Infrared target tracking based on fissile bootstrap particle filters

In order to overcome the particle depletion phenomenon in the particle filter algorithm and the problem of heavy large amount of calculation, a fission bootstrap particle filtering method was used at the moment of measurement. At first, fissile breeding was carried out on the particles with big weights. The particles after fissile breeding are proportional to those before fissile breeding. Then the particles with small weights in the particle group were covered. The particle pre-smoothing was carried out with the characteristics of the particle group maintained. After sampling once again, the function of particle anti-depletion was compensated. An appropriate sampling threshold was set to eliminate the sampling points with smaller weights. Multiple sampling points were derived from the sampling points with bigger weights in the premise of maintaining the total number of sampling points. The root mean square (RMS) error and calculation steps were shown in the model and best tracking effect was obtained. Simulation was carried out with MATLAB. The results show that the RMS error of the algorithm is 0.36445, better than that of the basic particle filtering algorithm.