An improved real-time adaptive Kalman filter with recursive noise covariance updating rules

This paper proposes a new approach for slip prediction of walking biped robots. The slip prediction is a measurement-based and friction behavior-inspired approach. A measurement-based online algorithm is designed to estimate the Coulomb friction which is regarded as a slip threshold. To predict the slip, a safety margin is introduced in the negative vicinity of the estimated Coulomb friction. The estimation algorithm concludes that if the applied force is outside the safety margin, then the foot tends to slip. The proposed approach depends on the available type of measurements. Three options of measurements are discussed. Among them, the foot acceleration and ankle force measurements scenario is validated by experiments on the humanoid SURALP (Sabanci University Robotics Research Laboratory Platform). The results demonstrate the effectiveness of the proposed approach for slip prediction and detection.

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“…44 The initial values ofē 0 and¯ 0 are zeros. For big values of N Q and N R (for example > 1000), the AKF is close to Kalman filter.…”

Section: Base Velocity Estimationmentioning

confidence: 99%

A novel method for slip prediction of walking biped robots

et al. 2015

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“…Remote monitoring systems for cars and robots require accurate tracking of moving objects. Representative tracking algorithms include the Kalman filter [1][2][3][4][5] and its variants, such as the extended/unscented Kalman [6][7][8][9] and particle filters [10][11][12]. These can accurately track movement based on adaptive filtering by using a state-space model.…”

Section: Introductionmentioning

confidence: 99%

“…This model has been used in many applications because of its versatility, effectiveness, and simplicity. However, in almost conventional tracking systems, the selection of process noise (zero-mean white noise in the dynamic model) is conducted empirically [4,6,8]. This is because conventional studies tend to assume that process noise takes one of a limited number of forms, which is known as appropriate selections.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

Saho¹

2018

Kalman Filters - Theory for Advanced Applications

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This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.

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“…This type of filters can be mainly divided into innovation-based ones and the ones using multiple models [ 21 ]. Innovation-based filters [ 21 , 22 , 23 , 24 ] adopt covariance matching method or maximum likelihood estimation to modify the statistic of the process noise. Drawbacks, such as heavy computation burden, non-positive matrix, and offline estimation prevent these methods from being utilized in reality.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking

Jiang

Cai

2018

Sensors

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Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.

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An improved real-time adaptive Kalman filter with recursive noise covariance updating rules

Cited by 39 publications

References 24 publications

A novel method for slip prediction of walking biped robots

A novel method for slip prediction of walking biped robots

Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking

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