Optimized Real-Time MUSIC Algorithm With CPU-GPU Architecture

Abstract

Direction-of-arrival (DOA) estimation algorithm for uniform planar arrays has been applied in many fields. The multiple signal classification (MUSIC) algorithm has obvious advantage in high-resolution signal source estimation scenarios. However, the MUSIC algorithm has high computational costs, therefore it is hard to be used in real-time scenes. Many studies are dedicated to accelerating MUSIC algorithm by parallel hardware, especially by Graphics Processing Units (GPU). MUSIC algorithm based on Central Processing Unit (CPU) -GPU architecture acceleration is rarely investigated in previous literatures, and how well MUSIC Algorithm with CPU-GPU architecture could perform remains unknown. In this paper, we present and evaluate a model of search parallel MUSIC algorithm with CPU-GPU architecture. In the proposed model, the steering vector of each candidate incident signal and the corresponding value of 2D spatial pseudo-spectrum (SPS) function are sequentially calculated in a single core of the GPU, and the subsequent calculation of each elevation or azimuth is parallel in batches. Furthermore, in order to improve the peak search speed, we propose a new Coarse and Fine Traversal (CFT) peak search algorithm via CPU and a new parallel peak search algorithm based on GPU acceleration. Across strategy comparison, utilizing CPU-GPU architecture for processing, a 150-160x performance gain is achieved compared to using CPU only. Besides, the resolution of uniform planar arrays is also analyzed.