Cognitive FDA-MIMO With Channel Uncertainty Information for Target Tracking

In this paper, we propose a cognitive frequency diverse array multiple input multiple output (FDA-MIMO) design by utilizing channel uncertainty information. Time-variant non-uniform frequency offsets are utilized to decouple frequency diverse array (FDA) range-angle dependent beampattern and a design strategy is proposed to maximize the transmit energy toward the desired range-angle region by steering the beam to match the channel uncertainty for enhanced target detect and tracking performance. To unambiguously estimate target parameters, we divide the FDA elements into multiple fully overlapped subarrays using orthogonal waveforms. Furthermore, we adaptively update the transmit beamspace matrix with two optimization criterions, namely, signal-to-noise ratio maximization and Cramér–Rao bound minimization. All proposed approaches are verified by numerical results.