170911 - Frequency Domain and Digital Filtering

Question 1

Digital filtering is implemented using mathematical functions.

Answer 1

True

Question 2

A moving average filter is more robust than a median filter and cannot provide impossible values.

Answer 2

False

Question 3

An exponentially weighted moving average filter is a low-pass filter.

Answer 3

True

Question 4

A low pass filter introduces a delay and this delay is larger the higher the frequency is.

Answer 4

True

Question 5

A high-pass filter stops high-frequency noise.

Answer 5

False

Question 6

The higher order (e.g. more points in a moving average filter), the more selective it is.

Answer 6

True

Question 7

The higher order (e.g. more points in a moving average filter), the less sudden changes are timely detected.

Answer 7

True

Question 8

A Kalman filter is an algorithm for optimal state estimation for linear systems in which controls and measures are affected be Gaussian noise.

Answer 8

True

Question 9

It is sufficient one single measure (either a control or an output to implement Kalman filtering).

Answer 9

False

Question 10

Kalman filtering can be used for sensor fusion in active safety systems.

Answer 10

True

Question 11

Kalman filtering works in two steps, prediction and correction.

Answer 11

True

Question 12

Kalman filters make the best out of the known information by determining which information we should trust the most (at each loop) to estimate our state.

Answer 12

True

Question 13

Unscented KF and extended KF provide an alternative to kalman filter when the error is not Gaussian.

Answer 13

False

Question 14

Particle filters are very general, they do not require for a system to be linear nor for the error to be Gaussian, but they are very computationally demanding.

Answer 14

True