The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthes...

Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of predictive and posterior probability density function (pdf). This paper introduces a filter called adaptive kernel Kalman (AKKF). The AKKF approximates arbitrary pdf hidden states using mean embedding (KME) reproducing Hilbert space (RKHS). In parallel with KME, some particles data are used to capture ...

In this paper we propose a novel training algorithm for RBF networks that is based on extended kalman filter and fuzzy logic.After the user choose how many prototypes to include in the network, the extended kalman filter simultancously solves for the prototype vectors and the weight matrix.The fuzzy logic is used to cope with the devergence problem caused by the insufficiently known a priori fi...

Aiming at the multi-source heterogeneous of target tracking system information. On the basis of "current" statistical model, this paper researches the unscented Kalman filter information fusion method and analyzes its mathematical model. According to the mathematic model researching of optimal estimation real time tracking algorithm, it be able to describe and process the sensor information of ...

In this paper, we present a technique based on fuzzy logic to improve the performance of the inertial navigation system integrated with GPS, and magnetometer. The proposed fuzzy technique is primarily used to predict position and velocity measurements during GPS outage signals. As long as the GPS measurements are available, the Q-SUKF of INS/GPS/MAG (MAG: magnetometer) integrated system operate...

This paper analyses the velocity estimation of a target, from the Doppler filter using 1) Kalman filter 2) Adaptive Kalman filter 3) Kalman filter with state vector fusion 4) Adaptive Kalman filter with state vector fusion 5) State vector fused adaptive Kalman filter. Simulation through MATLAB gave good response for 4 and 5 algorithms under low signal to noise ratio. 2 and 3 algorithms gave bet...

This paper proposes two methods to enhance traditional extended Kalman filter for UAV navigation. One is based on using fuzzy rules to choose parameters of an adaptive Kalman filter. The other uses inherent parallelism to speed up iterations in Kalman filter computations. Both methods are described briefly and simulation results are presented. INTRODUCTION Unmanned Aerial Vehicles (UAV), such a...

-For the rapid development high-speed railway system, improvement approach of the velocity measurement accuracy has been studied based on multiple speed sensors on high-speed train. In this method, the velocity measurement data from multi-channel speed sensors were dealt through data fusion of arithmetic mean filter, weighted arithmetic mean filter, Federated Kalman filter and adaptive Federate...