CAE352 Aerodynamics of Drones Syllabus:

CAE352 Aerodynamics of Drones Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

1. To introduce students to the basic concepts of payloads in UAV.
2. To understand the various sensor system of an UAV.
3. To introduce with the concepts of data algorithms and architectures.
4. To introduce the concepts of artificial neural networks.
5. To expose students to the concept of fuzzy logic.

UNIT-I PAYLOAD FOR UAV

Introduction – Types – Non-dispensable Payloads – Electro-optic Payload Systems – Electro-optic Systems Integration – Radar Imaging Payloads – Other Non-dispensable Payloads – Dispensable Payloads – Payload Development.

UNIT-II SENSOR

Data fusion applications to multiple sensor systems – Selection of sensors – Benefits of multiple sensor systems – Influence of wavelength on atmospheric attenuation – Fog characterization – Effects of operating frequency on MMW sensor performance – Absorption of MMW energy in rain and fog – Backscatter of MMW energy from rain – Effects of operating wavelength on IR sensor performance – Visibility metrics – Atmospheric and sensor system computer simulation models

UNIT-III DATA FUSION ALGORITHMS AND ARCHITECTURES

Definition of data fusion – Level 1 processing – Detection, classification, and identification algorithms for data fusion – State estimation and tracking algorithms for data fusion – Level 2, 3, and 4 processing – Data fusion processor functions – Definition of an architecture – Data fusion architectures – Sensor-level fusion – Central-level fusion – Hybrid fusion

UNIT-IV ARTIFICIAL NEURAL NETWORKS

Applications of artificial neural networks – Adaptive linear combiner – Linear classifiers – Capacity of linear classifiers – Nonlinear classifiers – Madaline – Feedforward network – Capacity of nonlinear classifiers – Supervised and unsupervised learning – Supervised learning rules – Voting Logic Fusion

UNIT-V FUZZY LOGIC AND FUZZY NEURAL NETWORKS

Conditions under which fuzzy logic provides an appropriate solution – Illustration of fuzzy logic in an automobile antilock braking system – Basic elements of a fuzzy system – Fuzzy logic processing – Fuzzy centroid calculation

TOTAL: 45 PERIODS
COURSE OUTCOMES:

Students will be able to
CO1 Calculate the payloads in UAV.
CO2 Explain the concepts sensor systems.
CO3 Predict the data fusion algorithms and architectures.
CO4 Learn the basics neural network systems
CO5 Design various network schemes.

TEXT BOOKS:

1. Reg Austin Aeronautical Consultant, AJohn “Unmanned aircraft systems UAVs design, development and deployment” Wiley and Sons, Ltd., Publication,2010
2. David L. Hall, Sonya A. H. McMullen “Mathematical Techniques in Multi-sensor Data Fusion”, by Artech, 2004
3 Martin Liggins II David Hall, James “Handbook of Multisensor Data Fusion: Theory and Practice”, Second Edition (Electrical Engineering & Applied Signal Processing Series), 2008.

REFERENCES:

1. Lawrence A. Klein, “Sensor and Data Fusion: A Tool for Information Assessment and Decision Making”, Second Edition, SPIE Press, 2013.
2. Jitendra R. Raol, “Multi-Sensor Data Fusion with MATLAB”, CRC Press, 2010.