Accurate and efficient localization is a fundamental requirement for autonomous operation of robots, especially in areas that deny global navigation services. Localization is even more challenging in environments that present visual and geographic difficulties. This not only includes environmental aspects like darkness, fog and dust but also geometrically monotone areas. The solution that the Center for Applied Autonomous Sensor Systems at the Örebro university decided to develop is therefore a prototype of radar-only localization and mapping (SLAM) system. The radar modality is less susceptible to the environmental factors when compared to, for example, a lidar. Our goal is to support this effort by creating an odometry module that uses radar and inertial data to provide the localization for this SLAM prototype. This radar-inertial-odometry (RIO) takes radar point clouds and inertial gyroscopic data to output an odometry message usable by other components in the robot operating system (ROS). The module has been tested on two datasets representing areas typical for deployment, one consisting of underground tunnels and the other one being an outside forest environment. The dataset has been processed by two different mappers where the lidar has been used as the basic modality. This choice allows us to evaluate the odometry module in a more practical way. The final results are promising, the underground localization closely adheres to reality. The forest dataset is more challenging although it still resembles the ground-truth position in the horizontal dimension. The module's biggest shortcoming is a noticeable drift problem in the vertical z-dimension , for which we propose a constraint that limits this drift.

This thesis investigates channel-coded physical layer network coding, in which the relay directly transforms the noisy superimposed channel-coded packets received from the two end nodes, to the network-coded combination of the source packets. This is in contrast to the traditional multiple-access problem, in which the goal is to obtain each message explicitly at the relay. Here, the end nodes $A$ and $B$ choose their symbols, $S_A$ and $S_B$, from a small non-binary field, $\mathbb{F}$, and use non-binary PSK constellation mapper during the transmission phase. The relay then directly decodes the network-coded combination ${aS_A+bS_B}$ over $\mathbb{F}$ from the noisy superimposed channel-coded packets received from two end nodes. Trying to obtain $S_A$ and $S_B$ explicitly at the relay is overly ambitious when the relay only needs $aS_B+bS_B$. For the binary case, the only possible network-coded combination, ${S_A+S_B}$ over the binary field, does not offer the best performance in several channel conditions. The advantage of working over non-binary fields is that it offers the opportunity to decode according to multiple decoding coefficients $(a,b)$. As only one of the network-coded combinations needs to be successfully decoded, a key advantage is then a reduction in error probability by attempting to decode against all choices of decoding coefficients. In this thesis, we compare different constellation mappers and prove that not all of them have distinct performance in terms of frame error rate. Moreover, we derive a lower bound on the frame error rate performance of decoding the network-coded combinations at the relay. Simulation results show that if we adopt concatenated Reed-Solomon and convolutional coding or low density parity check codes at the two end nodes, our non-binary constellations can outperform the binary case significantly in the sense of minimizing the frame error rate and, in particular, the ternary constellation has the best frame error rate performance among all considered cases.

This thesis investigates channel-coded physical layer network coding, in which the relay directly transforms the noisy superimposed channel-coded packets received from the two end nodes, to the network-coded combination of the source packets. This is in contrast to the traditional multiple-access problem, in which the goal is to obtain each message explicitly at the relay. Here, the end nodes $A$ and $B$ choose their symbols, $S_A$ and $S_B$, from a small non-binary field, $\mathbb{F}$, and use non-binary PSK constellation mapper during the transmission phase. The relay then directly decodes the network-coded combination ${aS_A+bS_B}$ over $\mathbb{F}$ from the noisy superimposed channel-coded packets received from two end nodes. Trying to obtain $S_A$ and $S_B$ explicitly at the relay is overly ambitious when the relay only needs $aS_B+bS_B$. For the binary case, the only possible network-coded combination, ${S_A+S_B}$ over the binary field, does not offer the best performance in several channel conditions. The advantage of working over non-binary fields is that it offers the opportunity to decode according to multiple decoding coefficients $(a,b)$. As only one of the network-coded combinations needs to be successfully decoded, a key advantage is then a reduction in error probability by attempting to decode against all choices of decoding coefficients. In this thesis, we compare different constellation mappers and prove that not all of them have distinct performance in terms of frame error rate. Moreover, we derive a lower bound on the frame error rate performance of decoding the network-coded combinations at the relay. Simulation results show that if we adopt concatenated Reed-Solomon and convolutional coding or low density parity check codes at the two end nodes, our non-binary constellations can outperform the binary case significantly in the sense of minimizing the frame error rate and, in particular, the ternary constellation has the best frame error rate performance among all considered cases.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho objetivou reconhecer e analisar as mudanças na paisagem da Serra de Pacaraima, que está localizada no extremo norte do Estado de Roraima, Brasil. Para isso, foram utilizadas quatro imagens do satélite LANDSAT-5/TM referentes às datas de cobertura 17/02/1990, 04/04/1995, 17/05/1999 e 06/05/2004, sobre as quais se determinou uma área de estudo, e uma imagem de radar do projeto SRTM, para caracterizar o relevo regional. No processamento das imagens LANDSAT-5/TM aplicaram-se técnicas para minimizar os erros decorrentes de sua aquisição. Empregou-se a técnica de segmentação por crescimento de região, em conjunto ao trabalho de campo, a fim de reconhecer as unidades da paisagem local, e a técnica de classificação supervisionada MAXVER, com o intuito de estimar a representação das unidades da paisagem ao longo da série multitemporal. Na imagem SRTM o processamento consistiu na análise do MNT e geração dos mapas hipsométrico e de declividade. Deste modo, quatro unidades de paisagem foram reconhecidas e caracterizadas como: unidade da paisagem de floresta (UPF); savana (UPS); desflorestamento (UPD); e ocupação humana (UPOH) / This work aimed at to recognize and to analyze the changes in the landscape of the Pacaraima Mountain that is located in the extreme north of the Roraima State, Brazil. For that, four images of the satellite LANDSAT-5/TM were used referring the dates of covering 17/02/1990, 04/04/1995, 17/05/1999 and 06/05/2004, on which was determined a study area, and one radar image of the project SRTM, to characterize the regional relief. In the LANDSAT-5/TM images processing techniques were applied to minimize the current mistakes of your acquisition. The segmentation technique by region growing was employ, together at field work, in order to recognize the units of local landscape, and the technique of supervised classification MAXVER, with the intention of to estimate the representation of landscape units along the multitemporal series. In SRTM image the processing consisted in the analysis of MNT and generation of hipsometric and slope maps. This way, four units of landscape were recognized and characterized as: landscape unit of forest (UPF); savanna (UPS); deforestation (UPD); and human occupation (UPOH)