Investigation of an adapted tracking scheme for multi-frequency space observation radars based on data fusion

This paper analyses the performance of different tracking configurations in order to best combine the data of multiple radar systems operating on different frequencies. A first study aims to understand the improvement obtained by the multi-sensor data fusion when processing the tracking data of a space object with a batch weighted least squares filter. For this, the performance of a batch filter with and without data fusion are compared and discussed. Those results lay the foundations for the second study of the article that examines possible scenarios for a new tracking approach for the Tracking and Imaging Radar (TIRA). This investigation is based on the multi-sensor data fusion between the tracking and the imaging radar of TIRA and it is achieved through a performance analysis of the derived two-step tracking filter. The latter consists of two different tracking filters (an extended Kalman filter and a batch weighted least squares filter) running in parallel and interacting between each other.