Refine
Document Type
- Master's Thesis (2) (remove)
Language
- English (2) (remove)
Has Fulltext
- yes (2) (remove)
Keywords
Institute
- Fakultät Elektrotechnik und Informationstechnik (2) (remove)
This thesis deals with the object tracking problem of multiple extended objects. For instance, this tracking problem occurs when a car with sensors drives on the road and detects multiple other cars in front of it. When the setup between the senor and the other cars is in a such way that multiple measurements are created by each single car, the cars are called extended objects. This can occur in real world scenarios, mainly with the use of high resolution sensors in near field applications. Such a near field scenario leads a single object to occupy several resolution cells of the sensor so that multiple measurements are generated per scan. The measurements are additionally superimposed by the sensor’s noise. Beside the object generated measurements, there occur false alarms, which are not caused by any object and sometimes in a sensor scan, single objects could be missed so that they not generate any measurements.
To handle these scenarios, object tracking filters are needed to process the sensor measurements in order to obtain a stable and accurate estimate of the objects in each sensor scan. In this thesis, the scope is to implement such a tracking filter that handles the extended objects, i.e. the filter estimates their positions and extents. In context of this, the topic of measurement partitioning occurs, which is a pre-processing of the measurement data. With the use of partitioning, the measurements that are likely generated by one object are put into one cluster, also called cell. Then, the obtained cells are processed by the tracking filter for the estimation process. The partitioning of measurement data is a crucial part for the performance of tracking filter because insufficient partitioning leads to bad tracking performance, i.e. inaccurate object estimates.
In this thesis, a Gaussian inverse Wishart Probability Hypothesis Density (GIW-PHD) filter was implemented to handle the multiple extended object tracking problem. Within this filter framework, the number of objects are modelled as Random Finite Sets (RFSs) and the objects’ extent as random matrices (RM). The partitioning methods that are used to cluster the measurement data are existing ones as well as a new approach that is based on likelihood sampling methods. The applied classical heuristic methods are Distance Partitioning (DP) and Sub-Partitioning (SP), whereas the proposed likelihood-based approach is called Stochastic Partitioning (StP). The latter was developed in this thesis based on the Stochastic Optimisation approach by Granström et al. An implementation, including the StP method and its integration into the filter framework, is provided within this thesis.
The implementations, using the different partitioning methods, were tested on simulated random multi-object scenarios and in a fixed parallel tracking scenario using Monte Carlo methods. Further, a runtime analysis was done to provide an insight into the computational effort using the different partitioning methods. It emphasized, that the StP method outperforms the classical partitioning methods in scenarios, where the objects move spatially close. The filter using StP performs more stable and with more accurate estimates. However, this advantage is associated with a higher computational effort compared to the classical heuristic partitioning methods.
This diploma thesis is devoted to the design and analysis of a radar signal enabling an object classification capability in surveillance radar systems based on high-resolution radar range profiles. It picks up the research results from Kastinger (2006), who investigated classification algorithms for high-resolution radar range profiles, and Meier (2007), who programmed a MATLAB toolbox for the evaluation of radar signals. A classical, brief, introduction to radar fundamentals is given (Chapter 1) as well as the motivation for this thesis and certain basic parameters used. After high-resolution radar range profiles are discussed with special focus on surveillance radar systems (Chapter 2), the results of Kastinger (2006) are picked up (Chapter 3) as far as necessary for the following chapters of this thesis. Following the chapters on radar basics, high-resolution radar range profiles and classification, basic and advanced radar signals are discussed and analysed, especially their range resolution and sidelobe levels (Chapter 4). This includes linear frequency-modulated pulses and nonlinear frequency-modulated pulses as well as phase-coded pulses, coherent trains of identical pulses, and stepped-frequency waveforms. Their analysis is based on Meier's MATLAB toolbox. In Chapter 5 we will bring up additional points that have to be considered in radar system design for implementing a classification capability, before this thesis ends with an overall conclusion (Chapter 6).