Browsing by Author "Geragersian, Patrick"

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    GNSS/INS/VO fusion using gated recurrent unit in GNSS denied environments
    (AIAA, 2023-01-19) Negru, Sorin A.; Geragersian, Patrick; Petrunin, Ivan; Zolotas, Argyrios; Grech, Raphael
    Urban air mobility is a growing market, which will bring new ways to travel and to deliver items covering urban and suburban areas, at relatively low altitudes. To guarantee a safe and robust navigation, Unmanned Aerial Vehicles should be able to overcome all the navigational constraints. The paper is analyzing a novel sensor fusion framework with the aim to obtain a stable flight in a degraded GNSS environment. The sensor fusion framework is combining data coming from a GNSS receiver, an IMU and an optical camera under a loosely coupled scheme. A Federated Filter approach is implemented with the integration of two GRUs blocks. The first GRU is used to increase the accuracy in time of the INS, giving as output a more reliable position that it is fused, with the position information coming from, the GNSS receiver, and the developed Visual Odometry algorithm. Further, a master GRU block is used to select the best position information. The data is collected using a hardware in the loop setup, using AirSim, Pixhawk and Spirent GSS7000 hardware. As validation, the framework is tested, on a virtual UAV, performing a delivery mission on Cranfield university campus. Results showed that the developed fusion framework, can be used for short GNSS outages.
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    A hybrid deep learning approach for robust multi-sensor GNSS/INS/VO fusion in urban canyons
    (The Institute of Navigation, 2023-09-15) Geragersian, Patrick; Petrunin, Ivan; Guo, Weisi; Grech, Raphael
    This paper addresses the significant challenges of robust autonomous navigation in Unmanned Aerial Vehicles (UAVs) within densely populated environments. The focus is on enhancing the performance of Position, Navigation, and Timing (PNT), as specified by the International Civil Aviation Organization, in terms of accuracy, integrity, continuity, and availability. The novel contribution introduces a Robust Multi-Sensor Fusion Architecture (RMSFA) that utilizes a Bayesian-LSTM machine learning algorithm, fusing GNSS, INS, and monocular odometry. Unlike existing solutions that rely on sensor redundancies or methods such as Receiver Autonomous Integrity Monitoring (RAIM), which have limitations in performance, or adaptability to erroneous signals, the proposed system offers improvements in both positioning accuracy and integrity. Furthermore, GNSS data is preprocessed to remove NoneLine-of-Sight data (NLOS) to improve positioning accuracy. Additionally, INS data errors are corrected using a GRU-based error correction architecture to improve INS positioning and reduce drifting. The addition of these post-processing steps reduced the 95th percentile horizontal error by 97.4% and 71.5% respectively. A CNN-LSTM architecture is used to obtain a Visual Odometer (VO) from the camera sensor. The Bayesian-LSTM architecture fusion performance was then compared to a GNSS/IMU/VO EKF-GRU architecture. The comparison showed a 95th percentile error improvement in the horizontal direction of 30.1% for the BayesianLSTM. The architecture was tested in a realistic simulated environment utilizing Unreal Engine and AirSim for UAV simulation, Spirent GNSS7000 simulator for Hardware-in-the-Loop (HIL) simulation, and OKTAL-SE Sim3D to mimic the effects of multipath on GNSS signals. Overall, this work represents a step toward improving the safety and effectiveness of drone navigation by providing a more robust navigation system suitable for safety-critical situations, without the stated disadvantages in previously mentioned literatures.
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    An INS/GNSS fusion architecture in GNSS denied environment using gated recurrent unit
    (AIAA, 2021-12-29) Geragersian, Patrick; Petrunin, Ivan; Guo, Weisi; Grech, Raphael
    One of the most used Position, Navigation and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit line-of-sight and reduce satellite availability to receivers. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as transportation of organs which are time-sensitive. Therefore, higher accuracy for position and velocity information is required. This paper investigates the use of Gated Recurrent Units (GRU) as a suitable technique that can memorize previous information in conjunction with the inputs (consisting of attitude, change in attitude, and change in velocity) to reduce position and velocity error when GNSS is not available. The fusion approach is developed and tested using Spirent’s SimGEN GSS7000 hardware simulator which simulates GNSS signals and Spirent’s SimSENSOR software that simulates accelerometer and gyroscope stochastic and deterministic errors. GNSS outage is varied between 1 and 20 seconds randomly to affect predicted position and velocity. The data is collected and used to train the GRU to predict the position and velocity error measured by the Inertial Measurement Unit (IMU). From the performance evaluation, a 60% reduction in Root Mean Squared Error (RMSE) is observed compared to Recurrent Neural Networks (RNN). Comparing 95th percentile with Inertial Navigation System (INS), RNN, and GRU, an 80% reduction is observed between INS and RNN. Furthermore, a 35% drop in the 95th percentile is observed between RNN and GRU.
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    Multipath detection from GNSS observables using gated recurrent unit
    (IEEE, 2022-10-31) Geragersian, Patrick; Petrunin, Ivan; Guo, Weisi; Grech, Raphael
    One of the most used Position, Navigation, and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit Line-Of-Sight (LOS) and increase position ambiguity. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as the transportation of organs that are time-sensitive. Therefore, techniques to mitigate Non-Line-Of-Sight (NLOS) interference are required for improved positioning accuracy. This paper proposes a Gated Recurrent Unit-based (GRU) multipath detection algorithm that uses pseudorange, ephemerides, Doppler shift, Carrier-To-Noise Ratio (C/N0), and elevation data from each satellite to determine whether multipath is present. Signals from the satellite classified as multipath are then flagged and ignored for Position, Velocity, and Timing (PVT) calculations until they are deemed as LOS. The classification algorithm is developed and tested on Spirent GSS7000 to generate GNSS Radio Frequency (RF). OKTAL-SE Sim3D is used to simulate urban canyon environments in which signals propagate from the satellite to the receiver. RF signals are then transmitted to a Ublox F9P GNSS receiver that can receive GPS and GLONASS signals which are processed to output PVT information. The data collected is used to train the GRU to classify received signals as no multipath or multipath. From performance evaluation, GRU outperforms decision tree, K-Nearest Neighbor (KNN) classifiers, and Support Vector Machines (SVM). Furthermore, comparing GRU with SVM, a 50% increase in accuracy is observed with a 95% error of 0.85 m for GRU compared to 1.78 m for SVM.
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    Performance enhancement of low-cost INS/GNSS navigation system operating in urban environments
    (AIAA, 2023-01-19) Ozdemir, Yunus Emre; Isik, Oguz Kagan; Geragersian, Patrick; Petrunin, Ivan; Grech, Raphael; Wong, Ronald
    As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.
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    Resilient multi-sensor UAV navigation with a hybrid federated fusion architecture
    (MDPI, 2024-02-02) Negru, Sorin Andrei; Geragersian, Patrick; Petrunin, Ivan; Guo, Weisi
    Future UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations leads to NLOS (non-line-of-sight) and multipath effects, which in turn impact the accuracy of the PNT data. A key research question within the research community pertains to determining the appropriate hybrid fusion architecture that can ensure the resilience and continuity of UAV operations in urban environments, minimizing significant degradations of PNT data. In this context, we present a novel federated fusion architecture that integrates data from the GNSS, the IMU (inertial measurement unit), a monocular camera, and a barometer to cope with the GNSS multipath and positioning performance degradation. Within the federated fusion architecture, local filters are implemented using EKFs (extended Kalman filters), while a master filter is used in the form of a GRU (gated recurrent unit) block. Data collection is performed by setting up a virtual environment in AirSim for the visual odometry aid and barometer data, while Spirent GSS7000 hardware is used to collect the GNSS and IMU data. The hybrid fusion architecture is compared to a classic federated architecture (formed only by EKFs) and tested under different light and weather conditions to assess its resilience, including multipath and GNSS outages. The proposed solution demonstrates improved resilience and robustness in a range of degraded conditions while maintaining a good level of positioning performance with a 95th percentile error of 0.54 m for the square scenario and 1.72 m for the survey scenario.
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    Robust autonomous navigation for UAVS in urban environments using machine learning
    (Cranfield University, 2024-02) Geragersian, Patrick; Petrunin, Ivan; Guo, Weisi
    Urban canyons, characterized by their high-rise buildings and dense infrastructure, pose significant challenges to Unmanned Ariel Vehicle (UAV) navigation primarily due to the obstruction and interference of Global Navigation Satellite Systems (GNSS) signals. These challenges include multipath effects, where signals bounce off multiple surfaces before reaching the receiver, leading to inaccurate positioning data, and direct signal blockage, which causes intermittent loss of satellite visibility. The reliance on GNSS for UAV navigation in such environments is further compromised by potential denial of service, whether intentional or unintentional, through interference. These limitations highlight the need to explore alternative or supplementary navigation technologies to ensure the safe and efficient operation of UAVs in urban settings. To address these challenges, this research begins by focusing on reducing sensor errors in both GNSS and Inertial Navigation Systems (INS). Specifically, data-driven approaches have been proposed to mitigate errors from individual sensors. For INS, a Gated Recurrent Units (GRU)-based error prediction algorithm, trained on labelled sensor data, is utilised to reduce the effects of random walk caused by the sensor’s stochastic noise, which accumulates over time. For GNSS, a GRU classification algorithm is employed to detect and exclude Non-Line-Of-Sight (NLOS) signals from the list of available GNSS satellites. Once NLOS signals are removed, the remaining signals are ranked based on their contribution to Geometric Dilution of Precision (GDOP). The top 10 ranked signals are then used to compute the receiver's position, resulting in improved positioning accuracy and greater reliability over time. Building on these initial advancements, this thesis proposes a robust federated multi- sensor fusion architecture to further enhance positioning accuracy. The proposed architecture integrates GNSS and INS positioning, with additional enhancements provided by GRU-based corrections. Furthermore, Monocular Visual Odometry (VO) and a barometer are incorporated to refine positioning in GNSS-degraded environments. A novel Bayesian Long Short-Term Memory (LSTM) model with Monte Carlo dropouts is introduced to generate stochastic outputs, enabling the system to estimate navigation integrity by predicting position distributions and calculating Protection Levels (PL). By quantifying the reliability of navigation data, this approach ensures the safety and accuracy required for autonomous UAV operations in complex urban environments. The proposed architecture was validated using a comprehensive simulation setup. This includes GNSS Hardware-in-the-Loop (HIL) simulations with the Spirent GSS7000 and OKTAL-SE Sim3D for realistic modelling of multipath effects, as well as high-fidelity VO sensor data generated through AirSim and Unreal Engine. These simulations replicate urban canyon scenarios, accounting for variations in GNSS signal quality and adverse weather conditions, providing a robust environment to test navigation performance. The results indicate significant advancements in UAV navigation, including a 22.1% reduction in the 95th percentile horizontal error compared to state-of-the-art federated fusion approaches and a 98% reduction in misleading integrity information relative to traditional Extended Kalman Filters (EKF). These improvements demonstrate the architecture’s ability to deliver accurate, reliable navigation with a quantifiable measure of integrity, even in the most challenging urban environments. This research has profound implications for the future of Urban Air Mobility (UAM). UAM applications, such as passenger air taxis, last-mile deliveries, and emergency response, rely on precise and reliable navigation systems to ensure safe operation in urban settings. The ability to provide integrity information—quantifying the reliability of positioning data—is crucial for regulatory compliance, operational safety, and public trust in autonomous aerial systems. By addressing the challenges of GNSS-degraded environments and offering a robust framework for navigation with measurable integrity, this study lays a strong foundation for UAM. It enables scalable, reliable, and safe integration of UAVs into urban airspaces, bringing the vision of autonomous urban transportation closer to reality.
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    Uncertainty-based sensor fusion architecture using Bayesian-LSTM neural network
    (AIAA, 2023-01-19) Geragersian, Patrick; Petrunin, Ivan; Guo, Weisi; Grech, Raphael
    Uncertainty-based sensor management for positioning is an essential component in safe drone operations inside urban environments with large urban valleys. These canyons significantly restrict the Line-Of-Sight signal conditions required for accurate positioning using Global Navigation Satellite Systems (GNSS). Therefore, sensor fusion solutions need to be in place whichcan take advantage of alternative Positioning, Navigation and Timing (PNT) sensors such as accelerometers or gyroscopes to complement GNSS information. Recent stateof-art research has focused on Machine Learning (ML) techniques such as Support Vector Machines (SVM) that utilize statistical learning to provide an output for a given input. However, understanding the uncertainty of these predictions made by Deep Learning (DL) models can help improve integrity of fusion systems. Therefore, there is a need for a DL model that can also provide uncertainty-related information as the output. This paper proposes a Bayesian-LSTM Neural Network (BLSTMNN) that is used to fuse GNSS and Inertial Measurement Unit (IMU) data. Furthermore, Protection Level (PL) is estimated based on the uncertainty distribution given by the system. To test the algorithm, Hardware-In-the-Loop (HIL) simulationhas been performed,utilizingSpirent’s GSS7000 simulator and OKTAL-SE Sim3D to simulate GNSS propagation in urban canyons. SimSENSOR is used to simulate the accelerometer and gyroscope. Results show that Bayesian-LSTM provides the best fusion performance compared to GNSS alone, and GNSS/IMU fusion using EKF and SVM. Furthermore, regarding uncertainty estimates, the proposed algorithm can estimate the positioning boundaries correctly, with an error rate of 0.4% and with an accuracy of 99.6%.