Scenario Tracking for Airborne Bistatic Radar Systems

Artikel i vetenskaplig tidskrift, 2024

An effective space-time adaptive processing implementation provides an accurate estimate of the corresponding space-time covariance matrix of the distribution of the interference and the noise. Such an estimate is commonly calculated from secondary observations associated with the most recent coherent processing interval. In this paper, we derive the covariance matrix using a parametric model which depends on a few parameters describing the state of the radar scenario. With this formulation, it is sufficient to estimate the  scenario parameters to obtain the covariance matrix estimate. The scenario parameters represents 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 the covariance matrix estimate. Consequently, as the scenario parameters denotes motion characteristics of a platform, the scenario parameters can be tracked over time by assuming a motion model. Thus, the estimator of this paper uses a combination of the likelihood density of the most recent set of radar observations, together with a dynamical 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 of previous scenario parameter estimates through time 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 indicates that the maximum a posteriori estimate obtains a scenario parameter estimate which is closer to the true scenario parameters. Moreover, the maximum a posteriori estimate is more robust towards a low number of secondary data comprising the likelihood density compared to the maximum likelihood estimate.