MM
M. Mo
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This thesis investigates high-resolution direction-of-arrival (DOA) estimation in a distributed automotive MIMO radar system consisting of two radars and four bistatic/monostatic apertures. To achieve super-resolution while keeping the computational load feasible, a two-stage DOA framework is developed: coarse beamforming with multi-aperture data association, followed by fine sparse reconstruction using both non-coherent and coherent FOCUSS. A complete phase-synchronization and geometric compensation model is derived to enable coherent fusion, and several numerical-stabilization techniques are introduced to ensure reliable reconstruction. Finally, this work proposes a modified array configuration that suppresses sidelobes for large baselines, significantly improving coherent performance. The results demonstrate enhanced angular resolution, robustness, and the feasibility of coherent sensing in distributed automotive radar.
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This thesis investigates high-resolution direction-of-arrival (DOA) estimation in a distributed automotive MIMO radar system consisting of two radars and four bistatic/monostatic apertures. To achieve super-resolution while keeping the computational load feasible, a two-stage DOA framework is developed: coarse beamforming with multi-aperture data association, followed by fine sparse reconstruction using both non-coherent and coherent FOCUSS. A complete phase-synchronization and geometric compensation model is derived to enable coherent fusion, and several numerical-stabilization techniques are introduced to ensure reliable reconstruction. Finally, this work proposes a modified array configuration that suppresses sidelobes for large baselines, significantly improving coherent performance. The results demonstrate enhanced angular resolution, robustness, and the feasibility of coherent sensing in distributed automotive radar.