Preface These lecture notes are concerned with state estimation problem of linear and particularly nonlinear discrete and continuous-discrete stochastic systems. State estimation has a great variety of applications including The general solution of the state estimation problem is based on the Bayesian recursive relations and the Fokker-Planck equation which generate conditional probability dens...

This paper discusses a new Bayesian filter able to estimate the state of static systems (parameter estimation) with any kind of nonlinear measurement equation subject to Gaussian measurement uncertainty. The filter is also applicable to the state estimation for a limited class of dynamic systems. The core idea of the filter is to linearize the process and measurement equation in a higher-dimens...

This paper presents a new finite-dimensional Bayesian filter. The filter calculates the exact analytical expression for the posterior probability density function (pdf) of static systems with kind of nonlinear measurement equation subject to Gaussian measurement uncertainty. The paper also extends this filter to a limited class of dynamic systems. The filter is applied to the estimation of the ...

This paper presents a Rao-Blackwellized mixed state particle filter for joint head tracking and pose estimation. Rao-Blackwellizing a particle filter consists of marginalizing some of the variables of the state space in order to exactly compute their posterior probability density function. Marginalizing variables reduces the dimension of the configuration space and makes the particle filter mor...

We consider the localization problem for a unicycle robot equipped with range finders and moving in environments with nonsmooth geometry, i.e., whose obstacle-free region has a piecewise-linear boundary. Using the Multi-Hypothesis Density Filter, a novel multi-modal estimator based on the bayesian framework, an innovative localization system is devised and implemented on the ATRV-Jr robot. Expe...

The solution to the problem of mapping an environment and at the same time using this map to localize (the simultaneous localization and mapping, SLAM, problem) is a key prerequisite in the synthesis of truly autonomous vehicles. By far the most common formulation of the SLAM problem is founded on a vector based stochastic framework, where the sensor models and the vehicle models are represente...

The effective detection of unmanned aerial vehicle (UAV) targets is great significance to guarantee national military security and social stability. In recent years, with the development communication control technology, movement UAVs has become increasingly flexible complex, presenting diverse trajectory forms different motion models in phases. Gaussian mixture probability hypothesis density f...

Multiple unattended ground sensors are deployed for surveillance, monitoring the movement of troops, military vehicles, and targeting. Usually, the probability of detection ( D P ) of an UGS is low and the false alarm density (FAD) is high. The particle filter (PF) and range-parametrized extended Kalman filter (RPEKF) have been used previously to produce improved results for the single sensor s...