• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.8
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• pp.531-540
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• 2022

In this paper, we present an algorithm that identifies artillery type and rapidly predicts the impact point based on the IMM filter. The ballistic trajectory equation is used as a system model, and three models with different ballistic coefficient values are used. Acceleration was divided into three components of gravity, air resistance, and lift. And lift acceleration was added as a new state variable. The kinematic condition that the velocity vector and lift acceleration are perpendicular was used as a pseudo-measurement value. The impact point was predicted based on the state variable estimated through the IMM filter and the ballistic coefficient of the model with the highest mode probability. Instead of the commonly used Runge-Kutta numerical integration for impact point prediction, a semi-analytic method was used to predict impact point with a small amount of calculation. Finally, a state variable initialization method using the least-square method was proposed. An integrated algorithm including artillery type identification, impact point prediction and initialization was presented, and the validity of the proposed method was verified through simulation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.9
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• pp.65-72
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• 2011

A binary integration is one of sub-optimal pulse integration which decides detection based on discriminating m successful detections out of n trials in radar systems using multiple pulse repetition frequencies. This paper introduces target measurement error reduction technique to reduce azimuth errors in suboptimal binary integration radar which applies the near value by m rather than the optimal m and verifies the performance by analyzing the experimental data measured from real radar.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1858-1859
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• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The gradient descendant method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.345-353
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• 2015

In this paper, we proposed an information fusion method for enhancement of automatic dependent surveillance - broadcast (ADS-B) system which is one of the next generation navigation system. Although ADS-B provides better performance than traditional radar, ADS-B still has error due to dependence of global navigation satellite system (GNSS) information. In this paper, we improved the ADS-B performance using information fusion of multilateration (MLAT) and wide area multilateration (WAM). Information fusion provides accurate data compared to original data. Mostly, information fusion methods use Kalman filter or IMM(interacting multiple model) filter as a subfilter. However, we used Robust IMM filter as a subfilter to improve the aircraft tracking performance. Also, we use actual ADS-B data not virtual data to increase reliability of our information fusion method.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.499-501
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• 2005

When the target maneuver occurs, the estimate of the standard Kalman filter is biased and its performance may be seriously degraded. To solve this problem, this paper proposes a new intelligent estimation algorithm for a maneuvering target. This algorithm is to estimate the unknown target maneuver by a fuzzy system using the relation between the filter residual and its variation. The detected acceleration input is regarded as an additive process noise. To optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. And then, the modified filter is corrected by the new update equation method using the fuzzy system. The tracking performance of the proposed method is compared with those of an interacting multiple model (IMM).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.331-337
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• 2004

Target motion analysis with a sonar system in general uses a regular sampling time and thus obtains regular target information regardless of the target maneuver status. This often results in overconsumption of the limited sonar resources. We propose two methods of the IMM(interacting Multiple Model) PDAF algorithm for sonar resource management to improve target motion analysis performance and to save sonar resources in this paper. In the first method, two different process noise covariance which are used as mode sets are combined based on probability. In the second method, resource time which are processed from two mode sets is calculated based on probability and then considered as update time at next step. Performance of the proposed algorithms are compared with the other algorithms by a series of Monte Carlo simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.472-478
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• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The error back-propagation method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.26-33
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• 2001

Though speaker recognition based on conventional AR HMM shows good performance, its lack of modeling the environmental noise makes its performance degraded in case of practical noisy environment. In this paper, a robust speaker recognition system based on AR HMM is proposed, where noise is considered in the observation signal model for practical noisy environment and duration-term is considered to increase performance. Experimental results, using the digits database from 100 speakers (77 males and 23 females) under white noise and car noise, show improved performance.

• Journal of Auto-vehicle Safety Association
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• v.5 no.2
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• pp.17-23
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• 2013

This paper describes an algorithm for Advanced Emergency Braking(AEB) with tire-road friction coefficient estimation. The AEB is a system to avoid a collision or mitigate a collision impact by decelerating the car automatically when forward collision is imminent. Typical AEB system is operated by Time-to-collision(TTC), which considers only relative velocity and clearance from control vehicle to preceding vehicle. AEB operation by TTC has a limit that tire-road friction coefficient is not considered. In this paper, Tire-road friction coefficient is also considered to achieve more safe operation of AEB. Interacting Multiple Model method(IMM) is used for Tire-road friction coefficient estimation. The AEB algorithm consists of friction coefficient estimator and upper level controller and lower level controller. The numerical simulation has been conducted to demonstrate the control performance of the proposed AEB algorithm. The simulation study has been conducted with a closed-loop driver-controller-vehicle system using using MATLAB-Simulink software and CarSim Vehicle model.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1117-1124
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• 2011

An integrated fault detection and isolation method is proposed in this paper. The main objective of this paper is development fault detection, isolation and diagnosis algorithm based on the DKF (Decentralized Kalman Filter) and the bank of IMM (Interacting Multiple Model) filters using penalty scalar for both partial and total faults and the outlier detection algorithm for preventing false alarm also included. The proposed FDI (Fault Detection and Isolation) scheme is developed in four phases. In the first phase, the outlier detection filter is designed to prevent false alarm as a pre-filter. In the second phases, two local filters and master filter are designed to detect sensor faults. In the third phases, the proposed FDI scheme checks sensor residual to isolate sensor faults and 11 EKFs actuator fault models are designed to detect wherever actuator faults occur. In the last phases, four filters are designed to identify the fault type which is either the total fault or partial fault. The developed scheme can deal with not only sensor and actuator faults, but also preventing false alarm. An important feature of the proposed FDI scheme can decreases fault isolation time and figure out not only fault detection and isolation but also fault type identification. To verify the proposed FDI algorithm performance, the Simulator is also developed under the Matlab/Simulink environment.