The course will be divided into two parts: the millimeter wave part and the signal processing part. The first – PART A: Millimeter wave radar – part will focus on physical and hardware aspects of high-frequency radar applications, whereas the second – PART B: Signal processing – part will touch upon several aspects which are either involved with the design of the radar waveform or the evaluation of the received echo signals.

**PART A**

This lecture will give an introduction about radar system design, especially taking the demands of high resolution millimeter wave radars into account. It covers the discussion of the radar equation and the implications on hardware and antenna systems. The main principles of pulsed, continuous wave (CW) and frequency modulated (FMCW) radar systems are shown. Major parts of a radar system are the signal generation, the high frequency front-end as well as the back-end. These components are discussed in detail to demonstrate the propagation of the entire signal up to the point of the signal processing, which is covered in part B.

This part will be between 13:30 and 15:00.

**PART B**

In general, this part will mathematically
expand on topics touched upon by the “Radar systems desgin and application” course. Four topics will be considered: Signal models, Radar
waveforms, Dopppler processing and Detection If time allows for a fifths topic
will be included with Beamforming and Space-Time Adaptive Processing. The focus
of *Signal model* will be one the
mathematical modeling of the wave received by the systems. This includes the
(complex) amplitude modulation due to the target’s surface, i.e., scattering
effects, frequency modulation due to the target’s movement, i.e., Doppler
effect, and lastly additive terms due to noise and clutter. The courses on *Radar
Waveforms* will focus on the design of the optimal
filter for maximizing the SNR. Additionally the ambiguity function – describing
the behavior of the response of a certain waveform to its matched filter – and
several waveform types will be discussed. A focus will be on the linear
frequency modulated (LFM), also known as frequency modulated continuous wave,
signal. The third part will deal with the evaluation of the Doppler component
of the echo signal. Here, the basic operation of radars for range and Doppler
measurement will be introduced along with two ways to detect if a target is
moving (moving target indication) and how fast it is moving (range-doppler
processing). Finally, the focus will be turned to detection. In a perfect
world, there is no clutter or noise. But, there are all kinds of sources for
noise in the echo signal and it is necessary to automatically distinguish
between actual targets and noise. This is the task detection has to deal with.

*Important notice:*

*The course is planned to start in September 2021. Due to the fact that it is a MENELAOS-only, purpose-made course, lecture videos will be added/uploaded once the resepective lecture was given and recorded.*

- Teacher: Andreas Bathelt
- Teacher: Stephan Stanko

(Course of Mr. Peter Knott usually given at RWTH Aachen)

This course provides an introduction to radar systems. The focus is hence on addressing the variety of topics which one is faced with when working with radars, in particular the physical effects governing the operation – propagation of waves and scattering effects of an object – will receive attention. The first part – Radar Basics – describes the fundamental structure and operation of a radar system. Based on this basic description – and the therein introduced radar equation – the aforementioned physical aspects of propagation and scattering are discussed. Here, the topics are for example the influence of atmospheric effects or the radar cross section of object. The third part – Signals & Noise – will deal with a wide signal processing aspects such as ambiguity function, modeling of noise source, etc. Some aspects will be explained in more mathematical detail in the “Millimeter wave radar and signal processing” course. The fourth part focuses then on the applications. Among the discussed applications are Multi-static systems (co-located sender and (multiple) receivers), planetary and space observation systems (Special Applications) as well as a foresight to up-and-coming system designs (Trends).

*Important notice:*

*The
course is planned to start in September 2021. Due to the fact that it
is a MENELAOS-only, purpose-made course, lecture videos will be
added/uploaded once the resepective lecture was given and recorded.*

- Teacher: Andreas Bathelt
- Teacher: Stephan Stanko

The course provides knowledge on the area of signal processing for
cognitive and robotic systems.

Particular emphasis is placed on machine learning methods, especially Deep
Learning with Convolutional Neural Nets (CNNs), an approach

which is particularly successful in the context of autonomous driving or sensor
data processing in automation technology / Industry 4.0.

Here, emphasis is placed on a solid mathematical derivation of the theory and
the connecting points to other lectures (->Estimation Theory).

The students implement a simple neural network in Matlab and train it with the
backpropagation algorithm.

A second focus is on environment sensing in multisensory applications. Here,
the basics of Kalman and particle filters are first reviewed.

As application examples, methods for simultaneous localization and map
generation (SLAM), data fusion of sensor and GPS signals, and car tracking with
automotive radars are presented.

Finally, algorithms for resource optimization and management of active sensor
systems are presented.

The application examples in the course are based on radar sensor technology as
well as optical and LIDAR systems.

*Important notice:*

*The
course is planned to start in January 2023. Due to the fact that it
is a MENELAOS-only, purpose-made course, lecture videos will be
added/uploaded once the resepective lecture was given and recorded.*

- Teacher: Andreas Bathelt
- Teacher: Stefan Brüggenwirth
- Teacher: Peter Knott