A STOCHASTIC-MODEL OF THE TIME-OF-FLIGHT NOISE IN AIRBORNE SONAR RANGING SYSTEMS

A stochastic model is developed far describing the statistical propert ies of the noise present in the time-of-flight (TOF) measurements made by in-air ultrasonic (US) transducers. The proposed method of analysi s decomposes the TOF noise into three components with different physic al origin and properties: a deterministic time-varying mean, a correla ted random process and an uncorrelated random process. The physics of US waves propagating in air and the operating mode of typical sonar ra nging systems are considered in orienting the choice of the model stru cture. The time-varying mean correlates with global thermal changes an d drafts affecting the environment. The detrended data are assumed to result from the sum of a correlated random component, due to inhomogen eities in the medium, such as temperature gradients and air turbulence , and an uncorrelated random component, mainly due to the wide band el ectronic noise superimposed on the echo signal. Autoregressive-moving average (ARMA) modelling techniques are used to capture the correlatio n structure with exponential decay of the piecewise stationary correla ted random process. A method of adaptive segmentation allows to test f or weak stationarity of this component. Kalman filtering techniques ar e used for its estimation. The adequacy of the representation in typic al indoor environments is demonstrated by analyzing experimental data from Polaroid sensors.