Antenna designs for modern applications and smart radar for fall detection

The antenna is the most critical single component of a communication and sensing system. It not only provides the basic transformation between the electrical signals and electromagnetic waves, but also governs the signal-to-noise ratio which limits the performance of the signal processing and the entire system. The antenna design - both construction and pattern - must account for different system requirements. In diversity/MIMO applications, where the goal is to mitigate multipath-induced signal fading (or improve capacity, error rate, range, coverage, and a host of other performance metrics), it is normally beneficial to have broad patterns for matching to the broad angular range of the multipath, and polarization purity is not a priority. In a polarimetric radar, the target is usually within a narrow angular range, and different combinations of polarizations in the transmit and receive provide different information. These connections between the antenna and the signal processing, for different applications, motivate the new designs presented in this dissertation. The first part concerns multi-element designs for diversity and MIMO, used for portable terminals in broadcast, Wi-Fi and cellular systems. Performance evaluation using the patterns and statistical models for the multipath propagation is the key design tool. The von-Mises Fisher distribution is introduced for evaluating the impact of directivity in the multipath. The antenna construction is typically PCB-based since the products must be very low cost and compact. The second part strives for higher directivity using new designs of fixed arrays. These designs include dual-polarization, multiple frequency bands, and circular polarization. The construction is slotted metallic cavities because of the low loss in both the elements and the feed (distribution of power over the aperture), and the potential simplicity of manufacture, given the higher directivity of polarized illumination. The final part discusses new radar signal processing for indoor fall detection. A radar system was developed and tested, and demonstrates the detection of falls, breathing, and other movements, even when a person has fallen and is on the floor. Deep learning algorithms are used with new experiments providing the training data for distinguishing a person from other moving entities such as pets, reducing the false-alarm rate of the fall detection.