Adaptive waveform design for cognitive radar

Advances in technology, especially in sensing, robotics, wireless communications, hardware capabilities and the constant need to confront not only the existing but also new and advanced threats are pushing for the need of advanced radar techniques. In this context, Cognitive Radar (CR) is visualized as the next generation multifunctional, smart and adaptive radar that extends its capabilities and responsibilities far beyond the traditional radar. CR incorporates knowledge gained by the interaction with the environment into its operation therefore forming a closed-loop system aiming to enhance the system performance. A very important element of the CR operation is the ability to adaptively design the transmitted waveforms based on the radar objective and the changes in the environment. In this thesis, we present the different aspects involved in the Cognitive Radar concept with deeper focus on the adaptive waveform design of the system aiming to improve the tracking performance. A method of adaptive waveform design within the sensor management problem ensuring that the total transmitted power is reduced compared to the transmission of a fixed waveform is proposed and finally a promising direction towards the multi-sensor resource allocation and waveform design is presented

A two-stage approach for direct signal and clutter cancellation in passive radar on moving platforms

This paper addresses the problem of direct signal interference (DSI) and clutter cancellation for passive radar systems on moving platforms using displaced phase centre antenna (DPCA) approach in the presence of receive channels imbalance. First, we show that using the signal emitted by the illuminator of opportunity as a source for channels calibration might be ineffective when DSI and clutter echoes have different directions of arrival. Then, a calibration approach is presented, based on supervised selection of clutter areas in the range-Doppler map. Finally, a two-stage strategy is presented, composed of an ECA-based DSI removal prior to DPCA clutter cancellation, which doesn’t require supervised selection of the calibration area. The effectiveness of this scheme in the joint suppression of DSI and clutter is shown against real data