Kalman Filter-based Navigation Algorithm for Multi-Radio Integrated Navigation System

Jae Hoon Son,Sang Heon Oh,황동환 사단법인 항법시스템학회 2020 Journal of Positioning, Navigation, and Timing Vol.9 No.2

Since GNSS is easily affected by jamming and/or spoofing, alternative navigation systems can be operated as backup system to prepare for outage of GNSS. Alternative navigation systems are being researched over the world, and a multi-radio integrated navigation system using alternative navigation systems such as KNSS, eLoran, Loran-C, DME, VOR has been researched in Korea. Least Square or Kalman filter can be used to estimate navigation parameters in the navigation system. A large number of measurements of the Kalman filter may lead to heavy computational load. The decentralized Kalman filter and the federated Kalman filter were proposed to handle this problem. In this paper, the decentralized Kalman filter and the federated Kalman filter are designed for the multi-radio integrated navigation system and the performance evaluation result are presented. The decentralized Kalman filter and the federated Kalman filter consists of local filters and a master filter. The navigation parameter is estimated by local filters and master filter compensates navigation parameter from the local filters. Characteristics of three Kalman filters for a linear system and nonlinear system are investigated, and the performance evaluation results of the three Kalman filters for multi-radio integrated navigation system are compared.

Kalman Filter 복수 적용을 통한 Backprojection 기반 FMCW-SAR의 영상복원 품질평가

송주영 ( Juyoung Song ),김덕진 ( Duk-jin Kim ),황지환 ( Ji-hwan Hwang ),안상호 ( Sangho An ),김준우 ( Junwoo Kim ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.5

SAR SLC 영상을 취득하기 위해 원시 자료로부터 BPA 기반 영상복원을 수행할 때 정확한 GNSS-INS 센서의 위치 및 속도 정보를 획득하는 것은 중요하다. BPA 기반 영상복원을 수행한 연구에서 기기 오차 보정을 위해 Kalman Filter를 적용하였으나, 대부분 1회 적용하여 효과적으로 오차를 제거하였는지 판단하기 어렵다. 본 연구에서는 GNSS-INS 센서의 위치 및 속도 정보에 Kalman Filter를 복수회 적용한 뒤 BPA를 이용하여 영상복원을 수행하여 기기 오차 보정에 효과적인 필터링 횟수를 평가하고자 하였다. 이를 위해 2회의 항공기 실험을 진행하여 SAR 원시 자료를 취득하였고, 이들에 해당하는 GNSS-INS 센서 정보에 대해 실질적이고 연속적으로 Kalman Filter를 적용하였다. 본 연구를 통해 상이한 이동 경로를 가지는 GNSS-INS 정보가 상응하는 FMCW-SAR 영상의 BPA 기반 최적 영상복원에 필요한 Kalman Filter 적용 횟수에 영향을 미칠 수 있다는 것을 확인하였다. Acquisition of precise position and velocity information of GNSS-INS (Global Navigation Satellite System; Inertial Navigation System) sensors in obtaining SAR SLC (Single Look Complex) images from raw data using BPA (Backprojection Algorithm) was regarded decisive. Several studies on BPA were accompanied by Kalman Filter for sensor noise oppression, but often implemented once where insufficient information was given to determine whether the filtering was effectively applied. Multiple operation of Kalman Filter on GNSS-INS sensor was presented in order to assess the effective order of sensor noise calibration. FMCW (Frequency Modulated Continuous Wave)-SAR raw data was collected from twice airborne experiments whose GNSS-INS information was practically and repeatedly filtered via Kalman Filter. It was driven that the FMCW-SAR raw data with diverse path information could derive different order of Kalman Filter with optimum operation of BPA image restoration.

항공 통신 기술 : 영상 기반의 이차 칼만 필터를 이용한 객체 추적

박선배 ( Sun Bae Park ),유도식 ( Do Sik Yoo ) 한국항행학회 2016 韓國航行學會論文誌 Vol.20 No.1

우리는 본 논문에서 이차 칼만 필터를 이용한 영상 기반 객체 추적분야의 새로운 알고리즘을 제안한다. 최근에 발표된 이차 칼만 필터는 영상 기반의 객체의 실제 3차원 공간의 위치를 추적하는 것에는 아직 적용되지 않았다. 2차원 영상 내의 위치를 3차원 공간상의 위치로 환원시키는 것은 비선형적 변환을 수반하기 때문에 그에 맞는 추적 알고리즘을 사용해야만 한다. 이러한 상황에서, 비선형 수식을 이차식으로 근사화하는 이차 칼만 필터가 선형으로 근사화하는 확장 칼만 필터보다 더 정확한 성능을 낼 수 있다. 우리는 동일한 상황을 가정하여 확장 칼만 필터, 무향 칼만 필터, 파티클 필터, 그리고 우리가 제안한 이차 칼만 필터를 이용하여 객체를 추적하고, 그 결과를 비교해 본다. 결론적으로 이차 칼만 필터가 발산율이 확장 칼만 필터에 비해 거의 절반가량 감소하며, 추적 정확도 측면에서 무향 칼만 필터에 비해 1% 가량 우수한 성능을 나타낸다. In this paper, we propose a novel quadratic Kalman filter based object tracking algorithm using moving pictures. Quadratic Kalman filter, which is introduced recently, has not yet been applied to the problem of 3-dimensional (3-D) object tracking. Since the mapping of a position in 2-D moving pictures into a 3-D world involves non-linear transformation, appropriate algorithm must be chosen for object tracking. In this situation, the quadratic Kalman filter can achieve better accuracy than extended Kalman filter. Under the same conditions, we compare extended Kalman filter, unscented Kalman filter and sequential importance resampling particle filter together with the proposed scheme. In conculsion, the proposed scheme decreases the divergence rate by half compared with the scheme based on extended Kalman filter and improves the accuracy by about 1% in comparison with the one based on unscented Kalman filter.

퍼지 알고리즘을 적용한 적응 KALMAN 필터

노영환 우송대학교 1996 우송대학교 논문집 Vol.1 No.-

이 연구에서 두 종류의 퍼지 방법들이 적응 Kalman 필터에 적용된다. 퍼지처리는 추적된 신호에 대하여 SNR(신호와 잡음비)의 온-라인 평가로 유도된다. 필터이득계수들은 퍼지 멤버쉽 함수들을 사용하여 불확실한 신호/잡음 다이나믹의 50 dB 에 대하여 적응 시킨다. 여기서 두종류의 멤버쉽 함수들이 있는데 하나는 "결정 및 제어 함수"에 기초를 두고 다른 하나는 "시스템의 오차와 오차 변화분"에 기초한다. 특정한 시뮬레이션 결과들이 GPS 같은 추적의 응용 같은 위치와 속도상태를 가진 하나의 다이나믹 시스템 모델에 대하여 보여준다. 이 필터는 Gaussian 잡음의 인가로 위치를 측정하고 단입력 단출력을 가지고 있다. 하나의 강인 Bayes 구조는 신호 및 잡음 평가로부터 필터이득 계수들을 계산한다. 이 연구에서 정확하지 않은 신호 및 잡음 평가가 퍼지 멤버쉽 함수의 사용으로 교정된다. 이 결과 적응필터는 GPS 신호 및 잡음 환경에서 거의 최적적응 실행 결과를 보여준다. 생성 결과들은 Kalman 위치 및 속도 이득과 SNR 및 추적오차에 관하여 퍼지동조 Kalman 필터와 고정 Kalman 필터에 대하여 비교하고 있다. In this research, two kinds of fuzzy processing methods are applied to the adaptive Kalman filter. The fuzzy processing is driven by an inaccurate on-line estimate of signal-to-noise ratio (SNR) for the signal being tracked. The filter gain coefficients are adapted over a 50 dB range of unknown signal/noise dynamics, using fuzzy membership functions. There are two kinds of membership functions; one is based on the decision and control function, and another is on the system error and its change of error. Specific simulation results are shown for a dynamic system model which has position-velocity states, as in vehicle tracking applications such as the Glabal Positioning System (GPS). The filter is a single-input, single-output, driven by meausrements of position, corrupted by additive (Gaussian) noise. A robust Bayes scheme would calculate the filter gain coefficients from the signal-to noise estimate. In my implementation, the inaccurate signal-to-noise estimate is corrected by the use of fuzzy membership functions. The resulting adaptive filter produces near optimum performance in the GPS signal-noise environment. Performances are compared for fuzzy-tuned Kalman filter and fixed kalman filter in terms of Kalman position and velocity gains, SNR and tracking error.

Implementation of Fuzzy Aided Kalman Filter for Tracking a Moving Object in Two-Dimensional Space

Khondker Rawan Hamid,Azzama Talukder,A. K. M. Ehtesanul Islam 한국지능시스템학회 2018 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.18 No.2

The work presented in this paper deals with object tracking problem which has vast application prospects. Tracking of surrounding moving objects is important for the development of a navigation system with proper planning and motion techniques. Because of its accurate estimation characteristic, we have used Kalman filter to estimate the motion parameters of a moving object and then fuzzy logic technique is used to improve the performance of Kalman filter. The proposed fuzzy logic controller is a multiple input system with nine rules to adapt noise covariance matrices based on the innovation sequence of a Kalman filter. We have compared the results between conventional Kalman filter and fuzzy aided Kalman filter. The software based simulation results verified that this unique fuzzy aided Kalman filter has a positive effectiveness for the object tracking problem over conventional Kalman filter because of its capability to recover filter divergence problem.

樹高曲線式의 精度 提高를 위한 Kalman Filter 推定量의 利用

申萬鏞 國民大學校 山林科學硏究所 1995 山林科學 Vol.7 No.-

The Kalman filter estimation technique was employed to update height estimation equations. Two different sources of prior information were used to modify the estimates of height-diameter regression model from the sample data using the Kalman filter. The Kalman filter and two OLS estimators were evaluated based on the estimation ability of the resulting height eqations. The Kalman filter estimator performed better than the two OLS estimators for both validation data sets. Data collected inside of the study area formed better prior information than those from outside of the sample rage. This indicated that the quality of prior information was important in using feedback procedures such as Kalman filter approach.

필터링 이론 기반 건물 에너지 모델 파라메터 추정

김덕우(Deuk Woo Kim),김유민(Yu Min Kim),이승언(Seung Eon Lee) 대한설비공학회 2017 대한설비공학회 학술발표대회논문집 Vol.2017 No.6

Kalman filtering has been used to estimate building physical states such as temperatures of zones and surfaces or heat gain and loss rates of zones. However, the exact parameters of a building energy model, which are used in Kalman filtering, is uncertain in common, hence the final estimates are unreliable. It is one of the cons of the Kalman filtering technique. This weakness can be moderated by a nonlinear filtering technique which estimates the states and uncertain model parameters simultaneously. This paper explores the estimation performance of the representative nonlinear filter, a particle filter. A simple room model was developed for a virtual experiment and a series of experiments was conducted to verify the estimation performance under uncertain model parameters and a system input. The preliminary result shows that the particle filter can estimates states and parameters effectively under the uncertain building energy model.

非線形 狀態空間 模型에 대한 필터의 비교

유희경,송민구,오대호 三陟大學校 1997 論文集 Vol.30 No.1

This paper examines a nonlinear state space model and its filtering methods for the model. In the linear state space model, Kalman filter is optimal filter. But in the nonlinear case it is no more optimal one. Hence nonlinear model or equation is linearized and then adjusted to Kalman filter. This is called the Extended Kalman filter. But this filter is approximation method and therefore we need to reduce the approximation error from the linearization. Hence we would, here investigate the error reduction method which is called iteration filter. We, define the steps of the iteration filtering algorithm which is similar but iteration with Extended Kalman filter and is iterated for some number. And we compare Extended Kalman filter to iteration filter about the variation of state estimates via a Monte Carlo methods.

State Estimation of the Nonlinear Suspension System based on Nonlinear Kalman Filter

Sung-Soon Yim,Joon-Hong Seok,Ju-Jang Lee 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10

In reality, a system is almost nonlinear. To estimate the parameter or state of this system, nonlinear approach is needed. The Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF) are used to estimate this nonlinear problem. EKF uses first order Taylor expansion to approximate the nonlinear system, while UKF performs a stochastic linearization by using a weighted statistical linear regression process. The purpose of this paper is to estimate the state of the nonlinear suspension system based on the Extended Kalman Filter and the Unscented Kalman Filter. The simulation deals with state estimation of nonlinear suspension system by using these filters and is compared with the true state. Also LQR controller and output feedback PD controller will be designed by aid of UKF and EKF estimation. Simulation results show that two nonlinear Kalman filters are effective in estimating the state of a nonlinear suspension system.

Reduced-Order Unscented Kalman Filter for Sensorless Control of Permanent-Magnet Synchronous Motor

Cheol Moon,Young Ahn Kwon 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.2

The unscented Kalman filter features a direct transforming process involving unscented transformation for removing the linearization process error that may occur in the extended Kalman filter. This paper proposes a reduced-order unscented Kalman filter for the sensorless control of a permanent magnet synchronous motor. The proposed method can reduce the computational load without degrading the accuracy compared to the conventional Kalman filters. Moreover, the proposed method can directly estimate the electrical rotor position and speed without a back-electromotive force. The proposed Kalman filter for the sensorless control of a permanent magnet synchronous motor is verified through the simulation and experimentation. The performance of the proposed method is evaluated over a wide range of operations, such as forward and reverse rotations in low and high speeds including the detuning parameters.