GPS Integrity Monitoring Method Using Auxiliary Nonlinear Filters with Log Likelihood Ratio Test Approach

Ahn, Jiyun; Rosihan, Rosihan; Won, Daehee; Lee, Young Jae; Nam, Gi-Wook; Heo, Moon Beom; Sung, Sangkyung

doi:10.5370/jeet.2011.6.4.563

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…Grosch et al [15], Hewitson and Wang [18], Leppakoski et al [29], and Li et al [32] utilized residual-based RAIM algorithms to remove faulty GNSS measurements before updating the state estimate using KF. Boucher et al [4], Ahn et al [1], and Wang et al [52,53] constructed multiple filters associated with different groups of GNSS measurements and used the logarithmic likelihood ratio between the distributions tracked by the filters to detect and remove faulty measurements. Pesonen [41] proposed a Bayesian filtering framework that tracks indicators of multipath bias in each GNSS measurement along with the state.…”

Section: Position Estimation Under Faulty Gnss Measurementsmentioning

confidence: 99%

Reliable urban vehicle localization under faulty satellite navigation signals

Gupta,

Gao

2024

EURASIP J. Adv. Signal Process.

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Reliable urban navigation using global navigation satellite system requires accurately estimating vehicle position despite measurement faults and monitoring the trustworthiness (or integrity) of the estimated location. However, reflected signals in urban areas introduce biases (or faults) in multiple measurements, while blocked signals reduce the number of available measurements, hindering robust localization and integrity monitoring. This paper presents a novel particle filter framework to address these challenges. First, a Bayesian fault-robust optimization task, formulated through a Gaussian mixture model (GMM) measurement likelihood, is integrated into the particle filter to mitigate faults in multiple measurement for enhanced positioning accuracy. Building on this, a novel test statistic leveraging the particle filter distribution and the GMM likelihood is devised to monitor the integrity of the localization by detecting errors exceeding a safe threshold. The performance of the proposed framework is demonstrated on real-world and simulated urban driving data. Our localization algorithm consistently achieves smaller positioning errors compared to existing filters under multiple faults. Furthermore, the proposed integrity monitor exhibits fewer missed and false alarms in detecting unsafe large localization errors.

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Section: Position Estimation Under Faulty Gnss Measurementsmentioning

confidence: 99%

Reliable urban vehicle localization under faulty satellite navigation signals

Gupta,

Gao

2024

EURASIP J. Adv. Signal Process.

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“…In the RAIM algorithm based on a particle filter, a set of particles x i k ; i ¼ 1. . .M is first set up at time k to predict the measurement value for the next epoch (Ahn et al 2011;He et al 2013). The estimated measurement value of the ith particle for satellite j isq iðjÞ k .…”

Section: A New Test Statistic For Raimmentioning

confidence: 99%

“…However, in certain circumstances, these statistics do not obey the above-mentioned distributions, and there is no closedform formula for the threshold computation. For nonGaussian noise, the extended Kalman filter (Toledo-Moreo et al 2007), Markov Chain Monte Carlo particle filter (Wang et al 2009), auxiliary particle filter (Ahn et al 2011) and genetic resampling particle filter (He et al 2013) are used in GPS positioning and integrity monitoring. The test statistic used for particle filters, i.e., the cumulative log likelihood ratio (LLR), is generated using a stochastic generator and does not obey a Gaussian-related distribution.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear fault detection threshold optimization method for RAIM algorithm using a heuristic approach

Liu

Tan

et al. 2015

GPS Solut

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The threshold value used in receiver autonomous integrity monitoring algorithms to identify faults has a significant impact on positioning integrity and GPS/ GNSS availability. The value is usually selected empirically or under certain distribution assumptions; its calculation for a non-Gaussian test statistic has not been solved. For fault detection methods using a particle filter, a new heuristic method is proposed to select an appropriate fault detection threshold value using an optimization model. In this method, a non-Gaussian cumulative log likelihood ratio (LLR) value is used as the test statistic. Its threshold is determined using an integrity risk minimization problem with an availability constraint. Since there is no closed form for this optimization model, a genetic algorithm with a local search strategy is adopted to find a near-optimal solution. Experimental results show that this method can be used to compute the non-Gaussian fault detection threshold value subject to different availability constraints. Comparisons with empirical and distribution-based methods indicate that while meeting the same probability for falsealert constraint, the probability of missed detection in the optimized approach is much lower than for other methods, especially for small numbers of errors. Since the cumulative LLR value does not exhibit obvious statistical features for any distribution, the performance of our optimized approach is stable for different test cases and satellite data sets.

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“…This method also monitors fault in current measurement based on verified past information. In general, the snapshot method is robust against continuous faults because it can be performed with only a single epoch measurement, yet it is limited in that the multiple faults detection is difficult because it is based on redundant measurements [17]. On contrast, the method using past information is vulnerable to continuous faults because it may use past information including faults that have been already occurred.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of GPS Fault Detection and Isolation via Pseudorange Prediction Model based Test Statistics

Yoo¹,

Ahn²,

Lee³

et al. 2012

Journal of Electrical Engineering and Technology

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-Fault detection and isolation (FDI) algorithms provide fault monitoring methods in GPS measurement to isolate abnormal signals from the GPS satellites or the acquired signal in receiver. In order to monitor the occurred faults, FDI generates test statistics and decides the case that is beyond a designed threshold as a fault. For such problem of fault detection and isolation, this paper presents and evaluates position domain integrity monitoring methods by formulating various pseudorange prediction methods and investigating the resulting test statistics. In particular, precise measurements like carrier phase and Doppler rate are employed under the assumption of fault free carrier signal. The presented position domain algorithm contains the following process; first a common pseudorange prediction formula is defined with the proposed variations in pseudorange differential update. Next, a threshold computation is proposed with the test statistics distribution considering the elevation angle. Then, by examining the test statistics, fault detection and isolation is done for each satellite channel. To verify the performance, simulations using the presented fault detection methods are done for an ideal and real fault case, respectively.

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GPS Integrity Monitoring Method Using Auxiliary Nonlinear Filters with Log Likelihood Ratio Test Approach

Cited by 7 publications

References 14 publications

Reliable urban vehicle localization under faulty satellite navigation signals

Reliable urban vehicle localization under faulty satellite navigation signals

Nonlinear fault detection threshold optimization method for RAIM algorithm using a heuristic approach

Performance Comparison of GPS Fault Detection and Isolation via Pseudorange Prediction Model based Test Statistics

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