Sparse array motion can efficiently expand the numbers of achievable degrees of freedom (DOFs) and consecutive lags, improving direction-of-arrival (DOA) estimation. Sparse arrays on a moving platform benefit from motion translation that introduces new sensor positions, which collectively with the original positions can increase the number of spatial autocorrelation lags and lead to full array augmentability. This property has been recently used for the case of environment-independent sparse array configurations, such as those defined by nested and co-prime arrays. In this paper, we consider environment-dependent sparse arrays (EDSAs) design using Cramer-Rao bound (CRB) as the metric of optimality for DOA estimation. The CRB is derived for a sparse array on a moving platform, where the number of identifiable uncorrelated sources exceeds the number of sensors. The CRB expression is used to solve for the sparse array configuration by applying the Genetic algorithm. Simulation results are provided to validate the effectiveness of the proposed EDSA design.

稀疏阵列运动可以有效地扩展可达到的自由度（DOF）和连续滞后的数量，从而改善到达方向（DOA）的估计。移动平台上的稀疏阵列受益于引入了新传感器位置的运动平移，该传感器平移与原始位置共同可以增加空间自相关滞后的次数并导致全阵列可扩展性。此属性最近已用于与环境无关的稀疏数组配置的情况，例如由嵌套数组和互素数组定义的配置。在本文中，我们考虑使用Cramer-Rao界（CRB）作为DOA估计的最佳度量的环境相关稀疏阵列（EDSA）设计。CRB是针对移动平台上的稀疏数组派生的，可识别的不相关源的数量超过传感器的数量。通过应用遗传算法，CRB表达式用于求解稀疏数组配置。提供仿真结果以验证所提出的EDSA设计的有效性。