In this paper, a current control scheme, based on proportional-integral regulators using sinusoidal signal integrators (SSIs), is proposed for shunt type power conditioners. The aim is to simplify the implementation of SSI-based current harmonic compensation for industrial implementations where strict limitations on the harmonic distortion of the mains' currents are required. To compensate current harmonics, the SSIs are implemented to operate both on positive and negative sequence signals. One regulator, for the fundamental current component, is implemented in the stationary reference frame. The other regulators, for the current harmonics, are all implemented in a synchronous reference frame rotating at the fundamental frequency. This allows the simultaneous compensation of two current harmonics with just one regulator, yielding a significant reduction of the computational effort compared with other current control methods employing sinusoidal signal integrators implemented in stationary reference frame. A simple and robust voltage filter is also proposed by the authors to obtain a smooth and accurate position estimation of the voltage vector at the point of common coupling (PCC) under distorted mains' voltages. The whole control algorithm has been implemented on a 16-b, fixed-point digital signal processor (DSP) platform controlling a 20-kVA power conditioner prototype. The experimental results presented in this paper for inductive and capacitive loads show the validity of the proposed solutions.Index Terms-Digital signal processor (DSP), shunt type power conditioner, sinusoidal signal integrators (SSIs).

In this article we show that when targets are closely spaced, traditional tracking algorithms can be adjusted to perform better under a performance measure that disregards identity. More specifically, we propose an adjusted version of the Joint Probabilistic Data Association (JPDA) filter, which we call the Set JPDA (SJPDA) filter. Through examples and theory we motivate the new approach, and show its possibilities. To decrease the computational requirements, we further show that the SJPDA filter can be formulated as a continuous optimization problem which is fairly easy to handle. Optimal approximations are also discussed, and an algorithm, KLSJPDA, which provides optimal Gaussian approximations in the Kullback-Leibler sense is derived. Finally, we evaluate the SJPDA filter on two scenarios with closely spaced targets, and compare the performance in terms of the mean Optimal Subpattern Assignment (MOSPA) measure with the JPDA filter, and also with the Gaussian-mixture CPHD filter. The results show that the SJPDA filter performs substantially better than the JPDA filter, and almost as well as the more complex GM-CPHD filter.

Most area-defense formulations follow from the assumption that threats must first be identified and then neutralized. This is reasonable, but inherent to it is a process of labeling: threat A must be identified and then threat B, and then action must be taken. This manuscript begins from the assumption that such labeling (A & B) is irrelevant. The problem naturally devolves to one of Random Finite Set (RFS) estimation: we show that by eschewing any concern of target label we relax the estimation procedure, and it is perhaps not surprising that by such a removal of constraint (of labeling) performance (in terms of localization) is enhanced. A suitable measure for the estimation of unlabeled objects is the Mean OSPA (MOSPA). We derive a general algorithm which provided the optimal estimator which minimize the MOSPA. We call such an estimator a Minimum MOSPA (MMOSPA) estimator.

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