• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.759-762
• /
• 1996

In previous work Kwon and Yoo [5] have shown that the FIR tracking algorithm using the input estimation technique. However, it has not solved the problem of systems with parameter uncertainties. Therefore, in this paper we propose a new robust $H_{\infty}$ FIR tracking filter to solve the target tracking problems under systems with parameter uncertainties. Also, we use here the input estimation approach to account for the possibility of maneuver. Simulation results show that the robust $H_{\infty}$ FIR tracking filter proposed here still has good tracking performance for a maneuvering target tracking problem even under all system parameter uncertainties.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.2
• /
• pp.192-198
• /
• 1990

The performance of the proportional navigation guidance(PNG) law for a target with constant acceleration has been extensively studied in the prior literature. In this papaer, we consider the performance of the PNG law for a randomly maneuvering target. By means of Lyapunov method, we prove that an ideal missile guided by the conventional PNG law can always intercept a randomly maneuvering target if the initial missile heading error is small and the navigation constant is chosen sufficiently large. To the authors' best knowledge, this is the first analytic result on the performance of the PNG law for a target with time-varying acceleration.

• Proceedings of the KIEE Conference
• /
• 2002.11c
• /
• pp.87-91
• /
• 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 seriously degraded. In this paper, to solve this problem and track a maneuvering target effectively, a DNA coding-based interacting multiple model (DNA coding-based IMM) method is proposed. The proposed method can overcome the mathematical limits of conventional methods by using the fuzzy logic based on DNA coding method. The tracking performance of the proposed method is compared with those of the adaptive IMM algorithm and the GA-based IMM method in computer simulations.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.1
• /
• pp.16-21
• /
• 2003

The accuracy in maneuvering target tracking using multiple models is resulted in by the suitability of each target motion model to be used. The interacting multiple model (IMM) method and the adaptive IMM (AIMM) method require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers in order to construct multiple models. In this paper, to solve these problems, a genetic algorithm(GA) based-IMM method using fuzzy logic is proposed. In the proposed method, the acceleration input is regarded as an additive noise and a sub-model is represented as a set of fuzzy rules to calculate the time-varying variances of the process noises of a new piecewise constant white acceleration model. The proposed method is compared with the AIMM algorithm in simulation.

• Journal of the Society of Naval Architects of Korea
• /
• v.44 no.6
• /
• pp.572-578
• /
• 2007

A Rigid Inflatable Boat (RIB) is now widely used for commercial and military purpose. In this paper, it is supposed that seven-meter-class RIB be used as an unmanned target ship for naval training. In order to develop many tactical maneuvering patterns of a target ship, a simple horizontal maneuvering model of a RIB is needed. Therefore, models of speed and yaw rate are constructed as the first-order differential equations based on Lewandowski#s empirical formula for steady turning circle diameter of a conventional planning hull. Some parameters in the models are determined using the results of sea trial tests. Finally, proposed models are validated through the comparison of the simulation result with the sea trial result for a specific scenario. Even though a simple model does not represent the horizontal motion of a RIB precisely, however, it can be used enough to develop tactical trajectory patterns.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2340-2344
• /
• 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.

• 전기의세계
• /
• v.31 no.2
• /
• pp.119-125
• /
• 1982

A new approach to the maneuvering target tracking problem is proposed. Its basic concept is to take the maneuver variable from the measurements. Tracking scheme based on the Kalman filter estimates the maneuver varieble from the residual and uses the estimates to update the Kalman filter. The estimation process is independent of target types and a model of the maneuver characteristics. All the filtering algorithms are processed in polor coordinate. Simulation results are presented and compared to that of the extended Kalman filter.

• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.114-119
• /
• 1991

An input estimation technique for tracking filter(CHP algorithm) suggested by Y.T. Chan et. al. has bad performance for low maneuvering targets. In this paper, two maneuver detection algorithms are applied to Singer's target model. First, an CHP input estimation technique is applied to 9 state target model. Second, we construct a maneuver detection and correction technique using pseudo acceleration measurements, which are derived directly from measurements. These two filters have good performance for even the low maneuvering targets.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.49 no.3
• /
• pp.82-89
• /
• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Journal of Electrical Engineering and information Science
• /
• v.3 no.3
• /
• pp.373-384
• /
• 1998

This paper presents a maneuvering target model with the maneuver dynamics modeled as a jump process of Poisson-type. The jump process represents the deterministic maneuver(or pilot commands) and is described by a stochastic differential equation driven by a Poisson process taking values a set of discrete states. Employing the new maneuver model along with the noisy observations described by linear difference equations, the author has developed a new linear, recursive, unbiased minimum variance filter, which is structurally simple, computationally efficient, and hence real-time implementable. Futhermore, the proposed filter does not involve a computationally burdensome technique to compute the filter gains and corresponding covariance matrices and still be able to track effectively a fast maneuvering target. The performance of the proposed filter is assessed through the numerical results generated from the Monte-Carlo simulation.