Journal ArticleDOI
Robust adaptive Kalman filtering with unknown inputs
A. Moghaddamjoo,R.L. Kirlin +1 more
TLDR
This work presents a robust procedure for optimally estimating a polynomial-form input forcing function, its time of occurrence and the measurement error covariance matrix, R, based on a running window robust regression analysis.Abstract:
A method is proposed to adapt the Kalman filter to the changes in the input forcing functions and the noise statistics. The resulting procedure is stable in the sense that the duration of divergences caused by external disturbances are finite and short and, also, the procedure is robust with respect to impulsive noise (outlier). The input forcing functions are estimated by a running window curve-fitting algorithm, which concurrently provides estimates of the measurement noise covariance matrix and the time instant of any significant change in the input forcing functions. In addition, an independent technique for estimating the process noise covariance matrix is suggested which establishes a negative feedback in the overall adaptive Kalman filter. This procedure is based on the residual characteristics of the standard optimum Kalman filter and a stochastic approximation method. The performance of the proposed method is demonstrated by simulations and compared to the conventional sequential adaptive Kalman filter algorithm. >read more
Citations
More filters
Journal ArticleDOI
Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration
TL;DR: In this article, the authors evaluate the performance of adaptive Kalman filter methods with different adaptations and compare their limitations in real-life engineering applications and evaluate their performance in real data sets.
Journal ArticleDOI
A recursive multiple model approach to noise identification
X.R. Li,Yaakov Bar-Shalom +1 more
TL;DR: An approach is presented that is valid for nonstationary noise with rapidly or slowly varying statistics as well as stationary noise and the application of the proposed approach to failure detection is illustrated.
Journal ArticleDOI
Performance Model Estimation and Tracking Using Optimal Filters
TL;DR: This paper adapts Kalman filter estimators for performance model parameters, evaluates the approximations which must be made, and develops a systematic approach to setting up an estimator which converges under easily verified conditions.
ltering with unknown inputs via optimal state estimation of singular systems
TL;DR: In this paper, a new method for designing a Kalman filter for linear discrete-time systems with unkown inputs is presented, and necessary and sufficient conditions for the existence and stability of the filter are derived.
Journal ArticleDOI
Road Extraction From Satellite Images Using Particle Filtering and Extended Kalman Filtering
TL;DR: A method for dynamic clustering of the road profiles in order to maintain tracking when the road profile undergoes some variations due to changes in the road width and intensity is constructed.
References
More filters
Journal ArticleDOI
Robust Regression: Asymptotics, Conjectures and Monte Carlo
TL;DR: In this paper, a formal power series expansion of the initial terms of a power-series expansion with respect to the number of observations has been proposed, in most cases down to 4 observations per parameter.
Journal ArticleDOI
On the identification of variances and adaptive Kalman filtering
TL;DR: In this paper, it was shown that the steady-state optimal Kalman filter gain depends only on n \times r linear functionals of the covariance matrix and the number of unknown elements in the matrix.
Journal ArticleDOI
Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets
TL;DR: In this paper, an optimal Kalman filter has been derived for this purpose using a target model that is simple to implement and that represents closely the motions of maneuvering targets, using this filter, parametric tracking accuracy data have been generated as a function of target maneuver characteristics, sensor observation noise, and data rate and that permits rapid a priori estimates of tracking performance to be made when the target is to be tracked by sensors providing any combination of range, bearing, and elevation measurements.