An iterated-extended Kalman filter algorithm for tracking surface and sub-surface targets

Hyland, John C.

doi:10.1109/oceans.2002.1191824

Cited by 19 publications

(11 citation statements)

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“…For the cases with infinite uncertainty, the information filter tracks the inverse of error covariance instead of the covariance itself, which is an algebraically equivalent formulation of the KF [7]. Besides, some specific approaches such as neurofuzzy-oriented, state partitioning and probabilistic can also be found in various applications [8]- [10]. Although the above methods provide different tools to eliminate the negative effect caused by poor initial guessing, none of them resolves the initial assumption problem fundamentally.…”

Section: Introductionmentioning

confidence: 99%

Linear Optimal Unbiased Filter for Time-Variant Systems Without Apriori Information on Initial Conditions

Zhao

Huang

Liu

2017

IEEE Trans. Automat. Contr.

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In this paper, an optimal unbiased filter (OUF) is derived for time-variant systems to relax the initial condition assumption in Kalman filter (KF). By minimizing the mean square errors subject to the unbiasedness condition a solution is derived in a batch computation form first. To facilitate the on-line application, a recursive realization is further developed. The effect of removing the initial condition assumption on the estimation performance is analysed, and we show that the proposed algorithm converges to the KF asymptotically. Two-state harmonic model and four-state moving target tracking model are employed to demonstrate that the OUF can improve transient estimation performance significantly and can be used in place of the KF when the apriori information about the initial state values is not available.

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Section: Introductionmentioning

confidence: 99%

Linear Optimal Unbiased Filter for Time-Variant Systems Without Apriori Information on Initial Conditions

Zhao

Huang

Liu

2017

IEEE Trans. Automat. Contr.

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“…A number of estimation strategies exist, and include: the extended Kalman filter (EKF) [10,11], the iterated extended Kalman filter (IKF) [12], the higher-order extended Kalman filter (HOEKF) [13], the sigma-point Kalman filter (SPKF) including the unscented Kalman filter (UKF) [14], the particle filter (PF) [4], Slotine's sliding mode observer [15], Walcott's sliding mode observer [16], Edward's sliding mode observer [17], the variable structure filter (VSF) [18], the extended variable structure filter (EVSF) [19], and the smooth variable structure filter (SVSF) [20,21,22]. These filters have been modified and/or combined to improve their performance for a variety of linear and nonlinear system and measurement applications.…”

Section: Introductionmentioning

confidence: 99%

The cubature smooth variable structure filter estimation strategy applied to a quadrotor controller

2015

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Unmanned aerial systems (UAS) are becoming increasingly popular in industry, military, and social environments. An UAS that provides good operating performance and robustness to disturbances is often quite expensive and prohibitive to the general public. To improve UAS performance without affecting the overall cost, an estimation strategy can be implemented on the internal controller. The use of an estimation strategy or filter reduces the number of required sensors and power requirement, and improves the controller performance. UAS devices are highly nonlinear, and implementation of filters can be quite challenging. This paper presents the implementation of the relatively new cubature smooth variable structure filter (CSVSF) on a quadrotor controller. The results are compared with other state and parameter estimation strategies.

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“…Several researches have been developed to overcome this limitation. Those include linearizing the system by Taylor Series Approximation (TSA) up to the first order such as the Perturbation Kalman Filter [9] [13] [14], the Extended Kalman filter (EKF) [8] [15]- [17], and the Iterated Extended Kalman filter (IEKF) [7] [15] [18]- [20], or up to higher order such as the Higher Order Extended Kalman Filter (HOEKF) [15] [21]- [23]. The later shows that in order to increase the accuracy of high nonlinear application, TSA is not a suitable approach as it takes long computation time with complicated structure [24].…”

Section: Introductionmentioning

confidence: 99%

Sigma-Point Filters in Robotic Applications

Al-Shabi¹

2015

ICA

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Sigma-Point Kalman Filters (SPKFs) are popular estimation techniques for high nonlinear system applications. The benefits of using SPKFs include (but not limited to) the following: the easiness of linearizing the nonlinear matrices statistically without the need to use the Jacobian matrices, the ability to handle more uncertainties than the Extended Kalman Filter (EKF), the ability to handle different types of noise, having less computational time than the Particle Filter (PF) and most of the adaptive techniques which makes it suitable for online applications, and having acceptable performance compared to other nonlinear estimation techniques. Therefore, SPKFs are a strong candidate for nonlinear industrial applications, i.e. robotic arm. Controlling a robotic arm is hard and challenging due to the system nature, which includes sinusoidal functions, and the dependency on the sensors' number, quality, accuracy and functionality. SPKFs provide with a mechanism that reduces the latter issue in terms of numbers of required sensors and their sensitivity. Moreover, they could handle the nonlinearity for a certain degree. This could be used to improve the controller quality while reducing the cost. In this paper, some SPKF algorithms are applied to 4-DOF robotic arm that consists of one prismatic joint and three revolute joints (PRRR). Those include the Unscented Kalman Filter (UKF), the Cubature Kalman Filter (CKF), and the Central Differences Kalman Filter (CDKF). This study gives a study of those filters and their responses, stability, robustness, computational time, complexity and convergences in order to obtain the suitable filter for an experimental setup.

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An iterated-extended Kalman filter algorithm for tracking surface and sub-surface targets

Cited by 19 publications

References 0 publications

Linear Optimal Unbiased Filter for Time-Variant Systems Without Apriori Information on Initial Conditions

Linear Optimal Unbiased Filter for Time-Variant Systems Without Apriori Information on Initial Conditions

The cubature smooth variable structure filter estimation strategy applied to a quadrotor controller

Sigma-Point Filters in Robotic Applications

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