In the direction of arrival (DoA) estimation, typically sensor arrays are used where the number of required sensors can be large depending on the application. With the help of compressed sensing (CS), hardware complexity of the sensor array system can be reduced since reliable estimations are possible by using the compressed measurements where the compression is done by measurement matrices. After the compression, DoAs are reconstructed by using sparsity promoting algorithms such as alternating direction method of multipliers (ADMM). For the given procedure, both the measurement matrix design and the reconstruction algorithm may include computationally intensive operations, which are addressed in this study. The presented simulation results imply the feasibility of the system in real-time processing with energy efficient implementations. We propose employing parallel programming to satisfy the real-time processing requirements. While the measurement matrix design has been accelerated 16 with CPU based parallel version with respect to the fastest serial implementation, ADMM based DoA estimation has been improved 1.1 with GPU based parallel version compared to the fastest CPU parallel implementation. In addition, we achieved, to the best of our knowledge, the first energy-efficient real-time DoA estimation on embedded Jetson GPGPUs in 15 W power consumption without affecting the DoA accuracy performance.

中文翻译：

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基于压缩感知的到达方向估计的高效异构并行编程

在到达方向 (DoA) 估计中，通常使用传感器阵列，其中所需传感器的数量可能很大，具体取决于应用。在压缩感知 (CS) 的帮助下，可以降低传感器阵列系统的硬件复杂性，因为可以通过使用压缩测量来进行可靠的估计，其中压缩是由测量矩阵完成的。在压缩之后，通过使用诸如交替方向乘法器（ADMM）的稀疏促进算法来重构DoA。对于给定的过程，测量矩阵设计和重建算法都可能包括计算密集型操作，这在本研究中得到解决。所呈现的仿真结果表明系统在实时处理中具有节能实施的可行性。我们建议采用并行编程来满足实时处理要求。虽然测量矩阵设计已经加速 16基于 CPU 的并行版本相对于最快的串行实现，与最快的 CPU 并行实现相比，基于 ADMM 的 DoA 估计在基于 GPU 的并行版本中得到了改进 1.1。此外，据我们所知，我们在不影响 DoA 精度性能的情况下，首次在 15 W 功耗下在嵌入式 Jetson GPGPU 上实现了节能实时 DoA 估计。