• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.173-182
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• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• 제어로봇시스템학회논문지
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• 제7권5호
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• pp.445-453
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• 2001

The multiple targets tracking problem has been one of the main issues in the radar applications area in the last decade. Besides the standard Kalman filtering, various methods including the variable dimen-sion filter, input estimation filter, interacting multiple model(IMM) filter, dederated variable dimension filter with input estimation, etc., have proposed to address the tracking and sensor fusion issues. In this pa- per, two existing tracking algorithm, i.e, the IMM filter and the variable dimension filter with input estima-tion(VDIE), are combined for the purpose of improving the tracking performance for maneuvering targets. To evaluate the tracking performance of the proposed algorithm, three typical maneuvering patterns, i.e., waver, pop-up, and high-diver motions, are defined and are applied to the modified IMM filter as well as the standard IMM filter. The smaller RMS tracking errors, in position and velocity, of the modified IMM filter than the standard IMM filter are demonstrated though computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2340-2344
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• 2003

This paper considers a tracking filter algorithm which can track a maneuvering target. Multiresolutional Interacting Multiple Model (MRIMM) algorithm is proposed to reduce computational burden. In this paper multiresolutional state space model equation and multiresolutional measurement equation are derived by using wavelet transform. This paper shows the outline of MRIMM algorithm. Simulation results show that MRIMM algorithm maintains a good tracking performance and reduces computational burden.

• Journal of Power Electronics
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• 제8권4호
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• pp.317-324
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• 2008

This paper proposes a Multiband Shunt Hybrid Active Filter (SHAF) with sensorless control. A plant is modeled in the discrete- time domain and a controller is designed using the Pole shifting law in the polynomial domain. This control approach is very useful for filtering the load harmonics with reduced sensor counts where a low cost solution like SHAF is required. Multiple Synchronous Reference Frames (MSRF) and low pass filters are used to measure the $5^{th}$ and $7^{th}$ harmonic components separately from the load and filter currents. Individual current controllers are designed for the $5^{th}$ and $7^{th}$ harmonic currents. Control is realized in the stationary, three-phase (abc) reference frame. Performance of the controller is validated through simulation, using realistic plant and controller models, as well as experimentally on a full-scale distribution system.

• International Journal of Control, Automation, and Systems
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• 제2권3호
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• pp.310-318
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• 2004

In this paper, an unscented Kalman filter (UKF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, an UKF is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.

• 전기학회논문지
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• 제67권2호
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• pp.314-324
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• 2018

This paper proposes a linear distributed target tracking filter for multiple unmanned aerial vehicles(UAVs) sharing their passive sensor measurements through communication channels. Different from the conventional nonlinear filtering schemes, the distributed passive target tracking problem is newly formulated within the framework of a linear robust state estimation theory incorporated with a linear uncertain measurement equation including the coordinate transform uncertainty. To effectively cope with the performance degradation due to the coordinate transform uncertainty, a linear consistent robust Kalman filter(CRKF) theory is devised and applied for designing a distributed passive target tracking filter. Through the simulations for typical UAV surveillance mission, the superior performance of the proposed method over the existing schemes of distributed passive target tracking are demonstrated.

• 한국음향학회지
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• 제13권6호
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• pp.15-23
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• 1994

본 논문은 소음원이 여러개 존재할 때 다중입력 다중제어의 능동적응제어에 관한 연구이다. 다중채널 제어의 ANC(Active Noise Control)에 관한 현재까지의 연구는 소음원이 여러개 존재하는 경우, 적응필터의 입력신호사이에 상관이 없는 것으로 가정하고 연구되었다. 그러나 실제는 소음신호의 수신마이크로폰에서 제거하고자 하는 소음신호들 사이에 상관이 존재하며, 다수개의 소음원 신호를 독립적으로 검출하는 것이 곤란한 경우가 많고 여기에 관한 연구가 불충분하다. 본 연구에서는 적응필터의 입력신호간에 소음원의 상관을 제거하는 상관제거필터를 제안하였다. 제안한 상관제거 필터를 삽입하여 소음제거 시뮬레이션 결과, 혼합비율을 $b_{12}=0.95$일 때 상관을 제거한 경우가 약 2배 정도 빠른 수렴속도를 나타냈다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.211-211
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• 2000

The multiple targets tracking problem has been one of main issues in the radar applications area in the last decade. Besides the standard Kalman filtering, various methods including the variable dimension filter, input estimation filter, interacting multiple model (IMM) filter, federated variable dimension filter with input estimation, probable data association (PDA) filter etc. have been proposed to address the tracking and sensor fusion issues. In this paper, two existing tracking algorithms, i.e. the IMMPDA filter and the variable dimension filter with input estimation (VDIE), are combined for the purpose of improving the tracking performance of maneuvering targets in clutter. To evaluate the tracking performance of the proposed algorithm, three typical maneuvering patterns i.e. Waver, Pop-Up, and High-Diver motions, are defined and are applied to the modified IMMPDA filter considered as well as the standard IMM filter. The smaller RMS tracking errors, in position and velocity, of the modified IMMPDA filter than the standard IMM filter are demonstrated through computer simulations.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1220-1225
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• 2010

This paper proposes a novel active noise control (ANC) approach that uses an IIR filter and neural network techniques to effectively reduce interior noise. We construct a multiple-channel IIR filter module which is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimization based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Lastly, we present the results of a numerical study to test our ANC methodology with realistic interior noise measurement obtained from Korean railway trains.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1669-1674
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• 2004

In this paper, a nonlinear information filter (IF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, a nonlinear IF is used in place of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.