Abstract
The extended Kalman filter (EKF) is the nonlinear model of a Kalman filter (KF). It is a useful parameter estimation method when the observation model and/or the state transition model is not a linear function. However, the computational requirements in EKF are a difficulty for the system. With the help of cognition-based designation and the Taylor expansion method, a novel algorithm is proposed to ease the computational load for EKF in azimuth predicting and localizing under a nonlinear observation model. When there are nonlinear functions and inverse calculations for matrices, this method makes use of the major components (according to current performance and the performance requirements) in the Taylor expansion. As a result, the computational load is greatly lowered and the performance is ensured. Simulation results show that the proposed measure will deliver filtering output with a similar precision compared to the regular EKF. At the same time, the computational load is substantially lowered.
Highlights
In statistics and parameter estimation, the Kalman filter is a mathematical method named afterRudolf E
The majority of our work presents the novel designation for an extended Kalman filter (EKF)
In these figures: (1) the “error” means “root-mean-square error (RMSE)”; (2) the “observations” are the transformed observations, called “localization based on observation” in the aforementioned texts; and (3) the
Summary
In statistics and parameter estimation, the Kalman filter is a mathematical method named afterRudolf E. In statistics and parameter estimation, the Kalman filter is a mathematical method named after. The Kalman filter is a very useful facility in parameter estimation, target tracking and data fusion [1,4]. One of its conveniences is that, when the observation noise (or measure noise) is Gaussian, the predicting covariance matrix, the filtering gain and the filtering covariance matrix can be calculated offline [1,5]. If the target’s state equation and the sensor’s measure equation are either linear in the same coordinate and the measure noise is Gaussian with zero mean, the Kalman filter algorithm is fairly brief, and the request for minimum variance is easy to meet [6,7]
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