Real-Time DOA Estimation of Underwater Sound Sources Using GPU

Direction of Arrival (DOA) estimation of sound sources using phased array-based methods has a lot of importance in various fields, including sonar, robot vision and mechanical defect detection. Adaptive beamforming methods, such as the MVDR (Minimum Variance Distortionless Response) algorithm, have high resolution compared to non-adaptive methods; but this advantage is achieved in return for the computational complexity of these algorithms. This makes it hard to use these algorithms in applications that require real-time sound source DOA estimation. On the other hand, an important feature of the adaptive beamforming methods including MVDR is the high potential of these algorithms for parallelization. The purpose of this paper is the parallel implementation of the MVDR algorithm by employing GPU instead of CPU to increase the execution speed and achieve real-time mode. To achieve this purpose, the CUDA programming model has been used to implement the algorithm on the GPU. In order to investigate the performance of parallel implementation of the MVDR algorithm, two different GPUs, as well as CPUs, have been used. The performance validity of various implementations in this paper was confirmed by real sonar data as well as simulation data. The results show that using an array of 64 sensors, it is possible to estimate the DOA of underwater sound sources in real-time and with high resolution using the MVDR algorithm.