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Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user's interest points according to the user's behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error. [ABSTRACT FROM AUTHOR]

The original target tracking algorithm based on a single model has long been unable to meet the complex and changeable characteristics of the target, and then there are problems such as poor tracking accuracy, target loss, and model mismatch. The interactive multimodel algorithm uses multiple motion models to track the target, obtains the degree of adaptation between the actual motion state of the target and each model according to the calculated likelihood function, and then combines the updated weight values of each filter to obtain a weighted sum. Therefore, the interactive multimodel algorithm can achieve better performance. This paper proposes an improved interactive multimodel algorithm that can achieve player tracking and trajectory feature matching. First, this paper proposes an improved Kalman filtering (IKF) algorithm. This method is developed from the unbiased conversion measurement Kalman filter, which can obtain more accurate target state and covariance estimation. Secondly, using the parallel processing mode of the IMM algorithm to efficiently solve the data association between multiple filters, the IMM-IKF model is proposed. Finally, in order to solve the problem of low computational efficiency and high mismatch rate in image feature point matching, a method of introducing a minimum spanning tree in two-view matching is proposed. Experimental results show that the improved IMM-IKF algorithm can quickly respond to changes in the target state and can find the matching path with the lowest matching cost. In the case of ensuring the matching accuracy, the real-time performance of image matching is ensured. [ABSTRACT FROM AUTHOR]

In this paper, a simple extended Kalman Filter (EKF) controller for direct torque control (DTC) of a six-phase induction machine in all speed ranges is proposed. The aim of this paper is to decrease the execution time of EKF modeling of a six-phase induction motor. The proposed method is fast and can operate online. If the machine parameters are changed during the operation, the EKF algorithm is activated to find parameters used for controlling the motor. In low speed, not only the motor speed measurement but also the DTC of machine is difficult. The EKF model can estimate speed, flux, load torque, and stator resistance in low speed; thus, optimization can be performed in all loads and speed range. The proposed method increases the accuracy of DTC of the six-phase induction machine and decreases the computation cost of the system using the simplified algorithm. The simulation and experimental results verify the effectiveness and the robustness of the proposed method against parameter variations. [ABSTRACT FROM AUTHOR]

In this paper, through an in-depth study and analysis of dance motion capture algorithms in wearable sensor networks, the extended Kalman filter algorithm and the quaternion method are selected after analysing a variety of commonly used data fusion algorithms and pose solving algorithms. In this paper, a sensor-body coordinate system calibration algorithm based on hand-eye calibration is proposed, which only requires three calibration poses to complete the calibration of the whole-body sensor-body coordinate system. In this paper, joint parameter estimation algorithm based on human joint constraints and limb length estimation algorithm based on closed joint chains are proposed, respectively. The algorithm is an iterative optimization algorithm that divides each iteration into an expectation step and a great likelihood step, and the best convergence value can be found efficiently according to each iteration step. The feature values of each pose action are fed into the algorithm for model learning, which enables the training of the model. The trained model is then tested by combining the collected gesture data with the algorithmic model to recognize and classify the gesture data, observe its recognition accuracy, and continuously optimize the model to achieve accurate recognition of human gesture actions. [ABSTRACT FROM AUTHOR]

In order to improve the surveillance effect of the aviation target surveillance radar, this paper improves the traditional filtering algorithm and builds the channel optimization system of the ADS-B aviation target surveillance radar based on the improved filtering algorithm. Moreover, this paper uses algorithm improvement to ensure the positive definite or semipositive definiteness of the state covariance and uses the root mean square volume Kalman filter to avoid the filter divergence or tracking interruption caused by the nonpositive definiteness of the matrix; the filtering principle of the interactive multimodel is to use multiple filters for parallel processing and achieve the adaptive adjustment algorithm residual error by adjusting the one-step prediction covariance in the adjustment algorithm. In addition, this paper combines the actual needs to construct a system functional structure to optimize the channel of the ADS-B aviation target surveillance radar and uses software engineering methods to model and analyze the requirements. Finally, this paper designs experiments to verify system performance. The research results show that the performance of the system constructed in this paper meets actual needs. [ABSTRACT FROM AUTHOR]

According to the actual application system model which has bias, this paper analyzes the shortage of the conventional augmented algorithm, the two-stage cubature Kalman filtering algorithm, which is presented on the basis of a two-stage nonlinear transformation. The core ideas of the algorithm are to obtain the block diagonalization of the covariance matrix using the matrix transformation and avoid calculating the covariance of the state and bias to reduce the amount of calculation and ensure a smooth filtering process. Then, the equivalence of the two-stage cubature Kalman filtering algorithm and the cubature Kalman filtering algorithm is proved by updating equivalent transformation. Through the experiment of trajectory tracking of a wheeled robot, it is verified that the two-stage cubature Kalman filtering algorithm can obtain good tracking accuracy and stability with the presence of unknown random bias. Simultaneously, the equivalence of the two-stage cubature Kalman filtering algorithm and cubature Kalman filtering algorithm is verified again using the contrast experiment. [ABSTRACT FROM AUTHOR]

Multiobject Tracking (MOT) is one of the most important abilities of autonomous driving systems. However, most of the existing MOT methods only use a single sensor, such as a camera, which has the problem of insufficient reliability. In this paper, we propose a novel Multiobject Tracking method by fusing deep appearance features and motion information of objects. In this method, the locations of objects are first determined based on a 2D object detector and a 3D object detector. We use the Nonmaximum Suppression (NMS) algorithm to combine the detection results of the two detectors to ensure the detection accuracy in complex scenes. After that, we use Convolutional Neural Network (CNN) to learn the deep appearance features of objects and employ Kalman Filter to obtain the motion information of objects. Finally, the MOT task is achieved by associating the motion information and deep appearance features. A successful match indicates that the object was tracked successfully. A set of experiments on the KITTI Tracking Benchmark shows that the proposed MOT method can effectively perform the MOT task. The Multiobject Tracking Accuracy (MOTA) is up to 76.40% and the Multiobject Tracking Precision (MOTP) is up to 83.50%. [ABSTRACT FROM AUTHOR]

Height measurement for moving pedestrians is quite significant in many scenarios, such as pedestrian positioning, criminal suspect tracking, and virtual reality. Although some existing height measurement methods can detect the height of the static people, it is hard to measure height accurately for moving pedestrians. Considering the height fluctuations in dynamic situation, this paper proposes a real-time height measurement based on the Time-of-Flight (TOF) camera. Depth images in a continuous sequence are addressed to obtain the real-time height of the pedestrian with moving. Firstly, a normalization equation is presented to convert the depth image into the grey image for a lower time cost and better performance. Secondly, a difference-particle swarm optimization (D-PSO) algorithm is proposed to remove the complex background and reduce the noises. Thirdly, a segmentation algorithm based on the maximally stable extremal regions (MSERs) is introduced to extract the pedestrian head region. Then, a novel multilayer iterative average algorithm (MLIA) is developed for obtaining the height of dynamic pedestrians. Finally, Kalman filtering is used to improve the measurement accuracy by combining the current measurement and the height at the last moment. In addition, the VICON system is adopted as the ground truth to verify the proposed method, and the result shows that our method can accurately measure the real-time height of moving pedestrians. [ABSTRACT FROM AUTHOR]

Load is the main external disturbance of a parallel robot manipulator. This disturbance will cause dynamic coupling among different degrees of freedom and make heaps of model-based control methods difficult to apply. In order to compensate this disturbance, it is crucial to obtain an accurate dynamic model of load. However, in practice, the load is always uncertain and its dynamic parameters are arduous to know a priori. To cope with this problem, this paper proposes a novel and simple approach to identify the dynamic parameters of load. Firstly, the dynamic model of the parallel robot manipulator with uncertain load is established and the dynamic coupling caused by load is also analyzed. Then, according to the dynamic model, the excitation signal is designed and a weak nonlinear dynamic model is derived. Furthermore, the identification model is presented and the identification algorithm based on the extended Kalman filter is designed. Lastly, numerical simulation results, obtained using a six-degree-of-freedom Gough–Stewart parallel manipulator, demonstrate the good estimation performance of the proposed method. [ABSTRACT FROM AUTHOR]

When the multisensor self-adaptive weighted fusion algorithm fuses the data sources that were severely interfered by noise, its fusion precision, data smoothness, and algorithm stability will be reduced. To overcome this drawback, the idea was proposed with respect to an improved algorithm which optimized acquisition of fusion data sources with discrete Kalman filtering technique, thus reducing the negative impact on the fusion performance from noise. To verify the effectiveness of the improved algorithm, this paper simulated the fusion process of soil moisture data with fusion samples. The result proved that, under the same circumstance, the improved algorithm has a stronger restrain ability to noise and a better performance in fusion precision, data smoothness, and algorithm stability compared with the general algorithm. [ABSTRACT FROM AUTHOR]

With the development of the industry, the physical model of controlled object tends to be complicated and unknown. It is particularly important to estimate the state variables of a nonlinear system when the model is unknown. This paper proposes a state estimation method based on adaptive fusion of multiple kernel functions to improve the accuracy of system state estimation. First, a dynamic neural network is used to build the system state model, where the kernel function node is constructed by a weighted linear combination of multiple local kernel functions and global kernel functions. Then, the state of the system and the weight of the kernel functions are put together to form an augmented state vector, which can be estimated in real time by using high-degree cubature Kalman filter. The high-degree cubature Kalman filter performs adaptive fusion of the kernel function weights according to specific samples, which makes the neural network function approximate the real system model, and the state estimate follows the real value. Finally, the simulation results verify the feasibility and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the coverage control for a group of agents, where the density function over the given region is unknown and time-varying. A cost function, depending on the density function and a certain metric, is provided to evaluate the performance of coverage network. Then, while considering the sampling noise, a novel estimation algorithm is developed to approximate the density function based on the Kalman filter (KF) and the Radial Basis Function (RBF) neural network. Compared with the other estimation algorithms, a novel sampling regulation mechanism is designed to improve the estimation performance and reduce the computational load. On this basis, a coverage control scheme with estimated density function is proposed to drive the agents to the optimal deployment. Moreover, the stability and performance of proposed coverage control system are strictly analyzed. Finally, numerical simulation is provided to illustrate the effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]

As one of the most important communication tools for human beings, English pronunciation not only conveys literal information but also conveys emotion through the change of tone. Based on the standard particle filtering algorithm, an improved auxiliary traceless particle filtering algorithm is proposed. In importance sampling, based on the latest observation information, the unscented Kalman filter method is used to calculate each particle estimate to improve the accuracy of particle nonlinear transformation estimation; during the resampling process, auxiliary factors are introduced to modify the particle weights to enrich the diversity of particles and weaken particle degradation. The improved particle filter algorithm was used for online parameter identification and compared with the standard particle filter algorithm, extended Kalman particle filter algorithm, and traceless particle filter algorithm for parameter identification accuracy and calculation efficiency. The topic model is used to extract the semantic space vector representation of English phonetic text and to sequentially predict the emotional information of different scales at the chapter level, paragraph level, and sentence level. The system has reasonable recognition ability for general speech, and the improved particle filter algorithm evaluation method is further used to optimize the defect of the English speech rationality and high recognition error rate Related experiments have verified the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy and reliability of wireless location in NLOS environment, a wireless location algorithm based on artificial neural network (ANN) is proposed for NLOS positioning error caused by non-line-of-sight (NLOS) propagation, such as occlusion and signal reflection. The mapping relationship between TOA and TDOA measurement data and coordinates is established. The connection weights of neural network are estimated as the state variables of nonlinear dynamic system. The multilayer perceptron network is trained by the real-time neural network training algorithm based on extended Kalman (EKF). Combined with the statistical characteristics of NLOS error, the state component NLOS bias estimation is modified to realize TDOA data reconstruction. Simulation and experimental data analysis show that the algorithm can effectively weaken the influence of NLOS error. The localization method does not depend on the specific NLOS error distribution, nor does it need LOS and NLOS recognition. It can significantly improve the mobile positioning accuracy. [ABSTRACT FROM AUTHOR]

A fractional Kalman filter-based multirate sensor fusion algorithm is presented to fuse the asynchronous measurements of the multirate sensors. Based on the characteristics of multirate and delay measurement, the state is reestimated at the time when the delayed measurement occurs by using weighted fractional Kalman filter, and then the state estimation is updated at the current time when the delayed measurement arrives following the similar pattern of Kalman filter. The simulation examples are given to illustrate the effectiveness of the proposed fusion method. [ABSTRACT FROM AUTHOR]