Browsing by Author "Wu, KG"

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A Bayes-Marlsov based maneuver estimator for maneuver target tracking
(1996)
Liu, CM
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Wu, KG
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交大名義發表
;
資訊工程學系
;
National Chiao Tung University
This paper proposes a new method for estimating a target's maneuver value. The method consists of a Baysian scheme for identifying the possible range of a target's maneuver magnitude and a multiple-hypothesis scheme for estimating its value. The parameters of the multiple-hypothesis scheme are dynamically adjusted with the variation of a target's maneuvering behavior. The dynamic adjustment helps keep a low complexity while dealing with a large range of target maneuverability Simulation results show that the new method, although has a low computational complexity, can achieve the same accuracy as the original multiple-hypothesis method in [3].
Adaptive identification of non-Gaussian/non-stationary glint noise
(1999-12-01)
Wu, WR
;
Wu, KG
;
資訊工程學系
;
電信工程研究所
;
Department of Computer Science
Non-stationary glint noise is often observed in a radar tracking system. The distribution of glint noise is non-Gaussian anti heavy-tailed. Conventional recursive identification algorithms use the stochastic approximation (SA) method. However, the SP, method converges slowly and is invalid for nonstationary noise. This paper proposes an adaptive algorithm, which uses the stochastic gradient descent (SGD) method, to overcome th(:se problems. The SGD method retains the simple structure of the SA method and is suitable for real-world implementation. Convergence behavior of the SGD method is analyzed and closed-form expressions for sufficient step size bounds are derived. Since noise data are usually not available in practice, we then propose a noise extraction scheme. Combining the SGD method, we I:an perform on-line adaptive noise identification directly from radar measurements. Simulation results show that the performance of the SGD method is comparable to that of the maximum-likelihood (ML) method. Also, the noise extraction scheme is effective that the identification results from the radar measurements are close to those from pure glint noise data.
An IMM algorithm incorporating a maneuver estimation method for tracking maneuvering targets in airborne radar
(1996)
Liu, CM
;
Wu, KG
;
交大名義發表
;
資訊工程學系
;
National Chiao Tung University
This paper proposes a new maneuvering tracking algorithm which consists of a multiple-hypothesis method for estimating a target's maneuver value and the interactive multiple model (IMM) algorithm for estimating a target's state. The new algorithm contains two motion model: one constant-speed model and one acceleration model. The parameters of the acceleration model will be adapted with the target dynamic based on the maneuver estimation results. Simulation results show that during the linear constant acceleration periods the speed estimation errors of the new algorithm can be reduced to about 50% of the conventional IMM algorithm, which uses 25 motion models of fixed model parameters to cover a maneuver range of -10 similar to 10g.
Modified moose estimator for tracking highly manoeuvring targets
(1997-04-10)
Liu, CM
;
Wu, KG
;
交大名義發表
;
資訊工程學系
;
National Chiao Tung University
The Moose's adaptive state estimator has proved successful in tracking a manoeuvring submarine [1]. However, its application to track highly manoeuvring aircraft will encounter problems in complexity and tracking accuracy [2, 3]. The authors present a new algorithm that extends the Moose estimator to solve its problems. The new algorithm is verified through Monte-Carlo simulation and shows a better performance than the original Moose algorithm.
Modified moose estimator for tracking highly manoeuvring targets
(1997-04-10)
Liu, CM
;
Wu, KG
;
資訊工程學系
;
Department of Computer Science
The Moose's adaptive state estimator has proved successful in tracking a manoeuvring submarine [1]. However, its application to track highly manoeuvring aircraft will encounter problems in complexity and tracking accuracy [2, 3]. The authors present a new algorithm that extends the Moose estimator to solve its problems. The new algorithm is verified through Monte-Carlo simulation and shows a better performance than the original Moose algorithm.
Objective image quality measure for block-based DCT coding
(1997-08-01)
Liu, CM
;
Lin, JY
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Wu, KG
;
Wang, CN
;
交大名義發表
The block-based discrete cosine transform (DCT) coding has been widely adopted in image or video compression standards, such as JPEG and MPEG. Such type of coding will produce a noticeable artifact known as the blocking effect at very low bit rate applications. In the literature, there have been various postprocessing methods proposed to reduce the blacking effect. However, there is no suitable objective criterion evaluating the effectiveness of these various methods. This paper presents a new objective measure evaluating the postprocessing methods. The proposed measure also provides an analytical result on the underlying sources of artifacts.
Objective image quality measure for block-based DCT coding
(1997-08-01)
Liu, CM
;
Lin, JY
;
Wu, KG
;
Wang, CN
;
資訊科學與工程研究所
The block-based discrete cosine transform (DCT) coding has been widely adopted in image or video compression standards, such as JPEG and MPEG. Such type of coding will produce a noticeable artifact known as the blocking effect at very low bit rate applications. In the literature, there have been various postprocessing methods proposed to reduce the blacking effect. However, there is no suitable objective criterion evaluating the effectiveness of these various methods. This paper presents a new objective measure evaluating the postprocessing methods. The proposed measure also provides an analytical result on the underlying sources of artifacts.
Online glint noise identification for tracking manoeuvring targets
(1999-04-29)
Wu, WR
;
Wu, KG
;
資訊工程學系
;
電信工程研究所
;
Department of Computer Science
The glint noise arising from a radar tracking system exhibits non-Gaussian statistics. Pure noise data, usually not available in online applications, are required in conventional glint identification methods. A new algorithm is presented for online glint identification using radar measurement data. Simulations show that the identified results, of the proposed algorithm using measurement data are close to those using pure noise data.