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Sensor fusion algorithms allow the combination of many heterogeneous data types to make sophisticated decisions. In many situations, these algorithms give increased performance such as better detectability and/or reduced false alarm rates. To achieve these benefits, typically some system or signal model is given. This work focuses on the situation where the event signal is unknown and a false alarm criterion must be met. Specifically, the case where data from multiple passive infrared (PIR) sensors are processed to detect intrusion into a room while satisfying a false alarm constraint is analyzed. The central challenge is the space of intrusion signals is unknown and we want to quantify analytically the probability of false alarm. It is shown that this quantification is possible by estimating the background noise statistics and computing the Mahalanobis distance in the frequency domain. Using the Mahalanobis distance as the decision metric, a threshold is computed to satisfy the false alarm constraint.978-1-4799-6117-7/15/$31.00 ©2015 IEEE This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.

Communication using mid-ultraviolet radiation be tween 200 nm and 280 nm has received renewed attention due to advancements in UV LED emitters and unique propagation characteristics at these wavelengths. Atmospheric gases absorb light at mid-UV so that receivers or sensors operating on the earth's surface receive no interference from solar radiation. This so-called "solar-blind" region of the spectrum allows the use of single-photon detection techniques. Further, UV light is strongly scattered by molecules in the air, enabling non-Iine-of-sight (NLOS) communication. We extend previous work in this area by incorporating angle-dependent Mie scattering into one of the standard propagation models, in an effort to include the effects of aerosols. Experimental results from outdoor measurements using a fog generator are also presented.

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In this paper we analyze the performance of random beamforming schemes in a multi-user Gaussian broadcast channel utilizing SINR feedback. For generality, the receivers are allowed to have multiple receive antennas. The first scheme analyzed allows each receiver to feed back the largest SINR it observes for each transmit beam. The distribution function of the maximum SINR is derived and is noted to differ from previous work. In an effort to further reduce feedback, a scheme where each user feeds back the maximum SINR observed over its receive antennas and transmit beams. Using the Fréchet bounds and properties of chi-squared random variables, the throughput of this system is bounded and empirically shown to approach the performance of the previous scheme as the number of users increases.