电子科技
電子科技
전자과기
IT AGE
2014年
9期
97-100
,共4页
主被动雷达%信息融合%状态估计
主被動雷達%信息融閤%狀態估計
주피동뢰체%신식융합%상태고계
active-passive radar%information fusion%state estimation
针对主被动雷达复合导引头，研究了基于序贯扩展Kalman滤波的信息融合算法。利用主被动雷达复合导引头对目标角误差进行观测，将匹配后的测量角度进行最优加权，进而以角度信息作为量测，估计目标的运动信息。通过试验验证，基于主被动雷达信息融合状态估计比仅依赖主动雷达观测量的状态估计稳态误差小，且滤波器收敛速度更快。
針對主被動雷達複閤導引頭，研究瞭基于序貫擴展Kalman濾波的信息融閤算法。利用主被動雷達複閤導引頭對目標角誤差進行觀測，將匹配後的測量角度進行最優加權，進而以角度信息作為量測，估計目標的運動信息。通過試驗驗證，基于主被動雷達信息融閤狀態估計比僅依賴主動雷達觀測量的狀態估計穩態誤差小，且濾波器收斂速度更快。
침대주피동뢰체복합도인두，연구료기우서관확전Kalman려파적신식융합산법。이용주피동뢰체복합도인두대목표각오차진행관측，장필배후적측량각도진행최우가권，진이이각도신식작위량측，고계목표적운동신식。통과시험험증，기우주피동뢰체신식융합상태고계비부의뢰주동뢰체관측량적상태고계은태오차소，차려파기수렴속도경쾌。
In the present work , an information fusion algorithm based on sequential extended Kalman filtering ( SEKF) for the active-passive radar composite seeker is proposed.First, the angle error is achieved from the active-passive radar composite seeker , and then an optimization strategy is used to weight the matched angle errors , which can be used to estimate the dynamic information of the targets.The experiments show that the steady-state error of state estimation based on active-passive radar information fusion is smaller than that relies on active radar measure -ment only.Meanwhile , the former filter has a faster convergence.
