Rss Based Localization In A Rayleigh Fading Environment

The objective of this thesis is to quantify the improvement that can be obtained in sensor agent localization accuracy as a function of the number of multipath components that can be resolved. We assume that a known number sensor agents are located at unknown coordinates within a rectangular grid having anchors at the corner locations, whose locations are known. Further, we assume fading is Rayleigh and that the propagation constant is constant but unknown. Also, we assume that modulation is spread spectrum and that either the sensors or agents are capable of resolving multipath components down to the chip level and are capable of measuring the received signal strength in each of the resolved multipath components. An error function is formulated based upon the square of the distances between the actual sensor locations and their model-predicted locations, which are functions of the received signal strength of the various multipath components and the propagation constant, and the optimal sensor location estimates and propagation constant are determined through a multistage process of formulating and minimizing error functions. The effectiveness of this approach is investigated via extensive simulations in which the Saleh-Valenzuela model is used to generate multipath components. The simulation results indicate that for a given fixed propagation constant, resolving multipath results in improved localization accuracy and that this improvement is a non decreasing function of the propagation constant. For a distance-squared propagation environment, the results indicate that resolving 6 multipath components improves localization accuracy by at least 20 %, the improvement being with respect to the localization accuracy based on aggregate received signal strength

Dynamic Code Selection Method for Content Transfer in Deep Space Network

Space communications feature large round-trip time delays (for example, between 6.5 and 44 minutes for Mars to Earth and return, depending on the actual distance between the two planets) and highly variable data error rates, for example, bit error rate (BER) of 10−5 is very comand even higher BERs on the order of 10−1 is observed in the deep- space environment. We develop a new content transfer protocol based on RaptorQ codes and turbo codes together with a real-time channel prediction model to maximize file transfer from space vehicles to the Earth stations. While turbo codes are used to correct channel errors, RaptorQ codes are applied to eliminate the need for negative-acknowledgment of the loss of any specific packet. To reduce the effect of channel variation, we develop a practical signal-to-noise ratio (SNR) prediction model that is used to periodically adjust the turbo encoder in distant source spacecraft. This new protocol, termed as dynamic code selection method (DCSM), is compared with two other methods: turbo based genie method (upper bound of DCSM performance) assuming that the channel condition is perfectly known in advance and a static method in which a fixed turbo encoder is used throughout a communication pass. Simulation results demonstrate that the genie can increase telemetry channel throughput expressed in terms of the total number of successfully delivered files during a communication pass by about 20.3 % and DCSM achieves more than 99 % of genie, compared to the static approach being used currently