电子与信息学报
電子與信息學報
전자여신식학보
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY
2015年
7期
1739-1745
,共7页
李少毅%梁爽%张凯%董敏周%闫杰
李少毅%樑爽%張凱%董敏週%閆傑
리소의%량상%장개%동민주%염걸
目标跟踪%数据关联%Kalman滤波%压缩成像%压缩背景差分
目標跟蹤%數據關聯%Kalman濾波%壓縮成像%壓縮揹景差分
목표근종%수거관련%Kalman려파%압축성상%압축배경차분
Target tracking%Data association%Kalman filtering%Compressive imaging%Compressive background subtraction
目前压缩测量的应用研究主要集中在重构图像方面,但是很多应用中最终目的是检测和跟踪。直接基于压缩测量的检测和跟踪问题尚未解决。该文首次建立一种压缩域到空间域的映射模型,并提出一种无需重构任何图像且直接从低维压缩测量中经解码进行目标跟踪的方法,并分析其应用于天基红外探测的可能性。该方法利用Hadamard测量矩阵构建红外压缩成像系统,采用自适应压缩背景差分法从低维压缩测量信息中分离背景和前景,再从压缩前景信息中解码目标空间位置,并结合数据关联和Kalman滤波算法解决了杂波环境下点目标跟踪问题。理论分析和仿真实验结果表明,该方法能利用少量压缩测量实现目标跟踪任务,并减小探测器规格及相关算法的计算复杂度和存储代价。
目前壓縮測量的應用研究主要集中在重構圖像方麵,但是很多應用中最終目的是檢測和跟蹤。直接基于壓縮測量的檢測和跟蹤問題尚未解決。該文首次建立一種壓縮域到空間域的映射模型,併提齣一種無需重構任何圖像且直接從低維壓縮測量中經解碼進行目標跟蹤的方法,併分析其應用于天基紅外探測的可能性。該方法利用Hadamard測量矩陣構建紅外壓縮成像繫統,採用自適應壓縮揹景差分法從低維壓縮測量信息中分離揹景和前景,再從壓縮前景信息中解碼目標空間位置,併結閤數據關聯和Kalman濾波算法解決瞭雜波環境下點目標跟蹤問題。理論分析和倣真實驗結果錶明,該方法能利用少量壓縮測量實現目標跟蹤任務,併減小探測器規格及相關算法的計算複雜度和存儲代價。
목전압축측량적응용연구주요집중재중구도상방면,단시흔다응용중최종목적시검측화근종。직접기우압축측량적검측화근종문제상미해결。해문수차건립일충압축역도공간역적영사모형,병제출일충무수중구임하도상차직접종저유압축측량중경해마진행목표근종적방법,병분석기응용우천기홍외탐측적가능성。해방법이용Hadamard측량구진구건홍외압축성상계통,채용자괄응압축배경차분법종저유압축측량신식중분리배경화전경,재종압축전경신식중해마목표공간위치,병결합수거관련화Kalman려파산법해결료잡파배경하점목표근종문제。이론분석화방진실험결과표명,해방법능이용소량압축측량실현목표근종임무,병감소탐측기규격급상관산법적계산복잡도화존저대개。
Currently the application research of compressive measurements is still focused on the image recovery, but the ultimate purpose is a task of target detection and tracking in many special applications. And the issue performing target detection and tracking based on compressive measurements is not yet solved. The mapping model is firstly exploited to locate the target in the spatial domain through the measurements in the compressive domain. Further, a method tracking point targets through decoding targets location in the low-dimensional compressive measurements without reconstructed image is proposed for the possible application in space based infrared detection. The method uses the Hadamard matrix to design infrared compressive imaging system, and separates the background and foreground image from the low-dimensional compressive measurements by the adaptive compressive background subtraction. With the mapping relation from the compressive domain into the spatial domain, the target location is possibly decoded. Then the task of point target tracking in the clutter environment can be done by the associated data association and Kalman filtering algorithm. The theoretical analysis and numerical simulations demonstrate the approach proposed is able to accomplish a task of target tracking only by using less compressive measurements, and reduce detector scale, computation complexity and storage cost.