ABSTRACT: In the Kalman filter used for the integration of GPS/INS, the inertial sensor error model is usually considered as a random constant or random walk for both gyroscopes and accelerometers. However, the Inertial Measurement Unit (IMU) used in aerial remote sensing applications for sensor positioning and orientation is typically of tactical grade, i.e., the gyroscope drifts are on the or...

Map-matching, which reconciles a vehicle’s location with the underlying road map, is a fundamental function of a land vehicle navigation system. This paper presents an improved Kalman filter approach whose state space model is different from the conventional ones. The main objective of the research is to develop and apply a proper Kalman filter-based model for effectively correcting the Global ...

Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system as well estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network). The EKF applies the standard linear Kalman filter met...

A novel robust finite-horizon Kalman filter is presented for networked linear time-varying systems with norm-bounded parameter uncertainty whether, or not, the data packets in the network are time-stamped. Measured data loss and latency in the communication link are both described by a Bernoulli distributed random sequence. Then, a two-stage recursive structure is employed for the robust Kalman...

We consider estimation problems for a continuous-time linear system with a state disturbance and additive errors on the input and the output. The problem formulation and the estimation principle are deterministic. The derived filter is identical to the stochastic Kalman filter. The problem formulation with additive error on both the input and the output, however, is more symmetric then the clas...

abstract comparison of food prices in different periods, indicates fluctuations and continually upward trend. any change in agricultural sector variables, as main food supplier, will affect food price. productivity shocks and production gap are examples of such variables. in this paper ,hodrick prescott and kalman filters are used as generators of productivity shocks and production gap series. ...

I. SUMMARY Here are the key ideas and results of this important topic. • Section II reviews Kalman Filter. • A system is observable if its state can be determined from its outputs (after some delay). • A system is reachable if there are inputs to drive it to any state. • We explore the evolution of the covariance in a linear system in Section IV. • The error covariance of a Kalman Filter is bou...

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This...

Obviously navigation is one of the most complicated issues in mobile robots. Intelligent algorithms are often used for error handling in robot navigation. This Paper deals with the problem of Inertial Measurement Unit (IMU) error handling by using Extended Kalman Filter (EKF) as an Expert Algorithms. Our focus is put on the field of mobile robot navigation in the 2D environments. The main chall...

A method based on the interval Kalman filter for discrete uncertain linear systems is presented. The system under consideration is subject to bounded parameter uncertainties not only in the state and observation matrices, but also in the covariance matrices of Gaussian noises. The gain matrix provided by the filter is optimized to give a minimal upper bound on the state estimation error covaria...