The MIMO radar concept promises numerous advantages upon today’s radar architectures: flexibility for the transmitting beampattern design — including wide scene illumination and fine resolution after processing — and system complexity reduction, through the use of less antennas and the possibility to transfer system control and calibration to the digital domain. However, the MIMO radar is still at the stage of the theoretical concept, with insufficient consideration of the impacts of waveform lack of orthogonality and system hardware imperfections.

This thesis work, in its ambition to contribute to paving the way to the operational MIMO radar, consists in anticipating and compensating the imperfections of the real world with processing techniques. The first part deals with MIMO waveform design and we show that phase code waveforms are optimal in terms of spatial resolution. We also exhibit their limits in terms of sidelobes appearance at matched filter output. The second part consists in taking on the waveform intrinsic imperfections and proposing data-dependent processing schemes for the rejection of the residual sidelobes. We develop an extension for the Orthogonal Matching Pursuit (OMP) that satisfies operational requirements, especially localization error robustness, low computation complexity, and nonnecessity of training data. The third part deals with processing robustness to signal model mismatch, especially how it can be prevented or anticipated to avoid performance degradation. In particular, we propose a digital method of transmitter phase calibration. The last part consists in carrying out experimentations in real conditions with the Hycam MIMO radar testbed. We exhibit how the different encountered distortions affect the processing performance in terms of detection.

Keywords: MIMO radar, MIMO waveform, sidelobe mitigation, data-dependent processing, OMP, IAA, model mismatch, experimentation.