排序方式: 共有14条查询结果,搜索用时 15 毫秒
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利用格罗贝斯统计理论剔除系统误差数据,对余下的有效数据,利用模糊理论计算其与估计值之间的模糊贴近度,并以此确定每个传感器的重要性权重,最后提出数据融合公式实现多传感器的数据融合。 相似文献
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针对利用传统算法难以跟踪低空目标的问题,提出了一种可行的跟踪低空目标的最大似然-概率数据关联(ML-PDA)算法。在分析各种低空目标特性的基础上,首先建立了基于ML-PDA滤波算法的低空目标跟踪模型,然后对该模型进行了深入分析,最后通过计算机仿真对该模型进行了验证。结果表明:ML-PDA滤波算法对低空目标跟踪十分有效,并且提高了滤波实时性,具有较好的工程应用前景。 相似文献
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针对由单传感器概率假定密度滤波到多传感器情形推导困难的问题,提出了一种有序粒子概率假定密度跟踪算法。首先,推导出集中式多传感器粒子概率假定密度滤波模型,再根据集中式融合系统的特点,选取与多传感器相关的重要性密度函数,通过多传感器多步更新重采样粒子,从而实现多传感器多目标有序粒子概率假定密度跟踪。仿真结果表明,该算法的跟踪误差距离差要小于单传感器粒子概率假定密度跟踪算法,且具有更优越的跟踪性能。 相似文献
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文章首先介绍了线性调频脉冲压缩的特点,然后对影响线性调频脉冲压缩的各种因素进行了基于MATLAB平台的分析和仿真,最后得出结论. 相似文献
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This paper presents a method for the automatic adjustment of the laser defocusing amount in micro-laser-induced breakdown spectroscopy. A microscopic optical imaging system consisting of a CCD camera and a 20× objective lens was adopted to realize the method. The real-time auto-focusing of the system was achieved by detecting the effective pixels of the light spot generated by the laser pointer. The focusing accuracy of the method could achieve 3 μm. The element concentrations of Mn and Ni in low-alloy steels were analyzed at a crater diameter of about 35 μm using the presented method. After using the presented method, the determination coefficients of Mn and Ni both exceeded 0.997, with the root-mean-square errors being 0.0133 and 0.0395, respectively. Scanning analysis was performed on the inclined plane and the curved surface by means of focusing control and non-focusing control. Ten characteristic spectral lines of Fe were selected as the analysis lines. With the focusing control, the average relative standard deviations obtained on the inclined plane and curved surface were both less than 5%, and much less than the values without focusing control, 14.6% and 40.39%. 相似文献
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目前检测矿浆品位相对准确的方法是传统化学分析,但周期长、有滞后性,无法实现在线检测。实验利用激光诱导击穿光谱(Laser induced breakdown spectroscopy,LIBS)在线、原位、快速等优点,分析了铁矿选矿过程尾矿浆中铁元素的品位值。由于LIBS采集到的光谱数据中存在大量对成分分析无用的冗余信息,进而增加了建模复杂程度,导致建立的模型精确度不够、泛化能力不强。因此,在偏最小二乘(PLS)模型基础上,提出了基于互信息特征筛选的偏最小二乘模型。实验结果表明,与传统的PLS模型相比,基于互信息特征筛选的偏最小二乘模型在分析精度上得到了明显改善,测试样品的决定系数R2从0.52提高到0.90,测试样本的平均绝对误差(MAEP)从2.87%下降到1.38%,总样本的平均绝对误差(MAE)从1.0%下降到0.60%。 相似文献