TU Delft
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2016/2017 Aerospace Engineering Bachelor Aerospace Engineering
Instrumentation & Signals
Responsible Instructor
Name E-mail
Prof.dr.ir. M. Mulder    M.Mulder@tudelft.nl
Contact Hours / Week x/x/x/x
Education Period
Start Education
Exam Period
Course Language
Course Contents
Week 1, Lectures 1-2
Introduction; Basic principles of signal acquisition, conditioning, modulation and transfer;
Definitions (continuous time, discrete time, periodic/a-periodic); Basic signal shapes (unit pulse, step, ramp, sinusoid); Refresh complex algebra, Euler's theorem;
Introduce Unit impulse function (Dirac, sifting property)

Week 1, Lectures 3-4
Signal decomposition; Fourier Series (real and complex exponential versions); Sinc function; Examples.

Week 2, Lectures 5-6
Fourier Transform, Basic Transforms, Duality, Transform pairs, Properties of FT, Convolution; Examples.

Week 2, Lectures 7-8
Relation Fourier Transform and Fourier Series; Examples
Energy and Power, Parseval's Theorem; Definition of Energy and Power Spectral Density; Examples.

Week 3, Lectures 9-10
Introduction to linear time-invariant systems (LTI); Impulse response function, Transfer function; Examples.

Week 3, Lectures 11-12
Fourier Analysis, Frequency-response function; Filtering, filtering properties (bandwidth, rise time); Examples.

Week 4, Lectures 13-14
Sampling; A/D and D/A conversion; Impulse-train sampling and signal reconstruction; Nyquist sampling theorem, aliasing; Examples.

Week 4, Lectures 15-16
Introduction to Discrete Fourier Transform (DFT) and Fast-Fourier Transform (FFT); Examples

Week 5, Studio Classroom Session
Sampling, aliasing, windowing, leakage. Basic signal conditioning; Basic filter design: Low-pass, High-pass, Band-pass. Using data from actual aerospace sensors.

Week 6, Lectures 17-18
Basic principles of transferring information (communication); modulation (digital system, binary signaling); On-Off-Keying, and Binary Phase Shift Keying; Time and frequency representation.

Week 6, Lectures 19-20
Effects of noise; Additive White Gaussian Noise (AWGN); thermal noise, noise temperature, noise density, effective noise bandwidth; Signal-to-Noise-Ratio (SNR); Signal detection.

Week 7, Lectures 21-22
Optimal signal detection (false alarm and missed detection probabilities); range estimation for navigation and surveillance; examples in aerospace; signal bandwidth versus bit-rate (communication) link/channel capacity, and bit energy to noise density ratio Eb/N0. Signal bandwidth versus ranging accuracy (navigation), chip/pulse duration, and carrier to noise density ratio C/N0.

Week 7, Lectures 23-24
Design calculations for telecommunications sub-system in aerospace; basic radio (wireless) signal link budget (aand radar equation): Emitted Isotropic Radiation Power (EIRP), free space
loss, and antenna gain. Examples: satellite-Earth link, aircraft-tower link, and radar two-way sensing; quick review of transmitter and receiver building blocks.
Study Goals
At the end of this course, the student will be able to:

1. How to acquire and condition a signal (from a transducer) suitable for further processing?
2. How to transfer a signal from A to B?
3. Comprehend signal representation/decomposition in time domain and frequency domain
4. Design a filter to condition signal (including trade-offs in performance)
5. Design a sampling scheme
6. Apply filtering and sampling to an actual case, and evaluate the result
7. Comprehend limitations and constraints (quantization, signal-to-noise)
8. Know building block, electronic components, and comprehend main functions
9. Comprehend signal modulation techniques
10. Apply & implement modulation to a (simplified) real signal
11. Comprehend signal detection techniques
12. Produce a signal link budget
Education Method
Lecturing and self study. A studio classroom session is planned to demonstrate the concepts of signal conditioning, filtering, and sampling.
Literature and Study Materials
The material consists of:
[1] Selected chapters from the book "Signals and Systems - Continuous and Discrete", fourth edition, by Ziemer, Tranter and Fannin. Pearson International Edition.
[2] The lecture slides, which will be put on the Blackboard.
[3] Additional hand-outs, which will be put on the Blackboard.
[4] Reader for course AE2105 "Instrumentation and Signals - signal modulation and detection", by C.C.J.M. Tiberius, edition June 2011 (MicroWebEdu artikelnummer 06917710032).
Written exam (open and multiple choice questions). Additionally, the Studio Classroom session will require students (in groups of 3-4 persons) to write a short report of 2 pages each. When the reports are correct, and the grade for the written exam is 5.0 or higher, students can gain one additional bonus-point for the examination. The bonus point will be valid only in the academic year where the studio classroom sessions have been conducted.
Permitted Materials during Tests
Formula sheet and normal, non-programmable calculator.
On a typical day, a student participates in an active lecture. The lecturer explains the (mathematical) background of signal acquisition, conditioning and transfer problems and performs the basic calculations. Students are required to, occasionnally, do these calculations themselves, or have to answer multiple-choice questions (during which the lecture is paused).

On another day, some weeks later, the student participates in a group (3-4 students) in a studio classroom session, complementing the 12 more traditional lectures. The groups are working on experimental data (coming from real aerospace sensors) and are required to do some elementary manipulations that correspond with the progress made during the active lecture. The classroom sessions focus on two of the most important themes: Filtering, and Sampling. PYTHON is the platform of choice, and example py-files and data files will be made available to the students. The first two hours are used to let groups work on the basics of FFT on data; the second two hours are used to let groups work on their experimental set-up, measuring real-life data and working on that. The practical aims at including data coming from real aerospace sensors, like accelerometers, rate gyros and other sensors.

week 5: Day depends on group A-D, Filled-in answer sheet from the studio classroom