To improve the accuracy and resolution of the measurements, radar systems employ increasingly complex, resource-intensive signal processing, larger bandwidths, and higher carrier frequencies. However, implementing these improvements requires more expensive, complex electronics that are larger and use more power. Using RFSoC (Radio Frequency System on Chips) in radars can address these challenges. By combining processors, an FPGA (Field Programmable Gate Array), and RF data converters into a single integrated circuit, RFSoCs allow for radar electronics that are physically smaller, use less power, and are simpler to design. As using RFSoCs to perform RF data conversion for radars has been already explored, this work focuses on performing radar processing for a marine radar with an RFSoC. Performance specifications for a marine radar are defined based on the specifications for other modern marine radars, a suitable radar waveform is designed based on the derived performance specifications, and a processing chain to compute the range-doppler map is implemented based on the radar specifications and waveform. The processing chain, created as a block design in the Vivado Design Suite, consists of matched filtering and doppler processing. The processing chain is simulated in Vivado and implemented on the FPGA of a Xilinx ZCU216 RFSoC Evaluation Board. To verify the operation of the processing chain, the range-doppler maps generated by simulation and from running the design on the ZCU216 board are compared with range-doppler maps generated by a processing model implemented in MATLAB. The range-doppler map processing chain performs correctly as the outputs from simulation and the ZCU216 show close agreement with the MATLAB results with average percent differences on the order of 10-4 % to 10-5 % and average differences on the order of 10-7. The processing chain generates the range-doppler map quickly: the final range-doppler value is output 1.75 milliseconds after the final sample of a CPI (Coherent Processing Interval) of radar data (the total CPI duration is 10.49 milliseconds) is input. The FPGA resource usage of the processing chain is 85% of the BRAM, 0% of the URAM, 0.87% of the DSP slices, and approximately 1% of the configurable logic blocks. The low resource usage of the processing chain allows for possibilities of future expansion of the processing chain to include radar processing techniques such as digital beamforming or threshold estimation.

Im Zuge der immer größer werdenden Knappheit fossiler Ressourcen und des damit verbundenen Anstiegs des Rohölpreises ergibt sich ein Trend hin zur Elektromobilität. In den nächsten Jahren werden jedoch nur Elektrofahrzeuge mit deutlich eingeschränkter maximaler Reichweite im Vergleich zu Benzin- oder Dieselfahrzeugen produziert werden können. Um den täglichen Umgang des Kunden mit einem Elektrofahrzeug trotzdem möglichst reibungslos zu ermöglichen, wurde im Rahmen dieser Arbeit eine neuartige Anzeige der verbleibenden Reichweite auf der Navigationskarte entwickelt. Nach detaillierter Analyse vorhandener Ansätze wurde je ein empirisches und ein modellbasiertes Verfahren ausgearbeitet. Die Ansätze wurden systematisch verglichen und zu einem komplett neuartigen, hybriden Ansatz kombiniert. Die auftretenden Verbräuche des Fahrzeugs werden im Kundenbetrieb erfasst, je nach Fahrsituation klassifiziert und für eine Extrapolation in der Zukunft verwendet. Die entwickelte Methodik zur Untersuchung der erreichbaren Genauigkeit ergab ein erzielbares Fehlermaß von durchschnittlich unter 10%. / Due to the prospect of a worldwide shortage of fossil fuels and the correlated increase of prices for crude-oil, a global trend to invest in electric mobility has started. During the next couple of years, electric vehicles will still have restrictions on the maximum distance that can be driven before having the need to recharge the battery. The potential costumers face the so-called „range-anxiety“, the fear to be stranded prior to reaching the destination. In order to provide a safe and easy way of operating such a vehicle, the work conducted in the course of this doctoral thesis led to a new way of displaying the remaining range of the vehicle on a navigation map. After detailed analysis of the state of the art, an empirical- and a model-based solution for calculating the remaining range were developed utilizing predictive map-data from a roadnetwork. After a systematical optimization of the developed solutions, an embedded prototype was developed which captured the driving situation of the vehicle together with the corresponding energy-consumption in order to provide a context-aware interpolation of the remaining range, depending on where the costumer would drive next. A developed methodology of objectively determining the error produced by the system resulted in a mean-deviation of 10% of absolute value.

Im Zuge der immer größer werdenden Knappheit fossiler Ressourcen und des damit verbundenen Anstiegs des Rohölpreises ergibt sich ein Trend hin zur Elektromobilität. In den nächsten Jahren werden jedoch nur Elektrofahrzeuge mit deutlich eingeschränkter maximaler Reichweite im Vergleich zu Benzin- oder Dieselfahrzeugen produziert werden können. Um den täglichen Umgang des Kunden mit einem Elektrofahrzeug trotzdem möglichst reibungslos zu ermöglichen, wurde im Rahmen dieser Arbeit eine neuartige Anzeige der verbleibenden Reichweite auf der Navigationskarte entwickelt. Nach detaillierter Analyse vorhandener Ansätze wurde je ein empirisches und ein modellbasiertes Verfahren ausgearbeitet. Die Ansätze wurden systematisch verglichen und zu einem komplett neuartigen, hybriden Ansatz kombiniert. Die auftretenden Verbräuche des Fahrzeugs werden im Kundenbetrieb erfasst, je nach Fahrsituation klassifiziert und für eine Extrapolation in der Zukunft verwendet. Die entwickelte Methodik zur Untersuchung der erreichbaren Genauigkeit ergab ein erzielbares Fehlermaß von durchschnittlich unter 10%. / Due to the prospect of a worldwide shortage of fossil fuels and the correlated increase of prices for crude-oil, a global trend to invest in electric mobility has started. During the next couple of years, electric vehicles will still have restrictions on the maximum distance that can be driven before having the need to recharge the battery. The potential costumers face the so-called „range-anxiety“, the fear to be stranded prior to reaching the destination. In order to provide a safe and easy way of operating such a vehicle, the work conducted in the course of this doctoral thesis led to a new way of displaying the remaining range of the vehicle on a navigation map. After detailed analysis of the state of the art, an empirical- and a model-based solution for calculating the remaining range were developed utilizing predictive map-data from a roadnetwork. After a systematical optimization of the developed solutions, an embedded prototype was developed which captured the driving situation of the vehicle together with the corresponding energy-consumption in order to provide a context-aware interpolation of the remaining range, depending on where the costumer would drive next. A developed methodology of objectively determining the error produced by the system resulted in a mean-deviation of 10% of absolute value.