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In this paper, a fast decomposition algorithm of positive and negative sequence components is derived first. And based on analyzing the interaction between sampling noise and time (angle) delay, a sampling-noise-optimized decomposition algorithm is proposed. This optimized method can identify positive and negative sequence components quickly and limit the error caused by sampling noise. Compared with existing methods, the new algorithm has a rapid response speed and greatly reduces the sampling noise level under the same response time. Therefore it is more suitable for those time-critical applications. Simulation results are in correspond with analytical conclusions and the validity of the method is examined. • Noise-optimized fast decomposition method of positive & negative sequence components. • Faster response speed and relatively lower sampling noise level. • A generic algorithm for different time-critical applications. [ABSTRACT FROM AUTHOR]

In this paper we present a generic framework for the asymptotic performance analysis of subspace-based parameter estimation schemes. It is based on earlier results on an explicit first-order expansion of the estimation error in the signal subspace obtained via an SVD of the noisy observation matrix. We extend these results in a number of aspects. Firstly, we demonstrate that an explicit first-order expansion of the Higher-Order SVD (HOSVD)-based subspace estimate can be derived. Secondly, we show how to obtain explicit first-order expansions of the estimation error of arbitrary ESPRIT-type algorithms and provide the expressions for R-D Standard ESPRIT, R-D Unitary ESPRIT, R-D Standard Tensor-ESPRIT, as well as R-D Unitary Tensor-ESPRIT. Thirdly, we derive closed-form expressions for the mean square error (MSE) and show that they only depend on the second-order moments of the noise. Hence, to apply this framework we only need the noise to be zero mean and possess finite second order moments. Additional assumptions such as Gaussianity or circular symmetry are not needed. [ABSTRACT FROM PUBLISHER]

This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Aimed at the inherent flaws of stable deterministic error in conventional algorithm using Jerk model, a modified algorithm was proposed. Jerk model and current Jerk model were fused in the improved algorithm. Weak Jerk maneuver was described by using Jerk model and its system noise variance was adjusted by using the relations of position estimation error and acceleration change rate variance. The strong Jerk maneuver was described by using the current Jerk model, and the statistical characteristics of acceleration change rate variance was depicted by using modified Rayleigh distribution. The tracking accuracy for weak Jerk maneuver was improved, and the tracking ability for strong Jerk maneuver was enhanced by introducing fuzzy distribution function and strong track filter. The theoretical analysis and simulation results show that the defect of conventional Jerk model algorithm is overcome by using the improved algorithm, Jerk sudden maneuver with various intensity can be approached adaptively. Under different intensity signal-to-noise ratios and gauging noises, its error is relatively stable and it has better tracking accuracy. [ABSTRACT FROM AUTHOR]