基于神经网络的高超声速飞行器惯导系统精度提高方法

doi:10.12305/j.issn.1001-506X.2022.04.28

Abstract

Abstract:

Aiming at the problem that the current inertial system error compensation model is insufficient in processing static and dynamic errors, in order to meet the requirements of long-endurance and high-precision inertial navigation of hypersonic aircraft, an accelerometer fitting model based on neural network is proposed. When the hypersonic vehicle has accurate satellite navigation information in the early stage of flight, the navigation information and accelerometer pulse information are collected, and the powerful nonlinear fitting ability of the neural network is used to conduct online training during the flight to obtain an accurate inertial system model. The simulation results show that when the power-on error and the error compensation model method does not consider the quadratic term error coefficient, the position navigation deviation is on the order of several kilometers and hundreds of meters, the position navigation deviation of the proposed method is only on the order of tens of meters in the same time, which effectively improve the navigation accuracy of the hypersonic vehicle.

Key words: neural network, strapdown inertial navigation system (SINS), accelerometer, hypersonic vehicle, error compensation model

CLC Number:

V249.32

Yajie XU, Yong XIAN, Bangjie LI, Leliang REN, Shaopeng LI, Weilin GUO. Method for improving the precision of hypersonic vehicle inertial navigation system based on neural network[J]. Systems Engineering and Electronics, 2022, 44(4): 1301-1309.

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References 33

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节点数	训练误差
节点数	X方向	Y方向	Z方向
4	5.50×10^-6	8.33×10^-7	3.45×10^-7
5	5.08×10^-6	1.52×10^-6	2.02×10^-7
6	1.54×10^-6	4.04×10^-7	9.37×10^-8
7	2.30×10^-4	5.44×10^-5	7.78×10^-6
8	2.85×10^-5	4.58×10^-5	1.95×10^-5
9	3.53×10^-6	8.17×10^-7	6.26×10^-8
10	1.47×10^-3	2.23×10^-5	2.28×10^-5
11	5.24×10^-5	1.47×10^-5	2.61×10^-6
12	1.97×10^-4	2.26×10^-5	2.15×10^-7
13	8.59×10^-4	4.42×10^-5	2.86×10^-6
14	8.61×10^-4	1.62×10^-5	5.82×10^-6

误差参数	漂移值	单位
ΔK_0x	5×10^-4	g
ΔK_0y	5×10^-4	g
ΔK_0z	5×10^-4	g
ΔK_1x	1×10^-3	-
ΔK_1y	1×10^-4	-
ΔK_1z	1×10^-4	-

偏差/m	δX	偏差/m	δY	偏差/m	δZ
< 100	9	< 100	24	< 100	13
< 500	48	< 500	90	< 500	62
< 1 000	73	< 700	98	< 1 000	95
< 2 000	100	< 800	100	< 1 500	100

统计项	δX	δY	δZ
平均值	6.45×10²	2.59×10²	4.51×10²
最大值	1.98×10³	7.81×10²	1.46×10³
标准差	4.65×10²	1.85×10²	3.12×10²

偏差/m	δX	偏差/m	δY	偏差/m	δZ
< 10	5	< 10	12	< 10	33
< 100	79	< 100	82	< 50	93
< 200	98	< 200	99	< 100	99
< 400	100	< 300	100	< 200	100

Method for improving the precision of hypersonic vehicle inertial navigation system based on neural network

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统计项	δX	δY	δZ
平均值	7.16×10	5.56×10	2.12×10
最大值	3.87×10²	2.45×10²	1.77×10²
标准差	5.54×10¹	4.65×10¹	2.25×10¹

导航模型	δV_x	δV_y	δV_z
静态误差	2.71×10^-1	1.51×10^-1	2.85×10^-2
动态误差	1.54	1.15	5.65
在线辨识	7.25×10^-1	2.41×10^-1	6.04×10^-1
BPNN	2.47×10^-2	6.81×10^-2	1.79×10^-2

导航模型	δX	δY	δZ
静态误差	1.51×10²	1.53×10²	1.61×10²
动态误差	7.43×10³	8.11×10²	4.31×10³
在线辨识	3.91×10²	2.52×10²	4.85×10²
BPNN	8.02×10	2.16×10	1.04×10

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