- Fig. 1 – Signal to Disturbance Ration after STAP processing with (red) and without (blue) the use of the GIP test. The Generalized Inner Product (GIP) test measures the similarity between the unknown covariance matrix of a received signal vector for a particular range cell and a test covariance matrix that can be derived from a priori knowledge about clutter environment
- Fig. 2 – SDR versus the number of homogeneous clutter range cells used for the calculation of the clutter covariance matrix. The blue line represents the results of using the KB Bayesian STAP algorithm, while the red line indicates the performance in case of standard STAP processing. As can be noted as the number of homogeneous clutter range cells increase the Conventional STAP approached the KB STAP. It is worth t pointing out that the case in which a high number of homogeneous range cells is available is extremely sporadic.
The aim of this study is to present a state of the art assessment of KBR signal and data processing techniques together with a roadmap for the implementation of this capability in European Radar and Multifunction RF Systems . This study is envisaged to find ways of mitigating the problem of clutter returns by using smarter radar signal processing in the form of KBR processing, including self-learning and adaptive processing techniques.
The KbaReT project has been articulated on six WPs:
- WP1 – State of the art (SoA) of KBR and architecture assessment.
- WP2 – KBR Impact on Emerging Technologies.
- WP3 -KBR for CFAR and STAP.
- WP4 – KBR for Multifunction Sensors.
- WP5 – KBR for Non Cooperative Identification (NCI).
- WP6 – KBR Implementation Roadmap. A detailed description of each WP is provided in the following sub-sections.
CNIT-RaSS was involved in WP1, WP2, WP3, WP5 and WP6.
In particular, the CNIT-RaSS has performed the following technical activities:
- Design and validation of KBR signal processing algorithms aimed at enhancing the target detection performance. KB-CFAR and KB-STAP algorithms have been proposed and tested by means of simulated data. KB-STAP has been formulated for both a monostatic and a bistatic radar geometry. Moreover two different Knowledge based algorithm has been implemented, namely the ITFS (Intelligent Training and Filter Selection) approach and the Bayesian approach. The main results of this activity are presented in the figures below.
- Design and validation of a KB algorithm exploiting the polarimetry response of a target to enhance its detection in strong clutter conditions. Measures was used in this case to test and validate the developed algorithms.
Design and validation of a KB Bayesian classifier. Validation has been performed by using measures acquired in a controlled-type measurement campaigns.