• Journal of Positioning, Navigation, and Timing
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• v.10 no.2
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• pp.83-89
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• 2021

The extended Kalman filter (EKF) is widely used for global navigation satellite system (GNSS) applications. It is difficult to obtain precise positions with an EKF one-way (forward or backward) filter. In this paper, we propose an EKF smoother to improve the positioning accuracy by integrating forward and backward filters. For the EKF smoother experiment, we performed PPP using GNSS data received at the DAEJ reference station for a month. The effectiveness of the proposed approach is validated with multi-GNSS kinematic PPP experiments. The EKF smoother showed 35%, 6%, and 22% improvement in east, north, and up directions, respectively. In addition, accurate tropospheric zenith total delay (ZTD) values were calculated by a smoother. Therefore, the results from EKF smoother demonstrate that better accuracy of position can be achieved.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.2
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• pp.137-147
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• 2024

This paper presents a novel Long Short-Term Memory (LSTM) network architecture for the integration of an Inertial Measurement Unit (IMU) and Global Navigation Satellite Systems (GNSS). The proposed algorithm consists of two independent LSTM networks and the LSTM networks are trained to predict attitudes and velocities from the sequence of IMU measurements and mechanization solutions. In this paper, three GNSS receivers are used to provide Real Time Kinematic (RTK) GNSS attitude and position information of a vehicle, and the information is used as a target output while training the network. The performance of the proposed method was evaluated with both experimental and simulation data using a lowcost IMU and three RTK-GNSS receivers. The test results showed that the proposed LSTM network could improve positioning accuracy by more than 90% compared to the position solutions obtained using a conventional Kalman filter based IMU/GNSS integration for more than 30 seconds of GNSS outages.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1060-1068
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• 2016

The range-free localization using connectivity information has problems of mobile tracking. This paper proposes two Bayesian filter-based mobile tracking algorithms considering a propagation scenario. Kalman and Markov Chain Monte Carlo (MCMC) particle filters are applied according to linearity of two measurement models. Measurement models of the Kalman and MCMC particle filter-based algorithms respectively are defined as connectivity between mobiles, information fusion of connectivity information and received signal strength (RSS) from neighbors within one-hop. To perform the accurate simulation, we consider a real indoor map of shopping mall and degree of radio irregularity (DOI) model. According to obstacles between mobiles, we assume two types of DOIs. We show the superiority of the proposed algorithm over existing range-free algorithms through MATLAB simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.86.5-86
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• 2002

The measurement sensor may contain unknown bias in addition to the white noise in the measurement sequence. In this paper, fault detection and diagnosis scheme for the measurement sensor is developed based on the adaptive estimator. The proposed scheme consists of a parallel bank of Kalman-type filters each matched to a set of different possible biases, a mode probability evaluator, an estimate combiner at the outputs of the filters, a bias estimator, and a fault detection and diagnosis logic. Monte Carlo simulations for the PWR steam generator in the nuclear power plant are provided to illustrate the effectiveness of the proposed scheme.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.146-151
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• 2019

This paper reports a sliding window filtering approach for ground moving targets with cross-correlated sensor noise and uncertainty. In addition, the effect of uncertain parameters during a tracking error on the model performance is considered. A distributed fusion sliding window filter is also proposed. The distributed fusion filtering algorithm represents the optimal linear combination of local filters under the minimum mean-square error criterion. The derivation of the error cross-covariances between the local sliding window filters is the key to the proposed method. Simulation results of the motion of the ground moving target a demonstrate high accuracy and computational efficiency of the distributed fusion sliding window filter.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.427-434
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• 2011

This paper is focused on application of recursive least squares method to estimate rail irregularities from the acceleration measurement on an axle-box or a bogie for the rail condition monitoring with in-service high-speed trains. Generally, the rail condition was monitored by a special railway inspection vehicle but the monitoring method needs an expensive measurement system. A monitoring method using accelerometers on an axle-box or a bogie was already proposed in the previous study, and the displacement was successfully estimated from the acceleration data by using Kalman and frequency selective band-pass filters. However, it was found that the displacement included not only the rail irregularities but also phase delay of the applied filters, and effect of suspension of the bogie and conicity of the wheel. To identify the rail irregularities from the estimated displacement, a compensation filter method is proposed. The compensation filters are derived by using recursive least squares method with the estimated displacement as input and the measured rail irregularity as output. The estimated rail irregularities are compared with the true rail irregularity data from the rail inspection system. From the comparison, the proposed method is a useful tool for the measurement of lateral and vertical rail irregularity.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.646-647
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• 2005

A recently published paper demonstrates the utility of allowing delays in state estimators for linear systems with unknown inputs. In this note, we point out an error in the derivation of the estimator.

• International Journal of Aeronautical and Space Sciences
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• v.3 no.1
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• pp.19-29
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• 2002

Decentralized filtering for a formation flight instrumentation system by INS/GPS integration is considered in this paper. An elaborate tuning method of the measurement noise covariance is suggested to compensate modeling errors caused by decentralizing the extended Kalman filter. It does not require large data transfer between formation vehicles. Covariance analysis exhibits the superior performance of the proposed approach when compared with the existent decentralized filter and the global filter, which has the target-filter performance.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.388-396
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• 2007

This paper describes a multipath estimation method for Global Positioning System (GPS) dual frequency carrier phase measurements. Multipath is a major error source in high precision GPS applications, i.e., carrier phase measurements for precise positioning and attitude determinations. In order to estimate and remove multipath at carrier phase measurements, an array GPS antenna system has been used. The known geometry between the antennas is used to estimate multipath parameters. Dual frequency carrier phase measurements increase the redundancy of measurements, so it can reduce the number of antennas. The unscented Kalman filter (UKF) is recently applied to many areas to overcome some of the limitations of the extended Kalman filter (EKF) such as weakness to severe nonlinearity. This paper uses the UKF for estimating multipath parameters. A series of simulations were performed with GPS antenna arrays located on a straight line with one reflector. The geometry information of the antenna array reduces the number of estimated multipath parameters from four to three. Both the EKF and the UKF are used as estimation algorithms and the results of the EKF and the UKF are compared. When the initial parameters are far from true parameters, the UKF shows better performance than the EKF.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.3
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• pp.21-29
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• 2011

This paper presents a method that estimates distance and velocity of an object with reliability regardless of maneuver status of the target in stereo vision system. A stereo vision system can calculate a distance with disparity from left and right images. However, the distance estimation error may occur due to quantization error of image pixel. A sub-pixel interpolation method minimizes the quantization error and estimates accurate disparity with real value. Extended Kalman filter (EKF) was used to minimize the error covariance and estimate the object's velocity. However, divergence problem occurs due to model uncertainty when a target maneuvers highly, which makes the estimation error increase. In this paper, we propose a virtual model extended Kalman filter (VMEKF) method that minimizes the processing time and provides reliable estimation ability regardless of maneuver status. Computer simulations and experimental results in real road environments demonstrate that the proposed method gives a reliable estimation performance and reduces processing time under various maneuver status while comparing other estimation filters.