• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.976-983
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• 2002

We presents a modified variable dimension filter with input estimation for maneuvering target tracking. The conventional variable dimension filter with input estimation(VDIE) consists of the input estimation(IE) technique and the variable dimension(VD) filter. In the VDIE, the IE technique is used for estimation of a maneuver onset time and its magnitude in the least square sense. The detection of the maneuver is declared according to the estimated magnitude of the maneuver. The VD filter structure is applied for the adaptation to the maneuver of the target after compensating the filter parameter with respect to the estimated maneuver when the detection of the maneuver is declared. The VDIE is known as one of the best maneuvering target tracking filter based on a single filter. However, it requires too much computational burden since the IE technique is performed at every sampling instance and thus it is computationally inefficient. We propose another variable dimension filter with input estimation named 'Modified VDIE' which combines VD filter with If technique. Modified VDIE has less computational load than the original one by separating maneuver detection and input estimation. Simulation results show that the proposed VDIE is more efficient and outperforms in terms of computational load.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.47-54
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• 2001

In this paper, we present a variable dimension vector quantization for spectral magnitudes. Espectially, spectral magnitudes of the Harmonic coder, need variable dimension quantizer because those are not fixed dimension. So, this paper present efficient quantization methods. These methods use variable Discrete Cosine Transform(DCT) for spectral magnitude parameters and NSTVQ which is combined odd/even, split and multi-stage structure, proposed quantization methods use Spectral Distortion(SD) for performance measure. Consequently, Multi-Stage Nonsquare Transform Vector Quantization(MSNSTVQ) is the best in performance measure.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1988-1991
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• 2002

In this paper, we propose a method for realizing transfer functions containing variable parameter, by the state-space method. By using this method, variable transfer functions (VTF) can be often realized with a minimal dimension. In case that a minimal realization can not be obtained, the realization dimension can be fairly reduced.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.410-416
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• 2003

In the affine projection algorithm(APA), the projection dimension depends on a number of projection basis and of elements of input vector used for updating of coefficients of the adaptive filter. The projection dimension is closely related to a convergence speed of the APA, and it determines computational complexity. As the adaptive filter approaches to steady state, convergence speed is decreased. Therefore it is possible to reduce projection dimension that determines convergence speed. In this paper, we proposed the variable dimension affine projection algorithm (VDAPA) that controls the projection dimension and uses the relation between variations of coefficients of the adaptive filter and convergence speed of the APA. The proposed method reduces computational complexity of the APA by modifying the number of projection basis on convergence state. For demonstrating the good performances of the proposed method, simulation results are compared with the APA and normalized LMS algorithm in convergence speed and computational quantity.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.53.6-53
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• 2001

In this paper, we are concerned with a tracking filter algorithm which can track a maneuvering target. Among the novel tracking filter algorithms, the input estimation (IE) filter can be summarized as estimating the unknown maneuver input and compensating the state according to the estimated input, and the variable dimension filter (VDF) can be summarized as detecting the maneuver of target and changing the dimension of the target dynamics to accomodate the maneuver of target They have some goods and bads with respect to each other. The variable dimension filter with input estimation (VDIEF) is constructed by combining the two filtering algorithms. However, it requires too much computational burden while it has good performance. We propose another variable dimension with input estimation ...

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.9
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• pp.870-877
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• 2009

This study provides how the Dimension Reduction (DR) method as an efficient technique for reliability analysis can acquire its increased efficiency when it is applied to highly nonlinear problems. In the highly nonlinear engineering systems, 4N+1 (N: number of random variables) sampling is generally recognized to be appropriate. However, there exists uncertainty concerning the standard for judgment of non-linearity of the system as well as possibility of diverse degrees of non-linearity according to each of the random variables. In this regard, this study judged the linearity individually on each random variable after 2N+1 sampling. If high non-linearity appeared, 2 additional sampling was administered on each random variable to apply the DR method. The applications of the proposed sampling to the examples produced the constant results with increased efficiency.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.5
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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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• 1991.10a
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• pp.108-113
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• 1991

In this paper, an improved method for tracking maneuvering target is proposed. The proposed tracking filter is constructed by combining the input estimation approach with the variable dimension filtering approach. In this approach, the filter also provides the estimated time instant at which target starts maneuver, when the target maneuver is detected. Using this estimated maneuvering time, the maneuver input is estimated and the tracking system changes to the maneuver model. Simulations are performed to demonstrate the efficiency of the proposed tracking filter.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1162-1168
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• 2008

This study provides how the Dimension Reduction (DR) method as an efficient technique for reliability analysis can acquire its increased efficiency when it is applied to highly nonlinear problems. In the highly nonlinear engineering systems, 4N+1 (N: number of random variables) sampling is generally recognized to be appropriate. However, there exists uncertainty concerning the standard for judgment of non-linearity of the system as well as possibility of diverse degrees of non-linearity according to each of the random variables. In this regard, this study judged the linearity individually on each random variable after 2N+1 sampling. If high non-linearity appeared, 2 additional sampling was administered on each random variable to apply the DR method. The applications of the proposed sampling to the examples produced the constant results with increased efficiency.