基于空间稀疏采样的迭代优化框架用于GNSS直接位置估计
Spatial sparse sampling-based iterative optimization framework for GNSS Direct Position Estimation
AI总结：
针对GNSS直接位置估计中传统网格搜索计算量大的问题，提出基于空间稀疏采样的迭代优化框架，利用空间相干性和梯度，在保持精度的同时降低计算负载。
AI中文摘要：
直接位置估计（DPE）是全球导航卫星系统（GNSS）接收机中的一项有前景的技术，能够直接从相关器输出中估计位置、速度和时间的（PVT）解。传统的基于网格搜索（GS）的DPE计算量大，因为它仅依赖于定位互模糊函数（CAF）的峰值，并未充分利用相关值中存在的PVT信息。本文提出了一种迭代优化DPE框架，通过空间稀疏采样（SS）利用空间相干性和空间梯度。在SS-DPE框架中，空间采样的PVT点的相关输出均作为测量值，从中推导出空间梯度和空间相干性，以捕获不同相关值的信息密度和多样性。推导了解析的克拉美-罗界（CRB），表明空间相干性和梯度通过噪声协方差和雅可比矩阵分析决定理论性能极限。所提出的理论框架不仅验证了稀疏采样的可行性，还指导了不同相关值的权重优化，有效地将这些见解与通用的基于梯度的优化相结合。理论推导通过蒙特卡洛模拟得到验证。现场实验进一步证明了所提出的SS-DPE优化框架的实际可行性。对比分析表明，所提出的SS-DPE在PVT估计精度上与传统的GS-DPE相当，同时仅消耗稀疏采样的相关值，提高了信息利用效率并降低了计算负载。
英文摘要：
Direct position estimation (DPE), a promising technique in Global Navigation Satellite Systems (GNSS) receivers, enables estimation of position, velocity, and time (PVT) solutions directly from correlator outputs. The conventional grid search (GS)-based DPE is computationally intensive, as it relies solely on locating the peak of the cross ambiguity function (CAF), and it does not fully leverage the PVT information present in the correlation values. This paper proposes an iterative optimization DPE framework that capitalizes on spatial coherence and spatial gradient via spatial sparse sampling (SS). In SS-DPE framework, the correlation outputs of spatial sampled PVT points all serve as measurements, where the spatial gradient and spatial coherence are derived to capture the information density and diversity of different correlation values. The analytical Cramér-Rao Bound (CRB) is derived and indicates that both spatial coherence and gradient determine the theoretical performance limit via the noise covariance and Jacobian matrices analysis. The proposed theoretical framework not only validates the feasibility of sparse sampling but also guides weight optimization to distinct correlation values, effectively integrating these insights with a general gradient-based optimization. Theoretical derivations are validated via Monte Carlo simulations. Field experiments further demonstrate the practical feasibility of the proposed SS-DPE optimization framework. Comparative analysis shows that the proposed SS-DPE achieves comparable PVT estimation accuracy to the conventional GS-DPE while consumes only sparsely sampled correlation values, improving the information utilization efficiency and reducing the computation load.
