基于条件扩散的MIMO雷达DOA估计生成式大阵列仿真
Generative Large-Array Emulation for DOA Estimation in MIMO Radar via Conditional Diffusion
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中文总结 AI 辅助
针对小孔径MIMO雷达DOA估计精度受限问题，提出直接生成大阵列MUSIC谱的条件扩散方法，利用协阵列压缩和谱峰共识，在仅用小型阵列推理时提升角度估计性能。
中文摘要 AI 辅助
从小型多输入多输出（MIMO）雷达阵列进行精确的到达方向（DOA）估计受限于其孔径，而增加天线会提高硬件成本。基于快照的阵列仿真在应用多信号分类（MUSIC）之前重建大阵列观测，但重建误差可能扭曲用于DOA估计的谱峰。我们转而直接从小型阵列测量生成理想化的大阵列MUSIC谱。协阵列压缩合并冗余虚拟通道，所得协方差和MUSIC谱作为扩散模型的条件。多个生成谱中峰值的共识产生DOA估计。针对波动目标的仿真表明，该方法优于场景匹配的快照重建网络和匹配的确定性谱预测器。小型和大型阵列MUSIC及其Cramér-Rao界提供参考比较。该方法在推理时仅使用小型阵列即可改善角度估计。
英文摘要
Accurate direction-of-arrival (DOA) estimation from a small multiple-input multiple-output (MIMO) radar array is limited by its aperture, while adding antennas increases hardware cost. Snapshot-based array emulation reconstructs large-array observations before applying multiple signal classification (MUSIC), but reconstruction errors can distort the spectral peaks used for DOA estimation. We instead generate an idealized large-array MUSIC spectrum directly from small-array measurements. Coarray compression combines redundant virtual channels, and the resulting covariance and MUSIC spectrum condition a diffusion model. Consensus over peaks in multiple generated spectra yields the DOA estimates. Simulations with fluctuating targets show that the method outperforms a scene-matched snapshot-reconstruction network and an otherwise matched deterministic spectrum predictor. Small- and large-array MUSIC and their Cramér-Rao bounds provide reference comparisons. The method improves angle estimation using only the small array at inference.
发表机构
- Ruhr University Bochum（波鸿鲁尔大学）
- Robert Bosch GmbH（罗伯特·博世有限公司）
机构由 AI 辅助整理，请以论文原文为准。
