• Title/Summary/Keyword: 칼만필터

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An Application of the Kalman Filter for Attenuation of Colored Noise Superimposed on Speech Signal (칼만필터를 이용한 음성신호에 중첩된 유색잡음의 감쇠)

  • Gu, Bon-Eung
    • The Journal of the Acoustical Society of Korea
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    • v.13 no.2
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    • pp.76-85
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    • 1994
  • A speech enhancement algorithm which attenuates nonstationary colored noise is presented In this paper. The algorithm consists of a stationary Kalman filter and the simple speech/nonspeech detector. While the conventional enhancement systems are focused on a stationary and/or white background noise, this study Is focused on the mort realistic nonstationary and nonwhite noise. An AR model-based vector Kalman filter is used as a noise suppression system and a short-time energy threshold logic is used as a speech/nonspeech classifier. For Kalman filtering. noise coefficients are estimated in the nonspeech frame, and speech coefficients are estimated by applying the EM iteration algorithm. Simulation results using the car noise are presented based on the signal-to-noise ratio and informal listening tests. According to the experimental results, background noises in the nonspeech frames are eliminated almost completely, while some distortions are noticed in the speech frames. The distortion becomes severer as the SNR is reduced to 0dB and -5dB. Intelligibility, however, is not degraded significantly.

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A Study of multi-objects tracking to protect aquaculture farms by Kalman Filter (어장보호를 위한 다물체 추적 칼만필터에 관한 연구)

  • Nam T.K.;Yim J.B.;Jeong J.S.;Park S.H.;Ahn Y.S.
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2006.06b
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    • pp.227-232
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    • 2006
  • In this paper, a Kalman filter application for GDSS(Group Digital Surveillance System) developed to protect an aquaculture farms is discussed GDSS is composed by a WIWAS(Watching, Identification, Warning, and Action System) and a FDS(Fishery Detection System) that will monitor incoming and outgoing vessels in the aquaculture farms. In the FDS, a tracking function to track vessels without F-AIS(Fishery Automatic Identification System) is needed and the Kalman filter is applied to track vessels around the aquaculture farms. Some simulation results for the multi-objects with white noise is presented and the adaptation possibility for tracking system is discussed.

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Real-time Aircraft Upset Detection and Prevention Based On Extended Kalman Filter (확장칼만필터를 이용한 항공기 비정상 비행상황 판단 및 방지를 위한 실시간 대처법 연구)

  • Woo, Beomki;Park, On;Kim, Seungkeun;Suk, Jinyoung;Kim, Youdan
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.45 no.9
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    • pp.724-733
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    • 2017
  • Accidents caused by upset condition leads to fatal damage to both manned and unmanned aircraft. This paper deals with real-time detection of these aircraft upset situations to prevent further severe situations. Firstly, the difference between sensor measurement and predicted measurement from Extended Kalman filter is monitored to determine whether a target aircraft goes into an upset condition or not. In addition, repeating the time update stage of the Extended Kalman filter for a specific length of time can enable future upset situation prediction. The results of aforementioned both the approaches will build a bridge to upset prevention for future generation of manned/unmanned aircraft.

Kalman Filter for Estimation of Sensor Acceleration Using Six-axis Inertial Sensor (6축 관성센서를 이용한 센서가속도 추정용 칼만필터)

  • Lee, Jung Keun
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.39 no.2
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    • pp.179-185
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    • 2015
  • Although an accelerometer is a sensor that measures acceleration, it cannot be used by itself to measure the acceleration when the orientation of the sensor changes. This paper introduces a Kalman filter for the estimation of a sensor acceleration based on a six-axis inertial sensor (i.e., a three-axis accelerometer and three-axis gyroscope). The novelty of the proposed Kalman filter lies in the fact that its state vector includes not only the tilt angle variable but also the sensor acceleration. Thus, the filter can explicitly estimate the latter with a high accuracy. The accuracy of acceleration estimates were validated experimentally under three different dynamic conditions, using an optical motion capture system. It could be concluded that the performance of the proposed Kalman filter was comparable to that of the state-of-the-art estimation algorithm employed by the Xsens MTw. The proposed algorithm may be more suitable than inertial/magnetic sensor-based algorithms for various applications adopting six-axis inertial sensors.

Approaching Target above Ground Tracking Technique Based on Noise Covariance Estimation Method-Kalman Filter (잡음 공분산 추정 방식을 적용한 칼만필터 기반 지면밀착 접근표적 추적기법)

  • Park, Young-Sik
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.28 no.10
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    • pp.810-818
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    • 2017
  • This paper presents the approaching target above ground tracking based on Kalman filter applied to the proximity sensor for the active defense system. The proximity sensor located on the front of the countermeasure is not easy to detect when the anti-tank threat enters a fragment dispersion range due to limited antenna beamwidth. In addition, it is difficult for the proximity sensor to detect the anti-tank threat accurately at a terrestrial environment including various clutters. To solve these problems, this study presents the approaching target above ground tracking based on Kalman filter and applies the novel estimation method for a noise covariance matrix to improve a tracking performance. Then, a high tracking performance of Kalman filter applied the proposed noise covariance matrix is presented through field firing test results and the validity of the proposed study is examined.

Observability Analysis of INS/GNSS System for Vehicles Moving with a Large Pitch Angle Change (피치각 변화가 큰 궤적에서의 INS/GNSS 통합항법 시스템 가관측성 분석)

  • Kim, Hyun-seok;Baek, Seung-jun;Kim, Hyung-Soo;Jo, Min-Su
    • Journal of Advanced Navigation Technology
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    • v.22 no.3
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    • pp.220-227
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    • 2018
  • The most widely used method for constructing an inertial navigation system (INS)/global navigation satellite system (GNSS) coupling system is to construct an integrated navigation system using a Kalman filter. However, depending on the trajectory, non-observable state variables may be generated. In this case, the state variables are not estimated. To solve this problem, a integrated navigation system is constructed and then an observability analysis is performed. In this paper, a 24th order position-matched Kalman filter is defined to design an INS/GNSS integrated navigation system for vehicles moving with a large pitch angle change. To verify the appropriateness of the error state variables applied to the Kalman filter, an observability analysis was performed. The trajectory was divided into five segments, and the piece-wise constant system (PWCS) was assumed for each segment, and the results were analytically analyzed. The analytical results and the simulation results confirm that the error state parameters of the Kalman filter are well-designed to the estimation side.

Kalman Filter Design For Aided INS Considering Gyroscope Mixed Random Errors (자이로의 불규칙 혼합잡음을 고려한 보조항법시스템 칼만 필터 설계)

  • Seong, Sang-Man
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.34 no.4
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    • pp.47-52
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    • 2006
  • Using the equivalent ARMA model representation of the mixed random errors, we propose Klaman filter design methods for aided INS(Inertial Navigation System) which contains the gyroscope mixed random errors. At first step, considering the characteristic of indirect feedback Kalman filter used in the aided INS, we perform the time difference of equivalent ARMA model. Next, according to the order of the time differenced ARMA model, we achieve the state space conversion of that by two methods. If the order of AR part is greater than MA part, we use controllable or observable canonical form. Otherwise, we establish the state apace equation via the method that several step ahead predicts are included in the state variable, where we can derive high and low order models depending on the variable which is compensated from gyroscope output. At final step, we include the state space equation of gyroscope mixed random errors into aided INS Kalman filter model. Through the simulation, we show that both the high and low order filter models proposed give less navigation errors compared to the conventional filter which assume the mixed random errors as white noise.

Airspeed Estimation of Course Correction Munitions by Using Extended Kalman Filter (확장 칼만필터를 이용한 탄도수정탄의 대기속도 추정)

  • Sung, Jaemin;Kim, Byoung Soo
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.43 no.5
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    • pp.405-412
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    • 2015
  • This paper represents a filter design to estimate the airspeed of a spin-stabilized, trajectory-correctible artillery ammunition. Due to the limited power and space in operational point of view, the airspeed sensor is not installed, and thus the airspeed need to be estimated using limited sensor measurements. The only IMU measurements(three-axis specific forces and angular rates) are used in this application. The extended Kalman filter algorithm is applied since a linear filter can not cover the its wide operational range in airspeed and altitude. In the implementation of the EKF, the state and measurement equations are transformed into the no-roll frame for simple form of Jacobian matrix. The simulation study is conducted to evaluate the performance of the filter under various environment conditions of sensor noise and wind turbulence. In addition, the effect of the choice in filter design parameters, i.e. process error covariance matrices is analyzed on the performance of the estimation of airspeed and angular rates.

Non-linear Maneuvering Target Tracking Method Using PIP (PIP 개념을 이용한 비선형 기동 표적 추적 기법)

  • Son, Hyun-Seung;Park, Jin-Bae;Joo, Young-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.1
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    • pp.136-142
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    • 2007
  • This paper proposes a new approach on nonlinear maneuvering target tracking. In this paper, proposed algorithm is the Kalman filter based on the adaptive interactive multiple model using the concept of predicted impact point and utilize modified Kalman filter regarding the error between measurement position and predicted impact point. The unknown target acceleration is regarded as an additional process noise to the target model, and each sub-model is characterized in accordance with the valiance of the overall process noise which is obtained on the basis of each acceleration interval. To compensate the decreasing performance of Kalman filter in nonlinear maneuver, we construct optional algorithm to utilize proposed method or Kalman filter selectively. To effectively estimate the acceleration during the target maneuvering, the rapid increase of the noise scale is recognized as the acceleration to be used in maneuvering target's movement equation. And a few examples are presented to show suggested algorithm's executional potential.

A Study of Kalman Filter Adaptation for Protecting Aquaculture Farms (양식어장보호를 위한 칼만필터 적용에 관한 연구)

  • Nam, Taek-Kun;Jeong, Jung-Sik;Jong, Jae-Yong;Yang, Won-Jae;Ahn, Young-Sup
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • v.29 no.1
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    • pp.273-277
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    • 2005
  • In this paper, we study on adaptation of the kalman filter for FDS(fishery detection system) to protect and aquaculture farms. The FDS will detect a robbing vessel with real time and a variance of the position of fishing fields. The kalman filter for tracking system that can be detect and track the approaching object without mounting F-AIS(Fishery Automatic Identification System) is applied. Some simulation results for the acceleration object with white noise is showed and the possibility of adaptation for tracking system is discussed.

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