Particle Estimation Algorithm Using Anglebetween Observation Vectors for Nonlinear System State

A particle estimation algorithm where theweight of the particle is related to angle between observation vectors is presented for nonliear system state. Whenthe likelihood has a bimodal nature, this algorithm leads tomore accurate state estimates than Sequential importanceresampling (SIR), Auxiliary particle filter (APF), Regularized particle filter (RPF), and Gaussian particle filter(GPF).