Scenario Tracking for Airborne Bistatic Radar Systems

Space-time adaptive processing aims to provide an accurate estimate of the distribution of the clutter interference and the noise. Such an estimate is commonly calculated from secondary observations associated with the most recent coherent processing interval. In this article, we consider airborne bistatic radar systems and use a parameteric model of the clutter distribution that depends on a few parameters that describe the radar scenario. With this formulation, an estimate of the scenario parameters from the secondary data yields the clutter distribution through the parameteric model. The scenario parameters represent the position and velocities of the platforms comprising the airborne bistatic configuration. Moreover, the framework of radar scenarios enables information from previously considered coherent processing intervals to contribute to a clutter distribution estimate. Consequently, as the scenario parameters denote motion characteristics of a platform, the scenario parameters can be tracked over time by assuming a motion model. Thus, the estimator of this article uses a combination of the likelihood density of the most recent set of radar observations, together with a motion model which enables the propagation of the scenario parameters dynamics through time. Hence, the density of the likelihood and the prior density from the propagation through time of previous scenario parameter estimates is combined to calculate a maximum a-posteriori estimate of the scenario parameters. In numerical simulations, the maximum a-posteriori estimate is compared with the corresponding scenario parameter estimate considering only a maximum likelihood estimate. The numerical simulations show clearly that the maximum a-posteriori estimate has lower variance and RMSE compared to the maximum likelihood estimate.