Down-converter for GPS applications

An RF down/up converter system is presented for indoor GPS applications. Transmission of GPS signals directly into indoor environments are limited and in some cases prohibited for regular operation of GPS system. However, ISM frequency bands, especially 433MHz can be used to retransmit the GPS signals to indoors. In this paper, RF down-converter building blocks are designed and implemented for sending GPS signals in ISM band. The down-converter system has heterodyne architecture which has LNAs, mixer, oscillator and filters. Received signals from the satellites are amplified, downconverted, filtered and again amplified. The overall performance of the designed system is 54.3dB gain and 2 dB noise figure while it is drawing 78mA current with 3V supply

Passive RFID Rotation Dimension Reduction via Aggregation

Radio Frequency IDentification (RFID) has applications in object identification, position, and orientation tracking. RFID technology can be applied in hospitals for patient and equipment tracking, stores and warehouses for product tracking, robots for self-localisation, tracking hazardous materials, or locating any other desired object. Efficient and accurate algorithms that perform localisation are required to extract meaningful data beyond simple identification. A Received Signal Strength Indicator (RSSI) is the strength of a received radio frequency signal used to localise passive and active RFID tags. Many factors affect RSSI such as reflections, tag rotation in 3D space, and obstacles blocking line-of-sight. LANDMARC is a statistical method for estimating tag location based on a target tag’s similarity to surrounding reference tags. LANDMARC does not take into account the rotation of the target tag. By either aggregating multiple reference tag positions at various rotations, or by determining a rotation value for a newly read tag, we can perform an expected value calculation based on a comparison to the k-most similar training samples via an algorithm called K-Nearest Neighbours (KNN) more accurately. By choosing the average as the aggregation function, we improve the relative accuracy of single-rotation LANDMARC localisation by 10%, and any-rotation localisation by 20%