Spacecraft missions such as spacecraft docking and formation flying require high precision relative position and attitude data. Although Global Positioining Systems can provide this capability near the earth, deep space missions require the use of alternative technologies. One such technology is the vision-based navigation (VISNAV) sensor system developed at Texas A&M University. VISNAV comprises an electro-optical sensor combined with light sources or beacons. This patented sensor has an analog detector in the focal plane with a rise time of a few microseconds. Accuracies better than one part in 2000 of the field of view have been obtained. This research presents a new approach involving simultaneous activation of beacons with frequency division multiplexing as part of the VISNAV sensor system. In addition, it discusses the synchronous demodulation process using digital heterodyning and decimating filter banks on a low-power fixed point DSP, which improves the accuracy of the sensor measurements and the reliability of the system. This research also presents an optimal and computationally efficient six-degree-of-freedom estimation algorithm using a new measurement model based on the attitude representation of Modified Rodrigues Parameters.

Spacecraft missions such as spacecraft docking and formation flying require high precision relative position and attitude data. Although Global Positioining Systems can provide this capability near the earth, deep space missions require the use of alternative technologies. One such technology is the vision-based navigation (VISNAV) sensor system developed at Texas A&M University. VISNAV comprises an electro-optical sensor combined with light sources or beacons. This patented sensor has an analog detector in the focal plane with a rise time of a few microseconds. Accuracies better than one part in 2000 of the field of view have been obtained. This research presents a new approach involving simultaneous activation of beacons with frequency division multiplexing as part of the VISNAV sensor system. In addition, it discusses the synchronous demodulation process using digital heterodyning and decimating filter banks on a low-power fixed point DSP, which improves the accuracy of the sensor measurements and the reliability of the system. This research also presents an optimal and computationally efficient six-degree-of-freedom estimation algorithm using a new measurement model based on the attitude representation of Modified Rodrigues Parameters.

This master thesis is focused on the theoretical description and practical implementation of software defined transmitter. The main aim of this thesis was made the prototype of software defined transmitter in FM band. Theoretical part is determined to description of basic parts of equipment and working principles to understand the basic principle of digital transmitters and define the appropriate component base for construction. Discussed are used types of A/D and D/A converters, blocks of digital signal processing and the roles, which these components performs. The second part is focused practical. Specified are suitable types of components and block diagram is proposed for following electrical connection and printed circuit board in Eagle program as a plug-in modul for developmental platform Arduino. The main point is program, which sets and controls the transmitter. Next important part is impedance match and antenna tuning, which is explain in practical part of thesis. The result is prototype of software defined transmitter compatible with Arduino Uno platform.

Machine learning is rapidly being integrated into all areas of society, however, that puts a lot of pressure on resource costraint hardware such as embedded systems. The company Ericsson is gradually integrating machine learning based on neural networks, so-called deep learning, into their radio products. One promising product is their vectorized Digital Signal Processor (DSP) that are based upon the machine learning suitable Single Instruction, Multiple Data (SIMD) paradigm and Very Long Instruction Word (VLIW) architecture. However, despite the suitability of the SIMD paradigm, the embedded system needs to efficiently execute a computation-intensive deep learning algorithm with proper use of its limited resources. Therefore commonly used methods of implementing each layer of the computation-intensive Convolutional Neural Network (CNN), a type of Deep Neural Network (DNN), have been used and evaluated its implementation on the hardware and to assess the vectorized DSP’s deep learning suitability and capabilities. Despite the suitability of the hardware, the implementation utilized less than half of the available resources at all times during the execution. The main limitations were identified to be the limited 16-bit element instructions. To enhance the performance and improve the utilization of the available resources, easy-to-implement hardware instructions have been suggested. This work has made the first steps of implementing an efficiently performing CNN implementation on the examined vectorized DSP. / Integreringen av maskininlärning in i alla samhällsområden sker idag i rusande fart, men det sätter stor press på begränsad hårdvara som inbyggda system. Företaget Ericsson integrerar successivt maskininlärning baserad på neurala nätverk, så kallad djupinlärning, i sina radioprodukter. En lovande produkt är deras vektoriserade DSP som är baserade på maskininlärningspasset SIMD-paradigm och VLIW-arkitektur. Men trots lämpligheten av SIMD-paradigmet, är den största utmaningen att utnyttja de begränsade resurserna i inbyggda systemet för att effektivt exekvera en beräkningsintensiv djupinlärningsalgoritm. Därför har vanligt använda metoder för att implementera varje lager av den beräkningsintensiva CNN, en typ av DNN, använts och utvärderats på hårdvaran för att bedöma den vektoriserade DSP:s djupinlärningslämplighet samt förmågor. Trots hårdvarans lämplighet använde alla implementeringar mindre än hälften av de tillgängliga resurserna vid alla tidpunkter under exekveringen. De huvudsakliga begränsningarna identifierades vara den begränsade tillgången på 16-bitars element instruktioner. För att förbättra prestandan för ett närmare fullt utnyttjande av tillgängliga resurser har hårdvaruinstruktioner som är enkla att implementera föreslagits. Detta arbete har tagit de första stegen för att implementera ett effektivt förformande CNN på den undersökta vekotriserade DSP.