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The estimation of state-of-charge (SOC) is the fundamental technology to improve battery working status and life. But severe external environment, dynamic nonlinearity of battery model, and measurement errors make SOC estimation challengable. In this paper, a multi-strategy probabilities based fusion method is proposed. It can combine the dynamic tracking capability of Proportional-Integral Observer...
Although several powerful joint filters for cross-modal image pairs have been proposed, the existing joint filters generate severe artifacts when there are misalignments between a target and a guidance images. Our goal is to generate an artifact-free output image even from the misaligned target and guidance images. We propose a novel misalignment-robust joint filter based on weight-volume-based image...
In this paper, the decentralized robust output-feedback tracking control problem is investigated for a class of uncertain nonlinear interconnected systems subject to high order time varying disturbances. Firstly, generalized-proportional-integral observers (GPIOs) are designed for every subsystems such that the disturbances and unmeasured states can be recovered. Then, based on the estimation information...
Due to continuous and unplanned urbanization, biases and probability of occurrences of non-line-of-sight (NLOS) errors can be drastically enlarged in macro-cellular smart urban environments. This paper presents a new robust estimation approach to mobile tracking improvements based on adequately tackling NLOS errors. To cover the dynamics of a mobile station, we cast a wireless localization problem...
In this paper, we consider a millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication system with hybrid beamforming architecture. To search the optimal beam pair, the beam sweeping algorithm is complex and time-consuming in the case of initial access and beam switching. With the aim of improving the robustness, we propose a novel beam management scheme to quickly determine the...
Dynamic spectrum access (DSA) has seen a growing interest in recent years due to spectrum scarcity. Cognitive radio is a necessary component to enable DSA. One key requirement for cognitive radio is the ability to blindly detect the presence of signals in time and frequency. One popular method for blind detection is computing a power spectral density estimate using the Welch periodogram. The Welch...
With the increase of state dimension, the calculation of UKF algorithm increases rapidly, and UKF is more sensitive to model error, and it is not suitable for the system model with noise as non-Gaussian distribution. Aiming at this problem, this paper proposes a robust model predictive Unscented Kalman filter based on the study of robust estimation, model predictive filtering and UKF. The algorithm...
Crowd counting on still images is very challenging due to heavy occlusions and scale variations. In this paper, we aim to develop a method that can accurately estimate the crowd count from a still image. Recently, convolutional neural networks have been shown effective in many computer vision tasks including crowd counting. To this end, we propose a fully convolutional network (FCN) architecture to...
Synthetic aperture Radar tomography has been widely used in 3D reconstruction in urban area. It can distinguish multi persistent scatterers in the same range-azimuth resolution cell. But how to detect and estimate the number and parameters of persistent scatterers is still an important and difficult problem need to be resolved. In this paper, a new method based on RELAX and generalized likelihood...
We propose the use of a light-weight setup consisting of a collocated camera and light source – commonly found on mobile devices – to reconstruct surface normals and spatially-varying BRDFs of near-planar material samples. A collocated setup provides only a 1-D “univariate” sampling of a 3-D isotropic BRDF. We show that a univariate sampling is sufficient to estimate parameters of commonly used analytical...
Owing to the complexity of sampling processes and running environment of the system, it is hard to avoid abnormal data such as outliers as well as patchy outliers, which widely emerge out of the sampling series. These abnormal data have remarkably bad impact on state estimation, process monitoring, process control as well as safe running of the dynamic system. In this paper, a detailed review is given...
We present a new point set registration method with global-local correspondence and transformation estimation (GL-CATE). The geometric structures of point sets are exploited by combining the global feature, the point-to-point Euclidean distance, with the local feature, the shape distance (SD) which is based on the histograms generated by an elliptical Gaussian soft count strategy. By using a bidirectional...
Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense...
Label estimation is an important component in an unsupervised person re-identification (re-ID) system. This paper focuses on cross-camera label estimation, which can be subsequently used in feature learning to learn robust re-ID models. Specifically, we propose to construct a graph for samples in each camera, and then graph matching scheme is introduced for cross-camera labeling association. While...
Shape reconstruction techniques using structured light have been widely researched and developed due to their robustness, high precision, and density. Because the techniques are based on decoding a pattern to find correspondences, it implicitly requires that the projected patterns be clearly captured by an image sensor, i.e., to avoid defocus and motion blur of the projected pattern. Although intensive...
Deblurring images with outliers has attracted considerable attention recently. However, existing algorithms usually involve complex operations which increase the difficulty of blur kernel estimation. In this paper, we propose a simple yet effective blind image deblurring algorithm to handle blurred images with outliers. The proposed method is motivated by the observation that outliers in the blurred...
The robustness of adaptive beamforming is relate to the input signal-to-noise ratio (SNR). In order to further improve the performance of input SNR estimation, a modified method of input SNR estimation for robust adaptive beamforming is proposed. Comparatively accurate value of the input SNR can be obtained by the proposed method, especially when the input SNR exceeds 0 dB. When the proposed method...
One of the challenging aspects in directional electromagnetic measurements is to accurately estimate the formation bedding orientation angle, which is critical to help maintain drilling the wellbore in the sweetest spots of the reservoir. Various existing methods can be both noisy and susceptible to phase wrapping issues. Here we present a new approach for accurately computing the bedding orientation...
We describe the implementation of a single-phase estimation algorithm for phasor measurement unit (PMUs) compliant with the IEEE C37.118.1-2011 standard. It consists of three stages: The fist one is a bandpass FIR filter that allows the relaxation of the requirements of the following stages. The second one is a digital extension on state-space of the pseudo-linear enhanced phase locked loop (PL-EPLL)...
Non-linear methods are usually used to analyze and process random sequences with outliers or in presence of impulsive noise. One of these methods is based on order statistics, which includes rank information by increasing the problem size. In this article we use a rank one quadratic measurement model based on sketches and apply it to order statistics. We introduce a method to estimate the correlation...
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