Localizing a vehicle consists in estimating its state by merging data from proprioceptive sensors (inertial measurement unit, gyrometer, odometer, etc.) and exteroceptive sensors (GPS sensor). A well known solution in state estimation is provided by the Kalman filter. But, due to the presence of nonlinearities, the Kalman estimator is applicable only through some alternatives among which the Ex...

This paper is based on the analysis and variation of multi target tracking algorithm which is based on kalman filter a digital filter. In this paper integrated random coefficient matrices kalman filtering is analysed with a mathematical model and used to reduce error and results are compared. KeywordsKalman Filtering, multi target tracking, integrated random coefficient matrices.

Combined state and parameter estimation of dynamical systems plays a crucial role in extracting system response from noisy measurements. A wide variety of methods have been developed to deal with the joint state-parameter estimation of nonlinear dynamical systems. The Extended Kalman Filter method is a popular approach for the joint systemparameter estimation of nonlinear systems. This method c...

Both the approximate reduced updated Kalman filter (RUKF) and reduced order model Kalman filter (ROMKP) have been used in image processing for some time. This correspondence shows that the ROMKF can be interpreted as a special case of the approximate RUKF and in so doing, provides a way to estimate a needed error variance input to ROMKF.

The norm-optimal iterative learning control (ilc) algorithm for linear systems is extended to an estimation-based normoptimal ilc algorithm where the controlled variables are not directly available as measurements. A separation lemma is presented, stating that if a stationary Kalman filter is used for linear time-invariant systems then the ilc design is independent of the dynamics in the Kalman...

Improving a priori stochastic models of the process and measurement noise vectors in Kalman Filer (KF) has always been a challenge. As one preferable technique to address this challenge, the variance component estimation (VCE) applied on the Kalman Filter’s process and measurement noise covariance matrix (Q & R) has been proved in plenty of applications. Unsurprisingly, VCE was expected to re-e...

Nonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorit...

A new filtering algorithm, adaptive square root cubature Kalman filter-Kalman filter (SRCKF-KF) is proposed to reduce the problems of amount of calculation, complex formula-transform, low accuracy, poor convergence or even divergence. The method uses cubature Kalman filter (CKF) to estimate the nonlinear states of model while its linear states are estimated by the Kalman filter (KF). The simula...

Abstract: More and more services are based on knowing the location of pedestrians equipped with connected objects (smartphones, smartwatches, etc.). One part of the location estimation process is attitude estimation. Many algorithms have been proposed but they principally target open space areas where the local magnetic field equals the Earth’s field. Unfortunately, this approach is impossible ...