Structural-parametric synthesis of the tracking filter based on decomposition by the target functional with adaptation to trajectory disturbances

2021 ◽  
Author(s):  
A.A. Kostoglotov ◽  
A.S. Penkov ◽  
S.V. Lazarenko
Keyword(s):  
Numerical Modeling ◽  
State Assessment ◽  
Estimation Accuracy ◽  
Motion Model ◽  
Parametric Synthesis ◽  
Adaptive Motion ◽  
Accuracy Criterion ◽  
Dynamic Errors ◽  
Tracking Filter ◽  
Kinematic Motion

Traditional Kalman-type tracking filters are based on a kinematic motion model, which leads to the occurrence of dynamic errors, which significantly increase during target maneuvering. One of the solutions to this problem is to develop a model of motion dynamics with the ability to adapt its structure to external influences. It is shown that the use of a dynamic model of motion in the filter, which takes into account the inertia of the target and the forces acting on it, makes it possible to significantly increase the efficiency of the state assessment. Purpose is to development of an algorithm for assessing the position of a maneuvering object, effective in terms of accuracy criterion. The use of an adaptive motion model as part of the filter provides an increase in the estimation accuracy in comparison with the classical Kalman filter, which is confirmed by the performed numerical modeling.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

Adaptive Motion Model for Human Tracking Using Particle Filter

2010 ◽  
Author(s):  
Mohammad Hossein Ghaeminia ◽  
Amir Hossein Shabani ◽  
Shahryar Baradaran Shokouhi
Keyword(s):  
Particle Filter ◽  
Human Tracking ◽  
Motion Model ◽  
Adaptive Motion

scholarly journals Using the properties of vehicles in the conceptof the multi-environment multi-modal quantomobile

2020 ◽  
Vol 17 (6) ◽  
pp. 195-205
Author(s):  
Ju. G. Kotikov ◽  
Keyword(s):  
Numerical Modeling ◽  
Research And Development ◽  
Physical Vacuum ◽  
Motion Model ◽  
Single Family ◽  
Coordinate Systems ◽  
Hypothetical Model ◽  
Ground Vehicle ◽  
New Class ◽  
The One

The development of the concept of the quantum engine, that uses the energy of physical vacuum, makes it possible to create a new class of vehicles, namely, the quantomobile, designed as a quantum - powered vehicle. The type of quantum vehicles can be versatile, starting from the simplest version (with the ground vehicle driving modes) to the multi-environment multi-modal quantomobile that can function on land, in the air and in water. To work out a hypothetical model of the multi-environment multi-modal quantomobile, it is necessary to use all the heritage of research and development in the sphere of transport engineering. For 10 variants of the multi-environment multi-modal quantomobile movement - from the air quantum helicopter (quantocraft) to a quantum submarine (quantomarine) - there has been made an analysis of the numerical modeling specifics, the use of coordinate systems, the implementation of the traffic of existing transport vehicles that can be reflected in the concept of multi-environment quantomobile. Two extreme methods of modeling are distinguished: 1) the one based on a single family of coordinate systems and a common (end-to-end for all types of environment) motion model; 2) the one based on models by type of motion with possible switching of coordinate systems.

Modified Strong Tracking Filter for the SOC Estimation of Lithium Battery in Electric Vehicle

2012 ◽  
Vol 236-237 ◽  
pp. 1362-1367
Author(s):  
Ze Cheng ◽  
Yu Hui Zhang ◽  
Yan Li Liu
Keyword(s):  
Electric Vehicle ◽  
Lithium Battery ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Original Algorithm ◽  
Soc Estimation ◽  
Tracking Ability ◽  
Strong Tracking Filter ◽  
Vehicle Management ◽  
Tracking Filter

In electric vehicle management system, the key technology is to estimate the SOC (state of charge) of battery in dynamic process. This paper builds the state space equation of lithium battery based on improved Randle’s model, realizes the tracking estimation of SOC using modified STF(strong tracking filter) algorithm. The simulation result proves that compared with the original algorithm, modified algorithm can greatly improve the system’s tracking ability as well as the estimation accuracy of SOC.

scholarly journals Improved Velocity Estimation Method for Doppler Sonar Based on Accuracy Evaluation and Selection

2021 ◽  
Vol 9 (6) ◽  
pp. 576
Author(s):  
Yongshou Yang ◽  
Shiliang Fang
Keyword(s):  
Estimation Method ◽  
Velocity Estimation ◽  
Estimation Accuracy ◽  
Matched Filtering ◽  
Accuracy Evaluation ◽  
Optimal Velocity ◽  
Tracking Method ◽  
Filtering Method ◽  
Accuracy Criterion ◽  
Doppler Sonar

The matched filtering method and the waveform-tracking method cannot maintain optimal velocity estimation performance all of the time. In order to solve this problem, this paper proposes an improved velocity estimation method for Doppler sonar, based on accuracy evaluation and selection. The echo of Doppler sonar is divided into several segments with the same width as the transmitted pulse, and each segment is regarded as the echo of the corresponding water layer. According to our study’s results, the velocity estimation accuracy of each segment is positively correlated with the ratio of its autocorrelation modulus to its power. Based on this conclusion, a velocity accuracy criterion with high accuracy and low complexity is designed in order to select the optimal velocity estimation for water layers or bottoms. The proposed accuracy selection method flexibly selects the echo interval to be processed according to the accuracy criterion, so as to maintain the optimal estimation of the current’s or bottom’s velocity. Water tank and field experiments using a prototype Doppler sonar device demonstrates that, compared with the matched filtering method and the waveform-tracking method, the average velocity estimation accuracy and bias of the proposed method are superior.

scholarly journals A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 6891
Author(s):  
Yanjie Liu ◽  
Changsen Zhao ◽  
Yanlong Wei
Keyword(s):  
Indoor Environment ◽  
Response Speed ◽  
The State ◽  
Outdoor Environment ◽  
Estimation Accuracy ◽  
Map Building ◽  
Motion Model ◽  
Probability Hypothesis Density ◽  
Grid Map ◽  
Dynamic Objects

The PHD (Probability Hypothesis Density) filter is a sub-optimal multi-target Bayesian filter based on a random finite set, which is widely used in the tracking and estimation of dynamic objects in outdoor environments. Compared with the outdoor environment, the indoor environment space and the shape of dynamic objects are relatively small, which puts forward higher requirements on the estimation accuracy and response speed of the filter. This paper proposes a method for fast and high-precision estimation of the dynamic objects’ velocity for mobile robots in an indoor environment. First, the indoor environment is represented as a dynamic grid map, and the state of dynamic objects is represented by its grid cells state as random finite sets. The estimation of dynamic objects’ speed information is realized by using the measurement-driven particle-based PHD filter. Second, we bound the dynamic grid map to the robot coordinate system and derived the update equation of the state of the particles with the movement of the robot. At the same time, in order to improve the perception accuracy and speed of the filter for dynamic targets, the CS (Current Statistical) motion model is added to the CV (Constant Velocity) motion model, and interactive resampling is performed to achieve the combination of the advantages of the two. Finally, in the Gazebo simulation environment based on ROS (Robot Operating System), the speed estimation and accuracy analysis of the square and cylindrical dynamic objects were carried out respectively when the robot was stationary and in motion. The results show that the proposed method has a great improvement in effect compared with the existing methods.

Sign in / Sign up

Export Citation Format

Share Document