Fire Localization Based On Range-Range-Range Model for Limited Interior Space

Abstract

Fire localization problem is studied based on temperature data taken by wireless sensor arrays and a novel range-range-range (RRR) model is proposed to overcome shortcomings, which exists in the current range-point-range (RPR) model in this paper. For a single sensor array composed of four sensors deployed with a square, three angle estimates on fire bearing can be obtained using far-field localization technology. These angle estimates are used to get their statistical mean and variance at a single time. Based on the statistical features, we propose two fire localization methods under the RRR frame, which are angle bisector and nonlinear filtering methods. For the angle bisector method, a recursive formula of the mean and variance is presented in time series so that global angle estimates can be used. Furthermore, a fire coordinate estimate, which is actually the center of estimated-range circle, can be taken by use of intersecting two angle bisectors from two sensor arrays. Moreover, the estimation of a radius for the estimated fire region is also realized. In order to improve localization accuracy and robustness of fire estimation to non-Gaussian noise component, the fire localization is taken as a nonlinear bearing-only tracking issue for the case where the covariance of measurement noise is unknown and a specific variational Bayesian adaptive square-cubature Kalman filter is proposed to estimate the coordinate of the center. These proposed algorithms not only provide some new points of view on the fire localization for limited interior space, but are helpful for practical fire fighting applications.