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A reformulation of the information reduction factor (IRF) for tracking in the presence of measurement uncertainty is presented. A simple integral representation is employed to derive an expression for the IRF that can be calculated without approximation in terms of one‐dimensional integrals. This significantly reduces the computational requirements of the calculation and is relevant to the offline calculation of tracking performance and the selection of detection thresholds to optimise tracking accuracy. A comparison with Monte Carlo integration shows that the new technique is approximately 380 times faster.

Bistatic radar is a subject of considerable present interest. Despite this, current understanding of the properties of bistatic clutter, and in particular, bistatic sea clutter, is limited at its best. The purpose of this study is to present an analysis of the limited existing published radar data and to derive an empirical model, which expresses the variation of mean sigma super( 0) with the measurement geometry and sea conditions. This empirical model is then compared with electromagnetic (EM) scattering calculations using the composite model, (sometimes called the two-scale model) to show the extent, to which the EM model is able to reproduce the trends observed in the data. The results indicate, where improvements to bistatic EM modelling are required (very low grazing angles and out-of-plane scattering), and a general need for more radar data to extend the empirical sigma super( 0) model and expand it to the bistatic clutter statistics.