• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.563-566
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• 2004

An intelligent Kalman filter (IKF) is proposed for tracking an incoming anti-ship missile. In the proposed IKF, the unknown target acceleration is regarded as an additive process noise. When the target maneuver is occurred, the residual of the Kalman filter increases in proportion to its magnitude. From this fact, the overall process noise variance can be approximated from the filter residual and its variation at every sampling time. A fuzzy system is utilized to approximate this valiance, and the genetic algorithm (GA) is applied to optimize the fuzzy system. In computer simulations, the tracking performance of the proposed IKF is compared with those of conventional maneuvering target tracking methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.396-399
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• 2008

Kalman Filter is an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurement. The filter is very powerful in the field of autonomous and assisted navigation. In this paper, we carry out comparative stduy to validate the performance of the application of Kalman Filter. We will build personal localization system based on Cricket mote, our system can present the real-time position of person when the man with PDA moves around. The proposed system is composed of cricket sensor networks, PDA and host computer. There is one listener attached to the PDA. The PDA will get the distance data from the listener synchronously. It will calculate the position of the person in the coordinate of the Cricket system with the trilateration method. Furthermore, it sends the real-time position information to the host computer by Bluetooth. The host computer will use Kalman Filter to process data and get the final estimated track of the person.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.593-598
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• 2005

The standard Kalman filter has been used to estimate the states of the target, but in the presence of a maneuver, its error is occurred and performance may be seriously degraded. To solve this problem, this paper presents a new intelligent tracking algorithm using the fuzzy Kalman filter. In this algorithm, the unknown acceleration is regarded as an additive process noise by using the fuzzy logic based on genetic algorithm(GA) method. And then, the modified filter is corrected by the new update equation method which is a fuzzy system using the relation between the filter residual and its variation. To shows the feasibility of the suggested method with only one filter, the computer simulations system are provided, this method is compared with multiple model method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.552-559
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• 2005

Recurrent neural networks(RNNs) trained with gradient based such as real time recurrent learning(RTRL) has a drawback of slor convergence rate. This algorithm also needs the derivative calculation which is not trivialized in error back propagation process. In this paper a derivative free Kalman filter, so called the unscented Kalman filter(UKF), for training a fully connected RNN is presented in a state space formulation of the system. A derivative free Kalman filler learning algorithm makes the RNN have fast convergence speed and good tracking performance without the derivative computation. Through experiments of nonlinear channel equalization, performance of the RNNs with a derivative free Kalman filter teaming algorithm is evaluated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.118-121
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• 2002

The problem of maneuvering target tracking has been studied in the field of the state estimation over decades. The Kalman filter has been widely used to estimate the state of the target, but in the presence of a maneuver, its performance may be seliously degraded. In this paper, to solve this problem and track a maneuvering target effectively, DNA coding-based intelligent Kalman filter (DNA coding-based IKF) is proposed. The proposed method can overcome the mathematical limits of conventional methods and can effectively track a maneuvering target with only one filter by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive interacting multiple model (AIMM) method and the GA-based IKF in computer simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.537-542
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• 2008

Cricket is an excellent indoor location system and it can successfully solve many critical problems such as user privacy, decentralized administration. But in some practical applications, Cricket sometimes didn't provide location with enough accuracy, and was unable to determine when it was giving inaccurate information. For getting high-accuracy tracking performance from location data contaminated with noise, some types of filters are required. Kalman Filter is an efficient recursive filter that estimates the state of a dynamic system from a series of incomplete and noisy measurement. The filter is very powerful in the field of autonomous and assisted navigation. In this paper, we carry out comparative studies to validate the performance of the application of Kalman Filter to Cricket based localization system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1500-1504
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• 2005

This paper proposes the design methodology of genetic algorithm (GA)-based fuzzy Kalman filter for tracking the maneuvering target. The performance of the standard Kalman Filter (SKF) has been degraded because mismatches between the modeled target dynamics and the actual target dynamics. To solve this problem, we use the method to estimate the increment of acceleration by a fuzzy system using the relation between maneuver filter residual and non-maneuvering one. To optimize the fuzzy system, a genetic algorithm (GA) is utilized and this is then tuned by the fuzzy logic correction. Finally, the tracking performance of the proposed method has been compared with those of the input estimation (IE) technique and the intelligent input estimation (IIE) through computer simulations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.1-11
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• 2013

The determination of the pose of the imaging camera is a fundamental problem in computer vision. In the monocular case, difficulties in determining the scene scale and the limitation to bearing-only measurements increase the difficulty in estimating camera pose accurately. Many mobile phones now contain inertial measurement devices, which may lend some aid to the task of determining camera pose. In this study, by means of simulation and real-world experimentation, we explore an approach to monocular camera localization that incorporates both observations of the environment and measurements from accelerometers and gyroscopes. The unscented Kalman filter was implemented for this task. Our main contribution is a novel approach to landmark initialization in a Kalman filter; we characterize the tolerance to noise that this approach allows.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.133-139
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• 2013

This paper proposes a vision-based indoor localization method for autonomous vehicles. A single upward-facing digital camera was mounted on an autonomous vehicle and used as a vision sensor to identify artificial landmarks and any natural corner features. An interest point detector was used to find the natural features. Using an optical flow detection algorithm, information related to the direction and vehicle translation was defined. This information was used to track the vehicle movements. Random noise related to uneven light disrupted the calculation of the vehicle translation. Thus, to estimate the vehicle translation, a Kalman filter was used to calculate the vehicle position. These algorithms were tested on a vehicle in a real environment. The image processing method could recognize the landmarks precisely, while the Kalman filter algorithm could estimate the vehicle's position accurately. The experimental results confirmed that the proposed approaches can be implemented in practical situations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.168-173
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• 2008

본 연구는 UKF Algorithm을 이용한 정전용량형 원격RF센서시스템을 개발하였다. 원격 RF센서 시스템이란 wireless, implantable 그리고 batterless을 만족하는 센서 시스템을 의미한다. 기존의 원격 RF센서 시스템은 보편적으로 집적회로 타입을 채택하지만, 그 구조의 복잡성과 전력소모의 제약을 받는다. 이러한 제약을 해결하기 위해 본 연구에서는 R, L 그리고 C만으로 구성되어있는 유도결합원리를 이용한 원격 RF센서 시스템을 제안하였다. 제안된 RF 센서 시스템은 압력 혹은 습도와 같은 환경의 변화를 정전용량 값으로 측정할 수 있으며 센서의 정전 용량 값을 측정하기 위해 비선형시스템의 파라메타추정에 적합한 Unscented Kalman Filter(UKF) 기법을 채택하였다. UKF 기법을 이용하기 위해 제안된 시스템은 페이저법을 사용하여 수학적으로 모델링되었다. 마지막으로, 제안된 UKF 알고리즘을 이용한 원력 RF센서시스템이 잡음환경에서도 정전용량값을 비교적 정확하게 추정가능함을 확인하였다.