The burden distribution real-time estimation problem of multi-loop charging based on the real multi-radar data is resolved.Firstly , an iterative algorithm is introduced to calculate the radial coordinate of the pile-...The burden distribution real-time estimation problem of multi-loop charging based on the real multi-radar data is resolved.Firstly , an iterative algorithm is introduced to calculate the radial coordinate of the pile-top.Then , based on the multi-radar data , the burden profile is estimated by a cubic-curve equation at the end of the multi-loop charging.Furthermore , the burden profile before the next multi-loop charging is calculated based on multi-radar data by considering the impact of burden descent.On the basis of these burden profiles , a more accurate thickness ratio of ore to coke （ RO/C ） at the radial direction of blast furnace can be obtained.Finally , an example is given to calculate the burden profiles and RO/C by using the real multi-radar data sampled from Baosteel , which shows the effectiveness of the method introduced.展开更多
Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The ...Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, time-frequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such non stationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.展开更多
矿产资源开采引发的地表沉陷积水是高潜水位矿区耕地资源破坏的重要原因,也是制约矿山绿色发展的瓶颈之一。面向矿区沉陷积水早期监测识别需求,建立了基于多时相合成孔径雷达(SAR)遥感影像的矿区沉陷积水监测方法,命名为矿区雷达水分指...矿产资源开采引发的地表沉陷积水是高潜水位矿区耕地资源破坏的重要原因,也是制约矿山绿色发展的瓶颈之一。面向矿区沉陷积水早期监测识别需求,建立了基于多时相合成孔径雷达(SAR)遥感影像的矿区沉陷积水监测方法,命名为矿区雷达水分指数(Mining-area Radar Water Index,MRWI)。以山东省龙堌镇某井工(地下开采)煤矿区为研究区域,收集并处理了2017年哨兵1号(Sentinel-1)GRD产品IW模式29景10 m空间分辨率的SAR影像数据,进行了沉陷区积水的动态监测及其演变分析。利用查准率P(precision)、查全率R(recall)以及两者的调和平均数F1这3种定量指标,对比分析了MRWI与光学水体指数(Visible and Shortwave Infrared Drought Index,VSDI)和单时相雷达水体指数(Sentinel-1 Dual-polarized Water Index,SDWI)的监测性能。结果表明:对于存在明显地表水体覆盖区域,MRWI、VSDI以及SDWI均能有效识别出水体范围,其中MRWI与VSDI相比的P、R和F1平均值分别达到了94.45%、94.83%和94.46%;MRWI与SDWI相比,P、R和F1平均值分别达到了99.86%、94.03%和96.85%。MRWI具有较强的沉陷积水早期监测能力,以2号积水区为例,MRWI在2017年6月6日监测到了沉陷积水现象,此时VSDI和SDWI均不能指示这一现象;在2017年7月12日左右,沉陷积水导致地表被水体覆盖,此时MRWI、VSDI和SDWI均能指示出沉陷积水现象。MRWI具备较强的时序监测能力,基于Sentinel-1雷达数据的MRWI能够做到至少每12 d为一期的时序监测,并且能够利用长时序的结果对矿区沉陷区积水变化进行时空演变趋势分析。MRWI可为高潜水位矿区沉陷积水现象的早期监测与治理提供有力技术支撑。展开更多
基金Item Sponsored by Fundamental Research Funds for Central Universities of China ( FRF-TP-12-103A , FRF-AS-11-004B , FRF-SD-12-016A )Doctoral Program Foundation of Institutions of Higher Education of China ( 20110006120034 )
文摘The burden distribution real-time estimation problem of multi-loop charging based on the real multi-radar data is resolved.Firstly , an iterative algorithm is introduced to calculate the radial coordinate of the pile-top.Then , based on the multi-radar data , the burden profile is estimated by a cubic-curve equation at the end of the multi-loop charging.Furthermore , the burden profile before the next multi-loop charging is calculated based on multi-radar data by considering the impact of burden descent.On the basis of these burden profiles , a more accurate thickness ratio of ore to coke （ RO/C ） at the radial direction of blast furnace can be obtained.Finally , an example is given to calculate the burden profiles and RO/C by using the real multi-radar data sampled from Baosteel , which shows the effectiveness of the method introduced.
文摘Multi-radar signal separation is a critical process in modern reconnaissance systems. However, the complicated battlefield is typically confronted with increasing electronic equipment and complex radar waveforms. The intercepted signal is difficult to separate with conventional parameters because of severe overlapping in both time and frequency domains. On the contrary, time-frequency analysis maps the 1D signal into a 2D time-frequency plane, which provides a better insight into the signal than traditional methods. Particularly, the parameterized time-frequency analysis (PTFA) shows great potential in processing such non stationary signals. Five procedures for the PTFA are proposed to separate the overlapped multi-radar signal, including initiation, instantaneous frequency estimation with PTFA, signal demodulation, signal separation with adaptive filter and signal recovery. The proposed method is verified with both simulated and real signals, which shows good performance in the application on multi-radar signal separation.
文摘矿产资源开采引发的地表沉陷积水是高潜水位矿区耕地资源破坏的重要原因,也是制约矿山绿色发展的瓶颈之一。面向矿区沉陷积水早期监测识别需求,建立了基于多时相合成孔径雷达(SAR)遥感影像的矿区沉陷积水监测方法,命名为矿区雷达水分指数(Mining-area Radar Water Index,MRWI)。以山东省龙堌镇某井工(地下开采)煤矿区为研究区域,收集并处理了2017年哨兵1号(Sentinel-1)GRD产品IW模式29景10 m空间分辨率的SAR影像数据,进行了沉陷区积水的动态监测及其演变分析。利用查准率P(precision)、查全率R(recall)以及两者的调和平均数F1这3种定量指标,对比分析了MRWI与光学水体指数(Visible and Shortwave Infrared Drought Index,VSDI)和单时相雷达水体指数(Sentinel-1 Dual-polarized Water Index,SDWI)的监测性能。结果表明:对于存在明显地表水体覆盖区域,MRWI、VSDI以及SDWI均能有效识别出水体范围,其中MRWI与VSDI相比的P、R和F1平均值分别达到了94.45%、94.83%和94.46%;MRWI与SDWI相比,P、R和F1平均值分别达到了99.86%、94.03%和96.85%。MRWI具有较强的沉陷积水早期监测能力,以2号积水区为例,MRWI在2017年6月6日监测到了沉陷积水现象,此时VSDI和SDWI均不能指示这一现象;在2017年7月12日左右,沉陷积水导致地表被水体覆盖,此时MRWI、VSDI和SDWI均能指示出沉陷积水现象。MRWI具备较强的时序监测能力,基于Sentinel-1雷达数据的MRWI能够做到至少每12 d为一期的时序监测,并且能够利用长时序的结果对矿区沉陷区积水变化进行时空演变趋势分析。MRWI可为高潜水位矿区沉陷积水现象的早期监测与治理提供有力技术支撑。
