Correlation-weighted least squares residual algorithm for RAIM

Duan, Song; Shi, Chuang; Wang, Zhipeng; Wang, Cheng; Jing, Guifei

doi:10.1016/j.cja.2019.12.012

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…Fig. 10 shows the standard deviations of the position errors on the three axes over 5000 epochs, which were computed using (8). We collected training samples for P-RAIM and used the PSO algorithm to select the optimal smoothing parameter λ.…”

Section: B Modeling Of P-raimmentioning

confidence: 99%

“…Two styles of RAIM have attracted attention: the least-squares-residuals approach and the Bayes approach. First, RAIM based on classical least-squares theory was used to detect single faults in the Global Positioning System (GPS) [7], [8]. At the same time, Sturza [9] projected the observation matrix into the parity space and solved the fault detection problem using a parity method.…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

Zheng

Wang³

et al. 2021

IEEE Access

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In this article, the problem of receiver autonomous integrity monitoring (RAIM) is transformed into a modeling problem using dynamic data and an artificial neural network. A new RAIM method based on a probabilistic neural network (P-RAIM) is presented to improve integrity monitoring performance. Compared with existing RAIM methods, P-RAIM has a greater ability to meet the monitoring requirements for localizer performance with vertical guidance down to altitudes of 250 feet (LPV-250) in a single global navigation satellite system. First, by projecting the pseudorange error model from the measurement domain into the positioning domain through multiconvolution, patterns including a satellite fault pattern and a faultfree pattern are obtained based on variance inflation theory. Second, the P-RAIM model is proposed as a modified dynamic-data-driven probabilistic neural network with five layers; moreover, unique methods for training sample collection and integrity support are presented. Then, particle swarm optimization is applied to optimize a fitness function based on the false alarm probability and missed detection probability thereby improving the ability of P-RAIM to meet the LPV-250 requirements, including the false alarm probability, missed detection probability, vertical alarm limit and alarm time. Finally, utilizing real satellite data from a receiver located in Beijing to verify the effectiveness and universality of P-RAIM, evaluation experiments show that both the false alarm probability and missed detection probability can be effectively reduced to meet the LPV-250 requirements when the positioning bias is no less than 40 m. Compared with least-squaresresiduals RAIM, P-RAIM can more easily detect potential faulty satellites in a single constellation.INDEX TERMS Receiver autonomous integrity monitoring, LPV-250, global navigation satellite system, multi-layer neural network, alarm systems.

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Section: B Modeling Of P-raimmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

Zheng

Wang³

et al. 2021

IEEE Access

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“…The weights were designed both in the position domain and in the measurement domain. Song et al [ 23 ] proposed the correlation-weighted least squares residual (CW-LSR) algorithm in which the pseudo-range residuals were weighted with the characteristic slope, making it approximate to the optimal test statistic. However, the characteristic slope was valid only in a single outlier mode.…”

Section: Related Workmentioning

confidence: 99%

A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation

Wang

2020

Sensors

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The residual-based (RB) receiver autonomous integrity monitoring (RAIM) detector is a widely used receiver integrity enhancement technology that has the ability to rapidly respond to outliers. However, the sensitivity and vulnerability of the residuals to the outliers are the weaknesses of the method especially in the case of multi-outlier modes. It is an effective method for enhancing the validity of residuals by robust estimation instead of least squares (LS) estimation. In this paper, a modified RB RAIM detector based on a robust MM estimation with a higher detection performance under multi-outlier modes is presented. A fast subset selection method based on the characteristic slope that could reduce the number of subsets to be calculated is also presented. The experimental results show that the proposed algorithm maintains a more robust performance than the RB RAIM detector based on the LS estimator and M estimator with an IGG III function especially with the increase in the number of outliers. The proposed fast subset selection method can reduce the calculation time by at least 80%, demonstrating the practical application value of the algorithm.

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“…It employs a high-energy laser beam to selectively melt and solidify metal powder, layer by layer creating components with intricate structures [4][5]. Selective laser melting technology has a higher cooling solidification rate (10 6 °C/s~10 8 °C/s) during the forming process than conventional processing (� 10 2 °C/s) which prevents the segregation of alloying elements and grain growth from forming components with fine grains, uniform microstructure, and excellent mechanical properties [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Study on microstructure and properties of selective laser melting formed AlSi10Mg alloy

Jian,

Wu,

Pai

et al. 2023

Materialwissenschaft Werkst

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The microstructure and properties of an AlSi10Mg alloy produced by selective laser melting were examined using optical microscopes, scanning electron microscopes, electron backscattered diffraction, and x‐ray diffraction. The results show that the mechanical characteristics of selective laser melting formed AlSi10Mg alloy is superior to those of traditional sand cast AlSi10Mg alloy due to fine grain strengthening and solid solution strengthening. AlSi10Mg alloy formed by selective laser melting is mainly composed of α‐aluminum solid solution, eutectic silicon with network structure and a small amount of Magnesium silicon precipitated phase, and different cross‐sections have unique microstructures and properties. The XOY plane is composed primarily of equiaxed grains with an average grain size of 7.34 μm and a {001} <100> cubic texture, and it has a hardness value of 130.7 HV 0.2. The majority of the YOZ plane, which has a {111}<110> brass R texture and a hardness value of 108.9 HV 0.2, comprises columnar crystals with an average grain size of 8.42 μm.

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Correlation-weighted least squares residual algorithm for RAIM

Cited by 14 publications

References 16 publications

A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

A Dynamic-Data-Driven Method for Improving the Performance of Receiver Autonomous Integrity Monitoring

A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation

Study on microstructure and properties of selective laser melting formed AlSi10Mg alloy

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