标准的粒子滤波能够较好的解决闪烁噪声下机动目标跟踪问题,但在采样过程中有时会出现严重的退化现象。针对这种现象,文中提出了一种改进的粒子滤波算法,在采样过程中退化检测,若粒子退化严重,则重新采样,直到退化在允许范围之内。仿真结果表明,在高斯环境下,标准的粒子滤波和改进的粒子滤波性能相近,都优于不敏卡尔曼滤波,但在闪烁噪声环境下,其性能明显高于不敏卡尔曼滤波和标准的粒子滤波,但耗费时间长。

Standard particle filter can solve the problem of maneuvering target tracking effectively under glint noise, but severe degradation phenomenon sometimes exists in sampling process. For this, an improved particle filtering algorithm was presented; degradation detection was carried out in sampling process, if the particles are seriously degraded, re-sampling until degradation within the allowable range. Simulation results show that under the Gaussian environment, the improved particle filter has the same performance with that of standard particle filter, being superior to unscented Kalman filter, but under glint noise environment, its performance is significantly higher than that of unscented Kalman filter and standard particle filter, but consuming longer work time.