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We propose a complete solution to robust and accurate object tracking in face of various types of occlusions which pose many challenges to correct judgment of occlusion situation and proper update of target template. In order to tackle those challenges, we first propose a content-adaptive progressive occlusion analysis (CAPOA) algorithm. By combining the information provided by spatiotemporal context, reference target, and motion constraints together, the algorithm makes a clear distinction between the target and outliers. Accurate tracking of an occluded target is achieved by rectifying the target location using the variant-mask template matching (VMTM). In order to deal with template drift in the process of template update, we propose a drift-inhibitive masked Kalman appearance filter (DIMKAF) which accurately evaluates the influence of template drift when updating the masked template. Finally, we devise a local best match authentication (LBMA) algorithm to handle complete occlusions, so that we can achieve a much more trustworthy detection of the end of an arbitrarily long complete occlusion. Experimental results show that our proposed solution tracks targets reliably and accurately no matter when they are under: short-term, long-term, partial or complete occlusions. Index Terms--Kalman filter, object tracking, occlusion handling, template drift, template matching.

A control method that improves performance of series-resonant converters that operate with a wide input voltage and/or output voltage range by substantially reducing their switching frequency range is introduced. The switching-frequency-range reduction is achieved by controlling the output voltage with a combination of variable-frequency and delay-time control. Variable-frequency control is employed to control the primary switches, while delay-time control is used to control secondary-side rectifier switches provided in place of diode rectifiers. A series-resonant converter with the proposed control method is employed as the output stage of the on-board charger module that operates with a wide battery-voltage range. By substantially reducing the switching frequency range, the overall operating frequency is increased to reduce the sizes of the passive components, and hence, increase power density. The performance evaluation of the proposed series-resonant converter with delay-time control was done on a 3.3-kW prototype delivering energy from 400-V bus, which is the output of the power factor correction front end, to a battery operating with voltage range between 180 and 430 V. Two implementations of the prototype circuit, one employing gallium nitride (GaN) and the other employing silicon (Si) switches, were evaluated and compared. The prototype with Si switches that at full load over the entire output voltage range operates with a switching frequency variation from approximately 150 to 190 kHz exhibits the maximum full-load efficiency of 98.1%, whereas the corresponding frequency range and efficiency of the prototype with GaN devices are 145–190 kHz and 97.4%, respectively. [ABSTRACT FROM AUTHOR]

In Multidimensional Harmonic Retrieval (MHR) problems, it is understood that the multidimensional structure of the measurement data, with a tensor representation, can be exploited to improve the parameter estimation accuracy. In this paper, the mode-\cal R subspace of the tensor representation, based on the general matricization of the tensor, is first defined. It is found that there is a subordinate relation among the different mode-\cal R signal subspaces. As a result, the measurement tensor can be projected to the mode-\cal R signal subspaces in a bottom-up way, and the long-vector signal subspace required by the many signal subspace based parameter estimation algorithms can be refined. As an example, a mode-\cal R projection based Tensor-ESPRIT algorithm is presented. The reason why mode-\cal R subspace projections bring about performance improvement becomes obvious by the first order perturbation analyses. These analyses also generate two criteria on how the mode-\cal R subspace based projection technique should be carried out. Simulations are conducted to verify the effectiveness of the algorithm and the analytical results. [ABSTRACT FROM PUBLISHER]

In this paper, a novel parallel factor (PARAFAC) model for processing the nested vector-sensor array is proposed. It is first shown that a nested vector-sensor array can be divided into multiple nested scalar-sensor subarrays. By means of the autocorrelation matrices of the measurements of these subarrays and the cross-correlation matrices among them, it is then demonstrated that these subarrays can be transformed into virtual scalar-sensor uniform linear arrays (ULAs). When the measurement matrices of these scalar-sensor ULAs are combined to form a third-order tensor, a novel PARAFAC model is obtained, which corresponds to a longer vector-sensor ULA and includes all of the measurements of the difference co-array constructed from the original nested vector-sensor array. Analyses show that the proposed PARAFAC model can fully use all of the measurements of the difference co-array, instead of its partial measurements as the reported models do in literature. It implies that all of the measurements of the difference co-array can be fully exploited to do the 2-D direction of arrival (DOA) and polarization parameter estimation effectively by a PARAFAC decomposition method so that both the better estimation performance and slightly improved identifiability are achieved. Simulation results confirm the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]