A new synthesis method for signals for testing of flame-detection algorithms

In this contribution, the development of a method for generating input data. for testing and optimising fire recognition algorithms, is presented. This work concentrates on the analysis and evaluation of sensor signals for detecting electromagnetic radiation in the infrared spectrum (IR). A current problem is the differentiation between the signal of a flame and the effect of non-fire IR sources in the same IR wavelength area that cause disturbance signals. The objective is to achieve a highly sensitive, early detection of fire and at the same time suppress false alarms. For testing of signal-processing algorithms, a great number of input data must be available reflecting a great variety of combinations of flame and non-fire IR source scenarios. Generally, these data records are created by test fires and physical simulation of simultaneous non-fire IR sources. The aim of this work is to develop a method for generating such input data based on a reduced number of experimental data. Our approach is to gain model functions based on measured sensor signals that are used to create parametric models. Assuming the linearity of the sensor transfer function. the synthesised signal components are superposed linearly and simulation signals are generated as new input signals. A uniform approach was developed for the simulation of signals caused by flames and non-IR sources. Three signal components were defined by their frequency range, the base function with f < 1 Hz, the flickering component with 1 Hz less than or equal to f < 10 Hz and the noise component with 10 Hz less than or equal to f < 256 Hz. The modelling of the base function was carried out by cubic spline interpolation. Assuming that the flickering and noise components are stationary and non-deterministic, autoregressive models are used for the analysis of signals and for generating new random sequences. Comparison of the measured signal and the respective generated simulation data show a good consistency. Hence, considerably more effective input data records can be created for testing and optimisation of signal-processing algorithms. Generally, the method presented can also be applied for the simulation of signals generated by sensors utilising other physical detection principles. (C) 2002 Elsevier Science Ltd. All rights reserved.