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To enhance users’ novel experiences in gaming, the design method of multi-mode natural interaction of game animation characters in mixed reality is studied. Through the process of skeleton modeling, interpolation of position and orientation, time alignment and time transformation of character posture, the game animation character in mixed reality is constructed. The multi-role interactive game animation is synthesized using a random graph and uniform network, and the edge of the depth of field image collected by the Kinect somatosensory sensor is extracted. According to the transfer model and observation model of the Kalman filter algorithm, the motion trajectory of the game animation characters is obtained and the game animation characters are tracked. The IRS recognition algorithm is used for optical multi-touch processing in mixed reality, and the natural interaction of multi-mode game animation characters in interaction–perception mode, natural interaction mode and interactive feedback mode is realized. The experimental results demonstrate that this method can achieve multi-mode natural interaction of game animation characters in mixed reality and provide users with a novel game experience.

With the development of machine vision and multimedia technology, posture detection and related algorithms have become widely used in the field of human posture recognition. Traditional video surveillance methods have the disadvantages of slow detection speed, low accuracy, interference from occlusions, and poor real-time performance. This paper proposes a real-time pose detection algorithm based on deep learning, which can effectively perform real-time tracking and detection of single and multiple individuals in different indoor and outdoor environments and at different distances. First, a corresponding pose recognition dataset for complex scenes was created based on the YOLO network. Then, the OpenPose method was used to detect key points of the human body. Finally, the Kalman filter multi-object tracking method was used to predict the state of human targets within the occluded area. Real-time detection of human postures (sitting, stand up, standing, sit down, walking, fall down, and lying down) is achieved with corresponding alarms to ensure the timely detection and processing of emergencies.

The global extended Kalman filtering (EKF) algorithm for recurrent neural networks (RNNs) is plagued by the drawback of high computational cost and storage requirement. In this paper, we present a local EKF training-pruning approach that can solve this problem. In particular, the by-products, obtained along with the local EKF training, can be utilized to measure the importance of the network weights. Comparing with the original global approach, the proposed local approach results in much lower computational cost and storage requirement. Hence, it is more practical in solving real world problems. Simulation showed that our approach is an effective joint-training-pruning method for RNNs under online operation.

This paper deals with the blood glucose level modeling for Type 1 Diabetes Mellitus (T1DM) patients. The model is developed using a recurrent neural network trained with an extended Kalman filter based algorithm in order to develop an affine model, which captures the nonlinear behavior of the blood glucose metabolism. The goal is to derive a dynamical mathematical model for the T1DM as the response of a patient to meal and subcutaneous insulin infusion. Experimental data given by continuous glucose monitoring system is utilized for identification and for testing the applicability of the proposed scheme to T1DM subjects.

By equaling the detection of centroid jamming to the “outlier” detection in the process of tracking with Kalman filter, an approach to target reselection for anti-ship missile against centroid jamming with accurate tracking information is proposed in this paper considering the mutation of the law of motion at the tracking point under centroid jamming. For this approach, an accurate target tracking model is built on the basis of extended Kalman filter (EKF). Using the information collected by radar seeker including distance, velocity and angle, it could achieve the accurate tracking of the target and determine the motion state of the target accurately. On this basis, the orthogonality of innovation in the process of Kalman filtering is utilized to detect any mutation of the motion state of the target, so that the existence of centroid jamming is detected when there is any mutation of the law of motion at the tracking point in the process of chaff centroid jamming. This offers a new solution for remote beyond-visual-range (BVR) anti-ship missile against centroid jamming.

We show in this article that when the environment is piecewise linear, it provides a powerful constraint on the kind of matches that exist between two images of the scene when the camera motion is unknown. For points and lines located in the same plane, the correspondence between the two cameras is a collineation. We show that the unknowns (the camera motion and the plane equation) can be recovered, in general, from an estimate of the matrix of this collineation. The two-fold ambiguity that remains can be removed by looking at a second plane, by taking a third view of the same plane, or by using a priori knowledge about the geometry of the plane being looked at. We then show how to combine the estimation of the matrix of collineation and the obtaining of point and line matches between the two images, by a strategy of Hypothesis Prediction and Testing guided by a Kalman filter. We finally show how our approach can be used to calibrate a system of cameras.

An adaptive predictor for decouplable systems, which is optimal in the minimum mean-square error sense at each sample time is proposed, based on priori knowledge of the structure of the interactor matrix and a canonical form. The adaptive predictor includes the uncertainty associated with the parameter and state estimates. Moreover, it is shown that no optimal solution to indirect adaptive prediction exists for nondecouplable systems having unknown interactor matrix.

An adaptive predictor that is optimal in the minimum mean-square error (MMSE) sense at each step is obtained for single-input, single-output (SISO) linear time-invariant discrete-time systems having general delay and white noise perturbation. The adaptive d-step-ahead predictor includes the uncertainty associated with the parameter and state estimates whereas conventional adaptive predictors, that are asymptotically optimal, ignore the uncertainty in the parameter estimates.

In this paper, a new linearization algorithm of power amplifier (PA), based on Kalman filtering theory is proposed for obtaining fast convergence of the adaptive digital predistortion. The proposed method uses the real-time digital processing of baseband signals to compensate the nonlinearities and memory effects in radio-frequency power amplifier. To reduce the complexity of computing in classical Kalman filtering, a sliding time-window has been inserted which combines offline measurement and online parameter estimation with high sampling time to track the changes in the PA characteristics. The performance of the proposed linearization scheme is evaluated through simulation and experiments. Using digital signal processing, experimental results with commercial power amplifier are presented for multicarrier signals to demonstrate the effectiveness of this new approach.

Instantaneous observability is used to watch a system output with very fast signals as well as it is a system property that enables to estimate system internal states. This property depends on the pair of discrete matrices $\{\mathbf{\text{A}}(k),\mathbf{\text{C}}(k)\}$ and it considers that the system state equations are known. The problem is that the system states are inside and they are not always accessible directly. A process, which is a time-varying running program in four parts composes the system under investigation here. It is shown it is possible to apply Kalman filtering on a digital personal computer’s system with particularly the four parts like the ones under investigation. A computing process is performed during a period of time called latency. The calculation of latency considers it as a random variable with Gaussian distribution. The potential application of the results attained is the forecasting of data traffic-jam on a digital personal computer, which has very fast signals inside. In a broader perspective, this method to calculate latency can be applied on other digital personal computer processes such as processes on random access memory. It is also possible to apply this method on local area networks and mainframes.

Mobile target tracking remains a significant issue in smart cities. Due to complex changes in time and space of targets, real-time tracking remains a challenging problem. As a result, this paper proposes a real-time tracking approach for moving objects by combining the advantages of YOLOv7 and SORT algorithms. First, we use the YOLOv7 algorithm for object detection, which has the characteristics of high accuracy and efficiency. Then, we apply the SORT algorithm to the target tracking stage, which estimates and updates the target state through Kalman filtering. The collaborative function of the two parts is expected to achieve high-quality tracking of moving targets. Besides, this paper also demonstrates experiments and analysis on image datasets. The experimental results show that the proposed algorithm has achieved good performance in real-time tracking of moving targets. Compared with traditional methods, it can more accurately predict the position and trajectory of targets and has better real-time performance. In addition, the proposed algorithm is equally effective for target tracking in complex scenes, such as multi-target tracking and target occlusion. Future research can further optimize the performance of algorithms to cope with more complex scenarios and problems.

An embedded, real-time, loosely-coupled INS/GPS integration system has been developed and used for an unmanned land vehicle's control and navigation. For this integrated system, a Kalman filter software is used for INS error damping and corrections via the weighted aiding of a GPS output. The detailed development work will be presented in this paper including algorithm simplification, sensor selection and critical problems solving. Vehicular trial is also conducted. Simulated outage in GPS availability is made to assess the bridging accuracy of this system.

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a certain shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a small number of variables and the membership optimization problem can be reduced to a parameter optimization problem. This is the approach that is typically taken, but it results in membership functions that are not (in general) sum normal. That is, the resulting membership function values do not add up to one at each point in the domain. This optimization approach is modified in this paper so that the resulting membership functions are sum normal. Sum normality is desirable not only for its intuitive appeal but also for computational reasons in the real time implementation of fuzzy logic systems. The sum normal constraint is applied in this paper to both gradient descent optimization and Kalman filter optimization of fuzzy membership functions. The methods are illustrated on a fuzzy automotive cruise controller.

The accuracy of attitude and heading measurement, as well as the system real-time performance are basic indicators used to evaluate an attitude and heading reference system (AHRS). In order to improve the attitude and heading measurement accuracy under dynamic complex environment, the AHRS system should also have numerical stability and calculation robustness. The AHRS system based on MEMS multi-sensor fusion can realize fusion processing of data measured by multiple sensors, so as to calculate and obtain the optimal carrier attitude and heading information, conduct real-time output, and improve the accuracy and reliability of attitude and heading measurement. For the AHRS system consisting of MEMS gyroscope, accelerometer and triaxial magnetometer, attitude and heading detection principle and algorithm based on MEMS multi-sensor fusion were proposed in this study: The information of the system itself was firstly used to discriminate motion state of the carrier within the filtering cycle, and then Kalman filtering was conducted using different measured information according to motion state to correct the attitude error angle caused by gyroscopic drift. On this basis, an attitude fusion algorithm based on extended Kalman filtering technology was designed for time update process of Kalman filtering, output information of accelerometer was taken as observed quantity under certain conditions to realize measurement updating process of Kalman filtering, and then attitude angle was calculated. In an optical fiber attitude and heading system project in practical engineering, a vehicle field test analysis was carried out simultaneously with the system using ordinary attitude algorithm, and the results showed that the extended Kalman filtering algorithm designed according to the simulation results could realize multi-sensor information fusion, improve measurement accuracy and realize accurate attitude positioning, so as to provide simpler and more flexible criteria for carrier motion status. The results have verified the accuracy and reliability of the algorithm, so it is feasible in practical engineering.

In this study, we introduce new estimation methods for the required rate of returns on equity of private and public companies using the stochastic dividend discount model (DDM). To estimate the required rate of return on equity, we use the maximum likelihood method, the Bayesian method, and the Kalman filtering. We apply the model to a set of firms from the S&P 500 index using historical dividend and price data over a 32-year period. Overall, the suggested methods can be used to estimate the required rate of returns.

Displacement is an important parameter for evaluating structural performance. However, the accurate measurement of global dynamic displacement remains a challenging task. To solve this problem, this paper proposes a global dynamic displacement reconstruction method for lattice tower structures, fusing limited acceleration and strain data. First, the strain-displacement mapping method for cantilever beams with variable cross-sections is presented and then extended to lattice towers. Thereafter, a modified multi-rate data fusion algorithm incorporating Kalman filtering and the proposed strain-displacement mapping methods is developed to fuse the acceleration and strain data to reconstruct the global dynamic displacement of the tower. The numerical simulation, model test and full-scale test demonstrate that the reconstructed dynamic displacements using the proposed method agree well with the reference values in both the time and frequency domains, and the parametric analysis has also been carried out, exhibiting great robustness and high reconstruction accuracy.

Prediction in real-time image sequences is a key-feature for visual servoing applications. It is used to compensate for the time-delay introduced by the image feature extraction process in the visual feedback loop. In order to track targets in a three-dimensional space in real-time with a robot arm, the target's movement and the robot end-effector's next position are predicted from the previous movements. A modular prediction architecture is presented, which is based on the Kalman filtering principle. The Kalman filter is an optimal stochastic estimation technique which needs an accurate system model and which is particularly sensitive to noise. The performances of this filter diminish with nonlinear systems and with time-varying environments. Therefore, we propose an adaptive Kalman filter using the modular framework of mixture of experts regulated by a gating network. The proposed filter has an adaptive state model to represent the system around its current state as close as possible. Different realizations of these state model adaptive Kalman filters are organized according to the divide-and-conquer principle: they all participate to the global estimation and a neural network mediates their different outputs in an unsupervised manner and tunes their parameters. The performances of the proposed approach are evaluated in terms of precision, capability to estimate and compensate abrupt changes in targets trajectories, as well as to adapt to time-variant parameters. The experiments prove that, without the use of models (e.g. the camera model, kinematic robot model, and system parameters) and without any prior knowledge about the targets movements, the predictions allow to compensate for the time-delay and to reduce the tracking error.

This paper contributes to a technical overview of recent progresses on stochastic iterative learning control (ILC), where stochastic ILC implies the learning control for systems with various random signals and factors such as stochastic noises, random data dropouts and inherent random asynchronism. The fundamental principles of ILC are first briefed with emphasis on the system formulations and typical analysis methods. Then the recent progresses on stochastic ILC are reviewed in three parts: additive randomness case, multiplicative randomness case, and coupled randomness case, respectively. Three major approaches, i.e., expectation-based method, Kalman filtering-based method, and stochastic approximation-based method, are clarified. Promising research directions are also presented for further investigation.

High performance liquid chromatography (HPLC) is a separation technique that can be used for the analysis of organic compounds, etc. HPLC pumping systems are required to deliver the mobile phase at a constant flow rate, hence pressure fluctuations is a difficult problem that should be minimized. This paper proposes a method based on measurement noise self-adaption to decrease the occurrence of pressure fluctuations. The measurement noise covariance matrix is estimated in real-time according to the observations made. Furthermore, error feedback correction is used to adjust the system output. The experiment results demonstrate that flow fluctuations are reduced significantly and pressure stability is improved.

Measuring power harmonics and improving the signal-to-noise ratio (SNR) is an important part of signal processing for optical current transducer (OCT). According to the low SNR of OCT and the demand of harmonic measurement for the power system, this paper proposes a harmonic measurement method based on linear discrete Kalman filter. An appropriate Kalman filtering model is established in order to meet the demand of measured current which is mixed current that contains direct current (DC), fundamental current, harmonics and white noise. The simulation results done on LabVIEW show that Kalman filter can measure harmonics, improve the SNR and the measurement accuracy.