This report discusses one part of a project consisting of three parts. The goal of the project is to design a battery less sensor powered by RF energy fields present in an office environment. The sensor is able to measure the temperature and is able to communicate wirelessly using a low power communication protocol.In this report the energy conversion and storage of the system will be discussed. A high-frequency signal coming from an antenna must be converted to an output signal which is functional for the communication module. Therefore a steady 3.3 V DC output needed to be created with a rectifier circuit. In order to achieve maximum power transfer to the load, a matching circuit with the RF energy harvesting antenna needed to be designed. The main challenges were to achieve a high enough input voltage to reach the threshold voltage of the diodes of the rectifier and to rectify the AC input as efficient as possible. To tackle these challenges a design with a Greinacher rectifier with low-threshold Schottky diodes and a DC/DC booster is presented.

Nowadays, many practical radar applications require an automatic interpretation of the received data, including data processing algorithms and target classification. The exploitation of additional polarimetric information is a very promising concept to improve the performance of automotive target classification. In this thesis work, we aim to identify target features that can be used for the classification and definition of sub-classes of moving automotive vehicles driving on a highway. This analysis is based on a multi-dimensional polarimetric feature database, created from real observations from a fully polarimetric-Doppler S-band FMCW radar (PARSAX). The polarimetric information of the vehicles is extracted while tracking the targets in a multi-target environment in the range-Doppler domain. Therefore, a multi-target tracking algorithm, based on an OS-CFAR detector, polarimetric data fusion algorithm, and a classical Kalman filter, is used. In order to cope with Doppler ambiguity, a novel MHT-based approach has been introduced. The feature extraction analysis shows that the polarimetric features of the observed targets provide well-defined reliable statistical relations between physically related features, but that blind classification based on our target feature database does not provide new insights that are useful for classification. Reliable clusters that are useful to describe the polarimetric signatures of the targets have not been found, except the polarimetric correlation coefficients, which, unfortunately, despite their physical clear sense, were not supported by the other analyzed features. Nevertheless, from similar feature analysis, it has been shown that the features originating from the incoherent polarimetric H/A/α-decomposition form compact and well-separated clusters corresponding to target scattering and clutter scattering. Therefore, it can be concluded that these features can be used to accurately distinguish moving vehicles from static clutter.