Model-based adaptive detection of fluctuating targets

Radar detection is a well-established area as illustrated by the development of target fluctuation models such as the Swerling models, diverse clutter models, different methods for coherent and non-coherent integration, different CFAR algorithms and signal processing methods such as STAP and adaptive matched filtering. In this contribution the idea is to use target models and adaptive processing to improve the radar detection performance and to reduce the radar resources required for detection. The proposed detection methods are primarily intended for multifunction phased array radar with flexible resource management. The model-based adaptive detection methods use the target fluctuations to find optimum parameter settings for the radar system such that the target radar cross section is maximized for each coherent processing interval. This in general results in a reduction of the target fluctuations and the detection performance is rather determined by background noise fluctuations than by target fluctuations. The main problem is to find relevant target models and adaptive processing methods that can give sufficient detection performance improvements.