Rain Clutter Modelling and Performance Assessment of Road Traffic Surveillance Radars

Abstract

Millimetre-wave road traffic surveillance radars are used for vehicle detection and tracking. Under rainy conditions, rain attenuation and rain backscatter severely degrade the vehicle detection performance of radars. In this paper, based on a big database of rain clutter collected by two radars at different rainfall levels, rain clutter modelling and the assessment of the vehicle detection ability of radars under rainy conditions are investigated. As the first contribution, the clutter-to-noise ratio (CNR) and signal-to-clutter-noise ratio (SCNR) of the road traffic surveillance radar under rainy conditions are derived as functions of radial distance, based on the rain attenuation and backscatter models. The derived formulas highly accord with the measured CNR change of radars under rainy conditions, which can be used to evaluate radar performance and optimise the radar operating mode. As the second contribution, a range-varying compound-Gaussian model (CGM) with a clutter map cell partition is introduced to model rain clutter with range-varying statistics, and a best-type selection method of amplitude distributions is proposed. Based on the analysis of a big database of rain clutter at different rainfall levels, the range-varying CGMs with gamma and lognormal textures are recommended to model rain clutter of millimetre-wave road traffic surveillance radars.