Method and Analysis of Spectrally Compressed Radio Images for Mobile-Centric Indoor Localization - 2018


Large databases with Received Signal Strength (RSS) measurements are essential for varied use cases in mobile wireless communications and navigation, including radio resource management algorithms and network-primarily based localization. As a result of of the constantly increasing variety of radio transmitters with varied wireless technologies and with the arrival of 5G cloud computing and Net of Things (IoT), the desired size of the RSS databases are changing into unmanageably giant. Thus, the necessities for the bandwidth and data rates for accessing the memory might become too costly. Therefore, so as to scale back the dimensions of the RSS database, while maintaining the information quality, we have a tendency to have previously proposed the tactic of spectrally compressed RSS pictures, that are in a position to attain considerable information compression of up to seventy %. In this Project, we deeply analyze the process of spectral compression and introduce error sources, that have an effect on the compression performance. Primarily based on the analysis, we have a tendency to propose a completely unique theoretical framework and ways to optimize the performance of the spectral compression. Still, we derive the Cramer-Rao Lower Certain (CRLB) for the RSS-primarily based localization error and compare the CRLB between separate baseline localization approaches. The theoretical analysis is justified and compared with experimental RSS measurements taken from several multi-storey buildings.

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