Indoor localization using analog output of pyroelectric infrared sensors

Abstract

Economical, low-power, and easy to deploy indoor localization schemes have always been a challenge. We propose a technique that utilizes the analog output of pyroelectric (or passive) infrared (PIR) sensors for indoor localization. We propose two models for distance estimation based on the characterization of the analog sensor output in terms of the peak to peak value and its relationship with distance from the sensor. The proposed distance estimation models are based on bijective functions: M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bh</sub> using a hyperbolic function and M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pl</sub> using a piecewise linear function. Once distances of the subject from the PIR sensors (or anchors) are estimated, multilateration or support vector regression (SVR) based techniques are used for computing the location coordinates of the subject. Using four PIR sensors, we demonstrate the localization of a human subject in a 7 m × 7.5 m area with the regression based technique outperforming the other techniques in terms of accuracy and achieving an RMS localization error of 0.65 m using the M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pl</sub> model for distance estimation. We also compare the computational complexity and memory or storage requirements of the proposed techniques which are an important consideration for distributed implementation on resource constrained devices such as sensor nodes.