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.
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.
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.
(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).
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.
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.
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.