Spin-image surface matching is a technique for locating objects in a scene by processing three-dimensional surface information from sources such as light detection and ranging (LIDAR), structured light photography, and tomography. It is attractive for parallel processing on graphics processing units (GPUs) because the two main computational steps - matching pairs of spin-images by correlation, and matching pairs of points between model and scene - are explicitly
parallel.
By implementing these parallel computations on the GPU, as well as recasting serial portions of the algorithm into a parallel form and structuring the algorithm to limit data exchanges between host and GPU, this project achieved an overall speedup of 20 times or more compared to conventional serial processing.
A demonstration application has been developed that allows users to select among a set of models and scenes and then applies the spin-image surface matching algorithm to match the selected models to the scene. It also has several user interface controls for changing parameters. One new parameter is a geometric consistency ratio (GCR) that quantifies the matching performance and provides a measure for discarding low-quality matches. By toggling between GPU- and
host-based processing, the application demonstrates the speedup achieved with parallelization on the GPU.
One of the main goals of the STAP-BOY program has been the implementation of a space-time adaptive processing (STAP) algorithm on graphics processing units (GPUs) with the goal of reducing the processing time. Within the context of GPU implementation, we have further developed algorithms that exploit data redundancy
inherent in particular STAP applications. Integration of these algorithms with GPU architecture is of primary importance for fast algorithmic processing times. STAP algorithms involve solving a linear system in which the transformation matrix is a covariance matrix. A standard method involves estimating a covariance matrix from a data matrix, computing its Cholesky factors by one of several methods, and then solving the system by substitution. Some STAP applications have redundancy in successive data matrices from which the covariance matrices are formed. For STAP applications in which a data matrix is updated with the addition of a new data row at
the bottom and the elimination of the oldest data in the top of the matrix, a sequence of data matrices have multiple rows in common. Two methods have been developed for exploiting this type of data redundancy when computing Cholesky factors. These two methods are referred to as
1) Fast QR factorizations of successive data matrices
2) Fast Cholesky factorizations of successive covariance matrices.
We have developed GPU implementations of these two methods. We show that these two algorithms exhibit reduced computational complexity when compared to benchmark algorithms that do not exploit data redundancy. More importantly, we show that when these algorithmic improvements are optimized for the GPU architecture,
the processing times of a GPU implementation of these matrix factorization algorithms may be greatly improved.
In this article we examine time evolution of the a posteriori probability distribution for the source location in an elementary passive sonar model. The approach employs Bayesian inversion to obtain the a posteriori distributions. The resulting multimodal distributions reflect the nonlinear relationship between source location and array output. This probabilistic approach allows us to include a priori information, utilize ocean acoustic modeling, and make direct use of increased data integration time.
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