An Improved Unscented Particle Filter with Global Sampling Strategy

Particle filter (PF) has many variations and one of the most popular is the unscented particle filter (UPF). UPF uses the unscented Kalman filter (UKF) to generate particles in the PF framework and has a better performance than the standard PF. However, UPF suffers from its high computation complexity because it has to execute UKF to each particle to obtain proposal distribution. This paper gives an improved UPF aiming at reducing the computation complexity of the algorithm. In comparison to the standard UPF, the new strategy generates proposal distribution from the mean and covariance value of the whole particles instead of from each particle. Thus the improved algorithm utilizes the characteristics of the whole particles and only needs to perform UKF algorithm once to get the proposal distribution at each time step. Experimental results show that, compared to standard UPF, the improved algorithm reduces the time consumption greatly almost without performance degradation. 1. Introduction Nonlinear and non-Gaussian filtering has a wide range of applications in many fields [1–3]. Among the many methods that have been proposed in the literature for these applications, particle filter (PF) has become one of the most popular. For decades PF has been applied to a variety of problems, such as computer vision, signal processing [4], target tracking [5], and financial pricing [6]. However when designing a PF a major problem is to choose a proper proposal distribution of the particles. Due to the fact that the particles are drawn from this distribution, and the weight values of particles are also related to this distribution, the performance of a PF is strongly influenced by the choice of the proposal distribution. To design better proposal distributions, several techniques based on linearization have been proposed. In one method, an extended Kalman filter (EKF) is used to generate the proposal distribution, and this is known as extended Kalman PF (EKPF). However, the linearization operation in EKPF introduces modeling errors, which can yield large estimation errors if the system is highly nonlinear [7]. To overcome this problem, a more accurate PF was proposed using unscented Kalman filter (UKF) to generate the proposal distribution [8]. The UKF can accurately compute the mean and covariance of nonlinear systems up to the second order of the Taylor series expansions. This type of particle filter using UKF to generate proposal distribution is known as the unscented particle filter (UPF). The UKF produces proposal distributions that exhibit a larger support