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Radar Signal Processor in FPGA
Přívara, Jan ; Musil, Petr (referee) ; Maršík, Lukáš (advisor)
This work describes design and implementation of radar processor in FPGA. The theoretical part is focused on Doppler radar, principles of radar signal processing methods and target platform Xilinx Zynq. The next part describes design of radar processor including its individual components and the solution is implemented. FPGA components are written in VHDL language. In the end, the implementation is evaluated and possible continuation of this work is stated.
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Object Classification Using Radar
Přívara, Jan ; Zemčík, Pavel (referee) ; Maršík, Lukáš (advisor)
The aim of this bachelor's thesis is to design and implement classification system using radar, specifically vehicle classification system. The first part describes both radar principles and radar signal processing methods. A brief introduction to machine learning is provided, with emphasis on Support Vector Machines classification model. Feature extraction methods from radar signal are discussed as well. The next part describes concept and implementation of system for vehicle classification. In the end, the implemented classification system is evaluated and the possible continuation of this work is stated.
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Radar Signal Processor in FPGA
Přívara, Jan ; Musil, Petr (referee) ; Maršík, Lukáš (advisor)
This work describes design and implementation of radar processor in FPGA. The theoretical part is focused on Doppler radar, principles of radar signal processing methods and target platform Xilinx Zynq. The next part describes design of radar processor including its individual components and the solution is implemented. FPGA components are written in VHDL language. In the end, the implementation is evaluated and possible continuation of this work is stated.
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Object Classification Using Radar
Přívara, Jan ; Zemčík, Pavel (referee) ; Maršík, Lukáš (advisor)
The aim of this bachelor's thesis is to design and implement classification system using radar, specifically vehicle classification system. The first part describes both radar principles and radar signal processing methods. A brief introduction to machine learning is provided, with emphasis on Support Vector Machines classification model. Feature extraction methods from radar signal are discussed as well. The next part describes concept and implementation of system for vehicle classification. In the end, the implemented classification system is evaluated and the possible continuation of this work is stated.
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