• Journal of Positioning, Navigation, and Timing
• /
• 제5권1호
• /
• pp.1-9
• /
• 2016

Range-based wireless localization system must measure accurate range between a mobile node (MN) and reference nodes. However, non-line-of-sight (NLOS) error caused by the spatial structures disturbs the localization system obtaining the accurate range measurements. Localization methods using the range measurements including NLOS error yield large localization error. But filter-based localization methods can provide comparatively accurate location solution. Motivated by the accuracy of the filter-based localization method, a filter residual-based NLOS error estimation method is presented in this paper. Range measurement-based residual contains NLOS error. By considering this factor with NLOS error properties, NLOS error is mitigated. Also a process noise covariance matrix tuning method is presented to reduce the time-delay estimation error caused by the single dynamic model-based filter when the speed or moving direction of a MN changes, that is the used dynamic model is not fit the current dynamic of a MN. The presented methods are evaluated by simulation allowing direct comparison between different localization methods. The simulation results show that the presented filter is more accurate than the iterative least squares- and extended Kalman filter-based localization methods.

• 제어로봇시스템학회논문지
• /
• 제17권9호
• /
• pp.947-953
• /
• 2011

Radome aberration error causes degradation of miss distance as well as stability of high maneuver missile system with RF seeker. A study about radome compensation method is important in this kind of missile system design. Several kinds of methods showed good compensation performance in their paper. Proposed adaptive Particle filter estimates line of sight rate excluding the radome induced error. This paper shows effectiveness of adaptive Particle filter as compensation method of radome aberration error. Robust performance of this filter depends on external aiding measurement, target acceleration. Tuning of system error covariance can make this filter unsensitive against the error of target acceleration information. This paper demonstrates practical usage of adaptive Particle filter for reducing miss distance and increasing stability against disturbance of radome aberration error through performance analysis.

• International Journal of Control, Automation, and Systems
• /
• 제1권4호
• /
• pp.520-526
• /
• 2003

In the case of a strapdown inertial navigation system (SDINS) with sizeable attitude errors, the uncertainty caused by linearization of the system degrades the performance of the filter. In this paper, a robust filter and various error models for the uncertainty are presented. The analytical characteristics of the proposed filter are also investigated. The results show that the filter does not require the statistical property of the system disturbance and that the region of the estimation error depends on a freedom parameter in the worst case. Then, the uncertainty of the SDINS is derived. Depending on the choice of the reference frame and the attitude error state, several error models are presented. Finally, various in-flight alignment methods are proposed by combining the robust filter with the error models. Simulation results demonstrate that the proposed filter effectively improves the performance.

• 제어로봇시스템학회논문지
• /
• 제12권2호
• /
• pp.93-100
• /
• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• 대한전자공학회논문지
• /
• 제20권4호
• /
• pp.1-9
• /
• 1983

MRAS 초안주 출력 오차 모델(MRAS hyperstable output-error model)을 이용한 적응 순환 필터(adaptive recursive filter)의 설계상 가장 어려운 점은 오차 완화 필터 (error-smoothing filter)의 설계이다. 본 논문에서는 적응 순환 필터의 대표적 알고리즘인 HARF(hyperstable adaptive recursive filter) 알고리즘을 적절히 변형시킴으로써 오차 완화 필터를 제거시킬 수 있고, 동시에 수산 속도도 바른 알고리즘을 얻을 수 있음을 보였다.

• 제어로봇시스템학회논문지
• /
• 제11권8호
• /
• pp.717-725
• /
• 2005

The adaptive filter is proposed for the INS/GPS. The proposed filter can estimate the variance of the process noise using the residual of the filter. To verify the efficiency of the adaptive filter, it is applied to the loosely-coupled INS/CPS that employs the additive quaternion error model. Simulation results demonstrate that the proposed filter is more effective in estimating the attitude error than EKF.

• 대한전자공학회논문지
• /
• 제19권2호
• /
• pp.23-30
• /
• 1982

본 논문에서는 관성 항해 시스뎀(INS) 수직 찬넬의 bias error 감소를 위해 Kalman filter 방법과 재래식 방법이 적용, 비교되어졌다. 이 두가지 방법들은 예측 error와 반응면에서 다른 보통 쓰이는 방법들 보다 더 잘 수행됨을 보였다. 비교 연구 결과에 의하면, Kalman filter 방법 방호이 별무리없이 재래식 방법보다 효과적으로 더 잘 수행됨을 알 수 있다.

• Journal of Positioning, Navigation, and Timing
• /
• 제9권3호
• /
• pp.221-227
• /
• 2020

In this paper, the Iterated Consider Extended Kalman Filter (ICEKF) is proposed for bearing-only localization of GNSS interference to improve the estimation performance and filter consistency. The ICEKF is an extended version of Consider KF (CKF) for Iterated EKF (IEKF) to consider an effect of bearing measurement bias error to filter covariance. The ICEKF can mitigate the EKF divergence problem which can occur when linearizing the nonlinear bearing measurement by a large initial state error. Also, it can mitigate filter inconsistency problem of EKF and IEKF which can occur when a weakly observable bearing measurement bias error state is not included in filter state vector. The simulation result shows that the localization error of the ICEKF is smaller than the EKF and IEKF, and the Root Mean Square (RMS) estimation error of ICEKF matches the covariance of filter.

• 한국통신학회논문지
• /
• 제21권5호
• /
• pp.1172-1179
• /
• 1996

General adaptive noise canceller(ANC) suffers from the misadjustment of adaptive filter weights, because of the gradient-estimate noise at steady state. In this paper, an adaptive noise cancellation algorithm with speech detector which is distinguishing speech from silence and adaptation-transient region is proposed. The speech detector uses property of adaptive prediction-error filter which can filter the highly correlated speech. To detect speech region, estimation error which is the output of the adaptive filter is applied to the adaptive prediction-error filter. When speech signal apears at the input of the adaptive prediction-error filter. The ratio of input and output energy of adaptive prediction-error filter becomes relatively lower. The ratio becomes large when the white noise appears at the input. So the region of speech is detected by the ratio. Sign algorithm is applied at speech region to prevent the weights from perturbing by output speech of ANC. As results of computer simulation, the proposed algorithm improves segmental SNR and SNR up to about 4 dBand 11 dB, respectively.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
• /
• pp.64-69
• /
• 1993

In this paper we present the performance bounds of the optimal FIR filter in continuous time systems with modeling uncertainty. The performance measure bounds are calculated from the estimation error covariance bounds of the optimal FIR filter and the suboptimal FIR filter. Performance error bounds range are expressed by the upper bounds on the estimation error covariance difference between the real and nominal values in case of the systems with noise uncertainty or model uncertainty. The performance bounds of the systems are derived on the assumption that the system uncertainty and the estimation error covariance are imperfectly known a priori. The estimation error bounds of the optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the estimation of the motion of an aircraft carrier at sea, which shows the former has better performances than the latter.