← 研踪 PaperTrailarXiv:2511.08326 · v2
A Two-Term Ziv-Zakai Bound for Multitarget DoA Estimation in MIMO Radar
首次提交 2025-11-11 22:59
版本更新 2026-11-06 21:27
雷达观测 2026-11-09 11:19
摘要
Multitarget direction of arrival (DoA) estimation is challenging when fluctuating targets are closely spaced: local Cramér--Rao bounds (CRBs) miss global errors, while direct vector Ziv--Zakai bound (ZZB) evaluation is computationally demanding. For colocated multiple-input multiple-output (MIMO) radar with Swerling-II fluctuations and an ordered angular prior, we derive a rigorous two-term Bayesian mean squared error lower bound. A finite separation contribution addresses nonlocal discrimination, while a correction based on the prior averaged Fisher information matrix (FIM) retains target coupling and virtual array geometry. We show that the prior averaged pointwise CRB diverges under unrestricted target spacing when the FIM is nonsingular almost everywhere, although the proposed bound remains finite. Once the averaged FIM is obtained, evaluation avoids displacement optimization and multidimensional binary error integration. The bound supports analysis of snapshot diversity, target spacing, and transmit and receive allocation. Numerical comparison with the vector ZZB shows close agreement at low SNR but greater conservatism at high SNR, quantifying the tradeoff in tightness.
作者备注：6 pages, 3 figures
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v2 · 2026-11-06 21:27 · 最新观测版本
A Two-Term Ziv-Zakai Bound for Multitarget DoA Estimation in MIMO Radar
Multitarget direction of arrival (DoA) estimation is challenging when fluctuating targets are closely spaced: local Cramér--Rao bounds (CRBs) miss global errors, while direct vector Ziv--Zakai bound (ZZB) evaluation is computationally demanding. For colocated multiple-input multiple-output (MIMO) radar with Swerling-II fluctuations and an ordered angular prior, we derive a rigorous two-term Bayesian mean squared error lower bound. A finite separation contribution addresses nonlocal discrimination, while a correction based on the prior averaged Fisher information matrix (FIM) retains target coupling and virtual array geometry. We show that the prior averaged pointwise CRB diverges under unrestricted target spacing when the FIM is nonsingular almost everywhere, although the proposed bound remains finite. Once the averaged FIM is obtained, evaluation avoids displacement optimization and multidimensional binary error integration. The bound supports analysis of snapshot diversity, target spacing, and transmit and receive allocation. Numerical comparison with the vector ZZB shows close agreement at low SNR but greater conservatism at high SNR, quantifying the tradeoff in tightness.
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