基于离格稀疏表示的近场信源定位方法

近场信源定位是下一代无线通信中的一个重要研究方向，现有的方法大多集中在传统子空间或在格稀疏方法。针对子空间类方法损失阵列孔径和稀疏表示类方法精度受网格划分效应制约的问题，该文提出了一种基于离格稀疏表示的定位方法。首先通过获得一个高阶累积量矩阵建立基于角度参数的离格信号模型，利用交替迭代优化方法实现角度的估计。然后根据角度估计值建立基于距离参数的离格信号模型，并采用交替迭代方法进行求解。仿真结果表明，所提方法不仅具有较高的估计精度，而且可以实现角度和距离参数的正确配对。

Off-grid Sparse Representation Based Localization Method for Near-field Sources

Near-field source localization is a potential research topic in next-generation wireless communications. Most existing methods focus on traditional subspace based methods or on-grid sparse methods. For the problem that the accuracy of subspace class method loss array aperture and sparse representation method is restricted by mesh effect, an off-grid sparse representation localization method is proposed in this paper. First, by obtaining a high-order cumulant matrix, an angle based off-grid signal model is constructed and then the alternatively iterating optimization method is employed to estimate the angles. For range estimation, a range parameter based off-grid signal model is constructed by using the angle estimation values and is solved by alternatively iterating method. Simulation results reveal that the proposed method not only possesses high estimation accuracy, but also can realize auto-pairing of angles and ranges.