scholarly journals A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages

2020 ◽  
Vol 12 (2) ◽  
pp. 256 ◽  
Author(s):  
Wei Fang ◽  
Jinguang Jiang ◽  
Shuangqiu Lu ◽  
Yilin Gong ◽  
Yifeng Tao ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Satellite System ◽  
Test Results ◽  
Dynamic Information ◽  
Vehicle Data ◽  
Global Navigation Satellite ◽  
Almost All ◽  
Navigation Accuracy ◽  
Ins Aiding

Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.

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

2021 ◽  
Vol 11 (3) ◽  
pp. 1270
Author(s):  
Uche Onyekpe ◽  
Vasile Palade ◽  
Stratis Kanarachos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Inertial Navigation ◽  
Satellite System ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Displacement Estimation ◽  
Global Navigation Satellite

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.

scholarly journals A Quaternion Gated Recurrent Unit Neural Network for Sensor Fusion

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 117
Author(s):  
Uche Onyekpe ◽  
Vasile Palade ◽  
Stratis Kanarachos ◽  
Stavros-Richard G. Christopoulos
Keyword(s):  
Neural Network ◽  
Sensor Fusion ◽  
Language Processing ◽  
Short Term Memory ◽  
Financial Analysis ◽  
Complex Structure ◽  
Satellite System ◽  
Computationally Efficient ◽  
Global Navigation Satellite ◽  
Gated Recurrent Unit

Recurrent Neural Networks (RNNs) are known for their ability to learn relationships within temporal sequences. Gated Recurrent Unit (GRU) networks have found use in challenging time-dependent applications such as Natural Language Processing (NLP), financial analysis and sensor fusion due to their capability to cope with the vanishing gradient problem. GRUs are also known to be more computationally efficient than their variant, the Long Short-Term Memory neural network (LSTM), due to their less complex structure and as such, are more suitable for applications requiring more efficient management of computational resources. Many of such applications require a stronger mapping of their features to further enhance the prediction accuracy. A novel Quaternion Gated Recurrent Unit (QGRU) is proposed in this paper, which leverages the internal and external dependencies within the quaternion algebra to map correlations within and across multidimensional features. The QGRU can be used to efficiently capture the inter- and intra-dependencies within multidimensional features unlike the GRU, which only captures the dependencies within the sequence. Furthermore, the performance of the proposed method is evaluated on a sensor fusion problem involving navigation in Global Navigation Satellite System (GNSS) deprived environments as well as a human activity recognition problem. The results obtained show that the QGRU produces competitive results with almost 3.7 times fewer parameters compared to the GRU. The QGRU code is available at https://github.com/onyekpeu/Quarternion-Gated-Recurrent-Unit.

Multi-DMEs for alternative position, navigation and timing (A-PNT)

2021 ◽  
pp. 1-21
Author(s):  
Xiao Liang ◽  
Carl Milner ◽  
Christophe Macabiau ◽  
Philippe Estival
Keyword(s):  
Global Navigation Satellite System ◽  
Measuring Equipment ◽  
Measurement Accuracy ◽  
Satellite System ◽  
Navigation Satellite ◽  
Test Results ◽  
Reference Area ◽  
Alternative Position ◽  
Alternate Source ◽  
Global Navigation Satellite

Abstract Distance measuring equipment (DME/DME) as the main reversionary method provides alternative positioning, navigation and timing (A-PNT) services for use during a Global Navigation Satellite System (GNSS) outage. Considering the geometry limitation of DME/DME, multi-DMEs with better geometry can be used to increase the accuracy and integrity performance of positioning. This paper discusses the opportunities and challenges related to use of multi-DMEs as an alternate source of positioning, navigation and timing. To support the performance for A-PNT, the basic idea is considering the existing installed equipment. In this paper, barometer altimeter and TACAN are used to help improve the performance of A-PNT provided by multi-DMEs both in accuracy and integrity. Based on the database of EUROCONTROL, the test results demonstrate that 79⋅7% of a reference area roughly matching with the continental European locations achieve RNP 1 using multi-DMEs when the DME measurement accuracy is 0⋅2 NM (95%). When the DME measurement accuracy is 0⋅1 NM (95%), 87⋅9% of the reference area can achieve RNP 1 using multi-DMEs. The usage of barometer/TACAN measurements aided multi-DMEs improves the performance of the accuracy and integrity monitoring.

scholarly journals A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation

2018 ◽  
Vol 8 (11) ◽  
pp. 2322 ◽  
Author(s):  
Lin Zhao ◽  
Mouyan Wu ◽  
Jicheng Ding ◽  
Yingyao Kang
Keyword(s):  
Navigation System ◽  
Density Ratio ◽  
Satellite System ◽  
The Arctic ◽  
Observation Data ◽  
Dual Frequency ◽  
Vector Tracking ◽  
Time Observation ◽  
Global Navigation Satellite ◽  
Navigation Accuracy

The strategic position of the polar area and its rich natural resources are becoming increasingly important, while the northeast and northwest passages through the Arctic are receiving much attention as glaciers continue to melt. The global navigation satellite system (GNSS) can provide real-time observation data for the polar areas, but may suffer low elevation problems of satellites, signals with poor carrier-power-to-noise-density ratio (C/N0), ionospheric scintillations, and dynamic requirements. In order to improve the navigation performance in polar areas, a deep-coupled navigation system with dual-frequency GNSS and a grid strapdown inertial navigation system (SINS) is proposed in the paper. The coverage and visibility of the GNSS constellation in polar areas are briefly reviewed firstly. Then, the joint dual-frequency vector tracking architecture of GNSS is designed with the aid of grid SINS information, which can optimize the tracking band, sharing tracking information to aid weak signal channels with strong signal channels and meet the dynamic requirement to improve the accuracy and robustness of the system. Besides this, the ionosphere-free combination of global positioning system (GPS) L1 C/A and L2 signals is used in the proposed system to further reduce ionospheric influence. Finally, the performance of the system is tested using a hardware simulator and semiphysical experiments. Experimental results indicate that the proposed system can obtain a better navigation accuracy and robust performance in polar areas.

scholarly journals Results of the GNSS receiver experiment OCAM-G on Ariane-5 flight VA 219

2016 ◽  
Vol 231 (6) ◽  
pp. 1100-1114 ◽  
Author(s):  
André Hauschild ◽  
Markus Markgraf ◽  
Oliver Montenbruck ◽  
Horst Pfeuffer ◽  
Elie Dawidowicz ◽  
...  
Keyword(s):  
French Guiana ◽  
Satellite System ◽  
Navigation Satellite ◽  
Navigation Solution ◽  
Gnss Receiver ◽  
Upper Stage ◽  
Lift Off ◽  
Global Navigation Satellite ◽  
Navigation Accuracy ◽  
First Time

The fifth Automated Transfer Vehicle was launched on 29 July 2014 with Ariane-5 flight VA 219 into orbit from Kourou, French Guiana. For the first time, the ascent of an Ariane rocket was independently tracked with a Global Navigation Satellite System (GNSS) receiver on this flight. The GNSS receiver experiment OCAM-G was mounted on the upper stage of the rocket. Its receivers tracked the trajectory of the Ariane-5 from lift-off until after the separation of the Automated Transfer Vehicle. This article introduces the design of the experiment and presents an analysis of the data gathered during the flight with respect to the GNSS tracking status, availability of navigation solution, and navigation accuracy.

scholarly journals GNSS/INS Fusion with Virtual Lever-Arm Measurements

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2228 ◽  
Author(s):  
Aviram Borko ◽  
Itzik Klein ◽  
Gilad Even-Tzur
Keyword(s):  
Field Experiments ◽  
Satellite System ◽  
Improve Performance ◽  
Lever Arm ◽  
Additional Improvement ◽  
Stationary Conditions ◽  
State Observability ◽  
Global Navigation Satellite ◽  
Navigation Accuracy ◽  
Improve State

The navigation subsystem in most platforms is based on an inertial navigation system (INS). Regardless of the INS grade, its navigation solution drifts in time. To avoid such a drift, the INS is fused with external sensor measurements such as a global navigation satellite system (GNSS). Recent publications showed that the lever-arm, defined as the relative position between the INS and aiding sensor, has a strong influence on navigation accuracy. Most research in this field is focused on INS/GNSS fusion with GNSS position or velocity updates while considering various maneuvers types. In this paper, we propose to employ virtual lever-arm (VLA) measurements to improve the accuracy and time to convergence of the observable INS error-states. In particular, we show that VLA measurements improve performance even in stationary conditions. In situations when maneuvering helps to improve state observability, VLA measurements manage to gain additional improvement in accuracy. These results are supported by simulation and field experiments with a vehicle mounted with a GNSS and an INS.

scholarly journals Extensive Investigation of the Land Subsidence Impressions on Gedebage District, Bandung, Indonesia

2021 ◽  
Vol 873 (1) ◽  
pp. 012044
Author(s):  
I Gumilar ◽  
TP. Sidiq ◽  
I Meilano ◽  
B Bramanto ◽  
G Pambudi
Keyword(s):  
Land Subsidence ◽  
Urban Areas ◽  
Average Rate ◽  
Satellite System ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Infrastructure Development ◽  
Global Navigation Satellite ◽  
Almost All ◽  
The Impact

Abstract Gedebage district is presently experiencing rapid and mass infrastructure development and becoming one of the developed districts in Bandung, Indonesia. A football stadium, several luxury housing, the grand mosque of West Java province, and a business center have been built in this district. However, it is well known that the Gedebage district has turned into one of the Bandung districts that suffers from land subsidence phenomena. Since 2000, the Gedebage district has suffered land subsidence at an average rate of 10 cm per year and becoming one of the fastest sinking districts in Bandung. This fast land subsidence phenomenon is suspected of affecting the infrastructure in this district. Therefore, this work aims to capture the current subsidence rate in the Gedebage district using the geodetic approach of the combination of the Global Navigation Satellite System (GNSS) with Interferometric Synthetic Aperture Radar (InSAR) and investigate the impact of land subsidence on infrastructures in Gedebage district. We use GNSS campaign datasets from the years 2016 and 2019. Each GNSS campaign was performed at least 10-12 hours of observations. We also utilize a similar period of 2016 to 2019 for the InSAR datasets. Utilizing both GNSS and InSAR datasets, we can capture the subsidence with the rate reaching 4 -15 cm per year between 2016 and 2019, and it occurs uniformly in this district. The impact of land subsidence occurred in almost all urban areas in the Gedebage district. These impacts include cracks in buildings, bridges and roads, and also tilted buildings.

scholarly journals High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network

2021 ◽  
Vol 10 (9) ◽  
pp. 623
Author(s):  
Yajie Shi ◽  
Chao Ren ◽  
Zhiheng Yan ◽  
Jianmin Lai
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Satellite System ◽  
Environmental Data ◽  
Navigation Satellite ◽  
Global Navigation Satellite

Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technology has become essential for monitoring soil moisture. However, the sparse distribution of GNSS-IR soil moisture sites has hindered the application of soil moisture products. In this paper, we propose a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and we finally obtain a soil moisture product with a high spatial and temporal resolution of 500 m per day. The multi-surface environmental data include latitude and longitude information, rainfall, air temperature, land cover type, normalized difference vegetation index (NDVI), and four topographic factors (elevation, slope, slope direction, and shading). To maximize the spatial and temporal resolution of the GNSS-IR technique within a machine learning framework, we obtained satisfactory results with a cross-validated R-value of 0.8660 and an ubRMSE of 0.0354. This indicates that the machine learning approach learns the complex nonlinear relationships between soil moisture and the input multi-surface environmental data. The soil moisture products were analyzed compared to the contemporaneous rainfall and National Aeronautics and Space Administration (NASA)’s soil moisture products. The results show that the spatial distribution of the GA-BP inversion soil moisture products is more consistent with rainfall and NASA products, which verifies the feasibility of using this experimental model to generate 500 m per day the GA-BP inversion soil moisture products.

scholarly journals Navigation Engine Design for Automated Driving Using INS/GNSS/3D LiDAR-SLAM and Integrity Assessment

2020 ◽  
Vol 12 (10) ◽  
pp. 1564 ◽  
Author(s):  
Kai-Wei Chiang ◽  
Guang-Je Tsai ◽  
Yu-Hua Li ◽  
You Li ◽  
Naser El-Sheimy
Keyword(s):  
Urban Areas ◽  
Satellite System ◽  
Multisensor Fusion ◽  
Automated Driving ◽  
Integrity Assessment ◽  
Localization And Mapping ◽  
Self Driving Cars ◽  
Global Navigation Satellite ◽  
3D Lidar ◽  
Ins Aiding

Automated driving has made considerable progress recently. The multisensor fusion system is a game changer in making self-driving cars possible. In the near future, multisensor fusion will be necessary to meet the high accuracy needs of automated driving systems. This paper proposes a multisensor fusion design, including an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LiDAR), to implement 3D simultaneous localization and mapping (INS/GNSS/3D LiDAR-SLAM). The proposed fusion structure enhances the conventional INS/GNSS/odometer by compensating for individual drawbacks such as INS-drift and error-contaminated GNSS. First, a highly integrated INS-aiding LiDAR-SLAM is presented to improve the performance and increase the robustness to adjust to varied environments using the reliable initial values from the INS. Second, the proposed fault detection exclusion (FDE) contributes SLAM to eliminate the failure solutions such as local solution or the divergence of algorithm. Third, the SLAM position velocity acceleration (PVA) model is used to deal with the high dynamic movement. Finally, an integrity assessment benefits the central fusion filter to avoid failure measurements into the update process based on the information from INS-aiding SLAM, which increases the reliability and accuracy. Consequently, our proposed multisensor design can deal with various situations such as long-term GNSS outage, deep urban areas, and highways. The results show that the proposed method can achieve an accuracy of under 1 meter in challenging scenarios, which has the potential to contribute the autonomous system.

scholarly journals A Unified Model for Multi-Frequency PPP Ambiguity Resolution and Test Results with Galileo and BeiDou Triple-Frequency Observations

2019 ◽  
Vol 11 (2) ◽  
pp. 116 ◽  
Author(s):  
Guorui Xiao ◽  
Pan Li ◽  
Yang Gao ◽  
Bernhard Heck
Keyword(s):  
Ambiguity Resolution ◽  
Satellite System ◽  
Unified Model ◽  
Test Results ◽  
Unified Modeling ◽  
Triple Frequency ◽  
Obvious Benefit ◽  
Modeling Strategy ◽  
Global Navigation Satellite ◽  
Globally Distributed

With the modernization of Global Navigation Satellite System (GNSS), triple- or multi-frequency signals have become available from more and more GNSS satellites. The additional signals are expected to enhance the performance of precise point positioning (PPP) with ambiguity resolution (AR). To deal with the additional signals, we propose a unified modeling strategy for multi-frequency PPP AR based on raw uncombined observations. Based on the unified model, the fractional cycle biases (FCBs) generated from multi-frequency observations can be flexibly used, such as for dual- or triple- frequency PPP AR. Its efficiency is verified with Galileo and BeiDou triple-frequency observations collected from globally distributed MGEX stations. The estimated FCB are assessed with respect to residual distributions and standard deviations. The obtained results indicate good consistency between the input float ambiguities and the generated FCBs. To assess the performance of the triple-frequency PPP AR, 11 days of MGEX data are processed in three-hour sessions. The positional biases in the ambiguity-fixed solutions are significantly reduced compared with the float solutions. The improvements are 49.2%, 38.3%, and 29.6%, respectively, in east/north/up components for positioning with BDS, while the corresponding improvements are 60.0%, 29.0%, and 21.1% for positioning with Galileo. These results confirm the efficiency of the proposed approach, and that the triple-frequency PPP AR can bring an obvious benefit to the ambiguity-float PPP solution.

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