0.12, 0.18)	(0.6, 0.25, 0.15)
$T_{5}$	(0.7, 0.21, 0.09)	(0.6, 0.32, 0.08)	(0.8, 0.15, 0.05)

Classification	Method	Ranking results
Proposed method	I-GIFSS	$T_1 >T_5 >T_2 >T_3 >T_4$
Comparison methods	IFS	$T_1 = T_5 >T_2 >T_4 >T_3$
	MIFS	$T_1 >T_5 >T_2 >T_4 >T_3$
	GIFSS	$T_1 >T_5 >T_3 >T_2 >T_4$

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[2]	Bin Chen, Zhixue Wang, and Chen Luo. Integrated evaluation approach for node importance of complex networks based on relative entropy [J]. Journal of Systems Engineering and Electronics, 2016, 27(6): 1219-1226.
[3]	Zhensong Chen, Shenghua Xiong, Yanlai Li, and Kwai-Sang Chin. Entropy measures of type-2 intuitionistic fuzzy sets and type-2 triangular intuitionistic trapezodial fuzzy sets [J]. Journal of Systems Engineering and Electronics, 2015, 26(4): 774-.
[4]	Rao Congjun & Zhao Yong. Multi-attribute decision making model based on optimal membership and relative entropy [J]. Journal of Systems Engineering and Electronics, 2009, 20(3): 537-542.