EE5020 Sensor signal and data processing

In this course a variety of modern signal and data processing techniques will be presented along with their application in sensor systems in general and surveillance (radar) systems in particular. Topics that will be presented include: detecting the presence and estimating the state of a single moving object based upon sensor measurements, modeling and estimation with multiple models, rejection of outliers in the measurements, the extension to a time-varying number of objects, the use of multiple sensors, and optimizing the resources of the sensor(s). Techniques that will be presented include: (extended) Kalman filtering, Interacting Multiple Model (IMM) filtering, non-linear filters such as the particle filter, Sequential Likelihood Ratio Testing (SLRT), Multiple Hypothesis Tracking (MHT), track-before-detect, data fusion concepts.

Study Goals

The main goal of this course is to teach students to apply basic detection and estimation theory to design algorithmic solutions to (complex) signal and data processing problems

Teachers Hans Driessen

Last modified: 2016-02-24


Credits: 5 EC
Period: 0/0/0/4
Contact: Hans Driessen