• Advances in aircraft and spacecraft science
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• v.7 no.6
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• pp.495-516
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• 2020

Nonlinear state estimation is a desirable and required technique for many situations in engineering (e.g., aircraft/spacecraft tracking, space situational awareness, collision warning, radar tracking, etc.). Due to high standards on performance in these applications, in the last few decades, there was an increasing demand for methods that are able to provide more accurate results. However, because of the mathematical complexity introduced by the nonlinearities of the models, the nonlinear state estimation uses techniques that, in practice, are not so well-established which, leads to sub-optimal results. It is important to take into account that each method will have advantages and limitations when facing specific environments. The main objective of this paper is to provide a comprehensive overview and interpretation of the most well-known methods for nonlinear state estimation with Gaussian priors. In particular, the Kalman filtering methods: EKF (Extended Kalman Filter), UKF (Unscented Kalman Filter), CKF (Cubature Kalman Filter) and EnKF (Ensemble Kalman Filter) with an aerospace perspective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2866-2880
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• 2014

We propose a video denoising method based on Kalman filter to reduce the noise in video sequences. Firstly, with the strong spatiotemporal correlations of neighboring frames, motion estimation is performed on video frames consisting of previous denoised frames and current noisy frame based on intensity and structure tensor. The current noisy frame is processed in temporal domain by using motion estimation result as the parameter in the Kalman filter, while it is also processed in spatial domain using the Wiener filter. Finally, by weighting the denoised frames from the Kalman and the Wiener filtering, a satisfactory result can be obtained. Experimental results show that the performance of our proposed method is competitive when compared with state-of-the-art video denoising algorithms based on both peak signal-to-noise-ratio and structural similarity evaluations.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1183-1187
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• 2011

In this paper, an optimal FIR (Finite-Impulse-Response) filter is proposed for discrete time-varying state-space models. The proposed filter estimates the current state using measured output samples on the recent time horizon so that the variance of the estimation error is minimized. It is designed to be linear, unbiased, with an FIR structure, and is independent of any state information. Due to its FIR structure, the proposed filter is believed to be robust for modeling uncertainty or numerical errors than other IIR filters, such as the Kalman filter. For a general system with system and measurement noise, the proposed filter is derived without any artificial assumptions such as the nonsingular assumption of the system matrix A and any infinite covariance of the initial state. A numerical example show that the proposed FIR filter has better performance than the Kalman filter based on the IIR (Infinite- Impulse-Response) structure when modeling uncertainties exist.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.4
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• pp.167-179
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• 2017

In this paper, a simplified Cubature Kalman Filter (SCKF) is proposed to reduce the computation load of CKF, which is then used as a filter for transfer alignment of shipboard INS. CKF is an approximate Bayesian filter that can be applied to non-linear systems. When an initial estimation error is large, convergence characteristic of the CKF is more stable than that of the Extended Kalman Filter (EKF), and the reliability of the filter operation is more ensured than that of the Unscented Kalman Filter (UKF). However, when a system degree is large, the computation amount of CKF is also increased significantly, becoming a burden on real-time implementation in embedded systems. A simplified CKF is proposed to address this problem. This filter is applied to shipboard inertial navigation system (INS) transfer alignment. In the filter design for transfer alignment, measurement type and measurement update rate should be determined first, and if an application target is a ship, lever-arm problem, flexure of the hull, and asynchronous time problem between Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS) should be taken into consideration. In this paper, a transfer alignment filter based on SCKF is designed by considering these problems, and its performance is validated based on simulations.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1830-1843
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• 2018

In this paper, the generation of a reference current and voltage signal based on a Kalman filter is offered for a 3-phase 4wire UPQC (Unified Power Quality Conditioner). The performance of the UPQC is improved with source voltages that are distorted due to harmonic components. Despite harmonic and frequency variations, the Kalman filter is capable enough to determine the amplitude and the phase angle of load currents and source voltages. The calculation of the first state is sufficient to identify the fundamental components of the current, voltage and angle. Therefore, the Kalman state estimator is fast and simple. A Kalman based control strategy is proposed and implemented for a UPQC in a distribution system. The performance of the proposed control strategy is assessed for all possible source conditions with varying nonlinear and linear loads. The functioning of the proposed control algorithm with a UPQC is scrutinized and validated through simulations employing MATLAB/Simulink software. Using a FPGA SPATRAN 3A DSP board, the proposed algorithm is developed and implemented. A small-scale laboratory prototype is built to verify the simulation results. The stated control scheme for the UPQC reduces the following issues, voltage sags, voltage swells, harmonic distortions (voltage and current), unbalanced supply voltage and unbalanced power factor under dynamic and steady-state operating conditions.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.175-181
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• 1998

This paper describes realtime visual tracking system of moving object for three dimensional target using EKF(Extended Kalman Filter). We present a new realtime visual tracking using EKF algorithm and image prediction algorithm. We demonstrate the performance of these tracking algorithm through real experiment. The experimental results show the effectiveness of the EKF algorithm and image prediction algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.939-947
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• 2008

The GPS tracking loop consists of three parts in general: discriminator, loop filter and DCO (Digitally Controlled Oscillator). The loop filter is the main part of the tracking loop designed to ensure a good tracking performance. Generally, the loop filter is designed using classical PI(Proportional Integral) control. Although the carrier Doppler and code Doppler are generated by the same relative movement between the satellite and the user, often, the loop filters for each tracking loop are designed separately and independently. Sometimes, they are used in a combined manner such as carrier aided code tracking, FLL assisted PLL, etc. For better GPS signal tracking, we need to design the FLL/PLL/DLL altogether optimally. The purpose of this paper is to design a GPS receiver tracking loop based on the Kalman filter in a combined manner. Also, the proposed GPS receiver tracking loop is compared with a conventional tracking loop in terms of the transfer function and the DCO input. This paper shows that conventional tracking loop is equal to the Kalman filter based tracking loop.

• Journal of KSNVE
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• v.2 no.2
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• pp.135-140
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• 1992

The Extended Kalman Filter(EKF) algorithm estimates variables and unknown parameters simultaneously and is applied to parameter identification of linear and nonlinear mechanical systems. In this paper, an EKF algorithm was developed through a computer simulation and then applied to a sealing test system as a practical example. Comparing with the frequency domain analysis, it was proved to be a useful alternative for the parameter identification.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1822-1825
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• 2003

A practical range estimator based on the robust Kalman filter is proposed to solve the range estimation problem for surface to air missile(SAM) homing guidance. Apart from the previous works based on the extended Kalman filter(EKF) with bearing only measurement, the proposed scheme makes use of line-of-sight(LOS) rate to ensure the fast convergency at long-range. In this reason, the robust Kalman filter is considered to deal with LOS rate measurement error. The recursive linear structure of proposed filter is easy to implement and make it possible to reduce computational burdens. Moreover, it shows good estimation performance without specific guidance law such as oscillation proportional navigation guidance(OPNG).

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1500-1504
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• 2005

This paper proposes the design methodology of genetic algorithm (GA)-based fuzzy Kalman filter for tracking the maneuvering target. The performance of the standard Kalman Filter (SKF) has been degraded because mismatches between the modeled target dynamics and the actual target dynamics. To solve this problem, we use the method to estimate the increment of acceleration by a fuzzy system using the relation between maneuver filter residual and non-maneuvering one. To optimize the fuzzy system, a genetic algorithm (GA) is utilized and this is then tuned by the fuzzy logic correction. Finally, the tracking performance of the proposed method has been compared with those of the input estimation (IE) technique and the intelligent input estimation (IIE) through computer simulations.