概率神经网络多历元残差RAIM算法

doi:10.12305/j.issn.1001-506X.2023.12.27

Abstract

Abstract:

We propose a probabilistic neural network multi-epoch residual receiver autonomous integrity monitoring (RAIM) algorithm for civil aviation which can improve the detection capability of RAIM algorithm for fault deviation and reduce the minimum detectable deviation. A four-layer fault satellite detection model based on probabilistic neural network is constructed. Fault class and fault-free class training samples of pseudorange residuals are established using variance inflation model. The smoothing parameter of probabilistic neural network are optimized by particle swarm optimization algorithm to meet the false alarm rate. Thus, the similarity between the input multi-epoch pseudorange residual and the fault samples and fault-free samples can be calculated to determine whether the satellite is faulty. Simulation results suggest that optimizing the smoothing parameter can improve the fault detection ability of the proposed algorithm. Compared with the weighted least squares RAIM algorithm and the advanced RAIM (ARAIM) algorithm, the proposed algorithm can improve the detection performance of small pseudorange deviation and reduce the minimum detectable deviation under different fault conditions.

Key words: receiver autonomous integrity monitoring (RAIM), fault detection, probabilistic neural network, variance inflation

CLC Number:

V249.31

Ming WU, Chengdong XU, Guoxian HUANG, Rui SUN, Zhiwei LU. Probabilistic neural network multi-epoch residual RAIM algorithm[J]. Systems Engineering and Electronics, 2023, 45(12): 3967-3974.

Figures/Tables 12

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P_fa	σ_ω
P_fa	1.2	1.3	1.4	1.5	1.6	1.7	1.8	1.9	2.0	2.1	2.2	2.3
10^－5	2.61	2.72	2.84	3.20	3.46	3.53	3.73	3.84	4.08	4.44	4.68	4.77
10^－6	3.11	3.47	3.53	3.96	4.07	4.47	4.70	4.86	5.00	5.11	5.28	5.48

误差	误差标准差
用户测距精度	2.4
对流层误差	0.12×1.001/(0.002 001+sin²θ)^1/2
多径误差	0.13+0.53e^－θ/10
接收机噪声	0.04－0.02×(θ－5)/85

卫星PRN码编号	PMR-RAIM	WLS-RAIM
G05	66.0	1.0
G02	49.0	1.67
G06	87.67	4.33
G13	69.33	3.67
G29	14.67	0
G15	17.33	0
G30	70.67	0.33
G07	33.0	0.67

卫星PRN码编号	PMR-RAIM	WLS-RAIM
G05	13.42	24.83
G02	14.86	26.94
G06	13.10	22.07
G13	12.93	24.88
G29	20.13	36.61
G15	19.20	36.50
G30	13.56	24.57
G07	16.42	29.15

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Probabilistic neural network multi-epoch residual RAIM algorithm

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