GPS/INS/Odometer Data Fusion for Land Vehicle Localization in GPS Denied Environment

Aftatah, Mohammed; Lahrech, Abdelkabir; Abounada, Abdelouahed; Soulhi, Aziz

doi:10.5539/mas.v11n1p62

Cited by 22 publications

(6 citation statements)

References 11 publications

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“…The continuous improvement and optimization of the Kalman filter have improved the filter's performance in terms of nonlinearity, adaptability, robustness, and fault tolerance [17,18]. However, the inherent defects limit the further improvement of their performance.…”

Section: Multi-source Fusion Localization Based On Factor Graphmentioning

confidence: 99%

Resilient Factor Graph-Based GNSS/IMU/Vision/Odo Integrated Navigation Scheme Enhanced by Noise Approximate Gaussian Estimation in Challenging Environments

Li,

Meng,

Shen

et al. 2024

Remote Sensing

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The signal blockage and multipath effects of the Global Navigation Satellite System (GNSS) caused by urban canyon scenarios have brought great technical challenges to the positioning and navigation of autonomous vehicles. In this paper, an improved factor graph optimization algorithm enhanced by a resilient noise model is proposed. The measurement noise is resilient and adjusted based on an approximate Gaussian distribution-based estimation. In estimating and adjusting the noise parameters of the measurement model, the error covariance matrix of the multi-sensor fusion positioning system is dynamically optimized to improve the system accuracy. Firstly, according to the approximate Gaussian statistical property of the GNSS/odometer velocity residual sequence, the measured data are divided into an approximate Gaussian fitting region and an approximate Gaussian convergence region. Secondly, the interval is divided according to the measured data, and the corresponding variational Bayesian network and Gaussian mixture model are used to estimate the innovation online. Further, the noise covariance matrix of the adaptive factor graph-based model is dynamically optimized using the estimated noise parameters. Finally, based on low-cost inertial navigation equipment, GNSS, odometer, and vision, the algorithm is implemented and verified using a simulation platform and real-vehicle road test. The experimental results show that in a complex urban road environment, compared with the traditional factor graph fusion localization algorithm, the maximum improvement in accuracy of the proposed algorithm can reach 65.63%, 39.52%, and 42.95% for heading, position, and velocity, respectively.

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Section: Multi-source Fusion Localization Based On Factor Graphmentioning

confidence: 99%

Resilient Factor Graph-Based GNSS/IMU/Vision/Odo Integrated Navigation Scheme Enhanced by Noise Approximate Gaussian Estimation in Challenging Environments

Li,

Meng,

Shen

et al. 2024

Remote Sensing

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show abstract

“…Global Navigation Satellite System (GNSS) technology has become an integral part of our daily lives. It is used in various applications, such as navigation, transportation, and time synchronization [1] and [6]. However, the widespread use of GNSS technology has also made it vulnerable to attacks, such as spoofing.…”

Section: Introductionmentioning

confidence: 99%

GNSS Spoofing - Advanced Mechanizms of Detection

Šimák¹

2023

Transp. commun.

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GNSS spoofing is a technique used to deceive Global Navigation Satellite Systems (GNSS) receivers by broadcasting fake signals that appear to be genuine. To detect GNSS spoofing, a receiver can use various techniques such as monitoring signal strength, cross-checking data from multiple satellites, comparing the signal characteristics with the expected patterns, and analyzing the timing and location information. Advanced detection methods may use machine learning algorithms to identify anomalies and patterns in the signal data. In addition, the use of encrypted signals and multiple frequency bands can make spoofing more difficult, and the implementation of spoofing-resistant hardware and software can further enhance detection capabilities. In this article various techniques of manipulation and detection of spoofing and experiments are described. There is no 100% method for spoofing detection.

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“…The challenge, therefore, becomes one of accurately predicting the position of the vehicle in the absence of the GNSS signal needed for positioning and correction. Traditionally, Kalman filters are used in modelling the error between the Global Positioning System (GPS) and INS positions [6][7][8]. However, they have limitations when modelling highly non-linear dependencies, stochastic relationships and non-Gaussian noise measurements [6].…”

Section: Introductionmentioning

confidence: 99%

Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

2021

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An approach based on Artificial Neural Networks is proposed in this paper to improve the localisation accuracy of Inertial Navigation Systems (INS)/Global Navigation Satellite System (GNSS) based aided navigation during the absence of GNSS signals. The INS can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, however, it suffers from unbounded exponential error drifts cascaded over time during the multiple integrations of the accelerometer and gyroscope measurements to position. More so, the error drift is characterised by a pattern dependent on time. This paper proposes several efficient neural network-based solutions to estimate the error drifts using Recurrent Neural Networks, such as the Input Delay Neural Network (IDNN), Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), and Gated Recurrent Unit (GRU). In contrast to previous papers published in literature, which focused on travel routes that do not take complex driving scenarios into consideration, this paper investigates the performance of the proposed methods on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences. The results obtained show that the Neural Network-based approaches are able to provide up to 89.55% improvement on the INS displacement estimation and 93.35% on the INS orientation rate estimation.

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GPS/INS/Odometer Data Fusion for Land Vehicle Localization in GPS Denied Environment

Cited by 22 publications

References 11 publications

Resilient Factor Graph-Based GNSS/IMU/Vision/Odo Integrated Navigation Scheme Enhanced by Noise Approximate Gaussian Estimation in Challenging Environments

Resilient Factor Graph-Based GNSS/IMU/Vision/Odo Integrated Navigation Scheme Enhanced by Noise Approximate Gaussian Estimation in Challenging Environments

GNSS Spoofing - Advanced Mechanizms of Detection

Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

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