Drone Systems and Control Nptel Week 3 Answers
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Drone Systems and Control Nptel Week 3 Answers (July-Dec 2025)
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Question 1. What is the main assumption for using a standard Kalman Filter?
a) System is highly non-linear
b) System is stochastic and time-invariant
c) System is linear with Gaussian noise
d) Measurement noise is always zero
Question 2. In Kalman Filtering, the matrix K, known as the Kalman Gain, determines:
a) System stability
b) Prediction of the state
c) Weight given to the measurement update
d) Time update accuracy
These are Drone Systems and Control Nptel Week 3 Answers
Question 3. Which step comes first in a Kalman Filter cycle?
a) Correction (Update)
b) Prediction
c) Sensor Fusion
d) Smoothing
Question 4. The Kalman Filter provides an optimal estimate in the least squares sense when:
a) Measurement noise is zero
b) Noise is uniform and independent
c) Noise is white and Gaussian
d) States are completely known
Question 5. EKF is used instead of a standard Kalman Filter when the system is:
a) High-dimensional
b) Discrete
c) Linear
d) Non-linear
Question 6. In EKF, the non-linear state transition function is linearized using:
a) Taylor Series expansion
b) Laplace Transform
c) Fourier Transform
d) Principal Component Analysis
Question 7. What matrix replaces the state transition matrix A in the EKF for linearization?
a) Covariance matrix
b) Kalman gain matrix
c) Jacobian matrix of the process model
d) Transition probability matrix
These are Drone Systems and Control Nptel Week 3 Answers
Question 8. Which of the following is a disadvantage of EKF?
a) Cannot handle Gaussian noise
b) Cannot be used in real time
c) Linearization may introduce large errors
d) Too slow for implementation on embedded hardware
Question 9. Which of the following best describes the prediction step in EKF?
a) It directly uses linear matrices
b) It computes expected state using non-linear model
c) It updates the measurement
d) It computes eigenvalues of system matrix
Question 10. Kalman Filters are commonly used in drones for:
a) Obstacle avoidance
b) Propeller design
c) Attitude estimation and sensor fusion
d) Image compression
Question 11. Which of the following sensor combinations is most typically fused using Kalman Filtering in drones?
a) Magnetometer and gyroscope
b) Accelerometer, gyroscope, and GPS
c) Camera and barometer
d) Microphone and lidar
Question 12. In drone navigation, the role of the process model in a Kalman filter is to:
a) Update the sensor frequency
b) Estimate how the state evolves over time
c) Set the hardware limits
d) Calibrate the IMU
Question 13. For real-time attitude estimation in a drone with an IMU, which filter is most commonly used?
a) Particle filter
b) Standard Kalman filter
c) Extended Kalman filter
d) Wiener filter
Question 14. What does the line P = (I – K * H) * P represent in a MATLAB Kalman filter code?
a) Time update of the state
b) Jacobian linearization
c) Covariance update (correction step)
d) Sensor noise modeling
Question 15. In MATLAB-based EKF code, when dealing with a nonlinear measurement model h(x), what is the standard way to get the measurement Jacobian H?
a) Numerical approximation or symbolic Jacobian
b) Use eig(h(x))
c) Just use the identity matrix
d) MATLAB does not support measurement models
These are Drone Systems and Control Nptel Week 3 Answers
