• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1053-1059
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• 2007

This paper presents a target sate estimator(TSE) with low pass filter for improving the gun driving command. The ballistic computer uses target information such as predicted range, velocity, acceleration of a target to generate the gun command. We adopt the finite impulse response(FIR) filter as our TSE to shorten calculation time for the driving command and due to its inherent stability property. We also introduce a post-processing filter to reduce the high frequency components in the output signal of a TSE which may cause instability of gun driving. The first order low pass filter has been designed based on $H{\infty}$ criteria considering the noise characteristics. To show the validity of the present scheme, simulation results are given for the overall gun driving system including aircraft target information.

• Journal of Advanced Navigation Technology
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• v.2 no.1
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• pp.34-42
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• 1998

An adaptive state and input estimator for the tracking of a target with unknown randomly switching input is developed. In modeling the unknown inputs, it is assumed that the input sequence is governed by semi-Markov process. By incorporating the semi-Markov probability concepts into the Bayesian estimation theory, an effective adaptive state and input estimator which consists of parallel Kalman-type filters is obtained. Computer simulation results reveal that the proposed adaptive estimator have improved tracking performance in spite of the unknown randomly switching input.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1740-1741
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• 2011

This paper proposes a new linear recursive target state estimator for automotive collision warning system. The target motion is modeled in Cartesian coordinate system while the radar measurements such as range, line-of-sight angle and range rate are obtained in polar coordinate system. To solve the problem by nonlinear relation between these two coordinate system, a practical linear filter design scheme employing the predicted line-of-sight Cartesian coordinate system (PLCCS) is proposed. Especially, PLCCS can effectively incorporate range rate measurements into target tracking system. It is known that the utilization of range rate measurements enables the improvement of target tracking performance. Moreover, PLCCS based target tracking system is implemented by linear recursive filter structure and hence is more suitable scheme for the development of reliable collision warning system. The performance of the proposed method is demonstrated by computer simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.419-422
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• 2005

In this note, the Takagi-Sugeno (T-S) fuzzy-model-based state estimator using standard Kalman filter theory is investigated. In that case, the dynamic system model is represented the T-S fuzzy model with the fuzzy state estimation. The steady state solutions can be found for proposed modeling method and dynamic system for maneuvering targets can be approximated as locally linear system. And then, modeled filter is corrected by the fuzzy gain which is a fuzzy system using the relation between the filter residual and its variation. This paper studies the T-S fuzzy model-based state estimator which the dynamic system can be approximated as linear system.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.478-481
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• 1996

Tracking a target of versatile maneuver recently demands a stable adaptation of tracker, and the multiple model techniques are being developed because of its ability to produce useful information of target maneuver. This paper presents the way to apply the multiple model method in a moving-target and moving-platform scenario, and the estimation and prediction results better than those of single Kalman filter.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.305-310
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• 2002

The measured rate of the tracking sensor becomes biased under some operational situation. For a highly maneuverable aircraft in 3D space, the target dynamics changes from time to time, and the Kalman filter using position measurement only can not be used effectively to reject the rate measurement bias error. To cope with this problem, we present a new algorithm which incorporate FIR-type filter and FIR-type fixed-lag smoother, and demonstrate that it has the optimal performance in terms of both estimation accuracy and response time through an application example to the anti-aircraft gun fire control system(AAGFCS).

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.141.2-141
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• 2001

An approximate target adaptive guidance algorithm(TAG) is proposed on the basis of the assumption that angular acceleration of missile to target line-of-sight and start time for TAG can be obtained by IR seeker. The algorithm does not use any target state estimator. Instead, it avoids the problem of determining target attitude by using the observation that the missile using LOS rate guidance is nearly on the collision course in the later point of engagement. Computer simulation results show that the proposed algorithm can effectively perform target adaptive guidance.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.87.1-87
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• 2001

For a plant subject to several kinds of disturbances in the plant input side, we consider a problem of designing a controller based on the disturbance cancellation. The conventional loop transfer recovery (LTR) technique can not be used since the extended system consisting of the plant and the disturbance model is not necessarily stabilizable. We propose a new LTR technique that can be applied for our problem. As a target of the LTR, we choose a state feedback controller using a disturbance estimator. We find an LTR procedure based on the Riccati equation formalism where the stochastic model contains the filter gain matrix of the disturbance estimator in the target. The procedure recovers the target feedback ...

• Journal of Mechanical Science and Technology
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• v.19 no.9
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• pp.1742-1752
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• 2005

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.