共查询到20条相似文献，搜索用时 122 毫秒
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随着卫星导航定位技术及接收机的发展,软件接收机已成为当前研究的热点之一。根据北斗卫星导航定位系统（BDS）的接口控制文档（ICD）,分析了BDS B1I信号,提出了一种基于并行码相位和并行频率捕获的算法,实现了对BDS-B1I/GPS信号的捕获跟踪、导航电文解码。利用HG-SOFTGPS02采集器采集的数据进行BDS/GPS定位,验证了该算法的可行性与合理性。 相似文献
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传统的基于硬件的接收机由于芯片的封装性很难用来进行通道信号的分析研究,而基于软件无线电技术的软件接收机则很容易做到。软件接收机的跟踪通道信号统计分析可用于接收机算法的测试与比对以及卫星信号性能的分析与评估。这里搭建了GPS软件接收机L1中频数字信号处理平台,对跟踪通道输出的I、Q支路信号进行了均值、标准方差以及均方根(RMS)统计。分析了各统计值与通道对应卫星的高度角、信号强度以及噪声之间的关系。基于统计结果提出了一种估计信道信噪比的方法。最后比较了不同信号采样频率对相关三角形和导航定位解算精度的影响。 相似文献
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传统的基于硬件的接收机由于芯片的封装性很难用来进行通道信号的分析研究,而基于软件无线电技术的软件接收机则很容易做到.软件接收机的跟踪通道信号统计分析可用于接收机算法的测试与比对以及卫星信号性能的分析与评估.这里搭建了GPS软件接收机L1中频数字信号处理平台,对跟踪通道输出的I、Q支路信号进行了均值、标准方差以及均方根(RMS)统计.分析了各统计值与通道对应卫星的高度角、信号强度以及噪声之间的关系.基于统计结果提出了一种估计信道信噪比的方法.最后比较了不同信号采样频率对相关三角形和导航定位解算精度的影响. 相似文献
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在分析基于软件无线电GPS接收机结构的基础上,在基于PC软件接收机信号处理系统上采用模拟的数字中频信号,对软件接收机信号捕获、跟踪算法进行了分析和验证.信号捕获阶段给出了基于快速傅利叶变换FFT的快速搜索原理和结果,并采用跟踪阶段Q支路信号的统计特性分析了捕获门限和误警概率的关系,给出了一种捕获门限的优化方法;跟踪阶段对系统采用的数字锁相环(PLL)进行了分析,并对I/Q解调原理进行了解析和验证. 相似文献
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CPU/GPU异构混合系统是一种新型高性能计算平台,但现有并行空间插值算法仅依赖CPU或GPU进行加速,迫切需要研究协同并行空间插值算法以充分利用异构计算资源,进一步提升插值效率。以薄板样条函数插值为例,提出一种CPU/GPU协同并行插值算法以加速海量激光雷达（light detector & ranger,LiDAR）点云生成数字高程模型（DEM）。通过插值任务的分解与抽象封装以屏蔽底层硬件执行模式的差异性,同时在多级协同并行框架基础上设计了Greedy-SET动态调度策略,策略顾及底层硬件能力的差异性,以实现异构并行资源的充分利用和良好负载均衡。实验表明,协同并行插值算法在高性能工作站上取得19.6倍的加速比,相比单一CPU或GPU并行算法,其效率提升分别达到54%和44%,实现了高效的协同并行处理。 相似文献
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Jia Liu Dustin Feld Jochen Garcke Thomas Soddemann Peiyuan Pan 《International Journal of Digital Earth》2016,9(8):748-765
Quantitative remote sensing retrieval algorithms help understanding the dynamic aspects of Digital Earth. However, the Big Data and complex models in Digital Earth pose grand challenges for computation infrastructures. In this article, taking the aerosol optical depth (AOD) retrieval as a study case, we exploit parallel computing methods for high efficient geophysical parameter retrieval. We present an efficient geocomputation workflow for the AOD calculation from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. According to their individual potential for parallelization, several procedures were adapted and implemented for a successful parallel execution on multi-core processors and Graphics Processing Units (GPUs). The benchmarks in this paper validate the high parallel performance of the retrieval workflow with speedups of up to 5.x on a multi-core processor with 8 threads and 43.x on a GPU. To specifically address the time-consuming model retrieval part, hybrid parallel patterns which combine the multi-core processor’s and the GPU’s compute power were implemented with static and dynamic workload distributions and evaluated on two systems with different CPU–GPU configurations. It is shown that only the dynamic hybrid implementation leads to a greatly enhanced overall exploitation of the heterogeneous hardware environment in varying circumstances. 相似文献
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波形分解是机载激光雷达全波形数据处理的重要基础工作,通过求解波形函数模型的参数,将波形数据利用具体的函数模型拟合出来,实现对全波形及其中各个子波形函数表达。LM(Levenberg-Marquardt)算法及其改进的算法是波形分解中对参数进行拟合求解的常用方法。针对LM算法在参数拟合计算的过程中存在大量迭代和矩阵运算,提出了基于线程块组和线程两级并行粒度的并行计算方案。将串行多次循环迭代求解参数改为单次并行计算取最佳值实现对参数的选择,将矩阵运算进行线程块的协同并行计算,实现了LM算法在通用计算图形处理器上的并行计算。实验证明,在规定阈值条件下,并行LM降低了算法的迭代次数,提高了波形分解LM算法的计算效率,为提高波形分解的处理效率提供了研究思路。 相似文献
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The Markov chain random field (MCRF) model is a spatial statistical approach for modeling categorical spatial variables in multiple dimensions. However, this approach tends to be computationally costly when dealing with large data sets because of its sequential simulation processes. Therefore, improving its computational efficiency is necessary in order to run this model on larger sizes of spatial data. In this study, we suggested four parallel computing solutions by using both central processing unit (CPU) and graphics processing unit (GPU) for executing the sequential simulation algorithm of the MCRF model, and compared them with the nonparallel computing solution on computation time spent for a land cover post-classification. The four parallel computing solutions are: (1) multicore processor parallel computing (MP), (2) parallel computing by GPU-accelerated nearest neighbor searching (GNNS), (3) MP with GPU-accelerated nearest neighbor searching (MP-GNNS), and (4) parallel computing by GPU-accelerated approximation and GPU-accelerated nearest neighbor searching (GA-GNNS). Experimental results indicated that all of the four parallel computing solutions are at least 1.8× faster than the nonparallel solution. Particularly, the GA-GNNS solution with 512 threads per block is around 83× faster than the nonparallel solution when conducting a land cover post-classification with a remotely sensed image of 1000?×?1000 pixels. 相似文献
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基于GPGPU的并行影像匹配算法 总被引：7，自引：1，他引：6
提出一种基于GPGPU的CUDA架构快速影像匹配并行算法,它能够在SIMT模式下完成高性能并行计算。并行算法根据GPU的并行结构和硬件特点,采用执行配置技术、高速存储技术和全局存储技术三种加速技术,优化数据存储结构,提高数据访问效率。实验结果表明,并行算法充分利用GPU的并行处理能力,在处理1280×1024分辨率的8位灰度图像时可达到最高多处理器warp占有率,速度是基于CPU实现的7倍。CUDA在高运算强度数据处理中呈现出的实时处理能力和计算能力,为进一步加速影像匹配性能和GPU通用计算提供了新的方法和思路。 相似文献
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针对EM(Expectation Maximization)波形分解算法具有多次迭代和大量乘、除、累加等高密集运算的特点,提出一套将EM算法在通用计算图形处理器GPGPU上并行化的方案。针对通用并行计算架构CUDA的存储层次特点,设计总体的并行方案,充分挖掘共享存储器、纹理存储器的高速访存的潜能;根据波形采样值采用字节存储的特征,利用波形采样值的直方图求取中位数,从而降低求噪音阈值的计算复杂度;最后,采用求和规约的并行策略提高EM算法迭代过程中大量累加的计算效率。实验结果表明,当设置合理的并行参数、EM迭代次数大于16次、数据量大于64 M时,与单核CPU处理相比,GPU的加速比达到了8,能够显著地提高全波形分解的效率。 相似文献
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An efficient data organization and scheduling strategy for accelerating large vector data rendering 
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下载免费PDF全文 Rendering large volumes of vector data is computationally intensive and therefore time consuming, leading to lower efficiency and poorer interactive experience. Graphics processing units (GPUs) are powerful tools in data parallel processing but lie idle most of the time. In this study, we propose an approach to improve the performance of vector data rendering by using the parallel computing capability of many‐core GPUs. Vertex transformation, largely a mathematical calculation that does not require communication with the host storage device, is a time‐consuming procedure because all coordinates of each vector feature need to be transformed to screen vertices. Use of a GPU enables optimization of a general‐purpose mathematical calculation, enabling the procedure to be executed in parallel on a many‐core GPU and optimized effectively. This study mainly focuses on: (1) an organization and storage strategy for vector data based on equal pitch alignment, which can adapt to the GPU's calculating characteristics; (2) a paging‐coalescing transfer and memory access strategy for vector data between the CPU and the GPU; and (3) a balancing allocation strategy to take full advantage of all processing cores of the GPU. Experimental results demonstrate that the approach proposed can significantly improve the efficiency of vector data rendering. 相似文献
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This research develops a parallel scheme to adopt multiple graphics processing units (GPUs) to accelerate large‐scale polygon rasterization. Three new parallel strategies are proposed. First, a decomposition strategy considering the calculation complexity of polygons and limited GPU memory is developed to achieve balanced workloads among multiple GPUs. Second, a parallel CPU/GPU scheduling strategy is proposed to conceal the data read/write times. The CPU is engaged with data reads/writes while the GPU rasterizes the polygons in parallel. This strategy can save considerable time spent in reading and writing, further improving the parallel efficiency. Third, a strategy for utilizing the GPU's internal memory and cache is proposed to reduce the time required to access the data. The parallel boundary algebra filling (BAF) algorithm is implemented using the programming models of compute unified device architecture (CUDA), message passing interface (MPI), and open multi‐processing (OpenMP). Experimental results confirm that the implemented parallel algorithm delivers apparent acceleration when a massive dataset is addressed (50.32 GB with approximately 1.3 × 108 polygons), reducing conversion time from 25.43 to 0.69 h, and obtaining a speedup ratio of 36.91. The proposed parallel strategies outperform the conventional method and can be effectively extended to a CPU‐based environment. 相似文献
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《International Journal of Digital Earth》2013,6(6):471-486
Spatial analysis, including viewshed analysis, is an important aspect of the Digital Earth system. Viewshed analysis is usually performed on a large scale, so efficiency is important in any Digital Earth application making these calculations. In this paper, a real-time algorithm for viewshed analysis in 3D scenes is presented by using the parallel computing capabilities of a graphics processing unit (GPU). In contrast to traditional algorithms based on line-of-sight, this algorithm runs completely within the programmable 3D visualization pipeline to render 3D terrains with viewshed analysis. The most important difference is its integration of the viewshed calculation with the rendering module. Invisible areas are rendered as shadows in the 3D scene. The algorithm process is paralleled by rasterizer units in the graphics card and by vertex and pixel shaders executed on the GPU. We have implemented this method in our 3D Digital Earth system with the DirectX 9.0c API and tested on some consumer-level PC platforms with interactive frame-rates and high image quality. Our algorithm has been widely used in related systems based on Digital Earth. 相似文献
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Changqing Yan Jimin Liu Gang Zhao Chuanfa Chen Tianxiang Yue 《Transactions in GIS》2016,20(6):991-1003
A high accuracy surface modeling method (HASM) has been developed to provide a solution to many surface modeling problems such as DEM construction, surface estimation and spatial prediction. Although HASM is able to model surfaces with a higher accuracy, its low computing speed limits its popularity in constructing large scale surfaces. Hence, the research described in this article aims to improve the computing efficiency of HASM with a graphic processor unit (GPU) accelerated multi‐grid method (HASM‐GMG). HASM‐GMG was tested with two types of surfaces: a Gauss synthetic surface and a real‐world example. Results indicate that HASM‐GMG can gain significant speedups compared with CPU‐based HASM without acceleration on GPU. Moreover, both the accuracy and speed of HASM‐GMG are superior to the classical interpolation methods including Kriging, Spline and IDW. 相似文献