Abstract—The Kalman filter is widely used in many different fields. Many practical applications and theoretical results show that the Kalman filter is very sensitive to outliers in a measurement process. In this paper some reasons why the Kalman Filter is sensitive to outliers are analyzed and a series of outlier-tolerant algorithms are designed to be used as substitutes of the Kalman Filter. These outlier-tolerant filters are highly capable of preventing adverse effects from outliers similar with the Kalman Filter in complexity degree and very outlier-tolerant in the case there are some outliers arisen in sampling data set of linear stochastic systems. Simulation results show that these modified algorithms are safe and applicable. Keywords...
Impulsed noise outliers are data points that differs significantly from other observations. They are...
Impulsed noise outliers are data points that differs significantly from other observations.They are ...
In this paper, a statistical similarity measure is in-troduced to quantify the similarity between tw...
A Kalman Filtering algorithm which is robust to observational outliers is developed by assuming that...
A Kalman Filtering algorithm which is robust to observational outliers is developed by assuming that...
In this paper we discuss efficient methods of the state estimation which are robust against unknown ...
In this paper we discuss efficient methods of the state estimation which are robust against unknown ...
Kalman filter (KF), which is an algorithm that is utilized to estimate unknown variables based on no...
This paper proposes a new robust Kalman filter algorithm under outliers and system uncertainties. Th...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
Kalman filter is one of the best filter utilized as a part of the state estimation taking into accou...
Impulsed noise outliers are data points that differs significantly from other observations. They are...
Impulsed noise outliers are data points that differs significantly from other observations. They are...
Impulsed noise outliers are data points that differs significantly from other observations. They are...
Impulsed noise outliers are data points that differs significantly from other observations.They are ...
In this paper, a statistical similarity measure is in-troduced to quantify the similarity between tw...
A Kalman Filtering algorithm which is robust to observational outliers is developed by assuming that...
A Kalman Filtering algorithm which is robust to observational outliers is developed by assuming that...
In this paper we discuss efficient methods of the state estimation which are robust against unknown ...
In this paper we discuss efficient methods of the state estimation which are robust against unknown ...
Kalman filter (KF), which is an algorithm that is utilized to estimate unknown variables based on no...
This paper proposes a new robust Kalman filter algorithm under outliers and system uncertainties. Th...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
summary:The impact of additive outliers on a performance of the Kalman filter is discussed and less ...
Kalman filter is one of the best filter utilized as a part of the state estimation taking into accou...
Impulsed noise outliers are data points that differs significantly from other observations. They are...
Impulsed noise outliers are data points that differs significantly from other observations. They are...
Impulsed noise outliers are data points that differs significantly from other observations. They are...
Impulsed noise outliers are data points that differs significantly from other observations.They are ...
In this paper, a statistical similarity measure is in-troduced to quantify the similarity between tw...