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 All-Chalcogenide Programmable All-Optical Deep Neural Networks

2021 ◽  
Author(s):  
Ting Yu ◽  
Xiaoxuan Ma ◽  
Ernest Pastor ◽  
Jonathan George ◽  
Simon Wall ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Autonomous Vehicles ◽  
Structural Phase ◽  
Activation Function ◽  
Navigation Systems ◽  
Optical Neural Networks ◽  
Electro Optic ◽  
All Optical ◽  
On Chip

Abstract Deeplearning algorithms are revolutionising many aspects of modern life. Typically, they are implemented in CMOS-based hardware with severely limited memory access times and inefficient data-routing. All-optical neural networks without any electro-optic conversions could alleviate these shortcomings. However, an all-optical nonlinear activation function, which is a vital building block for optical neural networks, needs to be developed efficiently on-chip. Here, we introduce and demonstrate both optical synapse weighting and all-optical nonlinear thresholding using two different effects in one single chalcogenide material. We show how the structural phase transitions in a wide-bandgap phase-change material enables storing the neural network weights via non-volatile photonic memory, whilst resonant bond destabilisation is used as a nonlinear activation threshold without changing the material. These two different transitions within chalcogenides enable programmable neural networks with near-zero static power consumption once trained, in addition to picosecond delays performing inference tasks not limited by wire charging that limit electrical circuits; for instance, we show that nanosecond-order weight programming and near-instantaneous weight updates enable accurate inference tasks within 20 picoseconds in a 3-layer all-optical neural network. Optical neural networks that bypass electro-optic conversion altogether hold promise for network-edge machine learning applications where decision-making in real-time are critical, such as for autonomous vehicles or navigation systems such as signal pre-processing of LIDAR systems.

scholarly journals Relative Navigation in UAV Applications

2020 ◽  
Vol vm01 (is02) ◽  
pp. 52-65
Author(s):  
Tuncay Yunus Erkec ◽  
Chingiz Hajiyev
Keyword(s):  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Satellite System ◽  
Formation Flight ◽  
Navigation Systems ◽  
Relative Navigation ◽  
Inertial Navigation Systems ◽  
Estimation Algorithms ◽  
Aerial Vehicles ◽  
Global Navigation Satellite

This paper is committed to the relative navigation of Unmanned Aerial Vehicles (UAVs) flying in formation flight. The concept and methods of swarm UAVs technology and architecture have been explained. The relative state estimation models of unmanned aerial vehicles which are based on separate systems as Inertial Navigation Systems (INS)&Global Navigation Satellite System (GNSS), Laser&INS and Vision based techniques have been compared via various approaches. The sensors are used individually or integrated each other via sensor integration for solving relative navigation problems. The UAV relative navigation models are varied as stated in operation area, type of platform and environment. The aim of this article is to understand the correlation between relative navigation systems and potency of state estimation algorithms as well during formation flight of UAV.

Review On Relative Navigation Methods of Space Vehicles

2020 ◽  
Vol 01 ◽  
Author(s):  
T. Y. Erkec ◽  
C. Hajiyev
Keyword(s):  
Satellite System ◽  
Low Earth Orbit ◽  
Formation Flight ◽  
Space Vehicles ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Relative Navigation ◽  
Inertial Navigation Systems ◽  
Global Navigation Satellite ◽  
Usage Area

Abstract:: This paper is devoted to understand relative navigation models which are used for space vehicles. The relative navigations models and aproaches which are based on different sytems (Inertial Navigation Systems (INS)& Global Navigation Satellite System (GNSS) , Laser&INS, Vision Based, etc.) are compared. These models and aproaches can be used individually or combined with each other for solving the relative navigation problems. Advantages and disadvanteges of the models vary according to the usage area, platform type and environment. Different methods and aproaches exist in addition to different estimation and optimization algoritms for adaptation, control and sensor fusion. Most of the models assume perfect attitude conditions. This study consideres satellites position estimates according to eachother within formation on the Low Earth Orbit (LEO). Also the aim of this article is to understand corelation between the relative navigation systems and effectiveness of the algorithms which are used for the estimating states during constellation or formation flight.

scholarly journals A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2491
Author(s):  
Mauro Tropea ◽  
Angelo Arieta ◽  
Floriano De Rango ◽  
Francesco Pupo
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Propagation Delay ◽  
Satellite System ◽  
Transportation Systems ◽  
Navigation Systems ◽  
Vehicle Positioning ◽  
Satellite Navigation Systems ◽  
Global Navigation Satellite

Vehicle positioning is becoming an important issue related to Intelligent Transportation Systems (ITSs). Novel vehicles and autonomous vehicles need to be localized under different weather conditions and it is important to have a reliable positioning system to track vehicles. Satellite navigation systems can be a key technology in providing global coverage and providing localization services through many satellite constellations such as GPS, GLONASS, Galileo and so forth. However, the modeling of positioning and localization systems under different weather conditions is not a trivial objective especially considering different factors such as receiver sensitivity, dynamic weather conditions, propagation delay and so forth. This paper focuses on the use of simulators for performing different kinds of tests on Global Navigation Satellite System (GNSS) systems in order to reduce the cost of the positioning testing under different techniques or models. Simulation driven approach, combined with some specific hardware equipment such as receivers and transmitters can characterize a more realistic scenario and the simulation can consider other aspects that could be complex to really test. In this work, the main contribution is the introduction of the Troposphere Collins model in a GNSS simulator for VANET applications, the GPS-SDR-SIM software. The use of the Collins model in the simulator allows to improve the accuracy of the simulation experiments throughout the reduction of the receiver errors.

scholarly journals Semi-tightly coupled integration of multi-GNSS PPP and S-VINS for precise positioning in GNSS-challenged environments

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Xingxing Li ◽  
Xuanbin Wang ◽  
Jianchi Liao ◽  
Xin Li ◽  
Shengyu Li ◽  
...  
Keyword(s):  
Global Navigation Satellite System ◽  
Inertial Navigation ◽  
Satellite System ◽  
Navigation Satellite ◽  
Navigation Systems ◽  
Tight Coupling ◽  
Positioning Accuracy ◽  
3D Positioning ◽  
Global Navigation ◽  
Global Navigation Satellite

AbstractBecause of its high-precision, low-cost and easy-operation, Precise Point Positioning (PPP) becomes a potential and attractive positioning technique that can be applied to self-driving cars and drones. However, the reliability and availability of PPP will be significantly degraded in the extremely difficult conditions where Global Navigation Satellite System (GNSS) signals are blocked frequently. Inertial Navigation System (INS) has been integrated with GNSS to ameliorate such situations in the last decades. Recently, the Visual-Inertial Navigation Systems (VINS) with favorable complementary characteristics is demonstrated to realize a more stable and accurate local position estimation than the INS-only. Nevertheless, the system still must rely on the global positions to eliminate the accumulated errors. In this contribution, we present a semi-tight coupling framework of multi-GNSS PPP and Stereo VINS (S-VINS), which achieves the bidirectional location transfer and sharing in two separate navigation systems. In our approach, the local positions, produced by S-VINS are integrated with multi-GNSS PPP through a graph-optimization based method. Furthermore, the accurate forecast positions with S-VINS are fed back to assist PPP in GNSS-challenged environments. The statistical analysis of a GNSS outage simulation test shows that the S-VINS mode can effectively suppress the degradation of positioning accuracy compared with the INS-only mode. We also carried out a vehicle-borne experiment collecting multi-sensor data in a GNSS-challenged environment. For the complex driving environment, the PPP positioning capability is significantly improved with the aiding of S-VINS. The 3D positioning accuracy is improved by 49.0% for Global Positioning System (GPS), 40.3% for GPS + GLOANSS (Global Navigation Satellite System), 45.6% for GPS + BDS (BeiDou navigation satellite System), and 51.2% for GPS + GLONASS + BDS. On this basis, the solution with the semi-tight coupling scheme of multi-GNSS PPP/S-VINS achieves the improvements of 41.8–60.6% in 3D positioning accuracy compared with the multi-GNSS PPP/INS solutions.

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 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.

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