The common problem with standard particle filters is weight degradation. Resampling can solve this problem

but it causes another noted hindrance—sample impoverishment. To overcome sample impoverishment

an improved particle filter algorithm based on krill herd optimization is proposed. The algorithm combines the solving process of particle filters and guides the particles to move to the high likelihood area by the induced motion

foraging motion

and physical diffusion of krill individuals. Firstly

the state values of particles were regarded as the individual positions of the krill herd. Thus the state estimation of particles was transformed into the optimization of krill herd. Secondly

according to the characteristics of the particle filter

parameters that can be improved in the krill herd algorithm were analyzed. A new dynamic updating strategy was designed for the weights of the individual induced motion and foraging motion to ensure fast global optimization in the early stage and accurate local optimization in the later stage. At the same time

to maintain the diversity of particles

the crossover operation in the genetic algorithm was carried out on krill individuals

and a new crossover probability updating formula was designed for krill individuals. Then

the convergence of the improved algorithm was analyzed based on the standard krill herd algorithm. A single static non-growth model was selected for simulation. The simulation results show that the proposed algorithm has a higher accuracy of state estimation

smaller root mean square error

and more reasonable particle distribution compared with standard particle filters

particle swarm optimization algorithm optimized particle filters (PSO-PF)

and bat algorithm optimized particle filters (BA-PF).