Circular prediction algorithms-hybrid filters

Tenne, D.; Singh, Tarunraj

doi:10.1109/acc.2002.1024799

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…For this experiment, we compare the proposed filter (FKF) with a well known filter, the Circular Prediction Algorithm (CPA) (Tenne and Singh, 2002). In figure 7 (down), we can see the results of FKF and CPA algorithms.…”

Section: Visual Servoing Control Scheme Resultsmentioning

confidence: 99%

“…These results show that FKF can be used to improve the high speed visual servoing tasks. This section is organized in two parts: in the first one (Subsection 6.1), the analysis of the FKF behaviour is focussed and in the second one (Subsection 6.2) their results are compared those with achieved by Chroust and Vince (Chroust and Vincze, 2003) and with CPA (Tenne and Singh, 2002) algorithm (algorithm used for aeronautic/aerospace applications). Conclusions and future work are presented in section 7.…”

Section: Introductionmentioning

confidence: 99%

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IMPROVEMENT OF THE VISUAL SERVOING TASK WITH A NEW TRAJECTORY PREDICTOR - The Fuzzy Kalman Filter

Perez¹,

Garcia²,

Sabater³

et al. 2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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Visual Servoing is an important issue in robotic vision but one of the main problems is to cope with the delay introduced by acquisition and image processing. This delay is the reason for the limited velocity and acceleration of tracking systems. The use of predictive techniques is one of the solutions to solve this problem. In this paper, we present a Fuzzy predictor. This predictor decreases the tracking error compared with the classic Kalman filter (KF) for abrupt changes of direction and can be used for an unknown object's dynamics. The Fuzzy predictor proposed in this work is based on several cases of the Kalman filtering, therefore, we have named it: Fuzzy Kalman Filter (FKF). The robustness and feasibility of the proposed algorithm is validated by a great number of experiments and is compared with other robust methods.

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Section: Visual Servoing Control Scheme Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IMPROVEMENT OF THE VISUAL SERVOING TASK WITH A NEW TRAJECTORY PREDICTOR - The Fuzzy Kalman Filter

Perez¹,

Garcia²,

Sabater³

et al. 2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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show abstract

“…Because the calculation of center coordinates is very complicated and polar coordinates may appear discontinuous when switching, CAP is not suitable for further analysis of prediction stability, uncertainty, and performance. In order to solve these problems, a three-point circular prediction (3PC) algorithm that does not require calculation of center and radius in relative coordinates has been proposed [ 14 , 15 ]. The prediction states are completely defined by the three points used to construct the circle.…”

Section: Introductionmentioning

confidence: 99%

Using a New Circular Prediction Algorithm to Design an IMM Filter for Low Update Rate Radar System

Lee

2020

Sensors

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For radar systems with low update rates; such as track-while-scan (TWS) systems using rotating phased array antennas; reducing the prediction error is a very important issue. A good interacting multiple models (IMM) hybrid filter combined with circular and linear filters that are defined in relation to three measurements has been proposed in the literature. However; the algorithm requires three previous measurements; and too much prior information will result in a reduced ability to predict the future position of a highly maneuvering target. A new circular prediction algorithm for maneuvering target tracking is proposed as a non-linear prediction filter in this paper. Based on this new predictor; we also proposed a new type of IMM filter that has good estimation performance for high maneuvering targets. The proposed hybrid filter is entirely defined in relation to two measurements in a three-dimensional space to obtain a better maneuver following capability than the three measurements hybrid filter. Two target profiles are included for a comparison of the performance of our proposed scheme with that of the conventional circular; linear and IMM filters. The simulation results show that under low update rates; the proposed filter has a faster and more stable estimation response than other filters

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“…En (Chroust and Vincze, 2003) se desarrolla el SKF Switching Kalman Filter, el cual utiliza un "monitor de predicción" que supervisa la calidad de la predicción de un filtro adaptativo de Kalman (AKF). Si el monitor detecta una discontinuidad, se conmuta a un filtro de Kalman estacionario (αβ/γ) adecuado; En (Kawase et al, 1997) se combina un predictor alpha-beta con un predictor circular o elipsoidal; En (Kolodziej and Singh, 2000) y (Tenne and Singh, 2002) se emplea un filtro alpha-beta para los tramos rectilíneos del movimiento del objeto y un predictor circular dinámico para las zonas de maniobra; En (Yoo and Kimb, 2003) se usa un algoritmo que conmuta entre un filtro alpha-beta convencional cuando el movimiento del objeto alcanza el régimen permanente y un algoritmo de aproximación de ganancia cuando el objeto empieza a maniobrar; En (Moore and Wang, 2001) se presenta un algoritmo que combina cinco filtros de Kalman distintos.…”

Section: Introductionunclassified