Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

Gabela, Jelena; Kealy, Allison; Hedley, Mark; Moran, Bill

doi:10.1002/navi.433

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…Using the powerful self-learning ability and high-order feature mining ability of deep learning algorithm, a water infor-mation extraction model based on deep deconvolution game generation network and combined features is established. Visual Studio 2021 and Anaconda 3 are used to build an experimental simulation platform, and the effective features of water signal sum in remote sensing images are extracted by spectral and spatial coupling feature constraint algorithm [24]. The overall performance of the proposed water information extraction model is tested by experiments.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Remote Sensing Image Water Body Recognition Algorithm Based on Deep Convolution Generating Network and Combined Features

Zhao

Feng

2022

Wireless Communications and Mobile Computing

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The application of remote sensing images in water body recognition has become an effective method for ecological environment detection and evaluation, which has the disadvantages of low efficiency due to the existence of interpretation marks and rich interpretation experience in the current water body environment recognition, and overreliance on human experience. In this paper, the water body recognition method is applied to remote sensing images by combining the deep convolution generation network and the combined features, which has the advantage of high recognition accuracy. In the convolutional neural network, a five-layer convolutional neural network is used to construct a remote sensing water information extraction model, the transfer learning idea is introduced, and the densely connected feature fusion structure is added, so as to achieve the purposes of accelerating the convergence speed of the neural network, reducing the requirements of the neural network on the scale of training data, and reducing the loss of spatial hierarchical information and small object information. Compared with SVM, DBN, and CNN models, the experimental results show that the recognition accuracy of the proposed method is as high as 95. 69% under the constraint of scale window, which has a wide range of application scenarios and practical significance.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Remote Sensing Image Water Body Recognition Algorithm Based on Deep Convolution Generating Network and Combined Features

Zhao

Feng

2022

Wireless Communications and Mobile Computing

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“…The flow chart of the system is drawn, and numerical simulation is carried out. The Monte Carlo statistical method is used to evaluate and analyze the performance [22]. The proposed method is improved and applied to the navigation system.…”

Section: A System Implementationmentioning

confidence: 99%

Research on Intelligent Navigation System Based on Aerospace Big Data Traffic System

Songchun Wang, Tiezhong Liu

2024

jes

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This paper studies the navigation technology of the On-Orbit Servicing Spacecraft (OSS) and proposes an overall solution for the OSS navigation system. The composition of the OSS satellite navigation simulation system and simulation supporting environment are studied. Then a new SSUKF navigation filter is proposed based on the hyper spherical distributed feature sampling point transform algorithm (SSUT) and the non-scanning Kalman filter (UKF). The numerical simulation of the OSS system is studied based on MATLAB/RTW. Finally, the SSUKF algorithm and the conventional UKF algorithm are simulated digitally. The effectiveness and advanced nature of the hybrid Kalman filter applied to spacecraft autonomous navigation are verified.

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“…where min q, θ K, exp (−β mod q ∆t) / exp (−β q ∆t) q is the minimum value obtained over all values of K, , θ, and q (maximum q is (M − 1) for α 1 and (N − M − 1) for α 2 ). For a given q and θ, (exp (−β mod q ∆t) / exp (−β q ∆t) ) q is minimum when all Ks and s are set to their respective minimum values (see Equations ( 17), ( 19), (22), and ( 23)). This is greater than or equal to the value K min obtained by setting all Ks to K min .…”

Section: Uncertain a Qmentioning

confidence: 99%

“…It is shown to improve positioning accuracy over that without fault exclusion. RAIM algorithms for advanced nonlinear filters [22] and vector receivers [23] are also described in the literature.…”

Section: Introductionmentioning

confidence: 99%

Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

Bhattacharyya

2021

Sensors

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This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.

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Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

Cited by 13 publications

References 37 publications

Remote Sensing Image Water Body Recognition Algorithm Based on Deep Convolution Generating Network and Combined Features

Remote Sensing Image Water Body Recognition Algorithm Based on Deep Convolution Generating Network and Combined Features

Research on Intelligent Navigation System Based on Aerospace Big Data Traffic System

Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

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