光电工程
光電工程
광전공정
OPTO-ELECTRONIC ENGINEERING
2009年
11期
29-34
,共6页
检测前跟踪%多模型粒子滤波%粒子退化%滑窗平均法
檢測前跟蹤%多模型粒子濾波%粒子退化%滑窗平均法
검측전근종%다모형입자려파%입자퇴화%활창평균법
track-before-detect%multi-model particle filter%particle degeneration%sliding window
针对低信噪比条件下机动目标实时检测与跟踪问题,提出一种改进的基于多模型粒子滤波的检测前跟踪(MMPF-TBD)方法.通过滑窗平均法判断粒子集是否受上一时刻目标估计结果的影响,当判断值超过设定阈值时,则根据上一时刻的目标检测概率与状态估计分布添加新粒子集,再用扩展后的粒子集对目标进行检测与估计,与现有方法的仿真比较表明,本丈所提的改进方法能够有效地解决粒子退化问题,并在满足系统实时性的前提下,提高了对于机动微弱目标的检测概率和跟踪精度.
針對低信譟比條件下機動目標實時檢測與跟蹤問題,提齣一種改進的基于多模型粒子濾波的檢測前跟蹤(MMPF-TBD)方法.通過滑窗平均法判斷粒子集是否受上一時刻目標估計結果的影響,噹判斷值超過設定閾值時,則根據上一時刻的目標檢測概率與狀態估計分佈添加新粒子集,再用擴展後的粒子集對目標進行檢測與估計,與現有方法的倣真比較錶明,本丈所提的改進方法能夠有效地解決粒子退化問題,併在滿足繫統實時性的前提下,提高瞭對于機動微弱目標的檢測概率和跟蹤精度.
침대저신조비조건하궤동목표실시검측여근종문제,제출일충개진적기우다모형입자려파적검측전근종(MMPF-TBD)방법.통과활창평균법판단입자집시부수상일시각목표고계결과적영향,당판단치초과설정역치시,칙근거상일시각적목표검측개솔여상태고계분포첨가신입자집,재용확전후적입자집대목표진행검측여고계,여현유방법적방진비교표명,본장소제적개진방법능구유효지해결입자퇴화문제,병재만족계통실시성적전제하,제고료대우궤동미약목표적검측개솔화근종정도.
An improved Multiple Model Particle Filter based on Track-before-detect (MMPF-TBD) algorithm for maneuvering target detection and tracking in low signal-to-noise environment is proposed. The algorithm uses sliding window to determine whether the particles are affected by the estimation of the target. When the value exceeds threshold, it adds new particles in accordance with the state estimation of the previous moment. Then it uses the expanded particles to detect and estimate the target. Compared with the existing methods, the simulation results show that the proposed algorithm can effectively solve the particle degeneration problem and improve the probability of maneuvering target detection and tracking accuracy in real time.
