一种基于MMPF-TBD的机动弱目标检测方法

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.

An MMPF-TBD Algorithm for Maneuvering Weak Target Detection

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.