Improved Kalman Filter Algorithm Research Articles

Joint Optimization of Measurement Point Intelligent Selection and End-to-End Network Traffic Calculation in Datacenters

The effective design and management of data centers needs to follow the end-to-end traffic characteristics of data center networks (DCNs). However, directly measuring the end-to-end traffic of the network requires huge software and hardware costs. Since the particularity of the structure of DCNs, the flow estimation method used in the traditional computer network cannot be applied to existing DCNs. In this paper, we study the end-to-end traffic calculation of cloud computing DCNs. We propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> , which is an intelligent end-to-end traffic inference algorithm based on network tomography. Only using SNMP (simple network management protocol) data generally supported by switches, end-to-end traffic information can be calculated quickly and accurately. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> first devices a network traffic measurement point intelligent selection scheme based on node weighting. It first assigns weight to nodes through node criticality, and then uses node weighted incidence matrix approximation algorithm to calculate initial solution. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> then designs a network tomography method suitable for cloud computing network traffic calculation. It uses the time correlation of data center traffic to model the algorithm problem into a linear state space model, and finally calculates the traffic carried by each path through the improved Kalman filtering algorithm. Our evaluation and analysis demonstrate that <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> can effectively use the extracted coarse-grained traffic characteristics, and greatly improve the accuracy of the calculation on the premise of ensuring the computational efficiency.

Research on detecting moving targets with an improved Kalman filter algorithm

Research on detecting moving targets with an improved Kalman filter algorithm

An improved filtering algorithm for indoor localization based on DE-PSO-BPNN

Among the indoor localization algorithms, the algorithm based on traditional Back Propagation Neural Network (BPNN) has the problems of slow convergence and easy to fall into local optimum. It is difficult to apply the algorithm in noisy environments. Therefore, in this paper, we propose a novel indoor localization algorithm where the whole localization process is divided into two parts: data preprocessing and localization output. Data preprocessing means using filtering algorithm to process the Received Signal Strength Indication (RSSI) sequence. It is considered that the initial value of the received sequence has a significant impact on the performance of Kalman Filter (KF). An improved Kalman Filtering algorithm (DBSCAN-KF) is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. First, the RSSI values that are seriously disturbed by noise in the sequence are removed using the DBSCAN algorithm, and then the RSSI sequences are processed using KF so that the RSSI values can be closer to the theoretical values. The localization output part is to reduce the localization error caused by the BPNN. In this paper, the Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm are combined, and the Differential Evolution Particle Swarm Optimization (DE-PSO) algorithm is proposed. The BPNN weights and thresholds are optimized in parallel, which improves the speed and ability of global optimization search and further avoids the shortcomings of traditional BPNNs that are prone to fall into local optimization in the training process. Experimental results show that the BPNN localization algorithm based on DBSCAN-KF improves the average localization accuracy by 0.26m compared with the BPNN localization algorithm without filtering. After filtering, the localization algorithm based on DE-PSO improved BPNN (DE-PSO-BP) improves the average localization accuracy by about 24% compared with the localization algorithm based on DE-PSO-BP. The localization algorithm based on DE-PSO-BP improves the average localization accuracy by about 61% compared with the traditional BPNN.

Simultaneous estimation of time-varying temperature field and boundary heat flux in participating medium by the improved Kalman filter algorithm

Simultaneous estimation of time-varying temperature field and boundary heat flux in participating medium by the improved Kalman filter algorithm

An improved Kalman filter algorithm for tightly GNSS/INS integrated navigation system.

The Kalman filter based on singular value decomposition (SVD) can sufficiently reduce the accumulation of rounding errors and is widely used in various applications with numerical calculations. However, in order to improve the filtering performance and adaptability in a tightly GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation system, we propose an improved robust method to satisfy the requirements. To solve the issue of large fluctuations in GNSS signals faced by the conventional method that uses a fixed noise covariance, the proposed method constructs a correction variable through the innovation and the new matrix which is obtained by performing SVD on the original matrix, dynamically correcting the noise covariance and has better robustness. In addition, the derived SVD form of the information filter (IF) extends its application. The proposed method has higher positioning accuracy and can be better applied to tightly coupled GNSS/INS navigation simulations and physical experiments. The experimental results show that, compared with the traditional Kalman algorithm based on SVD, the proposed algorithm*s maximum error is reduced by 45.77%. Compared with the traditional IF algorithm, the root mean squared error of the proposed IF algorithm in the form of SVD is also reduced by 4.7%.

Online measurement method for roundness of large aluminum alloy ring based on laser ranging principle

Aluminum alloy rings are widely used in aerospace, energy, petrochemical, and equipment applications. The loss of roundness often occurs because of the weak rigidity and unstable plasticity of aluminum alloy rings. Therefore, an online measurement technique for the roundness of aluminum alloy rings is critical for ensuring the processing quality of aluminum alloy rings. In this paper, an online roundness measurement method based on the laser ranging principle and improved particle swarm optimization (IPSO) is proposed with the aid of the rotation of the ring during the rolling process. First, the distance from the outer surface of the ring to the laser rangefinder was measured using a triangular laser rangefinder with the help of the rotation of the ring in the rolling process. Second, the distance data were denoized using an improved Kalman filter algorithm to reduce the influence of random noise on the measurement results. Finally, the roundness and geometric center were evaluated using the IPSO algorithm combined with the minimum zone circle (MZC) method and compared with the results obtained using the standard particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and least square (LS) algorithm. The experimental results show that the roundness measured by the IPSO algorithm is less than 0.06 mm, demonstrating better performance in the roundness evaluation. The measurement method for roundness proposed in this paper is suitable for the online evaluation of large aluminum alloy rings, avoiding the influence of ring size on roundness measurement.

Stochastic propeller force and moment reconstruction at a shaft end based on an improved Kalman filter

Measuring the unsteady hydrodynamic excitation of a marine propeller at a shaft end accurately from multiple directions is crucial for minimizing the unwanted vibration and noise of ships. Due to the synchronous loading of high static thrust and torque, an innovative piezoelectric multicomponent balance embedded into a hollow shaft end is designed. However, complex coupling between the balance and shaft structure is formed. This leads to crosstalk disturbance and a modal dynamic amplification effect on all measuring channels. To overcome this, an improved Kalman filter algorithm based on the subspace modal identification (SMI) method is established, thus allowing us to reconstruct and locate equivalent unsteady excitations accurately in multiple directions with response-only networks. The effectiveness of proposed framework is test on a simulated nonrotating shaft end system with twelve force and three acceleration response channels. The selection of the smoothing delay parameter and whether crosstalk is considered are two main factors that influence the reconstruction accuracy.

Research on online monitoring system of fusion transmission line based on multi-source data

With the rapid development of China’s national economy, the length of transmission lines is increasing steadily, and the operating status of high-voltage transmission lines is directly related to the operational safety and efficiency of the entire power grid, but the sensors of transmission lines can only monitor one type of fault, and the fault analysis has limitations, which makes it impossible to comprehensively judge the operating status of transmission lines. This paper designs a transmission line online monitoring system based on multi-source data fusion, which adopts the improved Kalman filter algorithm. The data collected by the front-end system is fused, analyzed and shared in real time through the edge computing fusion terminal, so as to achieve a comprehensive judgment on the operation of transmission lines and improve the safe and stable operation level of transmission lines.

Construction of Computer English Corpus Assisted by Internet of Things Information Perception and Interaction Technology.

Aiming at the situation that multiple sensing devices in the sensing layer sense the same sensing quantity, multiple redundant measurement data will be generated. In this paper, an improved Kalman filtering algorithm is used. The algorithm combines with numerical test theory to remove abnormal error data before filtering, thereby reducing the error of filtering result and improving its reliability. The parallel multiprotocol processing mechanism of the gateway is designed, and the IoT gateway has a hierarchical modular processing design for different protocol signals of the heterogeneous perception layer. The web interaction function with good platform versatility is designed to provide a visual interaction interface for the application layer, which is convenient for the application layer terminal to monitor and manage the perception layer equipment. This paper realizes the retrieval and query function of keywords, wrong sentences, and English character errors. This corpus enriches the less existing Madagascar corpus and establishes a separate English character error query database. This paper describes the overall process of corpus from preliminary preparation to establishment and the related use instructions of corpus, makes a statistical analysis of the current corpus data, and puts forward some suggestions for building a computer English corpus according to the analysis results.

VSLAM method based on object detection in dynamic environments.

Augmented Reality Registration field now requires improved SLAM systems to adapt to more complex and highly dynamic environments. The commonly used VSLAM algorithm has problems such as excessive pose estimation errors and easy loss of camera tracking in dynamic scenes. To solve these problems, we propose a real-time tracking and mapping method based on GMM combined with YOLOv3. The method utilizes the ORB-SLAM2 system framework and improves its tracking thread. It combines the affine transformation matrix to correct the front and back frames, and employs GMM to model the background image and segment the foreground dynamic region. Then, the obtained dynamic region is sent to the YOLO detector to find the possible dynamic target. It uses the improved Kalman filter algorithm to predict and track the detected dynamic objects in the tracking stage. Before building a map, the method filters the feature points detected in the current frame and eliminates dynamic feature points. Finally, we validate the proposed method using the TUM dataset and conduct real-time Augmented Reality Registration experiments in a dynamic environment. The results show that the method proposed in this paper is more robust under dynamic datasets and can register virtual objects stably and in real time.

A real-time data processing method based on twice weighted improved Kalman filter algorithm

The data obtained by sensor is strong randomness and deviates from Gaussian distribution. The Extended Kalman Filter (EKF) is cost-effective and easy to cause large errors while processing data. At the same time, the accuracy of the data prediction is not high enough. Therefore, an improved filter based on the original EKF is proposed to improve the prediction accuracy and noise reduction effect in this paper, which combines the predicted value and the measured value of the EKF twice to find the root mean square to obtain a more accurate predicted value. The noise reduction effect of EKF and TWEKF is compared by using MATLAB. And the root means square error (RMSE), signal-to-noise ratio (SNR), smoothness (S) and other quantitative judgment indexes prove that TWEKF has better noise reduction effect than EKF.

Satellite/Inertial Navigation Integrated Navigation Method Based on Improved Kalman Filtering Algorithm

With the continuous development of positioning technology in today’s world, the accuracy requirements for navigation and positioning are also getting higher and higher. Global Positioning and Navigation System (GPS) can provide high-precision long-term navigation and positioning information. However, it has a strong dependence on the external environment, which means that it is easily disturbed by environmental changes and affects the accuracy of navigation and positioning and even leads to positioning failure. The inertial navigation system (INS) is an autonomous navigation system. It uses sensors to measure the specific force and angular velocity of the carrier for positioning and navigation, which means that it is less affected by the environment. However, the inertial navigation device will produce a certain initial error due to the restriction of the manufacturing level, and the error will increase with time, so the inertial navigation method is not suitable for long-term navigation. Therefore, it is of great practical significance to realize satellite/inertial navigation integrated navigation by combining the respective advantages of satellite navigation and inertial navigation methods and avoiding their respective disadvantages. This paper is aimed at studying the satellite/inertial navigation integrated navigation method based on the improved Kalman filter algorithm. The satellite inertial navigation integrated navigation experiment is carried out based on the improved Kalman filter algorithm. In the experiment, the noise reduction experiment of the designed satellite inertial navigation system was carried out by using the filtering noise reduction function of the improved Kalman filter algorithm, and the conclusion was drawn after the experiment. The navigation accuracy of the satellite inertial navigation system is improved by a total of 2 m after the improved Kalman filter algorithm is used to filter the noise reduction.

An AR Geo-Registration Algorithm for UAV TIR Video Streams Based on Dual-Antenna RTK-GPS

In emergency response and disaster rescue, unmanned aerial vehicles (UAVs) onboard thermal infrared (TIR) sensors are an essential means of acquiring ground information in the nighttime working environment. To enable field personnel to make better decisions based on TIR video streams returned from a UAV, the geographic information enhancement of TIR video streams is required. At present, it is difficult for low-cost UAVs to carry high-precision attitude sensors and thus obtain high-precision camera attitude information to meet the enhanced processing requirements of UAV TIR video streams. To this end, this paper proposes an improved Kalman filter algorithm to improve the geographic registration (geo-registration) accuracy by fusing the positioning and heading data from the dual-antenna real-time kinematic global positioning system (RTK-GPS) with onboard internal measurement unit (IMU) data. This method can yield high-precision position and attitude data in real time based on low-cost UAV hardware, based on which high-precision geo-registration results can be obtained. The computational complexity can be reduced compared with video stream feature tracking algorithms. Furthermore, the problem of unstable matching due to the low resolution and texture level of TIR video streams can be avoided. The experimental results prove that the proposed method can reduce the registration error by 66.15%, and significantly improve the geo-registration accuracy of UAV TIR video streams. Thus, it can strongly support the popularization and practicality of the application of augmented reality (AR) technology to low-cost UAV platforms.

Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm

In order to solve the rainfall estimation error caused by various noise factors such as clutter, super refraction, and raindrops during the detection process of Doppler weather radar. This paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman filter. After data preprocessing, the radar data should be classified according to the precipitation intensity. And then, they are respectively substituted into the improved filter for calibration. The state noise variance and the measurement noise variance can be adaptively calculated and updated according to the input observation data during this process. Then the optimal parameter value of each type of precipitation intensity can be obtained. The state noise variance and the measurement noise variance could be assigned optimal values when filtering the remaining data. This rainfall estimation based on semi-adaptive Kalman filter calibration not only improves the accuracy of rainfall estimation, but also greatly reduces the amount of calculation. It avoids errors caused by repeated calculations, and improves the efficiency of the rainfall estimation at the same time.

Dual-station direction finding location based on disturbance detection

The application of direction finding location technology in electronic warfare is becoming more and more extensive. Aiming at the problem that the traditional location method will greatly affect the location accuracy when the measurement information of each observation station is disturbed, a dual-station direction finding location algorithm based on disturbance detection is proposed. The algorithm uses the least squares method to obtain the target’s position information at each moment, then designs an improved Kalman filter algorithm with disturbance detection to improve the location accuracy. The simulation results show that the algorithm can still accurately track the target when the measurement information is disturbed, which has practical application value.

A portable measurement system for pavement surface macrotexture – a case study in China

ABSTRACT This study proposes a portable measurement system (PS) for pavement surface macrotexture, which improves the current detection technology and provides the necessary support for maintenance and evaluation works on urban roads, newly built roads, parking, footpath, tunnels, bicycle lanes, and short distance roads. The system can obtain the elevation values through a high precision laser sensor and a data acquisition card. Next, the outliers of natural elevation values are screened and interpolated by a statistical test method, and then the corrected data are filtered by an improved Kalman filter algorithm. Finally, the mean profile depth (MPD) is calculated based on the standard ASTM E1845-15. The replication experiments, the correlation experiments with sand patch test (SPT), and the comparative experiment with vehicle laser profiler (VLP) are carried out on 20 test sections. The results show that the variation coefficients of PS are less than 5%, the correlation indexes with SPT are more than 0.928, the mean square errors and the mean absolute percentage errors of PS are less than 0.0032 and 4.98%, respectively. Meanwhile, the conversion models of MPD with mean texture depth (MTD) are established to realise that the direct estimation of MTD data utilising MPD data for different pavement types.

Novel strategy based on improved Kalman filter algorithm for state of health evaluation of hybrid electric vehicles Li-ion batteries during short- and longer term operating conditions

To solve the problems in estimating the state of health (SOH) of Li-ion batteries due to real-time estimation difficulty and low precision under various operating conditions, the variations of the SOH caused by increases of the internal resistance have been analyzed. Based on the second-order RC equivalent circuit model, the short-term effect of the state of charge (SOC) on the internal resistance was considered, which was set under the discharge condition. In addition, the variation of the internal resistance was analyzed in two intervals of 0–1 s and 1–10 s. The extended Kalman filter (EKF) algorithm was improved to present a novel improved Kalman filter (IKF) algorithm to accurately predict the long-term internal resistance under different operating conditions. A computational formula based on the internal-resistance increasing was established and the SOH was estimated. The error of the calculated result when compared with the forgetting factor least square method based on the internal-resistance increasing was controlled to within 4.0% under the HPPC condition, 3.0% under the BBDST condition, and 6.0% under the DST condition. The proposed algorithm has good convergence, helps improve the SOH estimation, and encourages the application of Li-ion batteries.

An autonomous positioning method for fire robots with multi-source sensors

The present technology, based on laser and visual SLAM navigation and positioning, does not apply to the event of a fire in a room where there is a large amount of smoke, for its increasingly obvious defects. In addition, traditional track deduction technology based on photoelectric encoder has accumulated error, and noise disturbance exists in the INS inertial navigation measurement technology, and the UWB positioning technology is vulnerable to NLOS disturbance caused by site occlusion. To solve the problem of accurate positioning of Autonomous Mobile fire-fighting robot in smoke scenes, the system design is optimized by adopting the following methods: using IMU-assisted residual chi-square criterion to detect whether there is NLOS in UWB, introducing IMU instantaneous compensation positioning data and adopting Chan algorithm fitting of the second multiplication to ensure the stability and accuracy of UWB data; meanwhile, a tight combination model of navigation and positioning is designed: via the improved Kalman filter algorithm, fused with the magnetic encoder track estimation pose, UWB absolute and IMU heading angle pose to realize the accurate positioning of the fire robot in the smoke scene. Finally, the fusion simulation model and algorithm are verified by MATLAB, as it shows, the method has an average positioning accuracy of 98.63% in the X-axis direction, 99.52% in the Y-axis direction, and 97.24% in the heading angle, which solves the inherent physical defects of a single positioning sensor and serves as a reliable and accurate solution for indoor robot positioning.

A Wind Speed Prediction Model Based on ARIMA and Improved Kalman Filter Algorithm

This article is dedicated to solving the problem of wind speed prediction. A time series analysis method based on the establishment of a differential autoregressive sliding model for simple training and a prediction model to obtain the state is proposed. The Kalman filter method with adaptive weighting coefficients is used to predict the equation, and the experimental results show that the composite algorithm can effectively reduce the prediction error.

Development and Research of a Multi-Medium Motion Capture System for Underwater Intelligent Agents

A multi-medium motion capture system based on markers’ visual detection is developed and experimentally demonstrated for monitoring underwater intelligent agents such as fish biology and bionic robot-fish. Considering the refraction effect between air and water, a three-dimensional (3D) reconstruction model is established, which can be utilized to reconstruct the 3D coordinate of markers underwater from 2D data. Furthermore, the process of markers matching is undertaken through the multi-lens fusion perception prediction combined K-Means clustering algorithm. Subsequently, in order to track the marker of being occluded, according to the kinematics information of fish, an improved Kalman filtering algorithm is proposed. Finally, the feasibility and effectiveness of proposed system are verified through experimental results. The main models and methods in this paper can provide a reference and inspiration for measurement of underwater intelligent agents.