The paper proposes Gaussian mixture particle probability hypothesis density filter(PHD) algorithm ,which can effectively solve the problem that the object number is changing or unknown, based on particle PHD filter. This algorithm calculates the object number and state by recursive procedure, avoiding the uncertainty of target state estimation caused by particle sampling and clustering. Gaussia...

Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. Classical solutions such that Kalman filter and Particle filter are introduced in this report. Gaussian processes have been introduced as a non-parametric technique for system estimation from supervision learning. For the thesis project, we intend to propose a new, general methodology for infer...

This paper introduces a redundant localisation system, combining a low-cost Gaussian pose tracker and a particle filter localiser which is more CPU intensive. Ideally, the Gaussian pose tracker can be used for most of the time and the particle filter localiser switched on only when the Gaussian pose tracker fails. The problem studied here is to determine when the Gaussian pose tracker has faile...

Gaussian filtering is a basic operation commonly used in numerous image and volume processing algorithms. It is, therefore, desirable to perform it as efficiently as possible. Over the last decade CPUs have been successfully extended with several SIMD (Single Instruction Multiple Data) extensions, such as MMX, 3DNow!, and SSE series. In this paper we introduce a new technique for Gaussian filte...