scholarly journals Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7234
Author(s):  
Amaury Auguste ◽  
Wissam Kaddah ◽  
Marwa Elbouz ◽  
Ghislain Oudinet ◽  
Ayman Alfalou
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Urban Environments ◽  
Abnormal Behavior ◽  
Behavioral Analysis ◽  
Filter Method ◽  
New Approach ◽  
Kalman Filter Method ◽  
Using Data ◽  
Individual Tracking

In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).

scholarly journals Analysis and Forecast of the Number of Deaths, Recovered Cases, and Confirmed Cases From COVID-19 for the Top Four Affected Countries Using Kalman Filter

2021 ◽  
Vol 9 ◽  
Author(s):  
Abdullah Ali H. Ahmadini ◽  
Muhammad Naeem ◽  
Muhammad Aamir ◽  
Raimi Dewan ◽  
Shokrya Saleh A. Alshqaq ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
The United States ◽  
Filter Method ◽  
Crucial Information ◽  
Kalman Filter Algorithm ◽  
Kalman Filter Method ◽  
Dynamic Structures ◽  
Health Complications ◽  
Filtering Techniques

COVID-19 is a virus that spread globally, causing severe health complications and substantial economic impact in various parts of the world. The COVID-19 forecast on infections is significant and crucial information that will help in executing policies and effectively reducing the daily cases. Filtering techniques are important ways to model dynamic structures because they provide good valuations over the recursive Bayesian updates. Kalman filters, one of the filtering techniques, are useful in the studying of contagious infections. Kalman filter algorithm performs an important role in the development of actual and comprehensive approaches to inhibit, learn, react, and reduce spreadable disorder outbreaks in people. The purpose of this paper is to forecast COVID-19 infections using the Kalman filter method. The Kalman filter (KF) was applied for the four most affected countries, namely the United States, India, Brazil, and Russia. Based on the results obtained, the KF method is capable of keeping track of the real COVID-19 data in nearly all scenarios. Kalman filters in the archetype background implement and produce decent COVID-19 predictions. The results of the KF method support the decision-making process for short-term strategies in handling the COVID-19 outbreak.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

Fault diagnose of the rolling bearings vibration signals based on Kalman filter method

2021 ◽  
Author(s):  
Qingpeng Han ◽  
Xinhang Shen ◽  
Bin Wu ◽  
Rui Zhu ◽  
Daolei Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Filter Method ◽  
Fault Diagnose ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Kalman Filter Method

An Implicit Algorithm of Solving Nonlinear Filtering Problems

2014 ◽  
Vol 16 (2) ◽  
pp. 382-402
Author(s):  
Feng Bao ◽  
Yanzhao Cao ◽  
Xiaoying Han
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Nonlinear Filtering ◽  
State Equation ◽  
Nonlinear Filter ◽  
Filter Method ◽  
Simulation Methods ◽  
Current State ◽  
Numerical Simulation Methods ◽  
Kalman Filter Method

AbstractNonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

An Improved Adaptive Kalman Filter for Gravity Anomaly Distortion Correction

2011 ◽  
Vol 121-126 ◽  
pp. 1421-1425
Author(s):  
Li Ye Zhao ◽  
Hong Sheng Li
Keyword(s):  
Kalman Filter ◽  
Gravity Anomaly ◽  
Distortion Correction ◽  
The Self ◽  
Filter Method ◽  
Current Time ◽  
Gain Matrix ◽  
Gravity Signal ◽  
Kalman Filter Method ◽  
Correlation Sequences

Combined with the system state equation and the measurement equation, a new method of cascade Kalman filter is proposed and applied to the correction of gravity anomaly distortion. In the signal processing procedure, according to the self-correlation sequences of the measurement gravity signal, the relation of the gain matrix K and the self-correlation sequences could be obtain, and the gravity signal at current time can be calculated by the gain matrix K. Emulations and experiments indicate that both the cascade Kalman filter method and the single inverse Kalman filter method are effective in alleviating the distortion of the gravity anomaly signal, but the performance of the cascade Kalman filter method is better than that of single inverse Kalman filter method.

The Dangerous Rock Mass Deformation Analysis with Application of Kalman Filter Method Based on Material Properties and AR Model

2013 ◽  
Vol 738 ◽  
pp. 109-112
Author(s):  
Fu Min Lu ◽  
Ting Yao Jiang
Keyword(s):  
Kalman Filter ◽  
Rock Mass ◽  
Ar Model ◽  
Deformation Analysis ◽  
Filter Method ◽  
The Status ◽  
Rock Mass Deformation ◽  
Kalman Filter Method ◽  
Mass Deformation ◽  
Dangerous Rock Mass

Considering the material property of the rock to the dangerous rock mass,the paper Looks the model parameter of AR( 1) model as the status vector, and uses Kalman filter method to analysis the deformation of the dangerous rock mass. The result shows that the method can improve the accuracy of fitting and forecasting of the model.

Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method

2021 ◽  
Vol 7 (1) ◽  
pp. 20-30
Author(s):  
Fauziyah ◽  
Evita Purnaningrum
Keyword(s):  
Kalman Filter ◽  
Goal Programming ◽  
Stock Prices ◽  
Stock Price ◽  
Filter Method ◽  
Stock Portfolios ◽  
Stock Investment ◽  
Kalman Filter Method ◽  
Return Difference ◽  
Stock Portfolio

Long-term stock investment development is carried out by means of portfolio optimization. Selection of stocks for portfolios is not only based on high-value stock prices but also takes into account their fluctuations. Estimation of future stock price fluctuations has an indirect impact on future portfolio formation. This research has implemented the Kalman filter method to obtain the best estimation results from various stock prices with a high degree of accuracy. The results are then used to form a stock portfolio on the basis of Goal Programming. This study has compared the optimization results with the real value of stock prices. The results obtained, Kalman filter-based Goal Programming is more effective for predicting future portfolios compared to the Goal Programming method with a return difference of Rp. 178,039,848. This suggests that optimization with the Kalman Filter-based Objective Programming can be used as a tool to determine future stock portfolios.

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