GNSS/IMU laser quadrant detector hybridization techniques for artillery rocket guidance

Celis, Raúl de; Cadarso, Luís

doi:10.1007/s11071-017-4039-3

Cited by 13 publications

(8 citation statements)

References 15 publications

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“…Determining the position of the vehicle may consist of integrating accelerometers' measurements to be hybridized with GNSS sensor information. The details of these calculations are out of the scope of this paper, see [1,2,28,29] for details on this approach.…”

Section: Navigationmentioning

confidence: 99%

“…The control law presented in [29] is employed in the current research. Two control conditions are presented in the actuation device: the modulus and the angle for the control force.…”

Section: Control Systemmentioning

confidence: 99%

“…[1] shows the validation of this modeling and solving approach for aerial platforms. To demonstrate the precision of the novel methodology introduced in this research, which is based on neural networks, the obtained results are compared to the obtained outcomes in [29]. The methodology in [29] features a Kalman based hybridization [43,44] of GNSS, IMU and semiactive laser quadrant photo-detector sensors.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…To demonstrate the precision of the novel methodology introduced in this research, which is based on neural networks, the obtained results are compared to the obtained outcomes in [29]. The methodology in [29] features a Kalman based hybridization [43,44] of GNSS, IMU and semiactive laser quadrant photo-detector sensors. To integrate the equations of motion and their interactions with GNC system and environment, MATLAB/Simulink R2020a on a personal computer with a processor of 2.8 Ghz and 32 GB RAM is used.…”

Section: Numerical Simulationsmentioning

confidence: 99%

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Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

2020

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The Guidance, Navigation and Control (GNC) of air and space vehicles has been one of the spearheads of research in the aerospace field in recent times. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, these sensor-based GNC systems may cause significant errors in determining attitude and position. These effects can be ameliorated using additional sensors, independent of cumulative errors. The quadrant photodetector semiactive laser is a good candidate for such a purpose. However, GNC systems’ development and construction costs are high. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while eliminating sensors are cornerstone. The development and application of machine learning techniques to GNC poses an innovative path for reducing complexity and costs. Here, a new nonlinear hybridization algorithm, which is based on neural networks, to estimate the gravity vector is presented. Using a neural network means that once it is trained, the physical-mathematical foundations of flight are not relevant; it is the network that returns dynamics to be fed to the GNC algorithm. The gravity vector, which can be accurately predicted, is used to determine vehicle attitude without calling for gyroscopes. Nonlinear simulations based on real flight dynamics are used to train the neural networks. Then, the approach is tested and simulated together with a GNC system. Monte Carlo analysis is conducted to determine performance when uncertainty arises. Simulation results prove that the performance of the presented approach is robust and precise in a six-degree-of-freedom simulation environment.

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Section: Navigationmentioning

confidence: 99%

“…The control law presented in [29] is employed in the current research. Two control conditions are presented in the actuation device: the modulus and the angle for the control force.…”

Section: Control Systemmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

Section: Numerical Simulationsmentioning

confidence: 99%

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Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

2020

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“…Additionally, control forces and moments in the body axis for each of the four control surfaces must be assessed. For this reason, the model characterized in is utilized, substituting the aerodynamic parameters with those expressed in Table . The effective incident aerodynamic speed on each of the four control surfaces must be evaluated.…”

Section: Gravity Vector Estimationmentioning

confidence: 99%

An Estimator for UAV Attitude Determination based on Accelerometers, GNSS Sensors, and Aerodynamic Coefficients

Celis

Cadarso

2018

J Inst Navig

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Accurate navigation and control of Unmanned Air Vehicles (UAV) are strongly linked to the estimation of position and attitude. Rotation of an aircraft can be determined measuring two vectors, for example, velocity and gravity vectors, in two independent reference frameworks, for example, inertial and the body reference frames. The velocity vector can be determined in inertial reference frame from a GNSS‐based sensor and in the body reference frame by integrating accelerometer measurements. A novel approach is presented for gravity vector estimation in the body reference frame, which is based on UAV aerodynamics and mass properties. Combining both vectors enables determination of body rotation. Nonlinear simulations show that, utilizing GNSS sensors, strap‐down accelerometers and the proposed approach, aircraft attitude determination is accurate. The developed calculations are implemented on a fixed‐wing UAV, permitting the substitution of specific attitude determination gadgets, which are normally costly for several applications. © 2018 Institute of Navigation.

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Anisotropic Plasmonic Nanostructure Induced Polarization Photoresponse for MoS₂‐Based Photodetector

Chen

Cao

Chen

et al. 2020

Adv Materials Inter

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Techniques that give anisotropic properties to 2D materials with isotropic crystal structures will enable 2D material based photodetectors to possess polarization sensitivity in the detection of polarization, which will greatly improve the spatial resolution of photodetectors. Here, an MoS2‐based polarization photodetector induced by anisotropic plasmonic nanostructure is prepared. The integrating anisotropic plasmonic structures on a few‐layer MoS2 nanosheet can induce polarization photoresponse and enhance the responsivity of MoS2 device at the same time. The ratio of the maximum to minimum photocurrents reached 1.45 along two perpendicular polarizations. It proves that anisotropic nanostructure induced polarization sensitivity will pave a new way for the fabrication of polarization photodetectors.

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GNSS/IMU laser quadrant detector hybridization techniques for artillery rocket guidance

Cited by 13 publications

References 15 publications

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

An Estimator for UAV Attitude Determination based on Accelerometers, GNSS Sensors, and Aerodynamic Coefficients

Anisotropic Plasmonic Nanostructure Induced Polarization Photoresponse for MoS₂‐Based Photodetector

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GNSS/IMU laser quadrant detector hybridization techniques for artillery rocket guidance

Cited by 13 publications

References 15 publications

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

An Estimator for UAV Attitude Determination based on Accelerometers, GNSS Sensors, and Aerodynamic Coefficients

Anisotropic Plasmonic Nanostructure Induced Polarization Photoresponse for MoS2‐Based Photodetector

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Anisotropic Plasmonic Nanostructure Induced Polarization Photoresponse for MoS₂‐Based Photodetector