Knowledge-Aided (Potentially Cognitive) Transmit Signal and Receive Filter Design in Signal-Dependent Clutter
We propose convex optimization based waveform designs for cognitive radar networks. The Multiple Input Multiple Output (MIMO) nature of the considered model as well as the cognitive capabilities provided by the joint adaptation of receiver and transmitter and some initial knowledge on the environment, make these algorithms suitable for cognitive radar networks. In particular, we propose two complementary optimization techniques. The first one aims at optimizing the Signal to Interference and Noise Ratio (SINR) of a specific radar while keeping the SINR of the other radars at desired levels. The second approach optimizes the SINR of all radars using a Max-Min optimization criterion. The optimization framework includes many constraints on the waveforms such as mutual orthogonality and transmission power. The simulation results confirm the efficiency of the algorithms.