Passive infrared localization with a Probability Hypothesis Density filter

In passive infrared localization (PIL) humans are located based on their thermal radiation. Thus, an active tag is not required and privacy is guaranteed due to non-identifying sensors. However, in case of multi-target tracking, the non-identifying sensors result in missing associations between targets and measurements. As additionally the number of humans in the surveillance region is unknown and varying, conventional localization approaches like the JPDA or MHT filter cannot be applied since they require a fixed number of targets or are not tractable. The Probability Hypothesis Density (PHD) filter, on the other hand, that propagates only the first order moment of the multitarget probability distribution offers an efficient and elegant way to handle the aspects of missing association and varying target number. Furthermore, false measurements as well as missed detection are considered implicitly. In this paper we present an implementation of this filter for PIL that allows an accurate and reliable tracking of several humans. The accuracy and reliability are evaluated under the influence of noise. Thus, several simulations and real measurements are carried out that reveal a mean error of less than 30 cm in case of three humans. Moreover, efficiency tests show that on standard PCs update rates of more than 50 Hz are achievable.